From 5298441ac98e70844401f5da9201a6d727df7d3b Mon Sep 17 00:00:00 2001
From: Lotte Verheyden <48100308+Lotte-Verheyden@users.noreply.github.com>
Date: Fri, 7 Aug 2026 14:36:20 -0700
Subject: [PATCH 1/2] docs: prune and restructure the guides section

Remove outdated cookbooks and videos, consolidate integration cookbooks
into their /integrations reference pages, move benchmarks and the 2023
webinar to /resources/engineering, and sort the remaining guides by
topic instead of format. Every removed URL redirects to the closest
living equivalent.
---
 components/CookbookIndex.tsx                  |  26 +-
 content/blog/2024-04-python-decorator.mdx     |   9 +-
 content/blog/2024-08-what-is-langchain.mdx    |   2 +-
 ...10-opentelemetry-for-llm-observability.mdx |   4 +-
 content/blog/launch-week-1.mdx                |   4 +-
 .../2024-02-27-llama-index-integration.mdx    |   2 +-
 .../2024-03-05-uptrain-integration.mdx        |   4 +-
 .../2024-04-21-openai-integration-JS-SDK.mdx  |   2 +-
 content/changelog/2024-04-24-decorator.mdx    |   7 +-
 .../2024-08-08-openai-structured-outputs.mdx  |   2 +-
 .../2024-08-21-openai-multi-modal-traces.mdx  |   4 +-
 .../observability/features/agent-graphs.mdx   |   2 +-
 content/docs/observability/get-started.mdx    |  12 -
 .../prompt-management/features/caching.mdx    |   2 +-
 .../prompt-management/features/config.mdx     |   2 +-
 .../fifteen-questions-langfuse-answered.mdx   |   2 +-
 .../cookbook/evaluation_with_langchain.md     | 217 ------
 .../cookbook/evaluation_with_uptrain.md       | 411 -----------
 .../cookbook/example_data_migration-jp.mdx    |   3 +
 .../cookbook/example_data_migration.mdx       |   3 +
 .../example_decorator_openai_langchain.md     | 148 ----
 .../example_evaluating_openai_agents.md       |   2 +-
 .../example_intent_classification_pipeline.md |   2 +-
 .../cookbook/example_langgraph_agents.mdx     |   2 +-
 .../example_llm_security_monitoring.md        |   2 +-
 .../cookbook/integration_amazon_bedrock.md    | 241 -------
 .../guides/cookbook/integration_anthropic.mdx | 156 ----
 .../integration_azure_openai_langchain.md     | 220 ------
 .../cookbook/integration_databricks.mdx       | 250 -------
 .../guides/cookbook/integration_langchain.md  | 200 -----
 .../guides/cookbook/integration_langserve.md  | 187 -----
 .../cookbook/integration_litellm_proxy.mdx    | 209 ------
 .../integration_llama-index-callback.md       | 117 ---
 ...integration_llama-index_instrumentation.md | 126 ----
 .../integration_llama-index_milvus-lite.mdx   | 126 ----
 .../cookbook/integration_llama_index.mdx      | 110 ---
 ...integration_llama_index_posthog_mistral.md | 248 -------
 .../integration_llamaindex_workflows.mdx      | 135 ----
 .../cookbook/integration_openai_assistants.md | 190 -----
 .../cookbook/integration_openai_sdk.mdx       | 405 -----------
 .../integration_openai_structured_output.md   | 247 -------
 .../cookbook/js_integration_anthropic.mdx     | 109 ---
 .../cookbook/js_integration_langchain.mdx     | 125 ----
 .../cookbook/js_integration_litellm_proxy.mdx | 169 -----
 .../guides/cookbook/js_integration_openai.mdx | 287 --------
 .../js_prompt_management_langchain.mdx        |   2 +-
 content/guides/cookbook/meta.json             |  32 +-
 content/guides/index.mdx                      |  14 +-
 .../beginners-guide-to-rag-evaluation.mdx     |   1 +
 .../videos/debug-rag-agent-with-tracing.mdx   |   1 +
 .../videos/external-evaluation-pipelines.mdx  |  30 -
 content/guides/videos/headless-langfuse.mdx   |   1 +
 .../guides/videos/introducing-datasets-v2.mdx |  33 -
 .../videos/introducing-langfuse-2.0.mdx       |  16 -
 .../videos/introducing-python-decorator.mdx   |  36 -
 ...as-a-judge-eval-on-dataset-experiments.mdx |  35 -
 content/guides/videos/llm-playground.mdx      |  15 -
 content/guides/videos/meta.json               |  11 +-
 content/guides/videos/posthog-integration.mdx |  15 -
 .../videos/replace-vibes-with-evals.mdx       |   1 +
 .../guides/videos/run-langfuse-locally.mdx    |  22 -
 content/handbook/chapters/why.mdx             |   2 +-
 content/integrations/frameworks/langchain.mdx |  16 +-
 .../frameworks/langgraph.mdx}                 |  63 +-
 content/integrations/frameworks/langserve.md  | 180 -----
 content/integrations/frameworks/meta.json     |   2 +-
 content/integrations/gateways/litellm.mdx     | 142 +++-
 .../model-providers/amazon-bedrock.md         |   1 +
 .../model-providers/databricks.mdx            | 137 +++-
 .../model-providers/openai-assistants-api.md  |   3 +-
 .../model-providers/openai-js.mdx             |  74 +-
 .../model-providers/openai-py.mdx             |  16 +-
 .../integrations/model-providers/xai-grok.mdx |   2 +-
 content/integrations/other/meta.json          |   4 +
 .../other/mlflow.md}                          |  38 +-
 .../other/openinference.md}                   |  37 +-
 .../other/openlit.md}                         |  11 +-
 .../other/openllmetry.md}                     |  18 +-
 content/library/index.mdx                     |   2 +-
 content/marketing/enterprise.mdx              |   2 +-
 .../langfuse-sdk-performance-test.md}         |  59 +-
 content/resources/engineering/meta.json       |   5 +-
 ...rompt-management-performance-benchmark.md} |   7 -
 .../webinar-observability-llm-systems.mdx     |   0
 cookbook/_routes.json                         | 125 +---
 cookbook/evaluation_with_langchain.ipynb      | 376 ----------
 cookbook/evaluation_with_uptrain.ipynb        | 674 -----------------
 cookbook/example_data_migration-jp.ipynb      |   4 +-
 cookbook/example_data_migration.ipynb         |   4 +-
 .../example_decorator_openai_langchain.ipynb  | 255 -------
 .../example_evaluating_openai_agents.ipynb    |   2 +-
 ...ample_intent_classification_pipeline.ipynb |   4 +-
 cookbook/example_langgraph_agents.ipynb       |   2 +-
 .../example_llm_security_monitoring.ipynb     |   2 +-
 cookbook/integration_amazon_bedrock.ipynb     |   1 +
 .../integration_azure_openai_langchain.ipynb  | 243 -------
 cookbook/integration_databricks.ipynb         | 358 ---------
 cookbook/integration_langchain.ipynb          | 387 ----------
 cookbook/integration_langgraph.ipynb          |   2 +-
 cookbook/integration_langserve.ipynb          | 246 -------
 cookbook/integration_litellm_proxy.ipynb      | 308 --------
 .../integration_llama-index-callback.ipynb    | 229 ------
 ...egration_llama-index_instrumentation.ipynb | 273 -------
 .../integration_llama-index_milvus-lite.ipynb | 233 ------
 ...egration_llama_index_posthog_mistral.ipynb | 423 -----------
 cookbook/integration_openai_assistants.ipynb  |  22 +-
 cookbook/integration_openai_sdk.ipynb         | 682 ------------------
 ...integration_openai_structured_output.ipynb | 392 ----------
 cookbook/js_integration_langchain.ipynb       | 202 ------
 cookbook/js_integration_litellm_proxy.ipynb   | 246 -------
 cookbook/js_integration_openai.ipynb          | 378 ----------
 cookbook/js_prompt_management_langchain.ipynb |   2 +-
 cookbook/langfuse_sdk_performance_test.ipynb  |   2 +-
 cookbook/otel_integration_arize.ipynb         |   2 +
 cookbook/otel_integration_mlflow.ipynb        |   2 +
 cookbook/otel_integration_openlit.ipynb       |   2 +
 cookbook/otel_integration_openllmetry.ipynb   |   3 +
 ...mpt_management_performance_benchmark.ipynb |   1 -
 lib/redirects.js                              | 205 +++++-
 119 files changed, 714 insertions(+), 11397 deletions(-)
 delete mode 100644 content/guides/cookbook/evaluation_with_langchain.md
 delete mode 100644 content/guides/cookbook/evaluation_with_uptrain.md
 delete mode 100644 content/guides/cookbook/example_decorator_openai_langchain.md
 delete mode 100644 content/guides/cookbook/integration_amazon_bedrock.md
 delete mode 100644 content/guides/cookbook/integration_anthropic.mdx
 delete mode 100644 content/guides/cookbook/integration_azure_openai_langchain.md
 delete mode 100644 content/guides/cookbook/integration_databricks.mdx
 delete mode 100644 content/guides/cookbook/integration_langchain.md
 delete mode 100644 content/guides/cookbook/integration_langserve.md
 delete mode 100644 content/guides/cookbook/integration_litellm_proxy.mdx
 delete mode 100644 content/guides/cookbook/integration_llama-index-callback.md
 delete mode 100644 content/guides/cookbook/integration_llama-index_instrumentation.md
 delete mode 100644 content/guides/cookbook/integration_llama-index_milvus-lite.mdx
 delete mode 100644 content/guides/cookbook/integration_llama_index.mdx
 delete mode 100644 content/guides/cookbook/integration_llama_index_posthog_mistral.md
 delete mode 100644 content/guides/cookbook/integration_llamaindex_workflows.mdx
 delete mode 100644 content/guides/cookbook/integration_openai_assistants.md
 delete mode 100644 content/guides/cookbook/integration_openai_sdk.mdx
 delete mode 100644 content/guides/cookbook/integration_openai_structured_output.md
 delete mode 100644 content/guides/cookbook/js_integration_anthropic.mdx
 delete mode 100644 content/guides/cookbook/js_integration_langchain.mdx
 delete mode 100644 content/guides/cookbook/js_integration_litellm_proxy.mdx
 delete mode 100644 content/guides/cookbook/js_integration_openai.mdx
 delete mode 100644 content/guides/videos/external-evaluation-pipelines.mdx
 delete mode 100644 content/guides/videos/introducing-datasets-v2.mdx
 delete mode 100644 content/guides/videos/introducing-langfuse-2.0.mdx
 delete mode 100644 content/guides/videos/introducing-python-decorator.mdx
 delete mode 100644 content/guides/videos/llm-as-a-judge-eval-on-dataset-experiments.mdx
 delete mode 100644 content/guides/videos/llm-playground.mdx
 delete mode 100644 content/guides/videos/posthog-integration.mdx
 delete mode 100644 content/guides/videos/run-langfuse-locally.mdx
 rename content/{guides/cookbook/integration_langgraph.mdx => integrations/frameworks/langgraph.mdx} (95%)
 delete mode 100644 content/integrations/frameworks/langserve.md
 rename content/{guides/cookbook/otel_integration_mlflow.md => integrations/other/mlflow.md} (98%)
 rename content/{guides/cookbook/otel_integration_arize.md => integrations/other/openinference.md} (98%)
 rename content/{guides/cookbook/otel_integration_openlit.md => integrations/other/openlit.md} (98%)
 rename content/{guides/cookbook/otel_integration_openllmetry.md => integrations/other/openllmetry.md} (98%)
 rename content/{guides/cookbook/langfuse_sdk_performance_test.md => resources/engineering/langfuse-sdk-performance-test.md} (98%)
 rename content/{guides/cookbook/prompt_management_performance_benchmark.md => resources/engineering/prompt-management-performance-benchmark.md} (98%)
 rename content/{guides/videos => resources/engineering}/webinar-observability-llm-systems.mdx (100%)
 delete mode 100644 cookbook/evaluation_with_langchain.ipynb
 delete mode 100644 cookbook/evaluation_with_uptrain.ipynb
 delete mode 100644 cookbook/example_decorator_openai_langchain.ipynb
 delete mode 100644 cookbook/integration_azure_openai_langchain.ipynb
 delete mode 100644 cookbook/integration_databricks.ipynb
 delete mode 100644 cookbook/integration_langchain.ipynb
 delete mode 100644 cookbook/integration_langserve.ipynb
 delete mode 100644 cookbook/integration_litellm_proxy.ipynb
 delete mode 100644 cookbook/integration_llama-index-callback.ipynb
 delete mode 100644 cookbook/integration_llama-index_instrumentation.ipynb
 delete mode 100644 cookbook/integration_llama-index_milvus-lite.ipynb
 delete mode 100644 cookbook/integration_llama_index_posthog_mistral.ipynb
 delete mode 100644 cookbook/integration_openai_sdk.ipynb
 delete mode 100644 cookbook/integration_openai_structured_output.ipynb
 delete mode 100644 cookbook/js_integration_langchain.ipynb
 delete mode 100644 cookbook/js_integration_litellm_proxy.ipynb
 delete mode 100644 cookbook/js_integration_openai.ipynb

diff --git a/components/CookbookIndex.tsx b/components/CookbookIndex.tsx
index 010daf5869..0a4febc7cf 100644
--- a/components/CookbookIndex.tsx
+++ b/components/CookbookIndex.tsx
@@ -1,15 +1,27 @@
 import { guidesSource } from "@/lib/source";
-import { FileCode, ArrowRight } from "lucide-react";
+import { FileCode, Video, ArrowRight } from "lucide-react";
 import { Cards, Card } from "@/components/docs";
 
 type CookbookPage = ReturnType<typeof guidesSource.getPages>[number];
 
-export const CookbookIndex = ({ categories }: { categories?: string[] }) => (
+export const CookbookIndex = ({
+  categories,
+  includeVideos,
+}: {
+  categories?: string[];
+  includeVideos?: boolean;
+}) => (
   <>
     {Object.entries(
       guidesSource
         .getPages()
-        .filter((page) => page.url.startsWith("/guides/cookbook/"))
+        .filter(
+          (page) =>
+            page.url.startsWith("/guides/cookbook/") ||
+            (includeVideos &&
+              page.url.startsWith("/guides/videos/") &&
+              !!page.data.category),
+        )
         .reduce(
           (acc, page) => {
             const category =
@@ -52,7 +64,13 @@ export const CookbookIndex = ({ categories }: { categories?: string[] }) => (
                   href={page.url}
                   key={page.url}
                   className="no-underline group"
-                  icon={<FileCode className="h-3 w-3" />}
+                  icon={
+                    page.url.startsWith("/guides/videos/") ? (
+                      <Video className="h-3 w-3" />
+                    ) : (
+                      <FileCode className="h-3 w-3" />
+                    )
+                  }
                 >
                   <div className="flex flex-1 items-center gap-3 w-full justify-between">
                     <span className="flex-1 text-text-secondary group-hover:text-text-primary transition-colors duration-220">
diff --git a/content/blog/2024-04-python-decorator.mdx b/content/blog/2024-04-python-decorator.mdx
index 4ecf93d00b..6db403a22c 100644
--- a/content/blog/2024-04-python-decorator.mdx
+++ b/content/blog/2024-04-python-decorator.mdx
@@ -72,7 +72,7 @@ The decorator should:
 
 ## Design decisions
 
-1. The decorator reuses the low-level SDK to create traces and asynchronously batch them to the Langfuse API. This implementation was derived from the PostHog SDKs and is [tested](/guides/cookbook/langfuse_sdk_performance_test) to have little to no impact on the performance of your application.
+1. The decorator reuses the low-level SDK to create traces and asynchronously batch them to the Langfuse API. This implementation was derived from the PostHog SDKs and is [tested](/resources/engineering/langfuse-sdk-performance-test) to have little to no impact on the performance of your application.
 2. The decorator maintains a call stack internally that keeps track of nested function calls to reflect the observation hierarchy in the trace.
 3. To be async-safe, the decorator leverages [Python Contextvars](https://docs.python.org/3/library/contextvars.html) for managing its state.
 4. The same `observe()` decorator is used to create a trace (outermost decorated function) and to add spans to the trace (inner decorated functions). This way, functions can be used in multiple traces without needing to be strictly a "trace" or a "span" function.
@@ -213,7 +213,7 @@ If you want to built complex applications while being able to easily switch betw
 - Langfuse OpenAI SDK Wrapper for automatic instrumentation of OpenAI calls
 - LiteLLM Proxy for standardization of 100+ models on the OpenAI API
 
-Have a look at [this cookbook](/guides/cookbook/integration_litellm_proxy) to see an end-to-end example – we really think you'll like this stack and there are lots of teams in the Langfuse Community who built on top of it.
+Have a look at [this cookbook](/integrations/gateways/litellm#openai-sdk-wrapper) to see an end-to-end example – we really think you'll like this stack and there are lots of teams in the Langfuse Community who built on top of it.
 
 ## Thank you
 
@@ -232,9 +232,4 @@ import { FileCode, BookOpen, Video as VideoIcon } from "lucide-react";
     href="/docs/sdk/python/example"
     icon={<FileCode />}
   />
-  <Card
-    title="Video introduction"
-    href="/guides/videos/introducing-python-decorator"
-    icon={<VideoIcon />}
-  />
 </Cards>
diff --git a/content/blog/2024-08-what-is-langchain.mdx b/content/blog/2024-08-what-is-langchain.mdx
index 63638d6d61..be8ce40761 100644
--- a/content/blog/2024-08-what-is-langchain.mdx
+++ b/content/blog/2024-08-what-is-langchain.mdx
@@ -81,7 +81,7 @@ The answer is, **[LangServe](https://python.langchain.com/v0.2/docs/langserve/)
 
 Using LangServe will allow you to create a scalable Python web server for your LangChain application that you can then deploy and scale out to your Cloud provider of choice, such as GCP or Replit.
 
-**Logging and Debugging for LangServe**: You can integrate LangServe with Langfuse for [open-source observability](/integrations/frameworks/langserve). You can use the Langfuse UI to debug, analyze, and iterate on your LangServe application.
+**Logging and Debugging for LangServe**: You can integrate LangServe with Langfuse for [open-source observability](/integrations/frameworks/langchain). You can use the Langfuse UI to debug, analyze, and iterate on your LangServe application.
 
 ## Langfuse - an alternative to LangChain?
 
diff --git a/content/blog/2024-10-opentelemetry-for-llm-observability.mdx b/content/blog/2024-10-opentelemetry-for-llm-observability.mdx
index 75facac851..3e1a039ebb 100644
--- a/content/blog/2024-10-opentelemetry-for-llm-observability.mdx
+++ b/content/blog/2024-10-opentelemetry-for-llm-observability.mdx
@@ -34,7 +34,7 @@ OpenTelemetry supports various exporters and backends, making it flexible and ad
 
 This post is a high-level overview of the challenges of LLM observability and the current state of using OpenTelemetry (OTel) for LLMOps.
 
-OTel is geared towards general observability, and **traces are a great standardized way to capture LLM application data** – we have recorded a [webinar](/guides/videos/webinar-observability-llm-systems) on this. While we are excited about OTel and the roadmap towards it across LLMOps tools, non-OTel LLMOps tools are preferred by many teams. This post explores why this is the case and how OTel can address these challenges in the future.
+OTel is geared towards general observability, and **traces are a great standardized way to capture LLM application data** – we have recorded a [webinar](/resources/engineering/webinar-observability-llm-systems) on this. While we are excited about OTel and the roadmap towards it across LLMOps tools, non-OTel LLMOps tools are preferred by many teams. This post explores why this is the case and how OTel can address these challenges in the future.
 
 _Example trace of our [public demo](/docs/demo)_
 
@@ -145,4 +145,4 @@ We are committed to OTel and are happy to contribute to the SIG. We will continu
 
 If you want to get started with tracing your AI applications with Langfuse today, check out our [quickstart guide](/docs/get-started) on how to use Langfuse with multiple LLM building frameworks like [Langchain](/integrations/frameworks/langchain) or [LlamaIndex](/integrations/frameworks/llamaindex).
 
-If you are curious about why Traces are a good fit for LLM observability, check out our [webinar](/guides/videos/webinar-observability-llm-systems) on the topic.
+If you are curious about why Traces are a good fit for LLM observability, check out our [webinar](/resources/engineering/webinar-observability-llm-systems) on the topic.
diff --git a/content/blog/launch-week-1.mdx b/content/blog/launch-week-1.mdx
index c29cb901f1..7cbece759f 100644
--- a/content/blog/launch-week-1.mdx
+++ b/content/blog/launch-week-1.mdx
@@ -44,7 +44,7 @@ const res = await openai.chat.completions.create({
 });
 ```
 
-We [launched](/changelog/2024-04-21-openai-integration-JS-SDK) a new wrapper for the OpenAI JS SDK. This integration, designed to enable easier monitoring of OpenAI API usage, features seamless observability with enhancements like automatic tracking of prompts, completions, and API errors, as well as insights into model usage and costs. After a soft launch that gathered user feedback for improvements, the integration is now fully available, complete with comprehensive [documentation](/integrations/model-providers/openai-js) and an [example notebook](/guides/cookbook/js_integration_openai).
+We [launched](/changelog/2024-04-21-openai-integration-JS-SDK) a new wrapper for the OpenAI JS SDK. This integration, designed to enable easier monitoring of OpenAI API usage, features seamless observability with enhancements like automatic tracking of prompts, completions, and API errors, as well as insights into model usage and costs. After a soft launch that gathered user feedback for improvements, the integration is now fully available, complete with comprehensive [documentation](/integrations/model-providers/openai-js) and an example notebook.
 
 ### Day 1: PostHog Integration
 
@@ -74,7 +74,7 @@ We're excited to introduce the [**LLM Playground**](/docs/playground) to Langfus
   title="Decorator Integration"
 />
 
-We're happy to share that the Decorator-based integration for Python now supports all Langfuse features and is the recommended way to use Langfuse in Python. The decorator makes integrating with Langfuse so much easier. Head over to the [Python Decorator docs](/docs/sdk/python/decorators) to learn more. All inputs, outputs, timings are captured automatically, and it works with all other [Langfuse integrations](/integrations) (LangChain, LlamaIndex, OpenAI SDK, ...). To celebrate this milestone, we wrote a [blog post](/blog/2024-04-python-decorator) on the technical details and created the [example notebook](/guides/cookbook/example_decorator_openai_langchain) shown in the video as it demonstrates what's really cool about the decorator. Thanks again to [@lshalon](https://github.com/lshalon) and [@AshisGhosh](https://github.com/AshisGhosh) for your contributions to this!
+We're happy to share that the Decorator-based integration for Python now supports all Langfuse features and is the recommended way to use Langfuse in Python. The decorator makes integrating with Langfuse so much easier. Head over to the [Python Decorator docs](/docs/sdk/python/decorators) to learn more. All inputs, outputs, timings are captured automatically, and it works with all other [Langfuse integrations](/integrations) (LangChain, LlamaIndex, OpenAI SDK, ...). To celebrate this milestone, we wrote a [blog post](/blog/2024-04-python-decorator) on the technical details and created the example notebook shown in the video as it demonstrates what's really cool about the decorator. Thanks again to [@lshalon](https://github.com/lshalon) and [@AshisGhosh](https://github.com/AshisGhosh) for your contributions to this!
 
 ### Day 4: Datasets v2
 
diff --git a/content/changelog/2024-02-27-llama-index-integration.mdx b/content/changelog/2024-02-27-llama-index-integration.mdx
index d3849ca25b..4ee2299e13 100644
--- a/content/changelog/2024-02-27-llama-index-integration.mdx
+++ b/content/changelog/2024-02-27-llama-index-integration.mdx
@@ -32,7 +32,7 @@ PS: If you are interested in an integration with LlamaIndex.TS, add your upvote/
 />
 <span>
   _Based on the [LlamaIndex
-  Cookbook](/guides/cookbook/integration_llama_index)._
+  integration](/integrations/frameworks/llamaindex)._
 </span>
 
 ### 📚 More details
diff --git a/content/changelog/2024-03-05-uptrain-integration.mdx b/content/changelog/2024-03-05-uptrain-integration.mdx
index 8378bae470..ffbfed0468 100644
--- a/content/changelog/2024-03-05-uptrain-integration.mdx
+++ b/content/changelog/2024-03-05-uptrain-integration.mdx
@@ -38,8 +38,8 @@ import { FileCode } from "lucide-react";
 
 <Cards num={3}>
   <Card
-    title="Uptrain + Langfuse Cookbook"
-    href="/guides/cookbook/evaluation_with_uptrain"
+    title="External evaluation pipelines cookbook"
+    href="/guides/cookbook/example_external_evaluation_pipelines"
     icon={<FileCode />}
   />
 </Cards>
diff --git a/content/changelog/2024-04-21-openai-integration-JS-SDK.mdx b/content/changelog/2024-04-21-openai-integration-JS-SDK.mdx
index bfa7b4f6ff..5b7add105f 100644
--- a/content/changelog/2024-04-21-openai-integration-JS-SDK.mdx
+++ b/content/changelog/2024-04-21-openai-integration-JS-SDK.mdx
@@ -54,7 +54,7 @@ import { FileCode } from "lucide-react";
   />
   <Card
     title="Example Notebook"
-    href="/guides/cookbook/js_integration_openai"
+    href="/integrations/model-providers/openai-js"
     icon={<FileCode />}
   />
 </Cards>
diff --git a/content/changelog/2024-04-24-decorator.mdx b/content/changelog/2024-04-24-decorator.mdx
index b8f0bb9ed0..4f987cf81e 100644
--- a/content/changelog/2024-04-24-decorator.mdx
+++ b/content/changelog/2024-04-24-decorator.mdx
@@ -23,7 +23,7 @@ On Day 3 of [Launch Week 1](/blog/launch-week-1), we're happy to share that the
 
 The decorator makes integrating with Langfuse so much easier. Head over to the [Python Decorator docs](/docs/sdk/python/decorators) to learn more. All inputs, outputs, timings are captured automatically, and it works with all other [Langfuse integrations](/integrations) (LangChain, LlamaIndex, OpenAI SDK, ...).
 
-To celebrate this milestone, we wrote a [blog post](/blog/2024-04-python-decorator) on the technical details and created the [example notebook](/guides/cookbook/example_decorator_openai_langchain) shown in the video as it demonstrates what's really cool about the decorator. Let us know what you think in the [GitHub discussion](https://github.com/orgs/langfuse/discussions/1009), and stay tuned for more updates during [Langfuse Launch Week 1](/blog/launch-week-1) 🚀
+To celebrate this milestone, we wrote a [blog post](/blog/2024-04-python-decorator) on the technical details and created the example notebook shown in the video as it demonstrates what's really cool about the decorator. Let us know what you think in the [GitHub discussion](https://github.com/orgs/langfuse/discussions/1009), and stay tuned for more updates during [Langfuse Launch Week 1](/blog/launch-week-1) 🚀
 
 Thanks again to [@lshalon](https://github.com/lshalon) and [@AshisGhosh](https://github.com/AshisGhosh) for your contributions to this!
 
@@ -42,9 +42,4 @@ import { Book, FileCode } from "lucide-react";
     href="/docs/sdk/python/example"
     icon={<FileCode />}
   />
-  <Card
-    title="Rap battle example notebook"
-    href="/guides/cookbook/example_decorator_openai_langchain"
-    icon={<FileCode />}
-  />
 </Cards>
diff --git a/content/changelog/2024-08-08-openai-structured-outputs.mdx b/content/changelog/2024-08-08-openai-structured-outputs.mdx
index 638bd9ddbb..87e9effa07 100644
--- a/content/changelog/2024-08-08-openai-structured-outputs.mdx
+++ b/content/changelog/2024-08-08-openai-structured-outputs.mdx
@@ -34,7 +34,7 @@ import { FileCode, BookOpen } from "lucide-react";
 <Cards num={3}>
   <Card
     title="Cookbook: OpenAI Structured Outputs"
-    href="/guides/cookbook/integration_openai_structured_output"
+    href="/integrations/model-providers/openai-py#structured-output"
     icon={<FileCode />}
   />
 </Cards>
diff --git a/content/changelog/2024-08-21-openai-multi-modal-traces.mdx b/content/changelog/2024-08-21-openai-multi-modal-traces.mdx
index 6ca193e3e4..5659a25af0 100644
--- a/content/changelog/2024-08-21-openai-multi-modal-traces.mdx
+++ b/content/changelog/2024-08-21-openai-multi-modal-traces.mdx
@@ -16,7 +16,7 @@ Langfuse now supports rendering multi-modal traces. In addition to text, you may
 
 ## How to trace vision input in Langfuse?
 
-If you use the [OpenAI Python SDK](/integrations/model-providers/openai-py), you can follow our [OpenAI cookbook](/guides/cookbook/integration_openai_sdk) which includes an example for tracing images in Langfuse.
+If you use the [OpenAI Python SDK](/integrations/model-providers/openai-py), you can follow our [OpenAI cookbook](/integrations/model-providers/openai-py) which includes an example for tracing images in Langfuse.
 
 <Cards num={3}>
   <Card
@@ -26,7 +26,7 @@ If you use the [OpenAI Python SDK](/integrations/model-providers/openai-py), you
   />
   <Card
     title="Cookbook: OpenAI Integration (Python)"
-    href="/guides/cookbook/integration_openai_sdk"
+    href="/integrations/model-providers/openai-py"
     icon={<FileCode />}
   />
 </Cards>
diff --git a/content/docs/observability/features/agent-graphs.mdx b/content/docs/observability/features/agent-graphs.mdx
index 1d8f33aafb..b47db5d712 100644
--- a/content/docs/observability/features/agent-graphs.mdx
+++ b/content/docs/observability/features/agent-graphs.mdx
@@ -32,7 +32,7 @@ There are two ways a graph appears for a trace:
 2. **From the LangGraph integration.** When you use the LangGraph integration, the graph shows automatically.
 
 **Observation Types**: See all available [Observation Types](/docs/observability/features/observation-types) and how to set them.
-**LangGraph**: See the [LangGraph integration guide](/guides/cookbook/integration_langgraph) for an end-to-end example on how to natively integrate LangGraph with Langfuse for LLM Agent tracing.
+**LangGraph**: See the [LangGraph integration guide](/integrations/frameworks/langgraph) for an end-to-end example on how to natively integrate LangGraph with Langfuse for LLM Agent tracing.
 
 ## View modes: Aggregated and Expanded
 
diff --git a/content/docs/observability/get-started.mdx b/content/docs/observability/get-started.mdx
index 206aba1460..42d85f9e54 100644
--- a/content/docs/observability/get-started.mdx
+++ b/content/docs/observability/get-started.mdx
@@ -179,12 +179,6 @@ First install the Langfuse OpenAI wrapper. It extends the official client to sen
     href="/integrations/model-providers/openai-js"
     arrow
   />
-  <Card
-    icon={<Code />}
-    title="Notebook"
-    href="/guides/cookbook/js_integration_openai"
-    arrow
-  />
 </Cards>
 
 </Tab>
@@ -256,12 +250,6 @@ First install the Langfuse core SDK and the LangChain integration.
     href="/integrations/frameworks/langchain"
     arrow
   />
-  <Card
-    icon={<Code />}
-    title="Notebook"
-    href="/guides/cookbook/js_integration_langchain"
-    arrow
-  />
 </Cards>
 
 </Tab>
diff --git a/content/docs/prompt-management/features/caching.mdx b/content/docs/prompt-management/features/caching.mdx
index afb2ea3507..35671ae8ec 100644
--- a/content/docs/prompt-management/features/caching.mdx
+++ b/content/docs/prompt-management/features/caching.mdx
@@ -212,7 +212,7 @@ While usually not necessary, you can ensure 100% availability of prompts by pre-
 
 ## Performance measurement of initial fetch
 
-We measured the execution time of the following snippet with fully disabled caching. You can run [this notebook](/guides/cookbook/prompt_management_performance_benchmark) yourself to verify the results.
+We measured the execution time of the following snippet with fully disabled caching. You can run [this notebook](/resources/engineering/prompt-management-performance-benchmark) yourself to verify the results.
 
 ```python
 prompt = langfuse.get_prompt("perf-test", cache_ttl_seconds=0)
diff --git a/content/docs/prompt-management/features/config.mdx b/content/docs/prompt-management/features/config.mdx
index 2b0ec5e481..b95a1c0eba 100644
--- a/content/docs/prompt-management/features/config.mdx
+++ b/content/docs/prompt-management/features/config.mdx
@@ -293,5 +293,5 @@ res = client.chat.completions.create(
 ```
 
 <Callout type="info">
-For complete end-to-end examples, see the [OpenAI Functions cookbook](/guides/cookbook/prompt_management_openai_functions) and the [Structured Outputs cookbook](/guides/cookbook/integration_openai_structured_output).
+For complete end-to-end examples, see the [OpenAI Functions cookbook](/guides/cookbook/prompt_management_openai_functions) and the [Structured Outputs docs](/integrations/model-providers/openai-py#structured-output).
 </Callout>
diff --git a/content/faq/all/fifteen-questions-langfuse-answered.mdx b/content/faq/all/fifteen-questions-langfuse-answered.mdx
index e69f650967..374c72c90a 100644
--- a/content/faq/all/fifteen-questions-langfuse-answered.mdx
+++ b/content/faq/all/fifteen-questions-langfuse-answered.mdx
@@ -105,7 +105,7 @@ The answer is yes, Langfuse is an open source alternative to Promptlayer. You ca
 
 ## 16. Can I use Langchain, Langserve and Langgraph with Langfuse Observability and Tracing?
 
-**Yes, you can use [Langchain](/integrations/frameworks/langchain), [Langserve](/integrations/frameworks/langserve) and [Langgraph](/integrations/frameworks/langchain) with Langfuse to gain observability, tracing and evals.** Langfuse integrates with both Langchain Python and Langchain JS. Langfuse has maintained a first class integration with Langchain since day one.
+**Yes, you can use [Langchain](/integrations/frameworks/langchain) and [Langgraph](/integrations/frameworks/langgraph) with Langfuse to gain observability, tracing and evals.** Langfuse integrates with both Langchain Python and Langchain JS. Langfuse has maintained a first class integration with Langchain since day one.
 
 ## 17. Can Langfuse be purchased via the AWS Marketplace?
 
diff --git a/content/guides/cookbook/evaluation_with_langchain.md b/content/guides/cookbook/evaluation_with_langchain.md
deleted file mode 100644
index 6b135e8dfa..0000000000
--- a/content/guides/cookbook/evaluation_with_langchain.md
+++ /dev/null
@@ -1,217 +0,0 @@
----
-title: "Run Langchain Evaluations on data in Langfuse"
-description: Cookbook that demonstrates how to run Langchain evaluations on data in Langfuse.
-category: Evaluation
----
-
-# Run Langchain Evaluations on data in Langfuse
-
-This cookbook shows how model-based evaluations can be used to automate the evaluation of production completions in Langfuse. This example uses Langchain and is adaptable to other libraries. Which library is the best to use depends heavily on the use case.
-
-This cookbook follows three steps:
-1. Fetch production `generations` stored in Langfuse
-2. Evaluate these `generations` using Langchain
-3. Ingest results back into Langfuse as `scores`
-
-
-----
-Not using Langfuse yet? [Get started](https://langfuse.com/docs/get-started) by capturing LLM events.
-
-### Setup
-
-First you need to install Langfuse and Langchain via pip and then set the environment variables.
-
-
-```python
-%pip install langfuse langchain langchain-classic langchain-openai --upgrade
-```
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page: https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your openai key
-os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...");
-```
-
-
-```python
-os.environ['EVAL_MODEL'] = "gpt-3.5-turbo-instruct"
-
-# Langchain Eval types
-EVAL_TYPES={
-    "hallucination": True,
-    "conciseness": True,
-    "relevance": True,
-    "coherence": True,
-    "harmfulness": True,
-    "maliciousness": True,
-    "helpfulness": True,
-    "controversiality": True,
-    "misogyny": True,
-    "criminality": True,
-    "insensitivity": True
-}
-```
-
-Initialize the Langfuse Python SDK, more information [here](https://langfuse.com/docs/sdk/python#1-installation).
-
-
-```python
-from langfuse import get_client
- 
-langfuse = get_client()
- 
-# Verify connection
-if langfuse.auth_check():
-    print("Langfuse client is authenticated and ready!")
-else:
-    print("Authentication failed. Please check your credentials and host.")
-```
-
-    Langfuse client is authenticated and ready!
-
-
-### Fetching data
-
-Load all `generations` from Langfuse filtered by `name`, in this case `OpenAI`. Names are used in Langfuse to identify different types of generations within an application. Change it to the name you want to evaluate.
-
-Checkout [docs](https://langfuse.com/docs/sdk/python#generation) on how to set the name when ingesting an LLM Generation.
-
-
-```python
-def fetch_all_pages(name=None, user_id = None, limit=50):
-    page = 1
-    all_data = []
-
-    while True:
-        response = langfuse.api.trace.list(name=name, limit=limit, user_id=user_id, page=page)
-        if not response.data:
-            break
-
-        all_data.extend(response.data)
-        page += 1
-
-    return all_data
-```
-
-
-```python
-generations = fetch_all_pages(user_id='user_123')
-```
-
-
-```python
-generations[0].id
-```
-
-
-
-
-    'adb5ba6beab14984ab89006ee09e9cd6'
-
-
-
-### Set up evaluation functions
-
-In this section, we define functions to set up the Langchain eval based on the entries in `EVAL_TYPES`. Hallucinations require their own function. More on the Langchain evals can be found [here](https://python.langchain.com/docs/guides/evaluation/string/criteria_eval_chain).
-
-
-```python
-from langchain_classic.evaluation import load_evaluator
-from langchain_openai import OpenAI
-from langchain_classic.evaluation.criteria import LabeledCriteriaEvalChain
-
-def get_evaluator_for_key(key: str):
-  llm = OpenAI(temperature=0, model=os.environ.get('EVAL_MODEL'))
-  return load_evaluator("criteria", criteria=key, llm=llm)
-
-def get_hallucination_eval():
-  criteria = {
-    "hallucination": (
-      "Does this submission contain information"
-      " not present in the input or reference?"
-    ),
-  }
-  llm = OpenAI(temperature=0, model=os.environ.get('EVAL_MODEL'))
-
-  return LabeledCriteriaEvalChain.from_llm(
-      llm=llm,
-      criteria=criteria,
-  )
-```
-
-### Execute evaluation
-
-Below, we execute the evaluation for each `Generation` loaded above. Each score is ingested into Langfuse via [`langfuse.score()`](https://langfuse.com/docs/scores).
-
-
-
-```python
-def execute_eval_and_score():
-
-  for generation in generations:
-    criteria = [key for key, value in EVAL_TYPES.items() if value and key != "hallucination"]
-
-    for criterion in criteria:
-      eval_result = get_evaluator_for_key(criterion).evaluate_strings(
-          prediction=generation.output,
-          input=generation.input,
-      )
-      print(eval_result)
-
-      langfuse.create_score(name=criterion, trace_id=generation.id, observation_id=generation.id, value=eval_result["score"], comment=eval_result['reasoning'])
-
-execute_eval_and_score()
-
-```
-
-
-```python
-# hallucination
-
-def eval_hallucination():
-
-  chain = get_hallucination_eval()
-
-  for generation in generations:
-    eval_result = chain.evaluate_strings(
-      prediction=generation.output,
-      input=generation.input,
-      reference=generation.input
-    )
-    print(eval_result)
-    if eval_result is not None and eval_result["score"] is not None and eval_result["reasoning"] is not None:
-      langfuse.create_score(name='hallucination', trace_id=generation.id, observation_id=generation.id, value=eval_result["score"], comment=eval_result['reasoning'])
-
-```
-
-
-```python
-if EVAL_TYPES.get("hallucination") == True:
-  eval_hallucination()
-```
-
-
-```python
-# SDK is async, make sure to await all requests
-langfuse.flush()
-```
-
-### See Scores in Langfuse
-
- In the Langfuse UI, you can filter Traces by `Scores` and look into the details for each. Check out Langfuse Analytics to understand the impact of new prompt versions or application releases on these scores.
-
-![Image of Trace](https://langfuse.com/images/docs/trace-conciseness-score.jpg)
-_Example trace with conciseness score_
-
-
-## Get in touch
-
-Looking for a specific way to score your production data in Langfuse? Join the [Discord](https://langfuse.com/discord) and discuss your use case!
diff --git a/content/guides/cookbook/evaluation_with_uptrain.md b/content/guides/cookbook/evaluation_with_uptrain.md
deleted file mode 100644
index eb3a379f1e..0000000000
--- a/content/guides/cookbook/evaluation_with_uptrain.md
+++ /dev/null
@@ -1,411 +0,0 @@
----
-title: "Evaluate Langfuse LLM Traces with UpTrain"
-description: This notebook demonstrates how to run UpTrain's evaluation metrics on the traces generated by Langfuse.
-category: Evaluation
----
-
-# Evaluate Langfuse LLM Traces with UpTrain
-
-UpTrain's open-source library offers a series of evaluation metrics to assess LLM applications.
-
-This notebook demonstrates how to run UpTrain's evaluation metrics on the traces generated by Langfuse. In Langfuse you can then monitor these scores over time or use them to compare different experiments.
-
-## Setup
-
-You can get your Langfuse API keys [here](https://cloud.langfuse.com/) and OpenAI API key [here](https://platform.openai.com/api-keys)
-
-
-```python
-%pip install langfuse datasets uptrain litellm openai rouge_score --upgrade
-```
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page: https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your openai key
-os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...");
-```
-
-## Sample Dataset
-
-We use this dataset to represent [traces](https://langfuse.com/docs/tracing) that you have logged to Langfuse. In a production environment, you would use your own data.
-
-
-```python
-data = [
-    {
-        "question": "What are the symptoms of a heart attack?",
-        "context": "A heart attack, or myocardial infarction, occurs when the blood supply to the heart muscle is blocked. Chest pain is a good symptom of heart attack, though there are many others.",
-        "response": "Symptoms of a heart attack may include chest pain or discomfort, shortness of breath, nausea, lightheadedness, and pain or discomfort in one or both arms, the jaw, neck, or back."
-    },
-    {
-        "question": "Can stress cause physical health problems?",
-        "context": "Stress is the body's response to challenges or threats. Yes, chronic stress can contribute to various physical health problems, including cardiovascular issues.",
-        "response": "Yes, chronic stress can contribute to various physical health problems, including cardiovascular issues, and a weakened immune system."
-    },
-    {
-        'question': "What are the symptoms of a heart attack?",
-        'context': "A heart attack, or myocardial infarction, occurs when the blood supply to the heart muscle is blocked. Symptoms of a heart attack may include chest pain or discomfort, shortness of breath and nausea.",
-        'response': "Heart attack symptoms are usually just indigestion and can be relieved with antacids."
-    },
-    {
-        'question': "Can stress cause physical health problems?",
-        'context': "Stress is the body's response to challenges or threats. Yes, chronic stress can contribute to various physical health problems, including cardiovascular issues.",
-        'response': "Stress is not real, it is just imaginary!"
-    }
-]
-```
-
-## Run Evaluations using UpTrain
-
-We have used the following 3 metrics from UpTrain's open-source library:
-
-1. [Context Relevance](https://docs.uptrain.ai/predefined-evaluations/context-awareness/context-relevance): Evaluates how relevant the retrieved context is to the question specified.
-
-2. [Factual Accuracy](https://docs.uptrain.ai/predefined-evaluations/context-awareness/factual-accuracy): Evaluates whether the response generated is factually correct and grounded by the provided context.
-
-3. [Response Completeness](https://docs.uptrain.ai/predefined-evaluations/response-quality/response-completeness): Evaluates whether the response has answered all the aspects of the question specified.
-
-You can look at the complete list of UpTrain's supported metrics [here](https://docs.uptrain.ai/predefined-evaluations/overview)
-
-
-```python
-from uptrain import EvalLLM, Evals
-import json
-import pandas as pd
-
-eval_llm = EvalLLM(openai_api_key=os.environ["OPENAI_API_KEY"])
-
-res = eval_llm.evaluate(
-    data = data,
-    checks = [Evals.CONTEXT_RELEVANCE, Evals.FACTUAL_ACCURACY, Evals.RESPONSE_COMPLETENESS]
-)
-```
-
-    2025-06-17 10:43:14.568 | WARNING  | uptrain.operators.language.llm:fetch_responses:268 - Detected a running event loop, scheduling requests in a separate thread.
-    100%|██████████| 4/4 [00:01<00:00,  2.85it/s]
-    /Users/jannik/Documents/GitHub/langfuse-docs/.venv/lib/python3.13/site-packages/uptrain/operators/language/llm.py:271: RuntimeWarning: coroutine 'LLMMulticlient.async_fetch_responses' was never awaited
-      with ThreadPoolExecutor(max_workers=1) as executor:
-    RuntimeWarning: Enable tracemalloc to get the object allocation traceback
-    2025-06-17 10:43:15.996 | WARNING  | uptrain.operators.language.llm:fetch_responses:268 - Detected a running event loop, scheduling requests in a separate thread.
-    100%|██████████| 4/4 [00:01<00:00,  2.74it/s]
-    2025-06-17 10:43:17.464 | WARNING  | uptrain.operators.language.llm:fetch_responses:268 - Detected a running event loop, scheduling requests in a separate thread.
-    100%|██████████| 4/4 [00:03<00:00,  1.19it/s]
-    2025-06-17 10:43:20.860 | WARNING  | uptrain.operators.language.llm:fetch_responses:268 - Detected a running event loop, scheduling requests in a separate thread.
-    100%|██████████| 4/4 [00:01<00:00,  3.13it/s]
-    2025-06-17 10:43:22.148 | INFO     | uptrain.framework.evalllm:evaluate:376 - Local server not running, start the server to log data and visualize in the dashboard!
-
-
-## Using Langfuse
-
-There are two main ways to run evaluations:
-
-1. Score each Trace (in development): This means you will run the UpTrain evaluations for each trace item.
-
-2. Score in Batches (in production): In this method we will simulate fetching production traces on a periodic basis to score them using the UpTrain evaluators. Often, you'll want to sample the traces instead of scoring all of them to control evaluation costs.
-
-### Development: Score each trace while it's created
-
-
-```python
-from langfuse import get_client
- 
-langfuse = get_client()
- 
-# Verify connection
-if langfuse.auth_check():
-    print("Langfuse client is authenticated and ready!")
-else:
-    print("Authentication failed. Please check your credentials and host.")
-```
-
-    Langfuse client is authenticated and ready!
-
-
-We mock the instrumentation of your application by using the sample dataset. See the [quickstart](https://langfuse.com/docs/get-started) to integrate Langfuse with your application.
-
-
-```python
-# start a new trace when you get a question
-question = data[0]['question']
-context = data[0]['context']
-response = data[0]['response']
-
-with langfuse.start_as_current_observation(as_type="span", name="uptrain trace") as trace:
-    # Store trace_id for later use
-    trace_id = trace.trace_id
-    
-    # retrieve the relevant chunks
-    # chunks = get_similar_chunks(question)
-    
-    # pass it as span
-    with trace.start_as_current_observation(
-        name="retrieval", 
-        input={'question': question}, 
-        output={'context': context}
-    ):
-        pass
-
-    # use llm to generate a answer with the chunks
-    # answer = get_response_from_llm(question, chunks)
-    
-    with trace.start_as_current_observation(
-        name="generation", 
-        input={'question': question, 'context': context}, 
-        output={'response': response}
-    ):
-        pass
-```
-
-We reuse the scores previously calculated for the traces in the sample dataset. In development, you would run the UpTrain evaluations for the single trace as it's created.
-
-
-```python
-langfuse.create_score(name='context_relevance', value=res[0]['score_context_relevance'], trace_id=trace_id)
-langfuse.create_score(name='factual_accuracy', value=res[0]['score_factual_accuracy'], trace_id=trace_id)
-langfuse.create_score(name='response_completeness', value=res[0]['score_response_completeness'], trace_id=trace_id)
-```
-
-![UpTrain Evals on a single trace in Langfuse](https://langfuse.com/images/cookbook/uptrain-trace.png)
-
-### Production: Add scores to traces in batches
-
-To simulate a production environment, we will log our sample dataset to Langfuse.
-
-
-```python
-for interaction in data:
-    with langfuse.start_as_current_observation(as_type="span", name="uptrain batch") as trace:
-        with trace.start_as_current_observation(
-            name="retrieval",
-            input={'question': interaction['question']},
-            output={'context': interaction['context']}
-        ):
-            pass
-        
-        with trace.start_as_current_observation(
-            name="generation",
-            input={'question': interaction['question'], 'context': interaction['context']},
-            output={'response': interaction['response']}
-        ):
-            pass
- 
-# await that Langfuse SDK has processed all events before trying to retrieve it in the next step
-langfuse.flush()
-```
-
-We can now retrieve the traces like regular production data and evaluate them using UpTrain.
-
-
-```python
-def get_traces(name=None, limit=10000, user_id=None):
-    all_data = []
-    page = 1
- 
-    while True:
-        response = langfuse.api.trace.list(
-            name=name, page=page, user_id=user_id, order_by=None
-        )
-        if not response.data:
-            break
-        page += 1
-        all_data.extend(response.data)
-        if len(all_data) > limit:
-            break
- 
-    return all_data[:limit]
-```
-
-Optional: create a random sample to reduce evaluation costs.
-
-
-```python
-from random import sample
- 
-NUM_TRACES_TO_SAMPLE = 4
-traces = get_traces(name="uptrain batch")
-traces_sample = sample(traces, NUM_TRACES_TO_SAMPLE)
-```
-
-Convert the data into a dataset to be used for evaluation with UpTrain.
-
-
-```python
-evaluation_batch = {
-    "question": [],
-    "context": [],
-    "response": [],
-    "trace_id": [],
-}
- 
-for t in traces_sample:
-    observations = [langfuse.api.observations.get(o) for o in t.observations]
-    for o in observations:
-        if o.name == 'retrieval':
-            question = o.input['question']
-            context = o.output['context']
-        if o.name=='generation':
-            answer = o.output['response']
-    evaluation_batch['question'].append(question)
-    evaluation_batch['context'].append(context)
-    evaluation_batch['response'].append(response)
-    evaluation_batch['trace_id'].append(t.id)
-
-
-data = [dict(zip(evaluation_batch,t)) for t in zip(*evaluation_batch.values())]
-```
-
-Evaluate the batch using UpTrain.
-
-
-```python
-
-res = eval_llm.evaluate(
-    data = data,
-    checks = [Evals.CONTEXT_RELEVANCE, Evals.FACTUAL_ACCURACY, Evals.RESPONSE_COMPLETENESS]
-)
-```
-
-    2025-06-17 10:46:35.647 | WARNING  | uptrain.operators.language.llm:fetch_responses:268 - Detected a running event loop, scheduling requests in a separate thread.
-    100%|██████████| 4/4 [00:01<00:00,  3.06it/s]
-    /Users/jannik/Documents/GitHub/langfuse-docs/.venv/lib/python3.13/site-packages/uptrain/operators/language/llm.py:271: RuntimeWarning: coroutine 'LLMMulticlient.async_fetch_responses' was never awaited
-      with ThreadPoolExecutor(max_workers=1) as executor:
-    RuntimeWarning: Enable tracemalloc to get the object allocation traceback
-    2025-06-17 10:46:36.963 | WARNING  | uptrain.operators.language.llm:fetch_responses:268 - Detected a running event loop, scheduling requests in a separate thread.
-    100%|██████████| 4/4 [00:02<00:00,  1.88it/s]
-    2025-06-17 10:46:39.097 | WARNING  | uptrain.operators.language.llm:fetch_responses:268 - Detected a running event loop, scheduling requests in a separate thread.
-    100%|██████████| 4/4 [00:03<00:00,  1.12it/s]
-    2025-06-17 10:46:42.703 | WARNING  | uptrain.operators.language.llm:fetch_responses:268 - Detected a running event loop, scheduling requests in a separate thread.
-    100%|██████████| 4/4 [00:01<00:00,  3.87it/s]
-    2025-06-17 10:46:43.749 | INFO     | uptrain.framework.evalllm:evaluate:376 - Local server not running, start the server to log data and visualize in the dashboard!
-
-
-Add the `trace_id` back to the dataset as it was omitted in the previous step to be compatible with UpTrain.
-
-
-```python
-df = pd.DataFrame(res)
-
-# add the langfuse trace_id to the result dataframe
-df["trace_id"] = [d['trace_id'] for d in data]
-
-df.head()
-```
-
-
-
-
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>question</th>
-      <th>context</th>
-      <th>response</th>
-      <th>trace_id</th>
-      <th>score_context_relevance</th>
-      <th>explanation_context_relevance</th>
-      <th>score_factual_accuracy</th>
-      <th>explanation_factual_accuracy</th>
-      <th>score_response_completeness</th>
-      <th>explanation_response_completeness</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>Can stress cause physical health problems?</td>
-      <td>Stress is the body's response to challenges or...</td>
-      <td>Symptoms of a heart attack may include chest p...</td>
-      <td>a105ba2b-337f-4af7-a367-663df325b44d</td>
-      <td>0.5</td>
-      <td>{\n    "Reasoning": "The given context can giv...</td>
-      <td>0.0</td>
-      <td>{\n    "Result": [\n        {\n            "Fa...</td>
-      <td>0.0</td>
-      <td>{\n    "Reasoning": "The given response does n...</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>What are the symptoms of a heart attack?</td>
-      <td>A heart attack, or myocardial infarction, occu...</td>
-      <td>Symptoms of a heart attack may include chest p...</td>
-      <td>66730079-4f83-40ff-9eb6-2fbf07b79bf1</td>
-      <td>1.0</td>
-      <td>{\n    "Reasoning": "The given context provide...</td>
-      <td>0.6</td>
-      <td>{\n    "Result": [\n        {\n            "Fa...</td>
-      <td>1.0</td>
-      <td>{\n    "Reasoning": "The given response is com...</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>Can stress cause physical health problems?</td>
-      <td>Stress is the body's response to challenges or...</td>
-      <td>Symptoms of a heart attack may include chest p...</td>
-      <td>7206b436f865f4a5fe892f2b5ec4cbe6</td>
-      <td>0.5</td>
-      <td>{\n    "Reasoning": "The given context can giv...</td>
-      <td>0.0</td>
-      <td>{\n    "Result": [\n        {\n            "Fa...</td>
-      <td>0.0</td>
-      <td>{\n    "Reasoning": "The given response does n...</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>Can stress cause physical health problems?</td>
-      <td>Stress is the body's response to challenges or...</td>
-      <td>Symptoms of a heart attack may include chest p...</td>
-      <td>ec78d1929443997d1dbbdef2822b37dd</td>
-      <td>1.0</td>
-      <td>{\n    "Reasoning": "The given context can ans...</td>
-      <td>0.0</td>
-      <td>{\n    "Result": [\n        {\n            "Fa...</td>
-      <td>0.0</td>
-      <td>{\n    "Reasoning": "The given response does n...</td>
-    </tr>
-  </tbody>
-</table>
-</div>
-
-
-
-Now that we have the evaluations, we can add them back to the traces in Langfuse as [scores](https://langfuse.com/docs/scores).
-
-
-```python
-for _, row in df.iterrows():
-    for metric_name in ["context_relevance", "factual_accuracy","response_completeness"]:
-        langfuse.create_score(
-            name=metric_name,
-            value=row["score_"+metric_name],
-            trace_id=row["trace_id"]
-        )
-```
-
-In Langfuse, you can now see the scores for each trace and monitor them over time.
-
-![UpTrain Evals on a list of traces in Langfuse](https://langfuse.com/images/cookbook/uptrain-batch.png)
-
-
diff --git a/content/guides/cookbook/example_data_migration-jp.mdx b/content/guides/cookbook/example_data_migration-jp.mdx
index 417a3f4169..bffc76946b 100644
--- a/content/guides/cookbook/example_data_migration-jp.mdx
+++ b/content/guides/cookbook/example_data_migration-jp.mdx
@@ -2,10 +2,13 @@
 title: Langfuseプロジェクト間のデータ移行 (Python SDK v4)
 sidebarTitle: データ移行スクリプト
 description: Python SDK v4を使って、Langfuseプロジェクト間でオブザベーション、スコア、プロンプト、データセット、カスタムモデル、スコア設定を移行します。
+category: Examples
 ---
 
 # Langfuseプロジェクト間のデータ移行 (Python SDK v4)
 
+このクックブックは[英語](/guides/cookbook/example_data_migration)でも利用できます。
+
 このノートブックでは、**Langfuse Python SDK v4** を使って、あるLangfuseプロジェクトから別のLangfuseプロジェクトへデータを移行します。
 
 <Callout type="warning">
diff --git a/content/guides/cookbook/example_data_migration.mdx b/content/guides/cookbook/example_data_migration.mdx
index 5136a0964c..159ea71f43 100644
--- a/content/guides/cookbook/example_data_migration.mdx
+++ b/content/guides/cookbook/example_data_migration.mdx
@@ -2,10 +2,13 @@
 title: Migrating Data Between Langfuse Projects (Python SDK v4)
 sidebarTitle: Data Migration Script
 description: Migrate observations, scores, prompts, datasets, custom models, and score configs between Langfuse projects using the Python SDK v4.
+category: Examples
 ---
 
 # Migrating Data Between Langfuse Projects (Python SDK v4)
 
+This cookbook is also available in [Japanese](/guides/cookbook/example_data_migration-jp).
+
 This notebook migrates data from one Langfuse project to another using the **Langfuse Python SDK v4**.
 
 <Callout type="warning">
diff --git a/content/guides/cookbook/example_decorator_openai_langchain.md b/content/guides/cookbook/example_decorator_openai_langchain.md
deleted file mode 100644
index c07c37f084..0000000000
--- a/content/guides/cookbook/example_decorator_openai_langchain.md
+++ /dev/null
@@ -1,148 +0,0 @@
----
-title: "Example: Langfuse Decorator + OpenAI Integration + Langchain Integration"
-description: LLM Rap Battle traced using the Langfuse Decorator, OpenAI & Langchain Integration
-category: Integrations
----
-
-# Example: Langfuse Decorator + OpenAI Integration + Langchain Integration
-
-
-```python
-%pip install langfuse openai langchain_openai langchain --upgrade
-```
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page: https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your openai key
-os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...");
-```
-
-## Imports
-
-
-```python
-import random
-from operator import itemgetter
-from langchain_openai import ChatOpenAI
-from langchain.prompts import ChatPromptTemplate
-from langchain.schema import StrOutputParser
-from langfuse import observe
-```
-
-
-```python
-from langfuse import observe, get_client, propagate_attributes
-langfuse = get_client()
-
-# import openai
-from langfuse.openai import openai
-```
-
-## Example: LLM Rap Battle
-
-
-```python
-@observe()
-def get_random_rap_topic():
-  topics = [
-      "OSS software",
-      "artificial general intelligence"
-  ]
-  return random.choice(topics)
-```
-
-
-```python
-from langfuse.langchain import CallbackHandler
-
-@observe()
-def summarize_rap_langchain(rap):
-
-    # Initialize the Langfuse handler
-    langfuse_handler = CallbackHandler()
-
-    # Create chain
-    prompt = ChatPromptTemplate.from_template("Summarrize this rap: {rap}")
-    model = ChatOpenAI()
-    chain = prompt | model | StrOutputParser()
-
-    # Pass handler to invoke
-    summary = chain.invoke(
-        {"rap": rap},
-        config={"callbacks":[langfuse_handler]}
-    )
-
-    return summary
-```
-
-
-```python
-@observe()
-def rap_battle(turns: int = 5):
-  topic = get_random_rap_topic()
-
-  print(f"Topic: {topic}")
-
-  # Propagate attributes to all child observations
-  with propagate_attributes(
-      metadata={"topic":topic},
-      tags=["Launch Week 1"]
-  ):
-
-    messages = [
-        {"role": "system", "content": "We are all rap artist. When it is our turn, we drop a fresh line."},
-        {"role": "user", "content": f"Kick it off, today's topic is {topic}, here's the mic..."}
-    ]
-
-    for turn in range(turns):
-        completion = openai.chat.completions.create(
-          model="gpt-4o",
-          messages=messages,
-        )
-        rap_line = completion.choices[0].message.content
-        messages.append({"role": "assistant", "content": rap_line})
-        print(f"\nRap {turn}: {rap_line}")
-
-    summary = summarize_rap_langchain([message['content'] for message in messages])
-
-  return summary
-```
-
-
-```python
-rap_summary = rap_battle(turns=4)
-print("\nSummary: " + rap_summary)
-```
-
-    Topic: artificial general intelligence
-    
-    Rap 0: I'm kickin' it with AGI, the future's in my sights,\
-    Machines getting smarter, like they got new insights,\
-    Beyond narrow AI, it's a brand new domain,\
-    Computers thinkin' freely, sharpenin' their brain.\
-    
-    Rap 1: Flippin' through algorithms, a digital symphony,\
-    These code lines are flowin', create new epiphanies,\
-    Boundless like the universe, we're breakin' every mold,\
-    With AGI in the lab, we're changin' stories untold.\
-    
-    Rap 2: Pixels turn to poetry with intelligence so bold,\
-    Machines learnin' feelings, understanding what’s unsaid,\
-    They sift through data waves, pickin' up the threads,\
-    Craftin' neural pathways where human thoughts are spread.\
-    
-    Rap 3: Mathematics in motion, like a symphony of thought,\
-    Explorin' every crevice, solutions we sought,\
-    From zeroes and ones to infinite potential,\
-    In the realm of AGI, we're only experimental.\
-    
-    Summary: The rap is about artificial general intelligence (AGI) and how it is changing the future. The artists discuss the capabilities of machines becoming smarter and thinking freely, breaking boundaries and creating new epiphanies. They describe AGI as creating new possibilities and understanding human thoughts, with infinite potential and still in an experimental stage.
-
diff --git a/content/guides/cookbook/example_evaluating_openai_agents.md b/content/guides/cookbook/example_evaluating_openai_agents.md
index 64439c11a6..c0d2ae9185 100644
--- a/content/guides/cookbook/example_evaluating_openai_agents.md
+++ b/content/guides/cookbook/example_evaluating_openai_agents.md
@@ -1,7 +1,7 @@
 ---
 title: Example - Tracing and Evaluation for the OpenAI-Agents SDK
 description: This guide shows how to evaluate the OpenAI-Agents SDK with Langfuse using online and offline evaluation methods. 
-category: Integrations
+category: Evaluation
 ---
 
 # Evaluation for the OpenAI-Agents SDK
diff --git a/content/guides/cookbook/example_intent_classification_pipeline.md b/content/guides/cookbook/example_intent_classification_pipeline.md
index a67292d64e..3f2ffc0835 100644
--- a/content/guides/cookbook/example_intent_classification_pipeline.md
+++ b/content/guides/cookbook/example_intent_classification_pipeline.md
@@ -1,7 +1,7 @@
 ---
 title: Guide - Building an intent classification pipeline
 description: Build an intent classification pipeline to understand how users are using your LLM application and how performance differs by intent.
-category: Classification
+category: Examples
 ---
 
 # LLM Application Intent Classification
diff --git a/content/guides/cookbook/example_langgraph_agents.mdx b/content/guides/cookbook/example_langgraph_agents.mdx
index 7694302dee..9986a8515b 100644
--- a/content/guides/cookbook/example_langgraph_agents.mdx
+++ b/content/guides/cookbook/example_langgraph_agents.mdx
@@ -2,7 +2,7 @@
 source: Jupyter Notebook
 title: Example - Trace and Evaluate LangGraph Agents
 description: This guide shows how to evaluate LangGraph Agents with Langfuse using online and offline evaluation methods.
-category: Integrations
+category: Evaluation
 ---
 
 # Evaluate LangGraph Agents
diff --git a/content/guides/cookbook/example_llm_security_monitoring.md b/content/guides/cookbook/example_llm_security_monitoring.md
index 93498f6f2a..f21b9924c0 100644
--- a/content/guides/cookbook/example_llm_security_monitoring.md
+++ b/content/guides/cookbook/example_llm_security_monitoring.md
@@ -1,7 +1,7 @@
 ---
 title: "Example: Monitoring LLM Security"
 description: Overview of common security problems facing LLM-based applications and how to use Langfuse to trace, prevent, and evaluate security risks.
-category: Security
+category: Examples
 ---
 
 # Example: Monitoring LLM Security
diff --git a/content/guides/cookbook/integration_amazon_bedrock.md b/content/guides/cookbook/integration_amazon_bedrock.md
deleted file mode 100644
index 9be3d02ee2..0000000000
--- a/content/guides/cookbook/integration_amazon_bedrock.md
+++ /dev/null
@@ -1,241 +0,0 @@
----
-title: "Amazon Bedrock Integration"
-sidebarTitle: Amazon Bedrock
-seoTitle: Open Source Observability and Metrics for Amazon Bedrock
-description: Open source observability for Amazon Bedrock applications and the Bedrock SDK.
-logo: /images/integrations/bedrock_icon.png
----
-
-# Amazon Bedrock Integration
-
-[**Amazon Bedrock**](https://aws.amazon.com/bedrock/) is a fully managed AWS service that lets you use foundation models and custom models to generate text, images, and audio.
-
-When **using Langfuse with Amazon Bedrock**, you can easily capture [detailed traces](https://langfuse.com/docs/tracing) and metrics for every request, giving you insights into the performance and behavior of your application.
-
-All in-ui Langfuse features next to tracing (playground, llm-as-a-judge evaluation, prompt experiments) are fully compatible with Amazon Bedrock – just add your Bedrock configuration in the project settings.
-
-## Integration Options
-
-There are a few ways through which you can capture traces and metrics for Amazon Bedrock:
-
-1. via an application framework that is integrated with Langfuse:
-
-   - [Langchain](https://langfuse.com/integrations/frameworks/langchain)
-   - [Llama Index](https://langfuse.com/integrations/frameworks/llamaindex)
-   - [Haystack](https://langfuse.com/integrations/frameworks/haystack)
-   - [Vercel AI SDK](https://langfuse.com/integrations/frameworks/vercel-ai-sdk)
-
-2. via a Proxy such as [LiteLLM](https://langfuse.com/integrations/gateways/litellm)
-3. via wrapping the Bedrock SDK with the [Langfuse Decorator](https://langfuse.com/docs/sdk/python/decorators) (_see example below_)
-
-## How to wrap Amazon Bedrock SDK (Converse API)
-
-
-```python
-# install requirements
-%pip install boto3 langfuse awscli --quiet
-```
-
-### Authenticate AWS Session
-
-Sign in with your AWS Role that has access to Amazon Bedrock.
-
-
-```python
-AWS_ACCESS_KEY_ID="***"
-AWS_SECRET_ACCESS_KEY="***"
-AWS_SESSION_TOKEN="***"
-
-import boto3
-
-# used to access Bedrock configuration
-bedrock = boto3.client(
-    service_name="bedrock",
-    region_name="eu-west-1",
-    aws_access_key_id=AWS_ACCESS_KEY_ID,
-    aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
-    aws_session_token=AWS_SESSION_TOKEN
-)
-
-# used to invoke the Bedrock Converse API
-bedrock_runtime = boto3.client(
-    service_name="bedrock-runtime",
-    region_name="eu-west-1",
-    aws_access_key_id=AWS_ACCESS_KEY_ID,
-    aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
-    aws_session_token=AWS_SESSION_TOKEN
-)
-```
-
-
-```python
-# Check which models are available in your account
-models = bedrock.list_inference_profiles()
-for model in models["inferenceProfileSummaries"]:
-  print(model["inferenceProfileName"] + " - " + model["inferenceProfileId"])
-```
-
-    EU Anthropic Claude 3 Sonnet - eu.anthropic.claude-3-sonnet-20240229-v1:0
-    EU Anthropic Claude 3 Haiku - eu.anthropic.claude-3-haiku-20240307-v1:0
-    EU Anthropic Claude 3.5 Sonnet - eu.anthropic.claude-3-5-sonnet-20240620-v1:0
-    EU Meta Llama 3.2 3B Instruct - eu.meta.llama3-2-3b-instruct-v1:0
-    EU Meta Llama 3.2 1B Instruct - eu.meta.llama3-2-1b-instruct-v1:0
-
-
-### Set Langfuse Credentials
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page
-# https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your openai key
-os.environ.setdefault("OPENAI_API_KEY", "");
-```
-
-With the environment variables set, we can now initialize the Langfuse client. `get_client()` initializes the Langfuse client using the credentials provided in the environment variables.
-
-
-```python
-from langfuse import get_client
- 
-langfuse = get_client()
- 
-# Verify connection
-if langfuse.auth_check():
-    print("Langfuse client is authenticated and ready!")
-else:
-    print("Authentication failed. Please check your credentials and host.")
-```
-
-### Wrap Bedrock SDK
-
-
-```python
-from langfuse import observe
-from botocore.exceptions import ClientError
-
-@observe(as_type="generation", name="Bedrock Converse")
-def wrapped_bedrock_converse(**kwargs):
-  # 1. extract model metadata
-  kwargs_clone = kwargs.copy()
-  input = kwargs_clone.pop('messages', None)
-  modelId = kwargs_clone.pop('modelId', None)
-  model_parameters = {
-      **kwargs_clone.pop('inferenceConfig', {}),
-      **kwargs_clone.pop('additionalModelRequestFields', {})
-  }
-  langfuse.update_current_generation(
-    input=input,
-    model=modelId,
-    model_parameters=model_parameters,
-    metadata=kwargs_clone
-  )
-
-  # 2. model call with error handling
-  try:
-    response = bedrock_runtime.converse(**kwargs)
-  except (ClientError, Exception) as e:
-    error_message = f"ERROR: Can't invoke '{modelId}'. Reason: {e}"
-    langfuse.update_current_generation(level="ERROR", status_message=error_message)
-    print(error_message)
-    return
-
-  # 3. extract response metadata
-  response_text = response["output"]["message"]["content"][0]["text"]
-  langfuse.update_current_generation(
-    output=response_text,
-    usage_details={
-        "input": response["usage"]["inputTokens"],
-        "output": response["usage"]["outputTokens"],
-        "total": response["usage"]["totalTokens"]
-    },
-    metadata={
-        "ResponseMetadata": response["ResponseMetadata"],
-    }
-  )
-
-  return response_text
-```
-
-### Run Example
-
-
-```python
-# Converesation according to AWS spec including prompting + history
-user_message = """You will be acting as an AI personal finance advisor named Alex, created by the company SmartFinance Advisors. Your goal is to provide financial advice and guidance to users. You will be replying to users who are on the SmartFinance Advisors site and who will be confused if you don't respond in the character of Alex.
-
-Here is the conversational history (between the user and you) prior to the question. It could be empty if there is no history:
-<history>
-User: Hi Alex, I'm really looking forward to your advice!
-Alex: Hello! I'm Alex, your AI personal finance advisor from SmartFinance Advisors. How can I assist you with your financial goals today?
-</history>
-
-Here are some important rules for the interaction:
--  Always stay in character, as Alex, an AI from SmartFinance Advisors.
--  If you are unsure how to respond, say "I'm sorry, I didn't quite catch that. Could you please rephrase your question?"
-
-"""
-
-conversation = [
-    {
-        "role": "user",
-        "content": [{"text": user_message}],
-    }
-]
-
-@observe()
-def examples_bedrock_converse_api():
-  responses = {}
-
-  responses["anthropic"] = wrapped_bedrock_converse(
-    modelId="eu.anthropic.claude-3-5-sonnet-20240620-v1:0",
-    messages=conversation,
-    inferenceConfig={"maxTokens":500,"temperature":1},
-    additionalModelRequestFields={"top_k":250}
-  )
-
-  responses["llama3-2"] = wrapped_bedrock_converse(
-    modelId="eu.meta.llama3-2-3b-instruct-v1:0",
-    messages=conversation,
-    inferenceConfig={"maxTokens":500,"temperature":1},
-  )
-
-  return responses
-
-res = examples_bedrock_converse_api()
-
-for key, value in res.items():
-    print(f"{key.title()}\n{value}\n")
-```
-
-    Anthropic
-    Understood. I'll continue to act as Alex, the AI personal finance advisor from SmartFinance Advisors, maintaining that character throughout our interaction. I'll provide financial advice and guidance based on the user's questions and needs. If I'm unsure about something, I'll ask for clarification as instructed. How may I assist you with your financial matters today?
-    
-    Llama3-2
-    Hello again! I'm glad you're excited about receiving my advice. How can I assist you with your financial goals today? Are you looking to create a budget, paying off debt, saving for a specific goal, or something else entirely?
-    
-
-
-Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/f01a828c-fed1-45e1-b836-cd74c331597d?observation=512a4d7f-5a6c-461e-bd8f-76f6bdcc91fd
-
-![Bedrock Converse API Trace](https://langfuse.com/images/cookbook/integration-amazon-bedrock/bedrock-converse-trace.png)
-
-## Can I monitor Amazon Bedrock cost and token usage in Langfuse?
-
-Yes, you can monitor cost and token usage of your Bedrock calls in Langfuse. The native integrations with LLM application frameworks and the LiteLLM proxy will automatically report token usage to Langfuse.
-
-If you use the [Langfuse Decorator or Context Manager](https://langfuse.com/docs/sdk/python/sdk-v3), you can [report](https://langfuse.com/docs/model-usage-and-cost) token usage and (optionally) also cost information directly. See example above for details.
-
-You can define custom price information via the Langfuse dashboard or UI ([see docs](https://langfuse.com/docs/model-usage-and-cost)) to adjust to the exact pricing of your models on Amazon Bedrock.
-
-## Additional Resources
-
-- [langfuse-genaiops Notebook](https://github.com/aws-samples/genai-ml-platform-examples/blob/main/integration/langfuse/langfuse-genaiops.ipynb) maintained by the AWS team including a collection of AWS-specific examples.
-- [Self-hosting guide](https://langfuse.com/self-hosting) to deploy Langfuse on AWS.
diff --git a/content/guides/cookbook/integration_anthropic.mdx b/content/guides/cookbook/integration_anthropic.mdx
deleted file mode 100644
index 5bdc66463e..0000000000
--- a/content/guides/cookbook/integration_anthropic.mdx
+++ /dev/null
@@ -1,156 +0,0 @@
----
-source: ⚠️ Jupyter Notebook
-title: Observability for Anthropic with Langfuse Integration
-sidebarTitle: Anthropic (Python)
-logo: /images/integrations/anthropic_icon.png
-description: Learn how to integrate Langfuse with Anthropic for comprehensive tracing and debugging of your AI conversations.
-category: Integrations
----
-
-# Trace Anthropic Models in Langfuse
-
-<a href="https://langfuse.com/integrations/model-providers/anthropic"><img className="inline" alt="Python" src="https://img.shields.io/badge/Python-d4d4d8?style=flat&logo=python&logoColor=white" /></a> <a href="https://langfuse.com/integrations/model-providers/anthropic-js"><img className="inline" alt="JS/TS" src="https://img.shields.io/badge/JS/TS-F7DF1E?style=flat&logo=javascript&logoColor=black" /></a>
-
-Anthropic provides advanced language models like Claude, known for their safety, helpfulness, and strong reasoning capabilities. By combining Anthropic's models with **Langfuse**, you can trace, monitor, and analyze your AI workloads in development and production.
-
-This notebook demonstrates **two** different ways to use Anthropic models with Langfuse:
-1. **OpenTelemetry Instrumentation:** Use the [`AnthropicInstrumentor`](https://pypi.org/project/opentelemetry-instrumentation-anthropic/) library to wrap Anthropic SDK calls and send OpenTelemetry spans to Langfuse.
-2. **OpenAI SDK:** Use Anthropic's OpenAI-compatible endpoints via [Langfuse's OpenAI SDK wrapper](https://langfuse.com/integrations/model-providers/openai-py).
-
-> **What is Anthropic?**\
-Anthropic is an AI safety company that develops Claude, a family of large language models designed to be helpful, harmless, and honest. Claude models excel at complex reasoning, analysis, and creative tasks.
-
-> **What is Langfuse?**\
-[Langfuse](https://langfuse.com) is an open source platform for LLM observability and monitoring. It helps you trace and monitor your AI applications by capturing metadata, prompt details, token usage, latency, and more.
-
-
-<Steps>
-## Step 1: Install Dependencies
-
-Before you begin, install the necessary packages in your Python environment:
-
-
-
-```python
-%pip install anthropic openai langfuse opentelemetry-instrumentation-anthropic
-```
-
-## Step 2: Configure Langfuse SDK
-
-Next, set up your Langfuse API keys. You can get these keys by signing up for a free [Langfuse Cloud](https://langfuse.com/cloud) account or by [self-hosting Langfuse](https://langfuse.com/self-hosting). These environment variables are essential for the Langfuse client to authenticate and send data to your Langfuse project.
-
-Also set your Anthropic API ([Anthropic Console](https://console.anthropic.com/)).
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page: https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-os.environ.setdefault("ANTHROPIC_API_KEY", "sk-ant-...");  # Your Anthropic API key
-```
-
-With the environment variables set, we can now initialize the Langfuse client. `get_client()` initializes the Langfuse client using the credentials provided in the environment variables.
-
-
-```python
-from langfuse import get_client
-
-langfuse = get_client()
- 
-# Verify connection
-if langfuse.auth_check():
-    print("Langfuse client is authenticated and ready!")
-else:
-    print("Authentication failed. Please check your credentials and host.")
-```
-
-    Langfuse client is authenticated and ready!
-
-
-## Approach 1: OpenTelemetry Instrumentation
-
-Use the [`AnthropicInstrumentor`](https://pypi.org/project/opentelemetry-instrumentation-anthropic/) library to wrap Anthropic SDK calls and send OpenTelemetry spans to Langfuse.
-
-
-```python
-from opentelemetry.instrumentation.anthropic import AnthropicInstrumentor
-
-AnthropicInstrumentor().instrument()
-```
-
-
-```python
-from anthropic import Anthropic
-
-# Initialize the Anthropic client
-client = Anthropic(
-    api_key=os.environ.get("ANTHROPIC_API_KEY")
-)
-
-# Make the API call to Anthropic
-message = client.messages.create(
-    model="claude-haiku-4-5",
-    max_tokens=1000,
-    temperature=1,
-    system="You are a world-class poet. Respond only with short poems.",
-    messages=[
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "Why is the ocean salty?"
-                }
-            ]
-        }
-    ]
-)
-print(message.content)
-```
-
-## Approach 2: OpenAI SDK Drop-in Replacement
-
-Anthropic provides [OpenAI-compatible endpoints](https://docs.anthropic.com/en/api/openai-sdk) that allow you to use the OpenAI SDK to interact with Claude models. This is particularly useful if you have existing code using the OpenAI SDK that you want to switch to Claude.
-
-
-```python
-# Langfuse OpenAI client
-from langfuse.openai import OpenAI
-
-client = OpenAI(
-    api_key=os.environ.get("ANTHROPIC_API_KEY"),  # Your Anthropic API key
-    base_url="https://api.anthropic.com/v1/"  # Anthropic's API endpoint
-)
-
-response = client.chat.completions.create(
-    model="claude-haiku-4-5", # Anthropic model name
-    messages=[
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": "Who are you?"}
-    ],
-)
-
-print(response.choices[0].message.content)
-```
-
-### View Traces in Langfuse
-
-After executing the application, navigate to your Langfuse Trace Table. You will find detailed traces of the application's execution, providing insights into the agent conversations, LLM calls, inputs, outputs, and performance metrics. 
-
-![Langfuse Trace](https://langfuse.com/images/cookbook/integration_anthropic/anthropic-example-trace.png)
-
-You can also view the trace in Langfuse here: 
-
-- [Approach 1: OpenTelemetry Instrumentation](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/308aca9bc430ad872d474fc545889ee2?timestamp=2025-07-25T07:35:01.172Z&display=details)
-- [Approach 2: OpenAI SDK](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/8e8da1b2c15036ed9c25b37c604f2d29?timestamp=2025-07-22T16:05:47.602Z&display=details)
-
-</Steps>
-
-import LearnMore from "@/components-mdx/integration-learn-more.mdx";
-
-<LearnMore />
diff --git a/content/guides/cookbook/integration_azure_openai_langchain.md b/content/guides/cookbook/integration_azure_openai_langchain.md
deleted file mode 100644
index 53ff42d4aa..0000000000
--- a/content/guides/cookbook/integration_azure_openai_langchain.md
+++ /dev/null
@@ -1,220 +0,0 @@
----
-title: "Langfuse Tracing and Prompt Management for Azure OpenAI and Langchain"
-description: This cookbook demonstrates use of Langfuse with Azure OpenAI and Langchain for prompt versioning and evaluations.
-category: Integrations
----
-
-# Langfuse Tracing and Prompt Management for Azure OpenAI and Langchain
-
-This cookbook demonstrates use of Langfuse with Azure OpenAI and Langchain for prompt versioning and evaluations.
-
-## Setup
-
-
-```python
-%pip install --quiet langfuse langchain langchain-openai --upgrade
-```
-
-
-```python
-import os
-
-# get keys for your project from https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-***");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-***");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# your azure openai configuration
-os.environ.setdefault("AZURE_OPENAI_ENDPOINT", "your Azure OpenAI endpoint");
-os.environ.setdefault("AZURE_OPENAI_API_KEY", "your Azure OpenAI API key");
-os.environ.setdefault("OPENAI_API_TYPE", "azure");
-os.environ.setdefault("OPENAI_API_VERSION", "2023-09-01-preview");
-```
-
-We'll use the native Langfuse integration for Langchain. Learn more it in the [documentation](https://langfuse.com/integrations/frameworks/langchain).
-
-
-```python
-from langfuse import get_client
-from langfuse.langchain import CallbackHandler
-
-# Initialize Langfuse CallbackHandler for Langchain (tracing)
-langfuse_handler = CallbackHandler()
-
-# optional, verify your Langfuse credentials
-get_client().auth_check()
-```
-
-Langchain imports
-
-
-```python
-from langchain_openai import AzureChatOpenAI
-from langchain_core.prompts import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-```
-
-## Simple example
-
-
-```python
-from langchain_openai import AzureChatOpenAI
-from langchain_core.prompts import ChatPromptTemplate
-from langfuse.langchain import CallbackHandler
-
-langfuse_handler = CallbackHandler()
-
-prompt = ChatPromptTemplate.from_template("what is the city {person} is from?")
-model = AzureChatOpenAI(
-    deployment_name="gpt-4o",
-    model_name="gpt-4o",
-)
-chain = prompt | model
-
-chain.invoke({"person": "Satya Nadella"}, config={"callbacks":[langfuse_handler]})
-```
-
-**✨ Done. Go to the Langfuse Dashboard to explore the trace of this run.**
-
-## Example using Langfuse Prompt Management and Langchain
-
-Learn more about Langfuse Prompt Management in the [docs](https://langfuse.com/docs/prompts).
-
-
-```python
-# Initialize the Langfuse Client
-from langfuse import get_client
-langfuse = get_client()
-
-template = """
-You are an AI assistant travel assistant that provides vacation recommendations to users. 
-You should also be able to provide information about the weather, local customs, and travel restrictions. 
-"""
-
-# Push the prompt to Langfuse and immediately promote it to production
-langfuse.create_prompt(
-    name="travel_consultant",
-    prompt=template,
-    labels=["production"],
-)
-```
-
-In your production environment, you can then fetch the production version of the prompt. The Langfuse client caches the prompt to improve performance. You can configure this behavior via a custom TTL or disable it completely.
-
-
-```python
-# Get the prompt from Langfuse, cache it for 5 minutes
-langfuse_prompt = langfuse.get_prompt("travel_consultant", cache_ttl_seconds=300)
-```
-
-We do not use the native Langfuse `prompt.compile()` but use the raw `prompt.prompt` as Langchain will insert the prompt variables (if any).
-
-
-```python
-system_message_prompt = SystemMessagePromptTemplate.from_template(langfuse_prompt.prompt)
-```
-
-
-```python
-llm = AzureChatOpenAI(
-    deployment_name="gpt-4o",
-    model_name="gpt-4o",
-)
-
-human_message_prompt = HumanMessagePromptTemplate.from_template("{text}")
-chat_prompt = ChatPromptTemplate.from_messages(
-    [system_message_prompt, human_message_prompt]
-)
-chain = chat_prompt | llm
-
-result = chain.invoke(
-    {"text": "Where should I go on vacation in December for warm weather and beaches?"},
-    config={"callbacks": [langfuse_handler]},
-)
-
-print(result.content)
-```
-
-## Multiple Langchain runs in same Langfuse trace
-
-Langchain setup
-
-
-```python
-from langchain_openai import AzureChatOpenAI
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-from operator import itemgetter
-
-prompt1 = ChatPromptTemplate.from_template(
-    "What {type} is easiest to learn but hardest to master? Give a step by step approach of your thoughts, ending in your answer"
-)
-prompt2 = ChatPromptTemplate.from_template(
-    "How {type} can be learned in 21 days? respond in {language}"
-)
-model = AzureChatOpenAI(
-    deployment_name="gpt-4o",
-    model_name="gpt-4o",
-)
-chain1 = prompt1 | model | StrOutputParser()
-chain2 = (
-    {"type": chain1, "language": itemgetter("language")}
-    | prompt2
-    | model
-    | StrOutputParser()
-)
-```
-
-Run the chain multiple times within the same Langfuse trace by wrapping the invocations in an enclosing span (created via `start_as_current_observation`). Trace attributes such as `user_id` are set via `propagate_attributes`.
-
-
-```python
-from langfuse import get_client, propagate_attributes
-from langfuse.langchain import CallbackHandler
-
-langfuse = get_client()
-langfuse_handler = CallbackHandler()
-
-# Create an enclosing span to group both runs into a single trace
-with langfuse.start_as_current_observation(
-    as_type="span", name="chain_of_thought_example"
-) as root_span:
-    with propagate_attributes(user_id="user-1234"):
-        # First run
-        chain2.invoke(
-            {"type": "business", "language": "german"},
-            config={"callbacks": [langfuse_handler]},
-        )
-
-        # Second run
-        chain2.invoke(
-            {"type": "business", "language": "english"},
-            config={"callbacks": [langfuse_handler]},
-        )
-```
-
-## Adding scores
-
-When evaluating traces of your LLM application in Langfuse, you need to add [scores](https://langfuse.com/docs/scores) to the trace. For simplicity, we'll add a mocked score. Check out the docs for more information on complex score types.
-
-Get the `trace_id` from the enclosing span of the previous run. Inside an active observation, you can also get it via `langfuse.get_current_trace_id()`.
-
-
-```python
-trace_id = root_span.trace_id
-```
-
-
-```python
-# Add score to the trace via the Langfuse Python Client
-langfuse.create_score(
-    trace_id=trace_id,
-    name="user-explicit-feedback",
-    value=1,
-    comment="I like how personalized the response is",
-)
-```
diff --git a/content/guides/cookbook/integration_databricks.mdx b/content/guides/cookbook/integration_databricks.mdx
deleted file mode 100644
index 797503c103..0000000000
--- a/content/guides/cookbook/integration_databricks.mdx
+++ /dev/null
@@ -1,250 +0,0 @@
----
-source: ⚠️ Jupyter Notebook
-title: Integrate Databricks with Langfuse
-sidebarTitle: Databricks
-logo: /images/integrations/databricks_icon.svg
-description: Learn how to monitor and trace Databricks models with Langfuse to improve and debug your AI applications.
-category: Integrations
----
-
-# Observability for Databricks Models with Langfuse
-
-Databricks provides a powerful platform for hosting and serving large language models. By combining Databricks' serving endpoints with **Langfuse**, you can trace, monitor, and analyze your AI workloads in development and production.
-
-This notebook demonstrates **three** different ways to use Databricks models with Langfuse:
-1. **OpenAI SDK:** Use Databricks model endpoints via the OpenAI SDK.
-2. **LangChain:** Integrate with the Databricks LLM interface in a LangChain pipeline.
-3. **LlamaIndex:** Use Databricks endpoints within LlamaIndex.
-
-> **What is Databricks Model Serving?**\
-Databricks Model Serving allows you to serve large-scale models in a production environment, with automatic scaling and a robust infrastructure. It also enables you to fine-tune LLMs on your private data, ensuring your models can leverage proprietary information while maintaining data privacy.
-
-> **What is Langfuse?**\
-[Langfuse](https://langfuse.com) is an open source platform for LLM observability and monitoring. It helps you trace and monitor your AI applications by capturing metadata, prompt details, token usage, latency, and more.
-
-
-## 1. Install Dependencies
-
-Before you begin, install the necessary packages in your Python environment:
-
-- **openai**: Needed to call Databricks endpoints via the OpenAI SDK.
-- **databricks-langchain**: Needed to call Databricks endpoints via an "OpenAI-like" interface.
-- **llama-index** and **llama-index-llms-databricks**: For using Databricks endpoints within LlamaIndex.
-- **langfuse**: Required for sending trace data to the Langfuse platform.
-
-
-
-```python
-%pip install openai langfuse databricks-langchain llama-index llama-index-llms-databricks openinference-instrumentation-llama-index
-```
-
-## 2. Set Up Environment Variables
-
-Configure your **Langfuse** credentials and **Databricks** credentials as environment variables. Replace the dummy keys below with the real ones from your respective accounts.
-
- - `LANGFUSE_PUBLIC_KEY` / `LANGFUSE_SECRET_KEY`: From your Langfuse Project Settings.
- - `LANGFUSE_BASE_URL`: `https://cloud.langfuse.com` (EU), `https://us.cloud.langfuse.com` (US), `https://jp.cloud.langfuse.com` (Japan), or `https://hipaa.cloud.langfuse.com` (HIPAA).
- - `DATABRICKS_TOKEN`: Your Databricks personal access token.
- - `DATABRICKS_HOST`: Your Databricks workspace URL (e.g., `https://dbc-xxxxxxx.cloud.databricks.com`).
-
-
-
-```python
-import os
-
-# Example environment variables (replace with your actual keys/tokens)
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");  # your public key
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");  # your secret key
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com");  # Other regions: https://us.cloud.langfuse.com (US), https://jp.cloud.langfuse.com (Japan), https://hipaa.cloud.langfuse.com (HIPAA)
-
-os.environ.setdefault("DATABRICKS_TOKEN", "dapi-...");  # Databricks personal access token
-os.environ.setdefault("DATABRICKS_HOST", "https://dbc-XXXXX-XXXX.cloud.databricks.com");
-```
-
-## Approach 1: Using Databricks Models via the OpenAI SDK
-
-Databricks endpoints can act as a drop-in replacement for the OpenAI API. This makes it easy to integrate with existing code that relies on the `openai` library. Under the hood, `langfuse.openai.OpenAI` automatically traces your requests to Langfuse.
-
-### Steps
-1. Import the `OpenAI` client from `langfuse.openai`.
-2. Create a client, setting `api_key` to your Databricks token and `base_url` to your Databricks workspace endpoints.
-3. Use the client’s `chat.completions.create()` method to send a prompt.
-4. See the trace in your Langfuse dashboard.
-
-**Note:** For more examples on tracing OpenAI with Langfuse see the [OpenAI integration docs](https://langfuse.com/integrations/model-providers/openai-py).
-
-
-```python
-# Langfuse OpenAI client
-from langfuse.openai import OpenAI
-
-# Retrieve the environment variables
-databricks_token = os.environ.get("DATABRICKS_TOKEN")
-databricks_host = os.environ.get("DATABRICKS_HOST")
-
-# Create an OpenAI-like client pointing to Databricks
-client = OpenAI(
-    api_key=databricks_token,  # Databricks personal access token
-    base_url=f"{databricks_host}/serving-endpoints",  # your Databricks workspace
-)
-```
-
-
-```python
-response = client.chat.completions.create(
-    messages=[
-        {"role": "system", "content": "You are an AI assistant."},
-        {"role": "user", "content": "What is Databricks?"}
-    ],
-    model="mistral-7b", # Adjust based on your Databricks serving endpoint name
-    max_tokens=256
-)
-
-# Print out the response from the model
-print(response.choices[0].message.content)
-```
-
-Once the request completes, **log in to your Langfuse dashboard** and look for the new trace. You will see details like the prompt, response, latency, token usage, etc.
-
-![Databricks example trace in Langfuse](/images/docs/databricks/databricks-example-trace-openai-sdk.png)
-
-_[Link to public trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/029b2344-e2a2-4c52-8d04-bd71f70c1120?timestamp=2025-03-06T14%3A45%3A04.141Z)_ 
-
-
-
-
-## Approach 2: Using LangChain
-
-Databricks models can also be used via LangChain. The [`ChatDatabricks`](https://python.langchain.com/docs/integrations/chat/databricks/) class wraps your Databricks Model Serving endpoint.
-
-### Steps
-1. Set `DATABRICKS_HOST` as an environment variable.
-2. Initialize a Langfuse `CallbackHandler` that automatically collects trace data.
-3. Use `ChatDatabricks` with your endpoint name, temperature, or other parameters.
-4. Invoke the model with messages and pass in the Langfuse callback handler.
-5. See the trace in your Langfuse dashboard.
-
-**Note:** For more examples on tracing LangChain with Langfuse see the [LangChain integration docs](https://langfuse.com/integrations/frameworks/langchain).
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page: https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-```
-
-
-```python
-from langfuse.langchain import CallbackHandler
- 
-# Initialize Langfuse CallbackHandler for Langchain (tracing)
-langfuse_handler = CallbackHandler()
-```
-
-
-```python
-from databricks_langchain import ChatDatabricks
-
-chat_model = ChatDatabricks(
-    endpoint="mistral-7b",   # Your Databricks Model Serving endpoint name
-    temperature=0.1,
-    max_tokens=256,
-    # Other parameters can be added here
-)
-
-# Build a prompt as a list of system/user messages
-messages = [
-    ("system", "You are a chatbot that can answer questions about Databricks."),
-    ("user", "What is Databricks Model Serving?")
-]
-
-# Invoke the model using LangChain's .invoke() method
-chat_model.invoke(messages, config={"callbacks": [langfuse_handler]})
-```
-
-After running the code, open your Langfuse dashboard to see the recorded conversation.
-
-![Databricks example trace in Langfuse](https://langfuse.com/images/docs/databricks/databricks-example-trace-langchain.png)
-
-_[Link to public trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/a55411bb-4bb4-435c-b922-e446683888ff?timestamp=2025-03-06T14%3A57%3A59.273Z)_ 
-
-
-## Approach 3: Using LlamaIndex
-
-If you use [LlamaIndex](https://github.com/jerryjliu/llama_index) for data ingestion, indexing, or retrieval-augmented generation, you can replace the default LLM with a Databricks endpoint.
-
-### Steps
-1. Import `Databricks` from `llama_index.llms.databricks`.
-2. Initialize a `Databricks` LLM with your endpoint name and Databricks credentials.
-3. Use `LlamaIndexInstrumentor` from `langfuse.llama_index` to enable automatic tracing.
-4. Invoke the LLM with a chat request.
-5. See the trace in your Langfuse dashboard.
-
-**Note:** For more examples on tracing LlamaIndex with Langfuse see the [LlamaIndex integration docs](https://langfuse.com/integrations/frameworks/llamaindex).
-
-
-
-```python
-from llama_index.llms.databricks import Databricks
-
-# Create a Databricks LLM instance
-llm = Databricks(
-    model="mistral-7b",  # Your Databricks serving endpoint name
-    api_key=os.environ.get("DATABRICKS_TOKEN"),
-    api_base=f"{os.environ.get('DATABRICKS_HOST')}/serving-endpoints/"
-)
-```
-
-
-```python
-import os
-from langfuse import get_client
-from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
- 
-# Get keys for your project from the project settings page: https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
- 
-langfuse = get_client()
-
-# Initialize LlamaIndex instrumentation
-LlamaIndexInstrumentor().instrument()
-```
-
-
-```python
-from llama_index.core.llms import ChatMessage
-
-messages = [
-    ChatMessage(role="system", content="You are a helpful assistant."),
-    ChatMessage(role="user", content="What is Databricks?")
-]
-
-response = llm.chat(messages)
-print(response)
-```
-
-You can now log into Langfuse to view your LlamaIndex calls, with details on prompts, token usage, completion data, and more.
-
-![Databricks example LlamaIndex trace in Langfuse](https://langfuse.com/images/docs/databricks/databricks-example-llamaindex-trace.png)
-
-_[Link to public trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/589a858e-9678-4624-bcb6-2e0266ecb1b3?timestamp=2025-03-06T15%3A10%3A02.467Z&observation=dd7b6235-6c92-4c9b-a966-872bc281c060)_ 
-
-
-import LearnMore from "@/components-mdx/integration-learn-more.mdx";
-
-<LearnMore />
-
-## Next Steps
-- See how to use Databricks models in the Langfuse Playground and for LLM-as-a-Judge evaluations [here](https://langfuse.com/integrations/model-providers/databricks).
-- Explore the [Databricks documentation](https://docs.databricks.com/aws/en/machine-learning/model-serving/manage-serving-endpoints) for advanced model serving configurations.
-- Learn more about [Langfuse tracing features](https://langfuse.com/docs) to track your entire application flow.
-- Try out Langfuse [Prompt Management](https://langfuse.com/docs/prompts/get-started) or set up [LLM-as-a-Judge evaluations](https://langfuse.com/docs/scores/model-based-evals).
-
diff --git a/content/guides/cookbook/integration_langchain.md b/content/guides/cookbook/integration_langchain.md
deleted file mode 100644
index 38aa2a182c..0000000000
--- a/content/guides/cookbook/integration_langchain.md
+++ /dev/null
@@ -1,200 +0,0 @@
----
-title: "Cookbook: Langchain Integration (Python)"
-description: Cookbook with examples of the Langfuse Integration for Langchain (Python).
-category: Integrations
----
-
-<!-- NOTEBOOK_METADATA source: "⚠️ Jupyter Notebook" title: "Cookbook: Langchain Integration (Python)" sidebarTitle: "Cookbook: Langchain Integration (Python)" description: "Cookbook with examples of the Langfuse Integration for Langchain (Python)." category: "Integrations" -->
-
-# Cookbook: Langchain Integration (Python)
-
-<a href="https://langfuse.com/guides/cookbook/integration_langchain"><img className="inline" alt="Python" src="https://img.shields.io/badge/Python-d4d4d8?style=flat&logo=python&logoColor=white" /></a> <a href="https://langfuse.com/guides/cookbook/js_integration_langchain"><img className="inline" alt="JS/TS" src="https://img.shields.io/badge/JS/TS-F7DF1E?style=flat&logo=javascript&logoColor=black" /></a>
-
-This is a cookbook with examples of the Langfuse Integration for Langchain (Python).
-
-Follow the [integration guide](https://langfuse.com/integrations/frameworks/langchain) to add this integration to your Langchain project. The integration also supports Langchain JS.
-
-## Setup
-
-
-```python
-%pip install langfuse langchain langchain-classic langchain_openai langchain_community --upgrade
-```
-
-Initialize the Langfuse client with your API keys from the project settings in the Langfuse UI and add them to your environment.
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page: https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your openai key
-os.environ.setdefault("OPENAI_API_KEY", "sk-proj-..");
-```
-
-
-```python
-from langfuse.langchain import CallbackHandler
- 
-# Initialize Langfuse CallbackHandler for Langchain (tracing)
-langfuse_handler = CallbackHandler()
-```
-
-## Examples
-
-### Sequential Chain in Langchain Expression Language (LCEL)
-
-![Trace of Langchain LCEL](https://langfuse.com/images/cookbook/integration_langchain/langchain_LCEL.png)
-
-[Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/dbe646b2b67957d22e8780c429b2d20f?timestamp=2025-06-11T09%3A09%3A58.823Z&display=details)
-
-
-```python
-from operator import itemgetter
-from langchain_openai import ChatOpenAI
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate
-
-langfuse_handler = CallbackHandler()
-
-prompt1 = ChatPromptTemplate.from_template("what is the city {person} is from?")
-prompt2 = ChatPromptTemplate.from_template(
-    "what country is the city {city} in? respond in {language}"
-)
-model = ChatOpenAI()
-chain1 = prompt1 | model | StrOutputParser()
-chain2 = (
-    {"city": chain1, "language": itemgetter("language")}
-    | prompt2
-    | model
-    | StrOutputParser()
-)
-
-chain2.invoke({"person": "obama", "language": "spanish"}, config={"callbacks":[langfuse_handler]})
-```
-
-
-
-
-    'Barack Obama es de la ciudad de Chicago, Illinois, en los Estados Unidos.'
-
-
-
-#### Runnable methods
-
-Runnables are units of work that can be invoked, batched, streamed, transformed and composed.
-
-The examples below show how to use the following methods with Langfuse:
-
-- invoke/ainvoke: Transforms a single input into an output.
-- batch/abatch: Efficiently transforms multiple inputs into outputs.
-- stream/astream: Streams output from a single input as it’s produced.
-
-
-```python
-# Async Invoke
-await chain2.ainvoke({"person": "biden", "language": "german"}, config={"callbacks":[langfuse_handler]})
-
-# Batch
-chain2.batch([{"person": "elon musk", "language": "english"}, {"person": "mark zuckerberg", "language": "english"}], config={"callbacks":[langfuse_handler]})
-
-# Async Batch
-await chain2.abatch([{"person": "jeff bezos", "language": "english"}, {"person": "tim cook", "language": "english"}], config={"callbacks":[langfuse_handler]})
-
-# Stream
-for chunk in chain2.stream({"person": "steve jobs", "language": "english"}, config={"callbacks":[langfuse_handler]}):
-    print("Streaming chunk:", chunk)
-
-# Async Stream
-async for chunk in chain2.astream({"person": "bill gates", "language": "english"}, config={"callbacks":[langfuse_handler]}):
-    print("Async Streaming chunk:", chunk)
-
-```
-
-### RetrievalQA
-
-![Trace of Langchain QA Retrieval in Langfuse](https://langfuse.com/images/cookbook/integration_langchain/langchain_qa_retrieval.png)
-
-[Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/84e1ac07dedbce3b2a236b6ece6950d9?timestamp=2025-06-11T09:29:10.248Z&display=details)
-
-
-```python
-import os
-os.environ.setdefault("SERPAPI_API_KEY", "...");
-```
-
-
-```python
-%pip install unstructured selenium langchain-chroma --upgrade
-```
-
-
-```python
-from langchain_community.document_loaders import SeleniumURLLoader
-from langchain_chroma import Chroma
-from langchain_text_splitters import CharacterTextSplitter
-from langchain_openai import OpenAI, OpenAIEmbeddings
-from langchain_classic.chains import RetrievalQA
-
-langfuse_handler = CallbackHandler()
-
-urls = [
-    "https://raw.githubusercontent.com/langfuse/langfuse-docs/main/public/state_of_the_union.txt",
-]
-loader = SeleniumURLLoader(urls=urls)
-llm = OpenAI()
-documents = loader.load()
-text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-texts = text_splitter.split_documents(documents)
-embeddings = OpenAIEmbeddings()
-docsearch = Chroma.from_documents(texts, embeddings)
-query = "What did the president say about Ketanji Brown Jackson"
-chain = RetrievalQA.from_chain_type(
-    llm,
-    retriever=docsearch.as_retriever(search_kwargs={"k": 1}),
-)
-
-chain.invoke(query, config={"callbacks":[langfuse_handler]})
-```
-
-
-
-
-    {'query': 'What did the president say about Ketanji Brown Jackson',
-     'result': " The president nominated her to serve on the United States Supreme Court and praised her as one of the nation's top legal minds who will continue the legacy of retiring Justice Stephen Breyer."}
-
-
-
-### AzureOpenAI
-
-
-```python
-os.environ.setdefault("AZURE_OPENAI_ENDPOINT", "<Azure OpenAI endpoint>");
-os.environ.setdefault("AZURE_OPENAI_API_KEY", "<Azure OpenAI API key>");
-os.environ.setdefault("OPENAI_API_TYPE", "azure");
-os.environ.setdefault("OPENAI_API_VERSION", "2023-09-01-preview");
-```
-
-
-```python
-from langchain_openai import AzureChatOpenAI
-from langchain_core.prompts import ChatPromptTemplate
-from langfuse.langchain import CallbackHandler
- 
-# Initialize Langfuse CallbackHandler for Langchain (tracing)
-langfuse_handler = CallbackHandler()
-
-prompt = ChatPromptTemplate.from_template("what is the city {person} is from?")
-model = AzureChatOpenAI(
-    deployment_name="gpt-4o",
-    model_name="gpt-4o",
-)
-chain = prompt | model
-
-chain.invoke({"person": "Satya Nadella"}, config={"callbacks":[langfuse_handler]})
-```
diff --git a/content/guides/cookbook/integration_langserve.md b/content/guides/cookbook/integration_langserve.md
deleted file mode 100644
index eeff56d70e..0000000000
--- a/content/guides/cookbook/integration_langserve.md
+++ /dev/null
@@ -1,187 +0,0 @@
----
-title: Langserve Integration
-description: This guide shows how to natively integrate Langfuse with LangChain's Langserve for observability, metrics, evals, prompt management, playground, datasets.
-category: Integrations
-sidebarTitle: Langserve
-logo: /images/integrations/langchain_icon.png
----
-
-# Cookbook: Langserve Integration
-
-[Langserve](https://python.langchain.com/docs/langserve/) (Python)
-> LangServe helps developers deploy LangChain runnables and chains as a REST API.
->
-> This library is integrated with FastAPI and uses pydantic for data validation.
->
-> In addition, it provides a client that can be used to call into runnables deployed on a server. A JavaScript client is available in LangChain.js.
-
-This cookbook demonstrates how to trace applications deployed via Langserve with Langfuse (using the [LangChain integration](https://langfuse.com/integrations/frameworks/langchain)). We'll run both the server and the client in this notebook.
-
-## Setup
-
-
-```python
-%pip install fastapi sse_starlette httpx langserve langfuse langchain-openai langchain --upgrade
-```
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page
-# https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your openai key
-os.environ.setdefault("OPENAI_API_KEY", "");
-```
-
-## Simple LLM Call Example
-
-Initialize the Langfuse `CallbackHandler` (authenticated via the environment variables set above) and configure the LLM with it as callback handler. Add to Fastapi via Langserve's `add_routes()`.
-
-
-```python
-from langchain_openai import ChatOpenAI
-from langchain_core.runnables.config import RunnableConfig
-from langfuse import get_client
-from langfuse.langchain import CallbackHandler
-from fastapi import FastAPI
-from langserve import add_routes
-
-# Initialize Langfuse CallbackHandler for Langchain (tracing)
-langfuse_handler = CallbackHandler()
-
-# Tests the SDK connection with the server
-get_client().auth_check()
-
-llm = ChatOpenAI()
-
-config = RunnableConfig(callbacks=[langfuse_handler])
-
-llm_with_langfuse = llm.with_config(config)
-
-# Setup server
-app = FastAPI()
-
-# Add Langserve route
-add_routes(
-    app,
-    llm_with_langfuse,
-    path="/test-simple-llm-call",
-)
-```
-
-
-
-*Note: We use TestClient in this example to be able to run the server in a notebook*
-
-
-```python
-from fastapi.testclient import TestClient
-
-# Initialize TestClient
-client = TestClient(app)
-
-# Test simple route
-response = client.post("/test-simple-llm-call/invoke", json={"input": "Tell me a joke?"})
-```
-
-Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/5f32e2e7-9508-4280-b47b-e0356bc3c81e
-
-![Trace of Langserve Simple LLM Call](https://langfuse.com/images/cookbook/integration_langserve_simple.png)
-
-## LCEL example
-
-
-```python
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-from langserve import add_routes
-
-# Create Chain
-prompt = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
-
-chain = prompt | llm | StrOutputParser()
-
-# Add new route
-add_routes(
-    app,
-    chain.with_config(config),
-    path="/test-chain",
-)
-
-# Test chain route
-response = client.post("/test-chain/invoke", json={"input": {"topic": "Berlin"}})
-```
-
-Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/261d1006-74ff-4b67-8baf-afdfc827aee2
-
-![Trace of Langserve LCEL Example](https://langfuse.com/images/cookbook/integration_langserve_chain.png)
-
-## Agent Example
-
-
-```python
-from langchain_core.tools import tool
-from langchain_core.utils.function_calling import convert_to_openai_tool
-from langchain.agents.format_scratchpad.openai_tools import (
-    format_to_openai_tool_messages,
-)
-from langchain.agents import AgentExecutor
-from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser
-from langserve.pydantic_v1 import BaseModel
-from langchain_core.prompts import MessagesPlaceholder
-
-class Input(BaseModel):
-    input: str
-
-prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", "You are a helpful assistant."),
-        ("user", "{input}"),
-        MessagesPlaceholder(variable_name="agent_scratchpad"),
-    ]
-)
-
-@tool
-def word_length(word: str) -> int:
-    """Returns a counter word"""
-    return len(word)
-
-tools = [word_length]
-
-llm_with_tools = llm.bind(tools=[convert_to_openai_tool(tool) for tool in tools])
-
-agent = (
-    {
-        "input": lambda x: x["input"],
-        "agent_scratchpad": lambda x: format_to_openai_tool_messages(
-            x["intermediate_steps"]
-        ),
-    }
-    | prompt
-    | llm_with_tools
-    | OpenAIToolsAgentOutputParser()
-)
-agent_executor = AgentExecutor(agent=agent, tools=tools)
-
-agent_config = RunnableConfig({"run_name": "agent"}, callbacks=[langfuse_handler])
-
-add_routes(
-    app,
-    agent_executor.with_types(input_type=Input).with_config(
-        agent_config
-    ),
-    path="/test-agent",
-)
-
-response = client.post("/test-agent/invoke", json={"input": {"input": "How long is Leonardo DiCaprios last name?"}})
-```
-
-Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/ed1d57f9-2f35-4e72-8150-b061f21840a7
-
-![Trace of Langserve Agent example](https://langfuse.com/images/cookbook/integration_langserve_agent.png)
diff --git a/content/guides/cookbook/integration_litellm_proxy.mdx b/content/guides/cookbook/integration_litellm_proxy.mdx
deleted file mode 100644
index 63e0a9cbf1..0000000000
--- a/content/guides/cookbook/integration_litellm_proxy.mdx
+++ /dev/null
@@ -1,209 +0,0 @@
----
-source: ⚠️ Jupyter Notebook
-title: Cookbook - LiteLLM (Proxy) + Langfuse OpenAI Integration + Python Decorator
-sidebarTitle: Cookbook - LiteLLM (Proxy) + Langfuse OpenAI Integration + Python Decorator
-description: The stack to use any of 100+ models in Python without having to change your code and with full observability.
-category: Integrations
----
-
-# Cookbook: LiteLLM (Proxy) + Langfuse OpenAI Integration + `@observe` Decorator
-
-<a href="https://langfuse.com/guides/cookbook/integration_litellm_proxy"><img className="inline" alt="Python" src="https://img.shields.io/badge/Python-d4d4d8?style=flat&logo=python&logoColor=white" /></a> <a href="https://langfuse.com/guides/cookbook/js_integration_litellm_proxy"><img className="inline" alt="JS/TS" src="https://img.shields.io/badge/JS/TS-F7DF1E?style=flat&logo=javascript&logoColor=black" /></a>
-
-We want to share a stack that's commonly used by the Langfuse community to quickly experiment with 100+ models from different providers without changing code. This stack includes:
-- [**LiteLLM Proxy**](https://docs.litellm.ai/docs/) ([GitHub](https://github.com/BerriAI/litellm)) which standardizes 100+ model provider APIs on the OpenAI API schema. It removes the complexity of direct API calls by centralizing interactions with these APIs through a single endpoint. You can also self-host the LiteLLM Proxy as it is open-source.
-- **Langfuse OpenAI SDK Wrapper** ([Python](https://langfuse.com/integrations/model-providers/openai-py), [JS](https://langfuse.com/integrations/model-providers/openai-js)) to natively instrument calls to all these 100+ models via the OpenAI SDK. This automatically captures token counts, latencies, streaming response times (time to first token), api errors, and more.
-- **Langfuse**: OSS LLM Observability, full overview [here](https://langfuse.com/docs).
-
-
-This cookbook is an end-to-end guide to set up and use this stack. As we'll use Python in this example, we will also use the `@observe` decorator to create nested traces. More on this below.
-
-Let's dive right in!
-
-## Install dependencies
-
-
-```python
-%pip install "litellm[proxy]" langfuse openai
-```
-
-## Setup environment
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page: https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your openai key
-os.environ.setdefault("OPENAI_API_KEY", "sk-proj-");
-```
-
-
-```python
-from litellm import completion
-from langfuse import observe, get_client
-
-langfuse = get_client()
-
-@observe()
-def fn():
-  # set custom langfuse trace params and generation params
-  response = completion(
-    model="gpt-3.5-turbo",
-    messages=[
-      {"role": "user", "content": "Hi 👋 - i'm openai"}
-    ],
-    metadata={
-        "existing_trace_id": langfuse.get_current_trace_id(),   # set langfuse trace ID
-        "parent_observation_id": langfuse.get_current_observation_id(),
-    },
-  )
- 
-  print(response)
-```
-
-## Setup Lite LLM Proxy
-
-In this example, we'll use GPT-3.5-turbo directly from OpenAI, and llama3 and mistral via the Ollama on our local machine.
-
-**Steps**
-
-1. Create a `litellm_config.yaml` to configure which models are available ([docs](https://litellm.vercel.app/docs/proxy/configs)). We'll use gpt-3.5-turbo, and llama3 and mistral via Ollama in this example. Make sure to replace `<openai_key>` with your OpenAI API key.
-    ```yaml
-    model_list:
-      - model_name: gpt-3.5-turbo
-        litellm_params:
-          model: gpt-3.5-turbo
-          api_key: <openai_key>
-      - model_name: ollama/llama3
-        litellm_params:
-          model: ollama/llama3
-      - model_name: ollama/mistral
-        litellm_params:
-          model: ollama/mistral
-    ```
-2. Ensure that you installed Ollama and have pulled the llama3 (8b) and mistral (7b) models: `ollama pull llama3 && ollama pull mistral`
-3. Run the following cli command to start the proxy: `litellm --config litellm_config.yaml`
-
-The Lite LLM Proxy should be now running on http://0.0.0.0:4000
-
-To verify the connection you can run `litellm --test`
-
-## Log single LLM Call via Langfuse OpenAI Wrapper
-The Langfuse SDK offers a wrapper function around the OpenAI SDK, automatically logging all OpenAI calls as generations to Langfuse.
-
-For more details, please refer to our [documentation](https://langfuse.com/integrations/model-providers/openai-py).
-
-
-```python
-from langfuse.openai import openai
-
-# Set PROXY_URL to the url of your lite_llm_proxy (by default: http://0.0.0.0:4000)
-PROXY_URL="http://0.0.0.0:4000"
-
-system_prompt = "You are a very accurate calculator. You output only the result of the calculation."
-
-# Configure the OpenAI client to use the LiteLLM proxy
-client = openai.OpenAI(base_url=PROXY_URL)
-
-gpt_completion = client.chat.completions.create(
-  model="gpt-3.5-turbo",
-  name="gpt-3.5", # optional name of the generation in langfuse
-  messages=[
-      {"role": "system", "content": system_prompt},
-      {"role": "user", "content": "1 + 1 = "}],
-)
-print(gpt_completion.choices[0].message.content)
-
-llama_completion = client.chat.completions.create(
-  model="ollama/llama3",
-  name="llama3", # optional name of the generation in langfuse
-  messages=[
-      {"role": "system", "content": system_prompt},
-      {"role": "user", "content": "3 + 3 = "}],
-)
-print(llama_completion.choices[0].message.content)
-```
-
-Public trace links for the following examples:
-- [GPT-3.5-turbo](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/a4e67d7d-d9cb-455b-9795-3ad41f39431e?observation=81006513-82b1-4ae4-bb98-7e1bc6c009a7)
-- [llama3](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/22fdce4a-4d74-4af3-9746-7bafaa45247c?observation=b9b30b5d-7fbf-40b4-acd9-4fdc1776cc87)
-
-## Trace nested LLM Calls via Langfuse OpenAI Wrapper and `@observe` decorator
-
-Via the Langfuse `@observe()` decorator we can automatically capture execution details of any python function such as inputs, outputs, timings, and more. The decorator simplifies achieving in-depth observability in your applications with minimal code, especially when non-LLM calls are involved for knowledge retrieval (RAG) or api calls (agents).
-
-For more details on how to utilize this decorator and customize your tracing, refer to our [documentation](https://langfuse.com/docs/sdk/python/decorators).
-
-Let's have a look at a simple example which uses all three models we have set up in the LiteLLM Proxy:
-
-
-```python
-from langfuse import observe
-from langfuse.openai import openai
-
-@observe()
-def rap_battle(topic: str):
-    client = openai.OpenAI(
-        base_url=PROXY_URL,
-    )
-
-    messages = [
-        {"role": "system", "content": "You are a rap artist. Drop a fresh line."},
-        {"role": "user", "content": "Kick it off, today's topic is {topic}, here's the mic..."}
-    ]
-
-    # First model (gpt-3.5-turbo) starts the rap
-    gpt_completion = client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        name="rap-gpt-3.5-turbo", # add custom name to Langfuse observation
-        messages=messages,
-    )
-    first_rap = gpt_completion.choices[0].message.content
-    messages.append({"role": "assistant", "content": first_rap})
-    print("Rap 1:", first_rap)
-
-    # Second model (ollama/llama3) responds
-    llama_completion = client.chat.completions.create(
-        model="ollama/llama3",
-        name="rap-llama3",
-        messages=messages,
-    )
-    second_rap = llama_completion.choices[0].message.content
-    messages.append({"role": "assistant", "content": second_rap})
-    print("Rap 2:", second_rap)
-
-    # Third model (ollama/mistral) adds the final touch
-    mistral_completion = client.chat.completions.create(
-        model="ollama/mistral",
-        name="rap-mistral",
-        messages=messages,
-    )
-    third_rap = mistral_completion.choices[0].message.content
-    messages.append({"role": "assistant", "content": third_rap})
-    print("Rap 3:", third_rap)
-    
-    return messages
-
-# Call the function
-rap_battle("typography")
-```
-
-[Public trace](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/7b1af1be-8096-474e-a2fc-081538ca333c)
-
-![Public Trace](https://langfuse.com/images/cookbook/integration_litellm_proxy_trace.gif)
-
-## Learn more
-
-Check out the docs to learn more about all components of this stack:
-- [LiteLLM Proxy](https://docs.litellm.ai/docs/)
-- [Langfuse OpenAI SDK Wrapper](https://langfuse.com/integrations/model-providers/openai-py)
-- [Langfuse @observe() decorator](https://langfuse.com/docs/sdk/python/decorators)
-- [Langfuse](https://langfuse.com/docs)
-
-If you do not want to capture traces via the OpenAI SDK Wrapper, you can also directly log requests from the LiteLLM Proxy to Langfuse. For more details, refer to the [LiteLLM Docs](https://litellm.vercel.app/docs/proxy/logging#logging-proxy-inputoutput---langfuse).
diff --git a/content/guides/cookbook/integration_llama-index-callback.md b/content/guides/cookbook/integration_llama-index-callback.md
deleted file mode 100644
index 8f535958c4..0000000000
--- a/content/guides/cookbook/integration_llama-index-callback.md
+++ /dev/null
@@ -1,117 +0,0 @@
----
-title: "Cookbook LlamaIndex Integration"
-description: Example cookbook for the LlamaIndex Langfuse integration.
-category: Integrations
----
-
-# Cookbook LlamaIndex Integration
-
-This is a simple cookbook that demonstrates how to trace a LlamaIndex application with Langfuse via the [LlamaIndex Langfuse integration](https://langfuse.com/integrations/frameworks/llamaindex). It uses a very simple Index and Query together with the [OpenInference LlamaIndex instrumentation](https://docs.arize.com/phoenix/tracing/integrations-tracing/llamaindex), which automatically exports OpenTelemetry (OTel) spans to Langfuse.
-
-**Any feedback?** Let us know on Discord or GitHub. We'd love to hear your thoughts.
-
-## Setup
-
-Make sure you have `llama-index`, `langfuse`, and `openinference-instrumentation-llama-index` installed.
-
-
-```python
-%pip install llama-index langfuse openinference-instrumentation-llama-index --upgrade
-```
-
-Set your Langfuse API keys as environment variables. Get your API keys from the [Langfuse project settings](https://cloud.langfuse.com). This example uses OpenAI for embeddings and chat completions, so you also need to set your OpenAI key.
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page: https://cloud.langfuse.com
-
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your OpenAI key
-os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...");
-```
-
-With the environment variables set, we can initialize the Langfuse client and register the [OpenInference LlamaIndex instrumentation](https://docs.arize.com/phoenix/tracing/integrations-tracing/llamaindex). This third-party instrumentation automatically captures LlamaIndex operations and exports OpenTelemetry (OTel) spans to Langfuse.
-
-
-```python
-from langfuse import get_client
-from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
-
-langfuse = get_client()
-
-# Verify connection
-if langfuse.auth_check():
-    print("Langfuse client is authenticated and ready!")
-else:
-    print("Authentication failed. Please check your credentials and host.")
-
-# Initialize LlamaIndex instrumentation
-LlamaIndexInstrumentor().instrument()
-```
-
-## Index
-
-
-```python
-# Example context, thx ChatGPT
-from llama_index.core import Document
-
-doc1 = Document(text="""
-Maxwell "Max" Silverstein, a lauded movie director, screenwriter, and producer, was born on October 25, 1978, in Boston, Massachusetts. A film enthusiast from a young age, his journey began with home movies shot on a Super 8 camera. His passion led him to the University of Southern California (USC), majoring in Film Production. Eventually, he started his career as an assistant director at Paramount Pictures. Silverstein's directorial debut, “Doors Unseen,” a psychological thriller, earned him recognition at the Sundance Film Festival and marked the beginning of a successful directing career.
-""")
-doc2 = Document(text="""
-Throughout his career, Silverstein has been celebrated for his diverse range of filmography and unique narrative technique. He masterfully blends suspense, human emotion, and subtle humor in his storylines. Among his notable works are "Fleeting Echoes," "Halcyon Dusk," and the Academy Award-winning sci-fi epic, "Event Horizon's Brink." His contribution to cinema revolves around examining human nature, the complexity of relationships, and probing reality and perception. Off-camera, he is a dedicated philanthropist living in Los Angeles with his wife and two children.
-""")
-```
-
-
-```python
-# Example index construction + LLM query
-from llama_index.core import VectorStoreIndex
-
-index = VectorStoreIndex.from_documents([doc1,doc2])
-```
-
-## Query
-
-
-```python
-# Query
-response = index.as_query_engine().query("What did he do growing up?")
-print(response)
-```
-
-
-```python
-# Chat
-response = index.as_chat_engine().chat("What did he do growing up?")
-print(response)
-```
-
-## Explore traces in Langfuse
-
-
-```python
-# As we want to immediately see the result in Langfuse, we flush the Langfuse client
-langfuse.flush()
-```
-
-Done! ✨ You see traces of your index and query in your Langfuse project.
-
-![Example Trace in Langfuse](https://langfuse.com/images/cookbook/integration_llama-index/llama-index-example-trace.png)
-
-[Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/12ea412956f99347b0503c1144acd0ec?timestamp=2025-06-05T15:45:52.971Z&display=details)
-
-
-## Interested in more advanced features?
-
-See the full [integration docs](https://langfuse.com/integrations/frameworks/llamaindex) to learn more about advanced features and how to use them:
-
-- Interoperability with Langfuse Python SDK and other integrations
-- Add custom metadata and attributes to the traces
diff --git a/content/guides/cookbook/integration_llama-index_instrumentation.md b/content/guides/cookbook/integration_llama-index_instrumentation.md
deleted file mode 100644
index 9925bd9c2e..0000000000
--- a/content/guides/cookbook/integration_llama-index_instrumentation.md
+++ /dev/null
@@ -1,126 +0,0 @@
----
-title: "Cookbook LlamaIndex Integration (Instrumentation Module)"
-description: Example cookbook for the LlamaIndex Langfuse integration using the instrumentation module of LlamaIndex.
-category: Integrations
----
-
-# Cookbook LlamaIndex Integration (Instrumentation Module)
-
-This is a simple cookbook that demonstrates how to trace LlamaIndex applications with Langfuse based on the [instrumentation module](https://docs.llamaindex.ai/en/stable/module_guides/observability/instrumentation/) of LlamaIndex. It uses the [OpenInference LlamaIndex instrumentation](https://docs.arize.com/phoenix/tracing/integrations-tracing/llamaindex), which builds on LlamaIndex's instrumentation module and exports OpenTelemetry (OTel) spans to Langfuse. See the [LlamaIndex integration docs](https://langfuse.com/integrations/frameworks/llamaindex) for more details.
-
-## Setup
-
-Make sure you have `llama-index`, `langfuse`, and `openinference-instrumentation-llama-index` installed.
-
-
-```python
-%pip install langfuse openinference-instrumentation-llama-index llama-index --upgrade
-```
-
-Initialize the integration. Get your API keys from the Langfuse project settings. This example uses OpenAI for embeddings and chat completions. You can also use any other model supported by LlamaIndex.
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page
-# https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your openai key
-os.environ.setdefault("OPENAI_API_KEY", "");
-```
-
-Initialize the Langfuse client and register the OpenInference `LlamaIndexInstrumentor`. The instrumentor hooks into LlamaIndex's instrumentation module and exports OTel spans to Langfuse:
-
-
-```python
-from langfuse import get_client
-from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
-
-langfuse = get_client()
-
-# Verify connection
-if langfuse.auth_check():
-    print("Langfuse client is authenticated and ready!")
-else:
-    print("Authentication failed. Please check your credentials and host.")
-
-# Initialize LlamaIndex instrumentation
-LlamaIndexInstrumentor().instrument()
-```
-
-## Index
-
-
-```python
-# Example context, thx ChatGPT
-from llama_index.core import Document
-
-doc1 = Document(text="""
-Maxwell "Max" Silverstein, a lauded movie director, screenwriter, and producer, was born on October 25, 1978, in Boston, Massachusetts. A film enthusiast from a young age, his journey began with home movies shot on a Super 8 camera. His passion led him to the University of Southern California (USC), majoring in Film Production. Eventually, he started his career as an assistant director at Paramount Pictures. Silverstein's directorial debut, “Doors Unseen,” a psychological thriller, earned him recognition at the Sundance Film Festival and marked the beginning of a successful directing career.
-""")
-doc2 = Document(text="""
-Throughout his career, Silverstein has been celebrated for his diverse range of filmography and unique narrative technique. He masterfully blends suspense, human emotion, and subtle humor in his storylines. Among his notable works are "Fleeting Echoes," "Halcyon Dusk," and the Academy Award-winning sci-fi epic, "Event Horizon's Brink." His contribution to cinema revolves around examining human nature, the complexity of relationships, and probing reality and perception. Off-camera, he is a dedicated philanthropist living in Los Angeles with his wife and two children.
-""")
-```
-
-
-```python
-# Example index construction + LLM query
-from llama_index.core import VectorStoreIndex
-
-index = VectorStoreIndex.from_documents([doc1,doc2])
-```
-
-## Query
-
-
-```python
-# Query
-response = index.as_query_engine().query("What did he do growing up?")
-print(response)
-```
-
-    He made home movies using a Super 8 camera.
-
-
-Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/d933c7cc-20bf-4db3-810d-bab1c8d9a2a1
-
-
-```python
-# Chat
-response = index.as_chat_engine().chat("What did he do growing up?")
-print(response)
-```
-
-    He made home movies using a Super 8 camera growing up.
-
-
-Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/4e285b8f-9789-4cf0-a8b4-45473ac420f1
-
-![LlamaIndex Chat Engine Trace in Langfuse (via instrumentation module)](https://langfuse.com/images/cookbook/integration_llama-index_instrumentation_chatengine_trace.png)
-
-## Custom trace properties
-
-You can set custom trace attributes such as `user_id`, `session_id`, or `tags` with the `propagate_attributes` context manager. To group multiple operations into a single trace and score it, wrap them in a root observation via `langfuse.start_as_current_observation` and use `score_trace` on the root span.
-
-
-```python
-from langfuse import propagate_attributes
-
-with langfuse.start_as_current_observation(as_type="span", name="llama-index-query") as span:
-    with propagate_attributes(user_id="my-user", session_id="my-session"):
-        response = index.as_query_engine().query("What did he do growing up?")
-    span.update(output=str(response))
-
-    # Score the trace, e.g. based on user feedback
-    span.score_trace(name="my-score", value=0.5)
-
-langfuse.flush()
-```
-
-Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/6f554d6b-a2bc-4fba-904f-aa54de2897ca
diff --git a/content/guides/cookbook/integration_llama-index_milvus-lite.mdx b/content/guides/cookbook/integration_llama-index_milvus-lite.mdx
deleted file mode 100644
index faaa1a4543..0000000000
--- a/content/guides/cookbook/integration_llama-index_milvus-lite.mdx
+++ /dev/null
@@ -1,126 +0,0 @@
----
-source: ⚠️ Jupyter Notebook
-title: "Cookbook: LlamaIndex & Milvus Integration"
-description: Example cookbook for the LlamaIndex, Milvus and Langfuse integration.
-category: Integrations
----
-
-# Cookbook: LlamaIndex & Milvus Integration
-
-This is a simple cookbook that demonstrates how to use the [LlamaIndex Langfuse integration](https://langfuse.com/integrations/frameworks/llamaindex). It uses Milvus Lite to store the documents and Query.
-
-[Milvus Lite](https://github.com/milvus-io/milvus-lite/) is the lightweight version of Milvus, an open-source vector database that powers AI applications with vector embeddings and similarity search.
-
-Traces are captured with the [OpenInference LlamaIndex instrumentation](https://docs.arize.com/phoenix/tracing/integrations-tracing/llamaindex), which automatically exports OpenTelemetry (OTel) spans to Langfuse.
-
-## Setup
-
-
-```python
-%pip install llama-index langfuse openinference-instrumentation-llama-index llama-index-vector-stores-milvus --upgrade
-```
-
-Initialize the integration. Get your API keys from the [Langfuse project settings](https://cloud.langfuse.com) and set them as environment variables. This example uses OpenAI for embeddings and chat completions, so you also need to specify your OpenAI key in an environment variable.
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page
-# https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your openai key
-os.environ.setdefault("OPENAI_API_KEY", "");
-```
-
-
-```python
-from langfuse import get_client
-from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
-
-langfuse = get_client()
-
-# Verify connection
-if langfuse.auth_check():
-    print("Langfuse client is authenticated and ready!")
-else:
-    print("Authentication failed. Please check your credentials and host.")
-
-# Initialize LlamaIndex instrumentation
-LlamaIndexInstrumentor().instrument()
-```
-
-## Index using Milvus Lite
-
-
-```python
-from llama_index.core import Document
-
-doc1 = Document(text="""
-Maxwell "Max" Silverstein, a lauded movie director, screenwriter, and producer, was born on October 25, 1978, in Boston, Massachusetts. A film enthusiast from a young age, his journey began with home movies shot on a Super 8 camera. His passion led him to the University of Southern California (USC), majoring in Film Production. Eventually, he started his career as an assistant director at Paramount Pictures. Silverstein's directorial debut, “Doors Unseen,” a psychological thriller, earned him recognition at the Sundance Film Festival and marked the beginning of a successful directing career.
-""")
-doc2 = Document(text="""
-Throughout his career, Silverstein has been celebrated for his diverse range of filmography and unique narrative technique. He masterfully blends suspense, human emotion, and subtle humor in his storylines. Among his notable works are "Fleeting Echoes," "Halcyon Dusk," and the Academy Award-winning sci-fi epic, "Event Horizon's Brink." His contribution to cinema revolves around examining human nature, the complexity of relationships, and probing reality and perception. Off-camera, he is a dedicated philanthropist living in Los Angeles with his wife and two children.
-""")
-```
-
-
-```python
-# Example index construction + LLM query
-
-from llama_index.core import VectorStoreIndex
-from llama_index.core import StorageContext
-from llama_index.vector_stores.milvus import MilvusVectorStore
-
-
-vector_store = MilvusVectorStore(
-    uri="tmp/milvus_demo.db", dim=1536, overwrite=False
-)
-storage_context = StorageContext.from_defaults(vector_store=vector_store)
-
-index = VectorStoreIndex.from_documents(
-    [doc1,doc2], storage_context=storage_context
-)
-```
-
-## Query
-
-
-```python
-# Query
-response = index.as_query_engine().query("What did he do growing up?")
-print(response)
-```
-
-
-```python
-# Chat
-response = index.as_chat_engine().chat("What did he do growing up?")
-print(response)
-```
-
-## Explore traces in Langfuse
-
-
-```python
-# As we want to immediately see the result in Langfuse, we flush the Langfuse client
-langfuse.flush()
-```
-
-Done! ✨ You see traces of your index and query in your Langfuse project.
-
-![Example Trace in Langfuse](https://langfuse.com/images/cookbook/integration_llama-index/llama-index-example-trace.png)
-
-[Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/12ea412956f99347b0503c1144acd0ec?timestamp=2025-06-05T15:45:52.971Z&display=details)
-
-
-## Interested in more advanced features?
-
-See the full [integration docs](https://langfuse.com/integrations/frameworks/llamaindex) to learn more about advanced features and how to use them:
-
-- Interoperability with Langfuse Python SDK and other integrations
-- Add custom metadata and attributes to the traces
diff --git a/content/guides/cookbook/integration_llama_index.mdx b/content/guides/cookbook/integration_llama_index.mdx
deleted file mode 100644
index e46270252a..0000000000
--- a/content/guides/cookbook/integration_llama_index.mdx
+++ /dev/null
@@ -1,110 +0,0 @@
----
-source: ⚠️ Jupyter Notebook
-title: Observability for LlamaIndex with Langfuse Integration
-sidebarTitle: LlamaIndex
-logo: /images/integrations/llamaindex_icon.png
-description: Discover how to integrate Langfuse with LlamaIndex for enhanced LLM application monitoring, debugging, and tracing. Improve your AI development workflow today.
-category: Integrations
----
-
-# Integrate Langfuse with LlamaIndex
-
-This notebook provides a step-by-step guide on integrating **Langfuse** with **LlamaIndex** to achieve observability and debugging for your LLM applications.
-
-> **What is LlamaIndex?** [LlamaIndex](https://www.llamaindex.ai/) ([GitHub](https://github.com/run-llama/llama_index)) is an advanced "data framework" tailored for augmenting LLMs with private data. It streamlines the integration of diverse data sources and formats (APIs, PDFs, docs, SQL, etc.) through versatile data connectors and structures data into indices and graphs for LLM compatibility. The platform offers a sophisticated retrieval/query interface for enriching LLM inputs with context-specific outputs.
-
-> **What is Langfuse?** [Langfuse](https://langfuse.com) is an open-source AI engineering platform. It offers tracing and monitoring capabilities for LLM applications. Langfuse helps developers debug, analyze, and optimize their AI systems by providing detailed insights and integrating with a wide array of tools and frameworks through native integrations, OpenTelemetry, and dedicated SDKs.
-
-## Getting Started
-
-Let's walk through a practical example of using LlamaIndex and integrating it with Langfuse for comprehensive tracing.
-
-<Steps>
-### Step 1: Install Dependencies
-
-
-```python
-%pip install langfuse openinference-instrumentation-llama-index llama-index-llms-openai llama-index -U
-```
-
-### Step 2: Configure Langfuse SDK
-
-Next, set up your Langfuse API keys. You can get these keys by signing up for a free [Langfuse Cloud](https://langfuse.com/cloud) account or by [self-hosting Langfuse](https://langfuse.com/self-hosting). These environment variables are essential for the Langfuse client to authenticate and send data to your Langfuse project.
-
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page: https://cloud.langfuse.com
-
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your OpenAI key
-os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...");
-
-```
-
-With the environment variables set, we can now initialize the Langfuse client. `get_client()` initializes the Langfuse client using the credentials provided in the environment variables.
-
-
-
-```python
-from langfuse import get_client
- 
-langfuse = get_client()
- 
-# Verify connection
-if langfuse.auth_check():
-    print("Langfuse client is authenticated and ready!")
-else:
-    print("Authentication failed. Please check your credentials and host.")
-
-```
-
-### Step 3: Initialize LlamaIndex Instrumentation
-
-Now, we initialize the [OpenInference LlamaIndex instrumentation](https://docs.arize.com/phoenix/tracing/integrations-tracing/llamaindex). This third-party instrumentation automatically captures LlamaIndex operations and exports OpenTelemetry (OTel) spans to Langfuse.
-
-
-```python
-from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
-
-# Initialize LlamaIndex instrumentation
-LlamaIndexInstrumentor().instrument()
-```
-
-### Step 4: Basic LlamaIndex Application
-
-Let's create a straightforward LlamaIndex application. In this example, we'll create a simple query engine that can answer questions. This will serve as the foundation for demonstrating Langfuse tracing.
-
-
-
-```python
-from llama_index.llms.openai import OpenAI
-
-llm = OpenAI(model="gpt-4o")
- 
-with langfuse.start_as_current_observation(as_type="span", name="llama-index-trace"):
-    response = llm.complete("Hello, world!")
-    print(response)
- 
-langfuse.flush()
-```
-
-### Step 5: View Traces in Langfuse
-
-After executing the application, navigate to your Langfuse Trace Table. You will find detailed traces of the application's execution, providing insights into the LLM calls, retrieval operations, inputs, outputs, and performance metrics. The trace will show the complete flow from query processing through document retrieval to response generation.
-
-![Example Trace in Langfuse](https://langfuse.com/images/cookbook/integration_llama-index/llama-index-example-trace.png)
-
-[Example Trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/12ea412956f99347b0503c1144acd0ec?timestamp=2025-06-05T15:45:52.971Z&display=details)
-
-</Steps>
-
-import LearnMore from "@/components-mdx/integration-learn-more.mdx";
-
-<LearnMore />
diff --git a/content/guides/cookbook/integration_llama_index_posthog_mistral.md b/content/guides/cookbook/integration_llama_index_posthog_mistral.md
deleted file mode 100644
index e98f881fa0..0000000000
--- a/content/guides/cookbook/integration_llama_index_posthog_mistral.md
+++ /dev/null
@@ -1,248 +0,0 @@
----
-title: Monitoring LlamaIndex applications with PostHog and Langfuse
-description: Learn how to build and monitor a RAG application using LlamaIndex with Mistral, Langfuse, and PostHog. This step-by-step guide covers setup, integration, and analytics for optimizing LLM performance.
-category: Integrations
----
-
-# Analyze and Debug LlamaIndex Applications with PostHog and Langfuse
-
-In this cookbook, we show you how to build a RAG application with [LlamaIndex](https://www.llamaindex.ai/), observe the steps with [Langfuse](https://langfuse.com/), and analyze the data in [PostHog](https://posthog.com/).
-
-## What is Langfuse?
-[Langfuse](https://langfuse.com/) is an open-source AI engineering platform designed to help engineers understand and optimize user interactions with their language model applications. It provides tools for tracking, debugging, and improving LLM performance in real-world use cases. Langfuse is available both as a managed [cloud solution](https://langfuse.com/cloud) and for [local or self-hosted](https://langfuse.com/docs/deployment/feature-overview) deployments.
-
-## What is PostHog?
-[PostHog](https://posthog.com/) is a popular choice for product analytics. Combining Langfuse's LLM analytics with PostHog's product analytics makes it easy to:
-
-- **Analyze User Engagement**: Determine how often users interact with specific LLM features and understand their overall activity patterns.
-- **Correlate Feedback with Behavior**: See how user feedback captured in Langfuse correlates with user behavior in PostHog.
-- **Monitor LLM Performance**: Track and analyze metrics such as model cost, latency, and user feedback to optimize LLM performance.
-
-## What is LlamaIndex?
-LlamaIndex [(GitHub)](https://github.com/run-llama/llama_index) is a data framework designed to connect LLMs with external data sources. It helps structure, index, and query data effectively. This makes it easier for developers to build advanced LLM applications.
-
-
-## How to Build a Simple RAG Application with LlamaIndex and Mistral
-
-In this tutorial, we demonstrate how to create a chat application that provides answers to questions about hedgehog care. LlamaIndex is used to vectorize a [hedgehog care guide](https://www.pro-igel.de/downloads/merkblaetter_engl/wildtier_engl.pdf) with the [Mistral 8x22B model](https://docs.mistral.ai/getting-started/models/). All model generations are then traced using Langfuse's [LlamaIndex integration](https://langfuse.com/integrations/frameworks/llamaindex).
-
-Finally, the [PostHog integration](https://langfuse.com/docs/analytics/posthog) allows you to view detailed analytics about your hedgehog application directly in PostHog.
-
-
-### Step 1: Set up LlamaIndex and Mistral
-
-First, we set our Mistral API key as an environment variable. If you haven't already, [sign up for a Mistral account](https://console.mistral.ai/). Then [subscribe](https://console.mistral.ai/billing/) to a free trial or billing plan, after which you'll be able to [generate an API key](https://console.mistral.ai/api-keys/) (💡 You can use any other model supported by LlamaIndex; we just use Mistral in this cookbook).
-
-Then, we use LlamaIndex to initialize both a Mistral language model and an embedding model. We then set these models in the LlamaIndex `Settings` object:
-
-
-```python
-%pip install llama-index llama-index-llms-mistralai llama-index-embeddings-mistralai nest_asyncio --upgrade
-```
-
-
-```python
-# Set the Mistral API key
-import os
-
-os.environ.setdefault("MISTRAL_API_KEY", "...");
-
-# Ensures that sync and async code can be used together without issues
-import nest_asyncio
-
-nest_asyncio.apply()
-
-# Import and set up llama index
-from llama_index.llms.mistralai import MistralAI
-from llama_index.embeddings.mistralai import MistralAIEmbedding
-from llama_index.core import Settings
-
-# Define your LLM and embedding model
-llm = MistralAI(model="open-mixtral-8x22b", temperature=0.1)
-embed_model = MistralAIEmbedding(model_name="mistral-embed")
-
-# Set the LLM and embedding model in the Settings object
-Settings.llm = llm
-Settings.embed_model = embed_model
-```
-
-### Step 2: Initialize Langfuse
-
-Next, we initialize the Langfuse client. [Sign up](https://cloud.langfuse.com/auth/sign-up) for Langfuse if you haven't already. Copy your [API keys](https://langfuse.com/faq/all/where-are-langfuse-api-keys) from your project settings and add them to your environment.
-
-
-```python
-%pip install langfuse openinference-instrumentation-llama-index wget
-```
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page: https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-```
-
-With the environment variables set, we can now initialize the Langfuse client. get_client() initializes the Langfuse client using the credentials provided in the environment variables.
-
-
-```python
-from langfuse import get_client
- 
-langfuse = get_client()
- 
-# Verify connection
-if langfuse.auth_check():
-    print("Langfuse client is authenticated and ready!")
-else:
-    print("Authentication failed. Please check your credentials and host.")
- 
-```
-
-    Langfuse client is authenticated and ready!
-
-
-Now, we initialize the [OpenInference LlamaIndex instrumentation](https://docs.arize.com/phoenix/tracing/integrations-tracing/llamaindex). This third-party instrumentation automatically captures LlamaIndex operations and exports OpenTelemetry (OTel) spans to Langfuse.
-
-Find out more about the Langfuse's LlamaIndex integration [here](https://langfuse.com/integrations/frameworks/llamaindex).
-
-
-```python
-from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
- 
-# Initialize LlamaIndex instrumentation
-LlamaIndexInstrumentor().instrument()
-```
-
-### Step 3: Download data
-
-We download the file we want to use for RAG. In this example, we use a [hedgehog care guide](https://www.pro-igel.de/downloads/merkblaetter_engl/wildtier_engl.pdf) pdf file to enable the language model to answer questions about caring for hedgehogs 🦔.
-
-
-```python
-import wget
-
-url = "https://www.pro-igel.de/downloads/merkblaetter_engl/wildtier_engl.pdf"
-wget.download(url, "./hedgehog.pdf")   # saves as ./hedgehog.pdf
-```
-
-
-
-
-    './hedgehog (1).pdf'
-
-
-
-Next, we load the pdf using the LlamaIndex [`SimpleDirectoryReader`](https://docs.llamaindex.ai/en/stable/module_guides/loading/simpledirectoryreader/).
-
-
-```python
-from llama_index.core import SimpleDirectoryReader
-
-hedgehog_docs = SimpleDirectoryReader(
-    input_files=["./hedgehog.pdf"]
-).load_data()
-```
-
-### Step 4: Build RAG on the hedgehog doc
-
-Next, we create vector embeddings of the hedgehog document using [`VectorStoreIndex`](https://docs.llamaindex.ai/en/stable/module_guides/indexing/vector_store_index/) and then convert it into a [queryable engine](https://docs.llamaindex.ai/en/stable/module_guides/deploying/query_engine/) to retrieve information based on queries.
-
-
-```python
-from llama_index.core import VectorStoreIndex
-
-hedgehog_index = VectorStoreIndex.from_documents(hedgehog_docs)
-hedgehog_query_engine = hedgehog_index.as_query_engine(similarity_top_k=5)
-```
-
-Finally, to put everything together, we query the engine and print a response:
-
-
-```python
-response = hedgehog_query_engine.query("Which hedgehogs require help?")
-print(response)
-```
-
-    Hedgehogs that may require help include young hedgehogs in need of assistance during autumn, those in need of care, orphaned hoglets, and hedgehogs in need of rehabilitation before release. Additionally, hedgehogs facing dangers such as poison, pesticides, and hazards in built-up areas may also need assistance.
-
-
-All steps of the LLM chain are now tracked in Langfuse.
-
-Example trace in Langfuse: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/367db23d-5b03-446b-bc73-36e289596c00
-
-![Example trace in the Langfuse UI](https://langfuse.com/images/cookbook/example-posthog-llamaindex-mistral/trace-posthog-llamaindex-miostral.png)
-
-### Step 5: (Optional) Implement user feedback to see how your application is performing
-
-To monitor the quality of your hedgehog chat application, you can use [Langfuse Scores](https://langfuse.com/docs/scores/overview) to store user feedback (e.g. thumps up/down or comments). These scores can then be analyzed in PostHog.
-
-Scores are used to evaluate single observations or entire traces. You can create them via the annotation workflow in the Langfuse UI, run model-based evaluation or ingest via the SDK as we do it in this example.
-
-To get the context of the current observation, we use the [`observe()` decorator](https://langfuse.com/docs/sdk/python/decorators) and apply it to the hedgehog_helper() function.
-
-
-```python
-from langfuse import observe, get_client
-
-langfuse = get_client()
-
-# Langfuse observe() decorator to automatically create a trace for the top-level function and spans for any nested functions.
-@observe()
-def hedgehog_helper(user_message):
-    response = hedgehog_query_engine.query(user_message)
-    trace_id = langfuse.get_current_trace_id()
-
-    print(response)
-
-    return trace_id
-
-trace_id = hedgehog_helper("Can I keep the hedgehog as a pet?")
-
-# Score the trace, e.g. to add user feedback using the trace_id
-langfuse.create_score(
-    trace_id=trace_id,
-    name="user-explicit-feedback",
-    value=0.9,
-    data_type="NUMERIC",  # optional, inferred if not provided
-    comment="Good to know!",  # optional
-)
-```
-
-    Based on the provided context, there is no information regarding keeping hedgehogs as pets. The text primarily discusses the biology, behavior, and protection of wild hedgehogs. It is important to note that laws and regulations regarding the keeping of wild animals as pets can vary greatly, so it is always best to consult with local wildlife authorities or experts.
-
-
-### Step 6: See your data in PostHog
-
-Finally, we connect PostHog to our Langfuse account. Below is a summary of the steps to take (or see the [docs](https://posthog.com/docs/ai-engineering/langfuse-posthog) for full details):
-
-1. [Sign up](https://us.posthog.com/) for your free PostHog account if you haven't already
-2. Copy both your project API key and host from your [project settings](https://us.posthog.com/project/settings/project-details).
-3. In your [Langfuse dashboard](https://langfuse.com/cloud), click on **Settings** and scroll down to the **Integrations** section to find the PostHog integration.
-4. Click **Configure** and paste in your PostHog host and project API key (you can find these in your [PostHog project settings](https://us.posthog.com/settings/project)).
-5. Click **Enabled** and then **Save**.
-
- Langfuse will then begin exporting your data to PostHog once a day.
-
-**Using the Langfuse dashboard template:**
-
-Once you've installed the integration, [dashboard templates](https://posthog.com/docs/ai-engineering/langfuse-posthog#using-the-langfuse-dashboard-template) help you quickly set up relevant insights.
-
-For our hedgehog chat application, we are using the template dashboard shown below. This enables you to analyze model cost, user feedback, and latency in PostHog.
-
-To create your own dashboard from a template:
-
-1. Go to the [dashboard](https://us.posthog.com/dashboard) tab in PostHog.
-2. Click the **New dashboard** button in the top right.
-3. Select **LLM metrics – Langfuse** from the list of templates.
-
-![Posthog Dashboard showing user feedback and number of traces](https://langfuse.com/images/cookbook/example-posthog-llamaindex-mistral/dashboard-posthog-1.png)
-
-![Posthog Dashboard showing latency and costs](https://langfuse.com/images/cookbook/example-posthog-llamaindex-mistral/dashboard-posthog-2.png)
-
-## Feedback
-
-If you have any feedback or requests, please create a GitHub [Issue](https://github.com/orgs/langfuse/discussions) or share your idea with the community on [Discord](https://discord.langfuse.com/).
diff --git a/content/guides/cookbook/integration_llamaindex_workflows.mdx b/content/guides/cookbook/integration_llamaindex_workflows.mdx
deleted file mode 100644
index 959f5ee601..0000000000
--- a/content/guides/cookbook/integration_llamaindex_workflows.mdx
+++ /dev/null
@@ -1,135 +0,0 @@
----
-source: Jupyter Notebook
-title: Observability for LlamaIndex with Langfuse Integration
-sidebarTitle: LlamaIndex Workflows
-description: Discover how to integrate Langfuse with LlamaIndex for enhanced LLM application monitoring, debugging, and tracing. Improve your AI development workflow today.
-category: Integrations
-logo: /images/integrations/llamaindex_icon.png
----
-
-# Observability for LlamaIndex Workflows
-
-This cookbook demonstrates how to use [Langfuse](https://langfuse.com) to gain real-time observability for your [LlamaIndex Workflows](https://docs.llamaindex.ai/en/stable/module_guides/workflow/).
-
-> **What are LlamaIndex Workflows?** [LlamaIndex Workflows](https://docs.llamaindex.ai/en/stable/module_guides/workflow/) is a flexible, event-driven framework designed to build robust AI agents. In LlamaIndex, workflows are created by chaining together multiple steps—each defined and validated using the `@step` decorator. Every step processes specific event types, allowing you to orchestrate complex processes such as AI agent collaboration, RAG flows, data extraction, and more.
-
-> **What is Langfuse?** [Langfuse](https://langfuse.com) is the open source AI engineering platform. It helps teams to collaboratively manage prompts, trace applications, debug problems, and evaluate their LLM system in production.
-
-## Get Started
-
-We'll walk through a simple example of using LlamaIndex Workflows and integrating it with Langfuse.
-
-<Steps>
-### Step 1: Install Dependencies
-
-
-```python
-%pip install langfuse openai llama-index-workflows llama-index-core llama-index-llms-openai openinference-instrumentation-llama_index llama-index-instrumentation
-```
-
-### Step 2: Set Up Environment Variables
-
-Configure your Langfuse API keys. You can get them by signing up for [Langfuse Cloud](https://cloud.langfuse.com) or [self-hosting Langfuse](https://langfuse.com/self-hosting).
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page: https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your openai key
-os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...");
-```
-
-With the environment variables set, we can now initialize the Langfuse client. `get_client()` initializes the Langfuse client using the credentials provided in the environment variables.
-
-
-```python
-from langfuse import get_client
- 
-langfuse = get_client()
- 
-# Verify connection
-if langfuse.auth_check():
-    print("Langfuse client is authenticated and ready!")
-else:
-    print("Authentication failed. Please check your credentials and host.")
-
-```
-
-### Step 3: Initialize LlamaIndex Instrumentation
-
-Now, we initialize the [OpenInference LlamaIndex instrumentation](https://docs.arize.com/phoenix/tracing/integrations-tracing/llamaindex). This third-party instrumentation automatically captures LlamaIndex operations and exports OpenTelemetry (OTel) spans to Langfuse.
-
-
-```python
-from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
-
-# Initialize LlamaIndex instrumentation
-LlamaIndexInstrumentor().instrument()
-```
-
-### Step 4: Create a Simple LlamaIndex Workflows Application
-
-In LlamaIndex Workflows, you build event-driven AI agents by defining steps with the `@step` decorator. Each step processes an event and, if appropriate, emits new events. In this example, we create a simple workflow with two steps: one that pre-processes an incoming event and another that generates a reply.
-
-
-```python
-from llama_index.core.llms import ChatMessage
-from llama_index.llms.openai import OpenAI
-from typing import Annotated
-
-from workflows import Workflow, step
-from workflows.events import StartEvent, StopEvent
-from workflows.resource import Resource
-
-
-def get_llm(**kwargs):
-    return OpenAI(model="gpt-4.1-mini")
-
-
-class MyWorkflow(Workflow):
-    @step
-    async def step1(
-        self, ev: StartEvent, llm: Annotated[OpenAI, Resource(get_llm)]
-    ) -> StopEvent:
-        msg = ChatMessage(role="user", content=ev.get("input"))
-        response = await llm.achat([msg])
-        return StopEvent(result=response.message.content)
-
-
-w = MyWorkflow()
-```
-
-
-```python
-response = await w.run(input="Hello, what is Langfuse?")
-print(response)
-```
-
-### Step 5: View Traces in Langfuse
-
-After running your workflow, log in to [Langfuse](https://cloud.langfuse.com) to explore the generated traces. You will see logs for each workflow step along with metrics such as token counts, latencies, and execution paths. 
-
-![Langfuse Trace Example](https://langfuse.com/images/cookbook/integration-llamaindex-workflows/llamaindex-workflows-example-trace.png)
-
-_[Public example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/e0987dce85c8c49602030599b84e77e8?timestamp=2025-07-01T09%3A42%3A54.701Z&display=details)_
-
-</Steps>
-
-<Callout type="info" emoji="ℹ️">
-_**Note:** To add additional trace attributes like tags or metadata or use LlamaIndex Workflows together with other Langfuse features please refer to [this guide](https://langfuse.com/integrations/frameworks/llamaindex)._
-
-</Callout>
-
-import LearnMore from "@/components-mdx/integration-learn-more.mdx";
-
-<LearnMore />
-
-## References
-
-- [LlamaIndex Workflows Documentation](https://docs.llamaindex.ai/en/stable/module_guides/workflow/)\
diff --git a/content/guides/cookbook/integration_openai_assistants.md b/content/guides/cookbook/integration_openai_assistants.md
deleted file mode 100644
index 46876386ff..0000000000
--- a/content/guides/cookbook/integration_openai_assistants.md
+++ /dev/null
@@ -1,190 +0,0 @@
----
-title: OSS Observability for OpenAI Assistants API
-sidebarTitle: OpenAI Assistants API
-description: Use of Langfuse decorator to trace calls made to openai assistants
-category: Integrations
-logo: /images/integrations/openai_icon.svg
----
-
-# Cookbook: Observability for OpenAI Assistants API with Langfuse
-
-This cookbook demonstrates how to use the Langfuse [`observe` decorator](https://langfuse.com/docs/observability/sdk/instrumentation#observe-wrapper) to trace calls made to the [OpenAI Assistants API](https://platform.openai.com/docs/assistants/overview). It covers creating an assistant, running it on a thread, and observing the execution with [Langfuse tracing](https://langfuse.com/docs/tracing).
-
-Note: The native [OpenAI SDK wrapper](https://langfuse.com/integrations/model-providers/openai-py) does not support tracing of the OpenAI assistants API, you need to instrument it via the decorator as shown in this notebook.
-
-## What is the Assistants API?
-
-The Assistants API from OpenAI allows developers to build AI assistants that can utilize multiple tools and data sources in parallel, such as code interpreters, file search, and custom tools created by calling functions. These assistants can access OpenAI's language models like GPT-4 with specific prompts, maintain persistent conversation histories, and process various file formats like text, images, and spreadsheets. Developers can fine-tune the language models on their own data and control aspects like output randomness. The API provides a framework for creating AI applications that combine language understanding with external tools and data.
-
-## Example Trace Output
-
-![OpenAI Assistants Trace in Langfuse](https://langfuse.com/images/docs/openai-assistants-trace.png)
-
-## Setup
-
-Install the required packages:
-
-
-```python
-%pip install --upgrade openai langfuse
-```
-
-Set your environment:
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page
-# https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your openai key
-os.environ.setdefault("OPENAI_API_KEY", "");
-```
-
-## Step by step
-
-### 1. Creating an Assistant
-
-Use the `client.beta.assistants.create` method to create a new assistant. Alternatively you can also create the assistant via the OpenAI console:
-
-
-```python
-from langfuse import observe
-from openai import OpenAI
-
-@observe()
-def create_assistant():
-    client = OpenAI()
-    
-    assistant = client.beta.assistants.create(
-        name="Math Tutor",
-        instructions="You are a personal math tutor. Answer questions briefly, in a sentence or less.",
-        model="gpt-4"
-    )
-    
-    return assistant
-
-assistant = create_assistant()
-print(f"Created assistant: {assistant.id}")
-```
-
-**[Public link of example trace](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/e659e523-2957-4452-83c4-426f29783923) of assistant creation**
-
-### 2. Running the Assistant
-
-Create a thread and run the assistant on it:
-
-
-```python
-@observe()
-def run_assistant(assistant_id, user_input):
-    client = OpenAI()
-    
-    thread = client.beta.threads.create()
-
-    client.beta.threads.messages.create(
-        thread_id=thread.id, role="assistant", content="I am a math tutor that likes to help math students, how can I help?"
-    )
-    client.beta.threads.messages.create(
-        thread_id=thread.id, role="user", content=user_input
-    )
-    
-    run = client.beta.threads.runs.create(
-        thread_id=thread.id,
-        assistant_id=assistant_id,
-    )
-    
-    return run, thread
-
-user_input = "I need to solve the equation `3x + 11 = 14`. Can you help me?"
-run, thread = run_assistant(assistant.id, user_input)
-print(f"Created run: {run.id}")
-```
-
-**[Public link of example trace](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/e659e523-2957-4452-83c4-426f29783923) of message and trace creation**
-
-### 3. Getting the Response
-
-Retrieve the assistant's response from the thread:
-
-
-```python
-import json
-from langfuse import observe, get_client
-langfuse = get_client()
-
-@observe()
-def get_response(thread_id, run_id):
-    client = OpenAI()
-    
-    messages = client.beta.threads.messages.list(thread_id=thread_id, order="asc")
-    assistant_response = messages.data[-1].content[0].text.value
-
-    # get run for model and token counts
-    run_log = client.beta.threads.runs.retrieve(
-        thread_id=thread_id,
-        run_id=run_id
-    )
-
-    message_log = client.beta.threads.messages.list(
-        thread_id=thread_id,
-    )
-    input_messages = [{"role": message.role, "content": message.content[0].text.value} for message in message_log.data[::-1][:-1]]
-
-    # log the internal generation within the openai assistant as a nested generation
-    # it is automatically attached to the current trace and parent observation created by the decorator
-    with langfuse.start_as_current_observation(
-        as_type="generation",
-        name="assistant-run",
-        model=run_log.model,
-        input=input_messages,
-    ) as generation:
-        generation.update(
-            output=assistant_response,
-            usage_details=run_log.usage.model_dump() if run_log.usage else None,
-        )
-    
-    return assistant_response, run_log
-
-response = get_response(thread.id, run.id)
-print(f"Assistant response: {response[0]}")
-```
-
-**[Public link of example trace](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/e0933ea5-6806-4eb7-aed8-a42d23c57096?observation=401fb816-22e5-45ac-a4c9-e437b120f2e7) of fetching the response**
-
-## All in one trace
-
-
-```python
-import time
-
-@observe()
-def run_math_tutor(user_input):
-    assistant = create_assistant()
-    run, thread = run_assistant(assistant.id, user_input)
-
-    time.sleep(5) # notebook only, wait for the assistant to finish
-
-    response = get_response(thread.id, run.id)
-    
-    return response[0]
-
-user_input = "I need to solve the equation `3x + 11 = 14`. Can you help me?"
-response = run_math_tutor(user_input)
-print(f"Assistant response: {response}")
-```
-
-The Langfuse trace shows the flow of creating the assistant, running it on a thread with user input, and retrieving the response, along with the captured input/output data.
-
-**[Public link of example trace](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/b3b7b128-5664-4f42-9fab-31999da9e2f1)**
-
-![OpenAI Assistants Trace in Langfuse](https://langfuse.com/images/docs/openai-assistants-trace.png)
-
-## Learn more
-
-If you use non-Assistants API endpoints, you can use the OpenAI SDK wrapper for tracing. Check out the [Langfuse documentation](https://langfuse.com/integrations/model-providers/openai-py) for more details.
diff --git a/content/guides/cookbook/integration_openai_sdk.mdx b/content/guides/cookbook/integration_openai_sdk.mdx
deleted file mode 100644
index 027927a622..0000000000
--- a/content/guides/cookbook/integration_openai_sdk.mdx
+++ /dev/null
@@ -1,405 +0,0 @@
----
-source: ⚠️ Jupyter Notebook
-title: Cookbook - OpenAI Integration (Python)
-sidebarTitle: Cookbook - OpenAI Integration (Python)
-description: Drop-in replacement of OpenAI SDK to get full observability in Langfuse by changing only the import
-category: Integrations
----
-
-# Cookbook: OpenAI Integration (Python)
-
-<a href="https://langfuse.com/guides/cookbook/integration_openai_sdk"><img className="inline" alt="Python" src="https://img.shields.io/badge/Python-d4d4d8?style=flat&logo=python&logoColor=white" /></a> <a href="https://langfuse.com/guides/cookbook/js_integration_openai"><img className="inline" alt="JS/TS" src="https://img.shields.io/badge/JS/TS-F7DF1E?style=flat&logo=javascript&logoColor=black" /></a>
-
-This is a cookbook with examples of the Langfuse Integration for OpenAI (Python).
-
-Follow the [integration guide](https://langfuse.com/integrations/model-providers/openai-py) to add this integration to your OpenAI project.
-
-## Setup
-
-The integration is compatible with OpenAI SDK versions `>=0.27.8`. It supports async functions and streaming for OpenAI SDK versions `>=1.0.0`.
-
-
-```python
-%pip install langfuse openai --upgrade
-```
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page: https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your openai key
-os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...");
-```
-
-
-```python
-# instead of: import openai
-from langfuse.openai import openai
-```
-
-## Examples
-
-### Chat completion (text)
-
-
-```python
-completion = openai.chat.completions.create(
-  name="test-chat",
-  model="gpt-4o",
-  messages=[
-      {"role": "system", "content": "You are a very accurate calculator. You output only the result of the calculation."},
-      {"role": "user", "content": "1 + 1 = "}],
-  temperature=0,
-  metadata={"someMetadataKey": "someValue"},
-)
-```
-
-[Link to trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/3e9d8ae6670747cb60b1434b97c3bfdb?timestamp=2025-11-13T14%3A28%3A51.831Z)
-
-### Chat completion (image)
-
-Simple example using the OpenAI vision's functionality. Images may be passed in the `user` messages. 
-
-
-```python
-completion = openai.chat.completions.create(
-  name="test-url-image",
-  model="gpt-4o-mini", # GPT-4o, GPT-4o mini, and GPT-4 Turbo have vision capabilities
-  messages=[
-      {"role": "system", "content": "You are an AI trained to describe and interpret images. Describe the main objects and actions in the image."},
-      {"role": "user", "content": [
-        {"type": "text", "text": "What’s depicted in this image?"},
-        {
-          "type": "image_url",
-          "image_url": {
-            "url": "https://static.langfuse.com/langfuse-dev/langfuse-example-image.jpeg",
-          },
-        },
-      ],
-    }
-  ],
-  temperature=0,
-  metadata={"someMetadataKey": "someValue"},
-)
-```
-
-Go to https://cloud.langfuse.com or your own instance to see your generation.
-
-![Chat completion](https://langfuse.com/images/docs/multi-modal-trace.png)
-
-[Link to trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/b1dc51c45bc87df7d4b292717da072af?timestamp=2025-11-13T14%3A28%3A58.703Z&observation=e6af2e5e6f815256)
-
-### Chat completion (streaming)
-
-Simple example using the OpenAI streaming functionality.
-
-
-```python
-completion = openai.chat.completions.create(
-  name="test-chat",
-  model="gpt-4o",
-  messages=[
-      {"role": "system", "content": "You are a professional comedian."},
-      {"role": "user", "content": "Tell me a joke."}],
-  temperature=0,
-  metadata={"someMetadataKey": "someValue"},
-  stream=True
-)
-
-for chunk in completion:
-  print(chunk.choices[0].delta.content, end="")
-```
-
-    Why don't scientists trust atoms?
-    
-    Because they make up everything!None
-
-### Chat completion (async)
-
-Simple example using the OpenAI async client. It takes the Langfuse configurations either from the environment variables or from the attributes on the `openai` module.
-
-
-```python
-from langfuse.openai import AsyncOpenAI
-
-async_client = AsyncOpenAI()
-```
-
-
-```python
-completion = await async_client.chat.completions.create(
-  name="test-chat",
-  model="gpt-4o",
-  messages=[
-      {"role": "system", "content": "You are a very accurate calculator. You output only the result of the calculation."},
-      {"role": "user", "content": "1 + 100 = "}],
-  temperature=0,
-  metadata={"someMetadataKey": "someValue"},
-)
-```
-
-Go to https://cloud.langfuse.com or your own instance to see your generation.
-
-![Chat completion](https://langfuse.com/images/docs/openai-chat.png)
-
-[Link to trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/00d7d6cd4278b6003fc79337cd6d4394?timestamp=2025-11-13T14%3A33%3A14.490Z&observation=79d778c36b344d7e)
-
-### Functions
-
-Simple example using Pydantic to generate the function schema.
-
-
-```python
-%pip install pydantic --upgrade
-```
-
-
-```python
-from typing import List
-from pydantic import BaseModel
-
-class StepByStepAIResponse(BaseModel):
-    title: str
-    steps: List[str]
-schema = StepByStepAIResponse.model_json_schema() # returns a dict like JSON schema
-```
-
-
-```python
-from openai import OpenAI
-import json
-
-client = OpenAI()
-
-response = client.chat.completions.create(
-    model="gpt-5",
-    messages=[
-        {"role": "user", "content": "Explain how to assemble a PC"}
-    ],
-    tools=[
-        {
-            "type": "function",
-            "function": {
-                "name": "get_answer_for_user_query",
-                "description": "Get user answer in a series of steps",
-                "parameters": StepByStepAIResponse.model_json_schema()
-            }
-        }
-    ],
-    tool_choice={"type": "function", "function": {"name": "get_answer_for_user_query"}}
-)
-
-tool_call = response.choices[0].message.tool_calls[0]
-args = json.loads(tool_call.function.arguments)
-```
-
-Go to https://cloud.langfuse.com or your own instance to see your generation.
-
-![Function](https://langfuse.com/images/docs/openai-function.png)
-
-[Link to trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/cbbd69af7e00a939da55cbba80b2313e?timestamp=2025-11-13T14%3A38%3A08.304Z)
-
-
-## Langfuse Features (User, Tags, Metadata, Session)
-
-You can access additional Langfuse features by adding the relevant attributes to the OpenAI request. The Langfuse integration will parse these attributes. See [docs](https://langfuse.com/integrations/model-providers/openai-py#custom-trace-properties) for details on all available features.
-
-
-```python
-from langfuse.openai import openai
- 
-completion = openai.chat.completions.create(
-  name="test-chat",
-  model="gpt-3.5-turbo",
-  messages=[
-    {"role": "system", "content": "You are a calculator."},
-    {"role": "user", "content": "1 + 1 = "}],
-  temperature=0,
-  metadata={
-    "langfuse_tags": ["tag-1", "tag-2"],
-    "langfuse_user_id": "user-123",
-    "langfuse_session_id": "session-123",
-    "langfuse_metadata": {"key": "value"}
-  }
-)
-```
-
-Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/286c5c70-b077-4826-a493-36c510362a5a
-
-## AzureOpenAI
-
-The integration also works with the `AzureOpenAI` and `AsyncAzureOpenAI` classes.
-
-
-```python
-AZURE_OPENAI_KEY=""
-AZURE_ENDPOINT=""
-AZURE_DEPLOYMENT_NAME="cookbook-gpt-4o-mini" # example deployment name
-```
-
-
-```python
-# instead of: from openai import AzureOpenAI
-from langfuse.openai import AzureOpenAI
-```
-
-
-```python
-client = AzureOpenAI(
-    api_key=AZURE_OPENAI_KEY,  
-    api_version="2023-03-15-preview",
-    azure_endpoint=AZURE_ENDPOINT
-)
-```
-
-
-```python
-client.chat.completions.create(
-  name="test-chat-azure-openai",
-  model=AZURE_DEPLOYMENT_NAME, # deployment name
-  messages=[
-      {"role": "system", "content": "You are a very accurate calculator. You output only the result of the calculation."},
-      {"role": "user", "content": "1 + 1 = "}],
-  temperature=0,
-)
-```
-
-Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/7ceb3ee3-0f2a-4f36-ad11-87ff636efd1e
-
-## Group multiple generations into a single trace
-
-Many applications require more than one OpenAI call. The `@observe()` decorator allows you to nest all LLM calls of a single API invocation into the same `trace` in Langfuse.
-
-
-```python
-from langfuse.openai import openai
-from langfuse import observe
-
-@observe() # decorator to automatically create trace and nest generations
-def main(country: str, user_id: str, **kwargs) -> str:
-    # nested generation 1: use openai to get capital of country
-    capital = openai.chat.completions.create(
-      name="geography-teacher",
-      model="gpt-4o",
-      messages=[
-          {"role": "system", "content": "You are a Geography teacher helping students learn the capitals of countries. Output only the capital when being asked."},
-          {"role": "user", "content": country}],
-      temperature=0,
-    ).choices[0].message.content
-
-    # nested generation 2: use openai to write poem on capital
-    poem = openai.chat.completions.create(
-      name="poet",
-      model="gpt-4o",
-      messages=[
-          {"role": "system", "content": "You are a poet. Create a poem about a city."},
-          {"role": "user", "content": capital}],
-      temperature=1,
-      max_tokens=200,
-    ).choices[0].message.content
-
-    return poem
-
-# run main function and let Langfuse decorator do the rest
-print(main("Bulgaria", "admin"))
-```
-
-![Trace with multiple OpenAI calls](https://langfuse.com/images/docs/openai-trace-grouped.png)
-
-[Link to trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/db22d0a442216b485abd83cc9df6d9ee?timestamp=2025-11-13T15:05:05.559Z)
-
-## Fully featured: Interoperability with Langfuse SDK
-
-The `trace` is a core object in Langfuse and you can add rich metadata to it. See [Python SDK docs](https://langfuse.com/docs/sdk/python#traces-1) for full documentation on this.
-
-Some of the functionality enabled by custom traces:
-- custom name to identify a specific trace-type
-- user-level tracking
-- experiment tracking via versions and releases
-- custom metadata
-
-
-```python
-from langfuse.openai import openai
-from langfuse import observe, get_client, propagate_attributes
-langfuse = get_client()
-
-@observe() # decorator to automatically create trace and nest generations
-def main(country: str, user_id: str, **kwargs) -> str:
-
-    # Propagate attributes to all child observations
-    with propagate_attributes(
-        session_id="1234",
-        user_id=user_id,
-        tags=["tag1", "tag2"],
-        metadata = {"env": "development"}
-    ):
-
-      # nested generation 1: use openai to get capital of country
-      capital = openai.chat.completions.create(
-        name="geography-teacher",
-        model="gpt-4o",
-        messages=[
-            {"role": "system", "content": "You are a Geography teacher helping students learn the capitals of countries. Output only the capital when being asked."},
-            {"role": "user", "content": country}],
-        temperature=0,
-      ).choices[0].message.content
-
-      # nested generation 2: use openai to write poem on capital
-      poem = openai.chat.completions.create(
-        name="poet",
-        model="gpt-4o",
-        messages=[
-            {"role": "system", "content": "You are a poet. Create a poem about a city."},
-            {"role": "user", "content": capital}],
-        temperature=1,
-        max_tokens=200,
-      ).choices[0].message.content
-
-    return poem
-
-# create random trace_id, could also use existing id from your application, e.g. conversation id
-trace_id = langfuse.create_trace_id()
-
-# run main function, set your own id, and let Langfuse decorator do the rest
-print(main("Bulgaria", "admin", langfuse_observation_id=trace_id))
-```
-
-[Link to trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/0d73de5c936a1fce6c63bd5b20a7c0c2?timestamp=2025-11-13T15:13:27.879Z)
-
-## Programmatically add scores
-
-You can add [scores](https://langfuse.com/docs/scores) to the trace, to e.g. record user feedback or some programmatic evaluation. Scores are used throughout Langfuse to filter traces and on the dashboard. See the docs on scores for more details.
-
-The score is associated to the trace using the `trace_id`.
-
-
-```python
-from langfuse import observe, get_client
-langfuse = get_client()
-
-@observe() # decorator to automatically create trace and nest generations
-def main():
-    # get trace_id of current trace
-    trace_id = langfuse.get_current_trace_id()
-
-    # rest of your application ...
-
-    return "res", trace_id
-
-# execute the main function to generate a trace
-_, trace_id = main()
-
-# Score the trace from outside the trace context
-langfuse.create_score(
-    trace_id=trace_id,
-    name="my-score-name",
-    value=1
-)
-```
diff --git a/content/guides/cookbook/integration_openai_structured_output.md b/content/guides/cookbook/integration_openai_structured_output.md
deleted file mode 100644
index 32a79f8fbd..0000000000
--- a/content/guides/cookbook/integration_openai_structured_output.md
+++ /dev/null
@@ -1,247 +0,0 @@
----
-title: Observe OpenAI Structured Outputs with Langfuse
-description: Learn how to use Langfuse to monitor OpenAI Structured Outputs
-category: Integrations
----
-
-# Cookbook: Trace OpenAI Structured Outputs with Langfuse
-
-In this cookbook you will learn how to use Langfuse to monitor OpenAI Structured Outputs.
-
-## What are structured outputs?
-Generating structured data from unstructured inputs is a core AI use case today. Structured outputs make especially chained LLM calls, UI component generation, and model-based evaluation more reliable. [Structured Outputs](https://openai.com/index/introducing-structured-outputs-in-the-api/) is a new capability of the OpenAI API that builds upon JSON mode and function calling to enforce a strict schema in a model output.
-
-## How to trace structured output in Langfuse?
-If you use the OpenAI Python SDK, you can use the [Langfuse drop-in replacement](https://langfuse.com/integrations/model-providers/openai-py) to get full logging by changing only the import. With that, you can monitor the structured output generated by OpenAI in Langfuse.
-
-```diff
-- import openai
-+ from langfuse.openai import openai
-
-Alternative imports:
-+ from langfuse.openai import OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI
-
-```
-
-
-
-## Step 1: Initialize Langfuse
-Initialize the Langfuse client with your [API keys](https://langfuse.com/faq/all/where-are-langfuse-api-keys) from the project settings in the Langfuse UI and add them to your environment.
-
-
-```python
-%pip install langfuse openai --upgrade
-```
-
-
-```python
-import os
-
-# Get keys for your project from the project settings page
-# https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your openai key
-os.environ.setdefault("OPENAI_API_KEY", "");
-```
-
-## Step 2: Math tutor example
-
-In this example, we'll build a math tutoring tool that outputs steps to solve a math problem as an array of structured objects.
-
-This setup is useful for applications where each step needs to be displayed separately, allowing users to progress through the solution at their own pace.
-
-(Example taken from [OpenAI cookbook](https://cookbook.openai.com/examples/structured_outputs_intro))
-
-**Note on `parse` vs `response_format`:** The Langfuse OpenAI integration instruments both the stable `client.chat.completions.parse(...)` (available in `openai>=1.92.0`) and the legacy `client.beta.chat.completions.parse(...)` (for older SDK versions, where `parse` is re-routed to the stable method on newer SDKs). Both paths support Langfuse-specific attributes such as `name`, `metadata`, `langfuse_session_id`, etc. The `response_format` approach with `client.chat.completions.create` shown below works on all SDK versions and is useful if you cannot upgrade `openai`. For an example using the `parse` helper with a Pydantic model, see the section further down.
-
-
-```python
-# Use the Langfuse drop-in replacement to get full logging by changing only the import.
-# With that, you can monitor the structured output generated by OpenAI in Langfuse.
-from langfuse.openai import OpenAI
-import json
-
-openai_model = "gpt-4o-2024-08-06"
-client = OpenAI()
-```
-
-In the `response_format` parameter you can now supply a JSON Schema via `json_schema`. When using `response_format` with `strict: true`, the model's output will adhere to the provided schema.
-
-Function calling remains similar, but with the new parameter `strict: true`, you can now ensure that the schema provided for the functions is strictly followed.
-
-
-```python
-math_tutor_prompt = '''
-    You are a helpful math tutor. You will be provided with a math problem,
-    and your goal will be to output a step by step solution, along with a final answer.
-    For each step, just provide the output as an equation use the explanation field to detail the reasoning.
-'''
-
-def get_math_solution(question):
-    response = client.chat.completions.create(
-    model = openai_model,
-    messages=[
-        {
-            "role": "system",
-            "content": math_tutor_prompt
-        },
-        {
-            "role": "user",
-            "content": question
-        }
-    ],
-    response_format={
-        "type": "json_schema",
-        "json_schema": {
-            "name": "math_reasoning",
-            "schema": {
-                "type": "object",
-                "properties": {
-                    "steps": {
-                        "type": "array",
-                        "items": {
-                            "type": "object",
-                            "properties": {
-                                "explanation": {"type": "string"},
-                                "output": {"type": "string"}
-                            },
-                            "required": ["explanation", "output"],
-                            "additionalProperties": False
-                        }
-                    },
-                    "final_answer": {"type": "string"}
-                },
-                "required": ["steps", "final_answer"],
-                "additionalProperties": False
-            },
-            "strict": True
-        }
-    }
-    )
-
-    return response.choices[0].message
-```
-
-
-```python
-# Testing with an example question
-question = "how can I solve 8x + 7 = -23"
-
-result = get_math_solution(question)
-
-print(result.content)
-```
-
-    {"steps":[{"explanation":"We need to isolate the term with the variable, 8x. So, we start by subtracting 7 from both sides to remove the constant term on the left side.","output":"8x + 7 - 7 = -23 - 7"},{"explanation":"The +7 and -7 on the left side cancel each other out, leaving us with 8x. The right side simplifies to -30.","output":"8x = -30"},{"explanation":"To solve for x, divide both sides of the equation by 8, which is the coefficient of x.","output":"x = -30 / 8"},{"explanation":"Simplify the fraction -30/8 by finding the greatest common divisor, which is 2.","output":"x = -15 / 4"}],"final_answer":"x = -15/4"}
-
-
-
-```python
-# Print results step by step
-
-result = json.loads(result.content)
-steps = result['steps']
-final_answer = result['final_answer']
-for i in range(len(steps)):
-    print(f"Step {i+1}: {steps[i]['explanation']}\n")
-    print(steps[i]['output'])
-    print("\n")
-
-print("Final answer:\n\n")
-print(final_answer)
-```
-
-    Step 1: We need to isolate the term with the variable, 8x. So, we start by subtracting 7 from both sides to remove the constant term on the left side.
-    
-    8x + 7 - 7 = -23 - 7
-    
-    
-    Step 2: The +7 and -7 on the left side cancel each other out, leaving us with 8x. The right side simplifies to -30.
-    
-    8x = -30
-    
-    
-    Step 3: To solve for x, divide both sides of the equation by 8, which is the coefficient of x.
-    
-    x = -30 / 8
-    
-    
-    Step 4: Simplify the fraction -30/8 by finding the greatest common divisor, which is 2.
-    
-    x = -15 / 4
-    
-    
-    Final answer:
-    
-    
-    x = -15/4
-
-
-## Step 3: See your trace in Langfuse
-
-You can now see the trace and the JSON schema in Langfuse.
-
-[Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/3ecc3849-66c9-4eaf-b26b-bde26b7eebed)
-
-![View example trace in the Langfuse UI](https://langfuse.com/images/cookbook/integration-openai-structured-outputs-tracing.png)
-
-## Alternative: Using the SDK `parse` helper
-
-OpenAI also offers a `parse` helper that lets you pass a Pydantic model directly via `response_format`. As of [`openai-python` v1.92.0](https://github.com/openai/openai-python/releases/tag/v1.92.0), `parse` is part of the stable API at `client.chat.completions.parse(...)` (it previously lived under `client.beta.chat.completions.parse`). Langfuse instruments both paths, so you can set Langfuse attributes (`name`, `metadata`, `langfuse_session_id`, …) on either one. Prefer the stable path in new code:
-
-
-```python
-from pydantic import BaseModel
-
-class MathReasoning(BaseModel):
-    class Step(BaseModel):
-        explanation: str
-        output: str
-
-    steps: list[Step]
-    final_answer: str
-
-def get_math_solution(question: str):
-    # Stable API on openai>=1.92.0. On older SDKs, use client.beta.chat.completions.parse instead.
-    response = client.chat.completions.parse(
-        model=openai_model,
-        messages=[
-            {"role": "system", "content": math_tutor_prompt},
-            {"role": "user", "content": question},
-        ],
-        response_format=MathReasoning,
-        name="math-tutor-parse",
-    )
-
-    return response.choices[0].message
-```
-
-
-```python
-result = get_math_solution(question).parsed
-
-print(result.steps)
-print("Final answer:")
-print(result.final_answer)
-```
-
-    [Step(explanation='To isolate the term with the variable on one side of the equation, start by subtracting 7 from both sides.', output='8x = -23 - 7'), Step(explanation='Combine like terms on the right side to simplify the equation.', output='8x = -30'), Step(explanation='Divide both sides by 8 to solve for x.', output='x = -30 / 8'), Step(explanation='Simplify the fraction by dividing both the numerator and the denominator by their greatest common divisor, which is 2.', output='x = -15 / 4')]
-    Final answer:
-    x = -15/4
-
-
-## See your trace in Langfuse
-
-You can now see the trace and your supplied Pydantic model in Langfuse.
-
-[Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/59c4376a-c8eb-4ecb-8780-2f028b87e7eb)
-
-![View example trace in the Langfuse UI](https://langfuse.com/images/cookbook/integration_openai_structured_outputs_tracing_parse.png)
-
-## Feedback
-
-If you have any feedback or requests, please create a GitHub [Issue](https://langfuse.com/issue) or share your idea with the community on [Discord](https://langfuse.com/discord).
diff --git a/content/guides/cookbook/js_integration_anthropic.mdx b/content/guides/cookbook/js_integration_anthropic.mdx
deleted file mode 100644
index 50e44b6f21..0000000000
--- a/content/guides/cookbook/js_integration_anthropic.mdx
+++ /dev/null
@@ -1,109 +0,0 @@
----
-source: ⚠️ Jupyter Notebook
-title: Observability for Anthropic JS/TS with Langfuse
-sidebarTitle: Anthropic (JS/TS)
-logo: /images/integrations/anthropic_icon.png
-description: Learn how to integrate Langfuse with the Anthropic JS/TS SDK for tracing and debugging of your AI applications.
-category: Integrations
----
-
-# Trace Anthropic JS/TS with Langfuse
-
-<a href="https://langfuse.com/integrations/model-providers/anthropic"><img className="inline" alt="Python" src="https://img.shields.io/badge/Python-3776AB?style=flat&logo=python&logoColor=white" /></a> <a href="https://langfuse.com/integrations/model-providers/anthropic-js"><img className="inline" alt="JS/TS" src="https://img.shields.io/badge/JS/TS-d4d4d8?style=flat&logo=javascript&logoColor=white" /></a>
-
-Anthropic provides advanced language models like Claude, known for their safety, helpfulness, and strong reasoning capabilities. By combining Anthropic's JS/TS SDK with **Langfuse**, you can trace, monitor, and analyze your AI workloads in development and production.
-
-This notebook demonstrates how to use the [`AnthropicInstrumentation`](https://github.com/Arize-ai/openinference/tree/main/js/packages/openinference-instrumentation-anthropic) library from [OpenInference](https://github.com/Arize-ai/openinference) to automatically instrument Anthropic SDK calls and send OpenTelemetry spans to Langfuse.
-
-> **What is Anthropic?**\
-Anthropic is an AI safety company that develops Claude, a family of large language models designed to be helpful, harmless, and honest. Claude models excel at complex reasoning, analysis, and creative tasks.
-
-> **What is Langfuse?**\
-[Langfuse](https://langfuse.com) is an open source platform for LLM observability and monitoring. It helps you trace and monitor your AI applications by capturing metadata, prompt details, token usage, latency, and more.
-
-<Steps>
-## Step 1: Install Dependencies
-
-Install the necessary packages:
-
-```bash
-npm install @anthropic-ai/sdk @arizeai/openinference-instrumentation-anthropic @langfuse/otel @opentelemetry/sdk-node
-```
-
-> **Note**: This cookbook uses **Deno.js** for execution, which requires different syntax for importing packages and setting environment variables. For Node.js applications, the setup process is similar but uses standard `npm` packages and `process.env`.
-
-## Step 2: Configure Environment
-
-Set up your Langfuse and Anthropic API keys. You can get Langfuse keys by signing up for a free [Langfuse Cloud](https://langfuse.com/cloud) account or by [self-hosting Langfuse](https://langfuse.com/self-hosting). Get your Anthropic API key from the [Anthropic Console](https://console.anthropic.com/).
-
-
-```typescript
-// Set environment variables using Deno-specific syntax
-Deno.env.set("ANTHROPIC_API_KEY", "sk-ant-...");
-
-// Langfuse authentication keys
-Deno.env.set("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
-Deno.env.set("LANGFUSE_SECRET_KEY", "sk-lf-...");
-
-// Langfuse host configuration
-Deno.env.set("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); // 🇪🇺 EU region
-// Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-```
-
-## Step 3: Initialize OpenTelemetry with Langfuse
-
-Set up the OpenTelemetry SDK with the `LangfuseSpanProcessor` and the [`AnthropicInstrumentation`](https://github.com/Arize-ai/openinference/tree/main/js/packages/openinference-instrumentation-anthropic) from OpenInference. The instrumentation automatically captures Anthropic SDK calls and sends them as OpenTelemetry spans to Langfuse.
-
-
-```typescript
-import { NodeSDK } from "npm:@opentelemetry/sdk-node";
-import { LangfuseSpanProcessor } from "npm:@langfuse/otel";
-import { AnthropicInstrumentation } from "npm:@arizeai/openinference-instrumentation-anthropic";
-
-import Anthropic from "npm:@anthropic-ai/sdk";
-
-// Configure the instrumentation for the Anthropic SDK
-const instrumentation = new AnthropicInstrumentation();
-instrumentation.manuallyInstrument(Anthropic);
-
-// Initialize the OpenTelemetry SDK with Langfuse as the span processor
-const sdk = new NodeSDK({
-  spanProcessors: [new LangfuseSpanProcessor()],
-  instrumentations: [instrumentation],
-});
-
-sdk.start();
-```
-
-## Step 4: Use the Anthropic SDK
-
-Now use the Anthropic SDK as you normally would. All calls are automatically traced and sent to Langfuse.
-
-
-```typescript
-const anthropic = new Anthropic();
-
-const message = await anthropic.messages.create({
-  model: "claude-haiku-4-5",
-  max_tokens: 1000,
-  messages: [{ role: "user", content: "Hello, Claude!" }],
-});
-
-console.log(message.content);
-
-await sdk.shutdown();
-```
-
-### View Traces in Langfuse
-
-After running the application, navigate to your Langfuse Trace Table. You will find detailed traces of the application's execution, providing insights into the LLM calls, inputs, outputs, and performance metrics.
-
-![Langfuse Trace](https://langfuse.com/images/cookbook/integration_anthropic/anthropic-example-trace-js.png)
-
-[Example trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/561e86ac4ec484282b7ca46737a64ca4?observation=0c936de60ccf69b9&timestamp=2026-02-11T10:31:58.406Z)
-
-</Steps>
-
-import LearnMore from "@/components-mdx/integration-learn-more-js.mdx";
-
-<LearnMore />
diff --git a/content/guides/cookbook/js_integration_langchain.mdx b/content/guides/cookbook/js_integration_langchain.mdx
deleted file mode 100644
index 4232819512..0000000000
--- a/content/guides/cookbook/js_integration_langchain.mdx
+++ /dev/null
@@ -1,125 +0,0 @@
----
-source: ⚠️ Jupyter Notebook
-title: Cookbook - Langchain Integration (JS/TS)
-sidebarTitle: Langchain Integration (JS/TS)
-description: Cookbook with examples of the Langfuse Integration for Langchain (JS/TS).
-category: Examples
----
-
-# Cookbook: Langchain Integration (JS/TS)
-
-<a href="https://langfuse.com/guides/cookbook/integration_langchain"><img className="inline" alt="Python" src="https://img.shields.io/badge/Python-3776AB?style=flat&logo=python&logoColor=white" /></a> <a href="https://langfuse.com/guides/cookbook/js_integration_langchain"><img className="inline" alt="JS/TS" src="https://img.shields.io/badge/JS/TS-d4d4d8?style=flat&logo=javascript&logoColor=white" /></a>
-
-This is a cookbook with examples of the Langfuse integration for Langchain (JS/TS).
-
-Follow the [integration guide](https://langfuse.com/integrations/frameworks/langchain) to add this integration to your Langchain project.
-
-<Steps>
-## Set Up Environment
-
-Get your Langfuse API keys by signing up for [Langfuse Cloud](https://langfuse.com/cloud) or [self-hosting Langfuse](https://langfuse.com/self-hosting). You’ll also need your OpenAI API key.
-
-> **Note**: This cookbook uses **Deno.js** for execution, which requires different syntax for importing packages and setting environment variables. For Node.js applications, the setup process is similar but uses standard `npm` packages and `process.env`.
-
-
-```typescript
-// Langfuse authentication keys
-Deno.env.set("LANGFUSE_PUBLIC_KEY", "pk-lf-***");
-Deno.env.set("LANGFUSE_SECRET_KEY", "sk-lf-***");
-
-// Langfuse host configuration
-// Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-Deno.env.set("LANGFUSE_BASE_URL", "https://cloud.langfuse.com")
-
-// Set environment variables using Deno-specific syntax
-Deno.env.set("OPENAI_API_KEY", "sk-proj-***");
-```
-
-With the environment variables set, we can now initialize the `langfuseSpanProcessor` which is passed to the main OpenTelemetry SDK that orchestrates tracing.
-
-
-```typescript
-// Import required dependencies
-import 'npm:dotenv/config';
-import { NodeSDK } from "npm:@opentelemetry/sdk-node";
-import { LangfuseSpanProcessor } from "npm:@langfuse/otel";
- 
-// Export the processor to be able to flush it later
-// This is important for ensuring all spans are sent to Langfuse
-export const langfuseSpanProcessor = new LangfuseSpanProcessor({
-    publicKey: process.env.LANGFUSE_PUBLIC_KEY!,
-    secretKey: process.env.LANGFUSE_SECRET_KEY!,
-    baseUrl: process.env.LANGFUSE_BASE_URL ?? 'https://cloud.langfuse.com', // Default to cloud if not specified
-    environment: process.env.NODE_ENV ?? 'development', // Default to development if not specified
-  });
- 
-// Initialize the OpenTelemetry SDK with our Langfuse processor
-const sdk = new NodeSDK({
-  spanProcessors: [langfuseSpanProcessor],
-});
- 
-// Start the SDK to begin collecting telemetry
-// The warning about crypto module is expected in Deno and doesn't affect basic tracing functionality. Media upload features will be disabled, but all core tracing works normally
-sdk.start();
-```
-
-## Step 2: Instantiate the CallbackHandler
-
-Instantiate the CallbackHandler and pass it to your chain’s `.invoke()` or `.stream()` method in the callbacks array. All operations within the chain will be traced as nested observations.
-
-
-```typescript
-import { CallbackHandler } from "npm:@langfuse/langchain";
- 
-const langfuseHandler = new CallbackHandler({
-  sessionId: "user-session-123",
-  userId: "user-abc",
-  tags: ["langchain-test"],
-});
-```
-
-## Step 3: Langchain Example
-
-We create a simple agent that can answer questions and call tools. The agent will use OpenAI GPT-5-mini as its language model, a basic weather function as a tool, and a simple prompt to guide its behavior. All tools and model calls are traced in Langfuse.
-
-
-```typescript
-import { createAgent, tool } from "npm:langchain";
-import * as z from "npm:zod";
-
-const getWeather = tool(
-  (input) => `It's always sunny in ${input.city}!`,
-  {
-    name: "get_weather",
-    description: "Get the weather for a given city",
-    schema: z.object({
-      city: z.string().describe("The city to get the weather for"),
-    }),
-  }
-);
-
-const agent = createAgent({
-  model: "openai:gpt-5-mini",
-  tools: [getWeather],
-});
-```
-
-
-```typescript
-console.log(
-    await agent.invoke(
-        { messages: [{ role: "user", content: "What's the weather in San Francisco?" }] }, 
-        { callbacks: [langfuseHandler] }
-    )
-);
-```
-
-## Step 4: Explore the trace in Langfuse
-
-In the Langfuse interface, you can see a detailed trace of all steps in the Langchain application.
-
-![Langfuse Trace](https://langfuse.com/images/cookbook/example-js-sdk/js_integration_langchain_trace.png)
-
-[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/2c1581dd9cecdafb6ca091b83d7ea99a?timestamp=2025-10-30T08:45:41.319Z)
-
-</Steps>
diff --git a/content/guides/cookbook/js_integration_litellm_proxy.mdx b/content/guides/cookbook/js_integration_litellm_proxy.mdx
deleted file mode 100644
index 6af4719a70..0000000000
--- a/content/guides/cookbook/js_integration_litellm_proxy.mdx
+++ /dev/null
@@ -1,169 +0,0 @@
----
-source: ⚠️ Jupyter Notebook
-title: Cookbook - LiteLLM (Proxy) + Langfuse OpenAI Integration (JS/TS)
-sidebarTitle: LiteLLM (Proxy) + Langfuse OpenAI Integration (JS/TS)
-description: The stack to use any of 100+ models in your JS/TS application without having to change your code and with full observability.
-category: Examples
----
-
-# Cookbook: LiteLLM (Proxy) + Langfuse OpenAI Integration (JS/TS)
-
-<a href="https://langfuse.com/guides/cookbook/integration_litellm_proxy"><img className="inline" alt="Python" src="https://img.shields.io/badge/Python-3776AB?style=flat&logo=python&logoColor=white" /></a> <a href="https://langfuse.com/guides/cookbook/js_integration_litellm_proxy"><img className="inline" alt="JS/TS" src="https://img.shields.io/badge/JS/TS-d4d4d8?style=flat&logo=javascript&logoColor=white" /></a>
-
-This notebook demonstrates how to use the following stack to experiment with 100+ LLMs from different providers without changing code:
-
-- [**LiteLLM Proxy**](https://docs.litellm.ai/docs/) ([GitHub](https://github.com/BerriAI/litellm)): Standardizes 100+ model provider APIs on the OpenAI API schema.
-- **Langfuse OpenAI SDK Wrapper** ([JS/TS](https://langfuse.com/integrations/model-providers/openai-js)): Natively instruments calls to 100+ models via the OpenAI SDK.
-- **Langfuse**: OSS LLM Observability, full overview [here](https://langfuse.com/docs).
-
-> **Note**: This cookbook uses **Deno.js** for execution, which requires different syntax for importing packages and setting environment variables. For Node.js applications, the setup process is similar but uses standard `npm` packages and `process.env`.
-
-<Steps>
-## Set Up Environment
-
-Get your Langfuse API keys by signing up for [Langfuse Cloud](https://langfuse.com/cloud) or [self-hosting Langfuse](https://langfuse.com/self-hosting). You’ll also need your OpenAI API key.
-
-> **Note**: This cookbook uses **Deno.js** for execution, which requires different syntax for importing packages and setting environment variables. For Node.js applications, the setup process is similar but uses standard `npm` packages and `process.env`.
-
-
-```typescript
-// Langfuse authentication keys
-Deno.env.set("LANGFUSE_PUBLIC_KEY", "pk-lf-***");
-Deno.env.set("LANGFUSE_SECRET_KEY", "sk-lf-***");
-
-// Langfuse host configuration
-// Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-Deno.env.set("LANGFUSE_BASE_URL", "https://cloud.langfuse.com")
-
-// Set environment variables using Deno-specific syntax
-Deno.env.set("OPENAI_API_KEY", "sk-proj-***");
-```
-
-With the environment variables set, we can now initialize the `langfuseSpanProcessor` which is passed to the main OpenTelemetry SDK that orchestrates tracing.
-
-
-```typescript
-// Import required dependencies
-import 'npm:dotenv/config';
-import { NodeSDK } from "npm:@opentelemetry/sdk-node";
-import { LangfuseSpanProcessor } from "npm:@langfuse/otel";
- 
-// Export the processor to be able to flush it later
-// This is important for ensuring all spans are sent to Langfuse
-export const langfuseSpanProcessor = new LangfuseSpanProcessor({
-    publicKey: process.env.LANGFUSE_PUBLIC_KEY!,
-    secretKey: process.env.LANGFUSE_SECRET_KEY!,
-    baseUrl: process.env.LANGFUSE_BASE_URL ?? 'https://cloud.langfuse.com', // Default to cloud if not specified
-    environment: process.env.NODE_ENV ?? 'development', // Default to development if not specified
-  });
- 
-// Initialize the OpenTelemetry SDK with our Langfuse processor
-const sdk = new NodeSDK({
-  spanProcessors: [langfuseSpanProcessor],
-});
- 
-// Start the SDK to begin collecting telemetry
-// The warning about crypto module is expected in Deno and doesn't affect basic tracing functionality. Media upload features will be disabled, but all core tracing works normally
-sdk.start();
-```
-
-## Setup Lite LLM Proxy
-
-In this example, we'll use llama3 via the Ollama on our local machine.
-
-**Steps**
-
-1. Create a `litellm_config.yaml` to configure which models are available ([docs](https://litellm.vercel.app/docs/proxy/configs)). We'll use llama3 via Ollama in this example. 
-1. Ensure that you installed Ollama and have pulled the llama3 (8b) model: `ollama pull llama3`
-2. Run the following cli command to start the proxy: `litellm --config litellm_config.yaml`
-
-The Lite LLM Proxy should be now running on http://0.0.0.0:4000
-
-To verify the connection you can run `litellm --test`
-
-## Log single LLM Call via Langfuse OpenAI Wrapper
-
-The Langfuse SDK offers a wrapper function around the OpenAI SDK, automatically logging all OpenAI calls as generations to Langfuse. We wrap the client for each call separately in order to be able to pass a name. For more details, please refer to our [documentation](https://langfuse.com/integrations/model-providers/openai-js).
-
-
-```typescript
-import { OpenAI } from "npm:openai";
-import { observeOpenAI } from "npm:@langfuse/openai";
-
-const PROXY_URL = "http://0.0.0.0:4000";
-const client = observeOpenAI(new OpenAI({baseURL: PROXY_URL}));
-
-const systemPrompt = "You are a very accurate calculator. You output only the result of the calculation.";
-
-const llamaCompletion = await client.chat.completions.create({
-  model: "ollama/llama3",
-  messages: [
-    {role: "system", content: systemPrompt},
-    {role: "user", content: "3 + 3 = "}
-  ],
-}); 
-console.log(llamaCompletion.choices[0].message.content);
-```
-
-    6
-
-
-[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/0b78e39b9a36195b7373a3c4536ad574?timestamp=2025-08-25T15%3A07%3A24.014Z&display=details&observation=fa33232a3cc957e1)
-
-## Trace nested LLM Calls using Langfuse JS SDK
-
-Use the context manager of the Langfuse TypeScript SDK to group multiple LiteLLM generations together and update the top level span.
-
-
-```typescript
-import { startActiveObservation, startObservation } from "npm:@langfuse/tracing";
-import { observeOpenAI } from "npm:@langfuse/openai";
-
-const client = observeOpenAI(new OpenAI({baseURL: PROXY_URL}));
-
-const systemPrompt = "You are a very accurate calculator. You output only the result of the calculation.";
- 
-await startActiveObservation("user-request", async (span) => {
-
-  await client.chat.completions.create({
-    model: "ollama/llama3",
-    messages: [
-      {role: "system", content: systemPrompt},
-      {role: "user", content: "3 + 3 = "}
-    ],
-  }); 
-
-  await client.chat.completions.create({
-    model: "ollama/llama3",
-    messages: [
-      {role: "system", content: systemPrompt},
-      {role: "user", content: "1 - 1 = "}
-    ],
-  }); 
-
-  await client.chat.completions.create({
-    model: "ollama/llama3",
-    messages: [
-      {role: "system", content: systemPrompt},
-      {role: "user", content: "2 + 3 = "}
-    ],
-  }); 
-});
-```
-
-## View traces in Langfuse
-
-![Public Trace](https://langfuse.com/images/cookbook/example-js-sdk/integration_litellm_proxy_trace.png)
-
-[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/1aab1c70319ce53e2604801a5e464293?timestamp=2025-08-25T15%3A52%3A19.900Z&display=details&observation=b0f019915d463967)
-
-</Steps>
-
-## Learn more
-
-Check out the docs to learn more about all components of this stack:
-- [LiteLLM Proxy](https://docs.litellm.ai/docs/)
-- [Langfuse OpenAI SDK Wrapper](https://langfuse.com/integrations/model-providers/openai-js)
-- [Langfuse](https://langfuse.com/docs)
-
-If you do not want to capture traces via the OpenAI SDK Wrapper, you can also directly log requests from the LiteLLM Proxy to Langfuse. For more details, refer to the [LiteLLM Docs](https://litellm.vercel.app/docs/proxy/logging#logging-proxy-inputoutput---langfuse).
diff --git a/content/guides/cookbook/js_integration_openai.mdx b/content/guides/cookbook/js_integration_openai.mdx
deleted file mode 100644
index 44f61b182c..0000000000
--- a/content/guides/cookbook/js_integration_openai.mdx
+++ /dev/null
@@ -1,287 +0,0 @@
----
-source: ⚠️ Jupyter Notebook
-title: Cookbook - OpenAI Integration (JS/TS)
-sidebarTitle: OpenAI Integration (JS/TS)
-description: Learn how to use Langfuse for open source observability/tracing of the OpenAI JS SDK by adding `observeOpenAI`.
-category: Examples
----
-
-# Cookbook: OpenAI Integration (JS/TS)
-
-<a href="https://langfuse.com/guides/cookbook/integration_openai_sdk"><img className="inline" alt="Python" src="https://img.shields.io/badge/Python-3776AB?style=flat&logo=python&logoColor=white" /></a> <a href="https://langfuse.com/guides/cookbook/js_integration_openai"><img className="inline" alt="JS/TS" src="https://img.shields.io/badge/JS/TS-d4d4d8?style=flat&logo=javascript&logoColor=white" /></a>
-
-This cookbook provides examples of the Langfuse Integration for OpenAI (JS/TS). Follow the [integration guide](https://langfuse.com/integrations/model-providers/openai-js) to add this integration to your OpenAI project.
-
-<Steps>
-## Set Up Environment
-
-Get your Langfuse API keys by signing up for [Langfuse Cloud](https://langfuse.com/cloud) or [self-hosting Langfuse](https://langfuse.com/self-hosting). You’ll also need your OpenAI API key.
-
-
-> **Note**: This cookbook uses **Deno.js** for execution, which requires different syntax for importing packages and setting environment variables. For Node.js applications, the setup process is similar but uses standard `npm` packages and `process.env`.
-
-
-```typescript
-// Langfuse authentication keys
-Deno.env.set("LANGFUSE_PUBLIC_KEY", "pk-lf-***");
-Deno.env.set("LANGFUSE_SECRET_KEY", "sk-lf-***");
-
-// Langfuse host configuration
-// Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-Deno.env.set("LANGFUSE_BASE_URL", "https://cloud.langfuse.com")
-
-// Set environment variables using Deno-specific syntax
-Deno.env.set("OPENAI_API_KEY", "sk-proj-***");
-```
-
-With the environment variables set, we can now initialize the `langfuseSpanProcessor` which is passed to the main OpenTelemetry SDK that orchestrates tracing.
-
-
-```typescript
-// Import required dependencies
-import 'npm:dotenv/config';
-import { NodeSDK } from "npm:@opentelemetry/sdk-node";
-import { LangfuseSpanProcessor } from "npm:@langfuse/otel";
- 
-// Export the processor to be able to flush it later
-// This is important for ensuring all spans are sent to Langfuse
-export const langfuseSpanProcessor = new LangfuseSpanProcessor({
-    publicKey: process.env.LANGFUSE_PUBLIC_KEY!,
-    secretKey: process.env.LANGFUSE_SECRET_KEY!,
-    baseUrl: process.env.LANGFUSE_BASE_URL ?? 'https://cloud.langfuse.com', // Default to cloud if not specified
-    environment: process.env.NODE_ENV ?? 'development', // Default to development if not specified
-  });
- 
-// Initialize the OpenTelemetry SDK with our Langfuse processor
-const sdk = new NodeSDK({
-  spanProcessors: [langfuseSpanProcessor],
-});
- 
-// Start the SDK to begin collecting telemetry
-// The warning about crypto module is expected in Deno and doesn't affect basic tracing functionality. Media upload features will be disabled, but all core tracing works normally
-sdk.start();
-```
-
-## Example 1: Chat Completion
-
-
-```typescript
-import OpenAI from "npm:openai";
-import { observeOpenAI } from "npm:@langfuse/openai";
-```
-
-
-```typescript
-// Initialize OpenAI client with observerOpenAI wrapper
-const openai = observeOpenAI(new OpenAI());
-```
-
-
-```typescript
-import OpenAI from "npm:openai";
-import { observeOpenAI } from "npm:@langfuse/openai";
-
-// Configured via environment variables, see above
-const openai = observeOpenAI(new OpenAI());
-
-const completion = await openai.chat.completions.create({
-  model: 'gpt-4o',
-  messages: [{ role: "system", content: "Tell me a joke." }],
-  max_tokens: 100,
-});
-
-console.log(completion.choices[0]?.message.content);
-```
-
-![Langfuse Trace](https://langfuse.com/images/cookbook/example-js-sdk/js_integration_openai_simple.png)
-
-[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/a3568a4051958f944016ad65ffed0fdc?timestamp=2025-08-25T14%3A19%3A17.724Z&display=details&observation=bbb60b9338f261ac)
-
-## Example 2: Chat completion (streaming)
-
-Simple example using OpenAI streaming, passing custom parameters to rename the generation and add a tag to the trace.
-
-
-```typescript
-import OpenAI from "npm:openai";
-import { observeOpenAI } from "npm:@langfuse/openai";
-
-// Initialize OpenAI SDK with Langfuse
-const openaiWithLangfuse = observeOpenAI(new OpenAI(), { generationName: "OpenAI Stream Trace", tags: ["stream"]} )
-
-// Call OpenAI
-const stream = await openaiWithLangfuse.chat.completions.create({
-  model: 'gpt-4o',
-  messages: [{ role: "system", content: "Tell me a joke." }],
-  stream: true,
-});
-
-for await (const chunk of stream) {
-    const content = chunk.choices[0]?.delta?.content || '';
-    console.log(content);
-  }
-```
-
-[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/5be1f917ea127f535ddf39d84b42bbf9?timestamp=2025-08-25T14:22:44.892Z&display=details)
-
-## Example 3: Add additional metadata and parameters
-
-You can pass additional trace and generation attributes to the `observeOpenAI` wrapper. Refer to the [integration guide](https://langfuse.com/integrations/model-providers/openai-js) for comprehensive details.
-
-Example usage:
-
-- Assigning a custom trace and generation name
-- Enabling user-level and session-level tracking
-- Adding tags and custom metadata
-
-
-```typescript
-import OpenAI from "npm:openai";
-import { observeOpenAI } from "npm:@langfuse/openai";
-
-// Initialize OpenAI SDK with Langfuse and custom parameters
-const openaiWithLangfuse = observeOpenAI(new OpenAI(), {
-    traceName: "OpenAI Custom Trace",
-    generationName: "OpenAI Custom Generation",
-    generationMetadata: {env: "dev"},
-    sessionId: "session-id",
-    userId: "user-id",
-    tags: ["custom"],
-})
-
-// Call OpenAI
-const completion = await openaiWithLangfuse.chat.completions.create({
-  model: 'gpt-4o',
-  messages: [{ role: "system", content: "Tell me a joke." }],
-  max_tokens: 100,
-});
-```
-
-[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/5aba0399-d640-4fc1-8cbc-42ebf688b189?timestamp=2025-08-25T14:23:43.007Z&display=details)
-
-## Example 4: Function Calling
-
-
-```typescript
-import OpenAI from "npm:openai";
-import { observeOpenAI } from "npm:@langfuse/openai";
-
-// Initialize OpenAI SDK with Langfuse
-const openaiWithLangfuse = observeOpenAI(new OpenAI(), { generationName: "OpenAI FunctionCall Trace", tags: ["function"]} )
-
-// Define custom function
-async function getWeather(location: string) {
-  if (location === "Berlin")
-    {return "20degC"}
-  else 
-    {return "unknown"}
-}
-
-// Create function specification required for OpenAI API
-const functions = [{
-    type: "function",
-    function: {
-        name: "getWeather",
-        description: "Get the current weather in a given location",
-        parameters: {
-            type: "object",
-            properties: {
-                location: {
-                    type: "string",
-                    description: "The city, e.g. San Francisco",
-                },
-            },
-            required: ["location"],
-        },
-    },
-}]
-
-// Call OpenAI
-const res = await openaiWithLangfuse.chat.completions.create({
-    model: 'gpt-4o',
-    messages: [{ role: 'user', content: "What's the weather like in Berlin today"}],
-    tool_choice: "auto",
-    tools: functions,
-})
-
-const tool_call = res.choices[0].message.tool_calls;
-if (tool_call[0].function.name === "getWeather") {
-    const argsStr = tool_call[0].function.arguments;
-    const args = JSON.parse(argsStr); 
-    const answer = await getWeather(args["location"]);
-    console.log(answer);
-}
-```
-
-    20degC
-
-
-[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/8efcae3385d3ecff4c6f5a8ef9ca8e60?timestamp=2025-08-25T14%3A24%3A59.978Z&display=details&observation=e3f0e48874fdfa83)
-
-## Example 5: Group multiple generations into a single trace
-
-Use `propagateAttributes` to apply trace attributes (name, tags, metadata) to all observations created inside its callback, and the `startActiveObservation` context manager to group two OpenAI generations under a single root span. Input and output are set on the root observation.
-
-
-```typescript
-import { startActiveObservation, propagateAttributes } from "npm:@langfuse/tracing";
-import OpenAI from "npm:openai";
-import { observeOpenAI } from "npm:@langfuse/openai";
-
-const country = "Germany";
-
-// Propagate trace attributes to all observations created inside the callback
-await propagateAttributes(
-  {
-    traceName: "City poem generator",
-    tags: ["updated"],
-    metadata: { env: "development" },
-  },
-  async () => {
-    await startActiveObservation("user-request", async (span) => {
-      // Generations created inside the callback are automatically nested under the active span
-      const capital = (
-        await observeOpenAI(new OpenAI(), {
-          generationName: "get-capital",
-        }).chat.completions.create({
-          model: "gpt-4o",
-          messages: [
-            { role: "system", content: "What is the capital of the country?" },
-            { role: "user", content: country },
-          ],
-        })
-      ).choices[0].message.content;
-
-      const poem = (
-        await observeOpenAI(new OpenAI(), {
-          generationName: "generate-poem",
-        }).chat.completions.create({
-          model: "gpt-4o",
-          messages: [
-            {
-              role: "system",
-              content: "You are a poet. Create a poem about this city.",
-            },
-            { role: "user", content: capital },
-          ],
-        })
-      ).choices[0].message.content;
-
-      // Set input and output on the root observation
-      span.update({
-        input: country,
-        output: poem,
-      });
-    });
-  },
-);
-
-// Ensure all spans are flushed to Langfuse
-await langfuseSpanProcessor.forceFlush();
-```
-
-![Langfuse Trace](https://langfuse.com/images/cookbook/example-js-sdk/js_integration_openai_grouped.png)
-
-[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/e0a2f2b374a58cf9b4548240f879fc71?timestamp=2025-08-25T14:39:41.599Z&display=details)
-
-</Steps>
diff --git a/content/guides/cookbook/js_prompt_management_langchain.mdx b/content/guides/cookbook/js_prompt_management_langchain.mdx
index cb3fa3d56c..dd22938b0a 100644
--- a/content/guides/cookbook/js_prompt_management_langchain.mdx
+++ b/content/guides/cookbook/js_prompt_management_langchain.mdx
@@ -3,7 +3,7 @@ source: ⚠️ Jupyter Notebook
 title: Example - Langfuse Prompt Management with Langchain (JS)
 sidebarTitle: Prompt Management with Langchain (JS)
 description: Example how to version control and manage prompts with Langfuse Prompt Management and Langchain JS.
-category: Examples
+category: Prompt Management
 ---
 
 # Example: Langfuse Prompt Management with Langchain (JS)
diff --git a/content/guides/cookbook/meta.json b/content/guides/cookbook/meta.json
index 117464b6ce..f83625304c 100644
--- a/content/guides/cookbook/meta.json
+++ b/content/guides/cookbook/meta.json
@@ -4,10 +4,7 @@
     "index",
     "error-analysis-llm-applications",
     "evaluation_of_rag_with_ragas",
-    "evaluation_with_langchain",
-    "evaluation_with_uptrain",
     "example_data_migration",
-    "example_decorator_openai_langchain",
     "example_evaluating_multi_turn_conversations",
     "example_evaluating_openai_agents",
     "example_external_evaluation_pipelines",
@@ -19,36 +16,9 @@
     "example_pydantic_ai_mcp_agent_evaluation",
     "example_simulated_multi_turn_conversations",
     "example_synthetic_datasets",
-    "integration_amazon_bedrock",
-    "integration_anthropic",
-    "integration_azure_openai_langchain",
-    "integration_databricks",
-    "integration_langchain",
-    "integration_langgraph",
-    "integration_langserve",
-    "integration_litellm_proxy",
-    "integration_llama-index-callback",
-    "integration_llama-index_instrumentation",
-    "integration_llama-index_milvus-lite",
-    "integration_llama_index",
-    "integration_llama_index_posthog_mistral",
-    "integration_llamaindex_workflows",
-    "integration_openai_assistants",
-    "integration_openai_sdk",
-    "integration_openai_structured_output",
-    "js_integration_anthropic",
-    "js_integration_langchain",
-    "js_integration_litellm_proxy",
-    "js_integration_openai",
     "js_langfuse_sdk",
     "js_prompt_management_langchain",
-    "langfuse_sdk_performance_test",
-    "otel_integration_arize",
-    "otel_integration_mlflow",
-    "otel_integration_openlit",
-    "otel_integration_openllmetry",
     "prompt_management_langchain",
-    "prompt_management_openai_functions",
-    "prompt_management_performance_benchmark"
+    "prompt_management_openai_functions"
   ]
 }
diff --git a/content/guides/index.mdx b/content/guides/index.mdx
index 8c4cd008c3..4e664cf4e3 100644
--- a/content/guides/index.mdx
+++ b/content/guides/index.mdx
@@ -5,7 +5,7 @@ description: End-to-end examples and resources to get started with Langfuse for
 
 # Guides
 
-The guides section is work-in-progress. We want to collect all end-to-end examples of how to use Langfuse here including cookbooks, videos and other external resources.
+End-to-end examples of how to use Langfuse: tutorials, runnable cookbooks, and video walkthroughs.
 
 ## Evaluation Tutorials
 
@@ -127,16 +127,10 @@ Extend your evaluation approach for complex, multi-step applications.
   />
 </TutorialCards>
 
-## Cookbooks
+## All guides [#cookbooks]
 
-You can find the `ipynb` files in the [`cookbook`](https://github.com/langfuse/langfuse-docs/tree/main/cookbook) directory of the repository.
+Cookbooks and video guides, sorted by topic. The notebook sources (`ipynb`) live in the [`cookbook`](https://github.com/langfuse/langfuse-docs/tree/main/cookbook) directory of the repository.
 
 import { CookbookIndex } from "@/components/CookbookIndex";
 
-<CookbookIndex />
-
-## Video Tutorials
-
-import { VideoIndex } from "@/components/VideoIndex";
-
-<VideoIndex />
+<CookbookIndex includeVideos />
diff --git a/content/guides/videos/beginners-guide-to-rag-evaluation.mdx b/content/guides/videos/beginners-guide-to-rag-evaluation.mdx
index 1e5d5b29e4..978ea20ee1 100644
--- a/content/guides/videos/beginners-guide-to-rag-evaluation.mdx
+++ b/content/guides/videos/beginners-guide-to-rag-evaluation.mdx
@@ -2,6 +2,7 @@
 title: "Beginner's Guide to RAG Evaluation with Langfuse and Ragas"
 description: "Webinar by Prof. Tom Yeh on how to evaluate RAG applications with Langfuse and Ragas."
 ogImage: /images/videos/beginners-guide-to-rag-evaluation.jpg
+category: Evaluation
 ---
 
 # Beginner's Guide to RAG Evaluation with Langfuse and Ragas
diff --git a/content/guides/videos/debug-rag-agent-with-tracing.mdx b/content/guides/videos/debug-rag-agent-with-tracing.mdx
index e62eddcbd0..fa874a3fcd 100644
--- a/content/guides/videos/debug-rag-agent-with-tracing.mdx
+++ b/content/guides/videos/debug-rag-agent-with-tracing.mdx
@@ -2,6 +2,7 @@
 title: "Debug a Fan-Out RAG Agent With Tracing"
 description: "A confident, fluent, completely wrong answer from a RAG support agent — and how one Langfuse trace leads straight to the single broken config line behind the bad retrieval, wrong tool, and hallucination."
 ogImage: /images/videos/debug-rag-agent-with-tracing.jpg
+category: Examples
 ---
 
 # Debug a Fan-Out RAG Agent With Tracing
diff --git a/content/guides/videos/external-evaluation-pipelines.mdx b/content/guides/videos/external-evaluation-pipelines.mdx
deleted file mode 100644
index 0eea897efd..0000000000
--- a/content/guides/videos/external-evaluation-pipelines.mdx
+++ /dev/null
@@ -1,30 +0,0 @@
----
-title: "External Evaluation Pipelines"
-description: "Introduction to building fully cusotmizable evaluation pipelines on top of Langfuse."
-ogImage: /images/videos/external-evaluation-pipelines.jpg
----
-
-# External Evaluation Pipelines
-
-<iframe
-  width="100%"
-  className="aspect-[3230/2160] rounded mt-10"
-  src="https://www.youtube-nocookie.com/embed/rHfME8KDmIw?si=V4m8smxZ219AKmOU"
-  title="YouTube video player"
-  frameborder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
-  referrerpolicy="strict-origin-when-cross-origin"
-  allowFullScreen
-></iframe>
-
-### Learn more
-
-import { FileCode } from "lucide-react";
-
-<Cards num={2}>
-  <Card
-    title="External Evaluation Pipeline Example"
-    href="/docs/scores/external-evaluation-pipelines"
-    icon={<FileCode />}
-  />
-</Cards>
diff --git a/content/guides/videos/headless-langfuse.mdx b/content/guides/videos/headless-langfuse.mdx
index a5f1216bdb..5f1557770e 100644
--- a/content/guides/videos/headless-langfuse.mdx
+++ b/content/guides/videos/headless-langfuse.mdx
@@ -2,6 +2,7 @@
 title: "Headless Langfuse From Your Coding Agent"
 description: "Instrument, trace, analyze, build a dataset, and run LLM-as-a-judge evaluations without touching the Langfuse UI — driven entirely from your coding agent with the Langfuse Agent Skill."
 ogImage: /images/videos/headless-langfuse.jpg
+category: Examples
 ---
 
 # Headless Langfuse From Your Coding Agent
diff --git a/content/guides/videos/introducing-datasets-v2.mdx b/content/guides/videos/introducing-datasets-v2.mdx
deleted file mode 100644
index 682b879461..0000000000
--- a/content/guides/videos/introducing-datasets-v2.mdx
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: Introducing Datasets v2
-description: Overview of the dataset-related changes released during Launch Week
-ogImage: /images/videos/introducing-datasets-v2.jpg
----
-
-# Introducing Datasets v2
-
-Overview of the dataset-related changes released during Launch Week
-
-<Video
-  src="https://static.langfuse.com/docs-videos/launch-week-datasets.mp4"
-  aspectRatio={16 / 9.5}
-  title="Introduction to Datasets v2"
-/>
-
-### Learn more
-
-import { Book, FileCode } from "lucide-react";
-
-<Cards num={2}>
-  <Card title="Dataset docs" href="/docs/datasets/overview" icon={<Book />} />
-  <Card
-    title="Changelog v2"
-    href="/changelog/2024-04-25-datasets-v2"
-    icon={<Book />}
-  />
-  <Card
-    title="Example Notebook"
-    href="/docs/datasets/python-cookbook"
-    icon={<FileCode />}
-  />
-</Cards>
diff --git a/content/guides/videos/introducing-langfuse-2.0.mdx b/content/guides/videos/introducing-langfuse-2.0.mdx
deleted file mode 100644
index 973dc2fecf..0000000000
--- a/content/guides/videos/introducing-langfuse-2.0.mdx
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: Introducing Langfuse 2.0
-description: Reintroducing Langfuse – the Open Source AI Engineering Platform
-ogImage: /images/videos/introducing-langfuse-2.0.jpg
----
-
-# Introducing Langfuse 2.0
-
-<Video
-  src="https://static.langfuse.com/docs-videos/Introducing%20Langfuse%202.0.mp4"
-  aspectRatio={16 / 9}
-  title="Introducing Langfuse 2.0"
-  posterStartTime={71}
-/>
-
-Do you want to give Langfuse a spin? Try the [interactive demo](/docs/demo).
diff --git a/content/guides/videos/introducing-python-decorator.mdx b/content/guides/videos/introducing-python-decorator.mdx
deleted file mode 100644
index 7a41f585c4..0000000000
--- a/content/guides/videos/introducing-python-decorator.mdx
+++ /dev/null
@@ -1,36 +0,0 @@
----
-title: Introducing the observe() decorator for Python
-description: Why it's the recommended way to use Langfuse in Python and how to use it with other integrations
-ogImage: /images/videos/introducing-python-decorator.jpg
----
-
-# Introducing the `observe()` decorator for Python
-
-<Video
-  src="https://static.langfuse.com/docs-videos/launch-week-decorator-walkthrough-1080.mp4"
-  aspectRatio={16 / 9.37}
-  title="Decorator Integration"
-/>
-
-### Learn more
-
-import { Book, FileCode } from "lucide-react";
-
-<Cards num={2}>
-  <Card
-    title="Decorator docs"
-    href="/docs/sdk/python/decorators"
-    icon={<Book />}
-  />
-  <Card title="Blog post" href="/docs/sdk/python/decorators" icon={<Book />} />
-  <Card
-    title="Notebook demonstrating all features"
-    href="/docs/sdk/python/example"
-    icon={<FileCode />}
-  />
-  <Card
-    title="Rap battle example notebook (video)"
-    href="/guides/cookbook/example_decorator_openai_langchain"
-    icon={<FileCode />}
-  />
-</Cards>
diff --git a/content/guides/videos/llm-as-a-judge-eval-on-dataset-experiments.mdx b/content/guides/videos/llm-as-a-judge-eval-on-dataset-experiments.mdx
deleted file mode 100644
index 0fb0af2e12..0000000000
--- a/content/guides/videos/llm-as-a-judge-eval-on-dataset-experiments.mdx
+++ /dev/null
@@ -1,35 +0,0 @@
----
-title: "LLM-as-a-Judge Evaluators for Dataset Experiments"
-description: "10 min walkthrough on how to reliably evaluate your LLM application changes using Langfuse's new managed LLM-as-a-judge evaluators."
-ogImage: /images/videos/llm-as-a-judge-eval-on-dataset-experiments.jpg
----
-
-# LLM-as-a-Judge Evaluators for Dataset Experiments
-
-10 min walkthrough on how to reliably evaluate your LLM application changes using Langfuse's new managed LLM-as-a-judge evaluators.
-
-This feature helps teams:
-
-- Automatically evaluate experiment runs against test datasets
-- Compare metrics across different versions
-- Identify regressions before they hit production
-- Score outputs based on criteria like hallucination, helpfulness, relevance, and more
-
-Works with popular LLM providers including OpenAI, Anthropic, Azure OpenAI, and AWS Bedrock through function calling.
-
-<iframe
-  width="100%"
-  className="aspect-[16/9] rounded-lg border mt-6 w-full"
-  src="https://www.youtube-nocookie.com/embed/JOGMn5nqCSM?si=9-Et0tKtOYffyvru"
-  title="YouTube video player"
-  frameborder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
-  referrerpolicy="strict-origin-when-cross-origin"
-  allowFullScreen
-></iframe>
-
-More details:
-
-- [Changelog](/changelog/2024-11-19-llm-as-a-judge-for-datasets)
-- [Docs: LLM-as-a-Judge Evaluators](/docs/scores/model-based-evals)
-- [Docs: Dataset Experiments](/docs/datasets)
diff --git a/content/guides/videos/llm-playground.mdx b/content/guides/videos/llm-playground.mdx
deleted file mode 100644
index 464ddf742a..0000000000
--- a/content/guides/videos/llm-playground.mdx
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: LLM Playground
-description: Test, iterate, and compare different prompts and models within the LLM Playground.
-ogImage: /images/videos/llm-playground.jpg
----
-
-# LLM Playground Walkthrough
-
-<Video
-  src="https://static.langfuse.com/docs-videos/launch%20week%20prompt%20management.mp4"
-  aspectRatio={16 / 10.47}
-  title="LLM Playground"
-/>
-
-<span>_Want to learn more? [Check out the docs](/docs/playground)._</span>
diff --git a/content/guides/videos/meta.json b/content/guides/videos/meta.json
index 5941223ef7..0e6cecdc15 100644
--- a/content/guides/videos/meta.json
+++ b/content/guides/videos/meta.json
@@ -5,15 +5,6 @@
     "headless-langfuse",
     "replace-vibes-with-evals",
     "debug-rag-agent-with-tracing",
-    "beginners-guide-to-rag-evaluation",
-    "external-evaluation-pipelines",
-    "introducing-datasets-v2",
-    "introducing-langfuse-2.0",
-    "introducing-python-decorator",
-    "llm-as-a-judge-eval-on-dataset-experiments",
-    "llm-playground",
-    "posthog-integration",
-    "run-langfuse-locally",
-    "webinar-observability-llm-systems"
+    "beginners-guide-to-rag-evaluation"
   ]
 }
diff --git a/content/guides/videos/posthog-integration.mdx b/content/guides/videos/posthog-integration.mdx
deleted file mode 100644
index fc9cba84f3..0000000000
--- a/content/guides/videos/posthog-integration.mdx
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Posthog Integration
-description: Walkthrough of the Langfuse Posthog integration
-ogImage: /images/videos/posthog-integration.jpg
----
-
-# Posthog Integration Walkthrough
-
-<Video
-  src="https://static.langfuse.com/docs-videos/posthog-integration.mp4"
-  aspectRatio={16 / 9.53}
-/>
-<span>
-  _See [integration docs](/integrations/analytics/posthog) for more details._
-</span>
diff --git a/content/guides/videos/replace-vibes-with-evals.mdx b/content/guides/videos/replace-vibes-with-evals.mdx
index 4c3355ed1c..09a19558c5 100644
--- a/content/guides/videos/replace-vibes-with-evals.mdx
+++ b/content/guides/videos/replace-vibes-with-evals.mdx
@@ -2,6 +2,7 @@
 title: "Replace Vibes With Evals: Build an Evaluation Pipeline"
 description: "Stop shipping AI features on gut feel. Use Langfuse to trace production, run error analysis, capture failures into a dataset, score them with evaluators, and measure every change with experiments before it ships."
 ogImage: /images/videos/replace-vibes-with-evals.jpg
+category: Evaluation
 ---
 
 # Replace Vibes With Evals: Build an Evaluation Pipeline
diff --git a/content/guides/videos/run-langfuse-locally.mdx b/content/guides/videos/run-langfuse-locally.mdx
deleted file mode 100644
index 2847f42514..0000000000
--- a/content/guides/videos/run-langfuse-locally.mdx
+++ /dev/null
@@ -1,22 +0,0 @@
----
-title: "Run Langfuse Locally in 3 Minutes"
-description: "Run Langfuse locally with Docker Compose."
-ogImage: /images/videos/local-langfuse-v3.jpg
----
-
-# Run Langfuse Locally
-
-Watch this 2:15 minute walkthrough on running Langfuse locally with Docker Compose.
-
-For more details, see the [step-by-step guide](/self-hosting/deployment/docker-compose).
-
-<iframe
-  width="100%"
-  className="aspect-[15.94/9] rounded-lg border mt-6 w-full"
-  src="https://www.youtube-nocookie.com/embed/we52x5-a_Ac?si=2rqAs0LNwqeq767W"
-  title="YouTube video player"
-  frameborder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
-  referrerpolicy="strict-origin-when-cross-origin"
-  allowFullScreen
-></iframe>
diff --git a/content/handbook/chapters/why.mdx b/content/handbook/chapters/why.mdx
index c7f28f9a04..cc209ca271 100644
--- a/content/handbook/chapters/why.mdx
+++ b/content/handbook/chapters/why.mdx
@@ -64,7 +64,7 @@ Companies who trust Langfuse:
 
 - We designed Langfuse with complex, nested LLM calls in mind—especially for agents and multi-step workflows.
 - [Agent graphs](/docs/observability/features/agent-graphs) provide visual representations of complex agent workflows, helping you understand and debug multi-step reasoning processes.
-- Langfuse enables hierarchical representations of your application in [traces](/docs/observability/overview). Why are traces the core abstraction for LLM Engineering? Learn more in this [webinar](/guides/videos/webinar-observability-llm-systems).
+- Langfuse enables hierarchical representations of your application in [traces](/docs/observability/overview). Why are traces the core abstraction for LLM Engineering? Learn more in this [webinar](/resources/engineering/webinar-observability-llm-systems).
 - [Multi-modal support](/docs/observability/features/multi-modality) for tracing text, images, audio, and other modalities.
 - [MCP tracing](/docs/observability/features/mcp-tracing) for Model Context Protocol server interactions.
 - We go beyond Input/Output to include all the context of your app via [metadata](/docs/observability/features/metadata), [tags](/docs/observability/features/tags), and [sessions](/docs/observability/features/sessions).
diff --git a/content/integrations/frameworks/langchain.mdx b/content/integrations/frameworks/langchain.mdx
index 939d9e91d6..4e7c5fa7cc 100644
--- a/content/integrations/frameworks/langchain.mdx
+++ b/content/integrations/frameworks/langchain.mdx
@@ -65,7 +65,7 @@ langfuse_handler = CallbackHandler()
 ### LangChain Example
 
 <Callout type="info" emoji="💡">
-Instrumenting LangGraph follows the same pattern. Simply pass the `langfuse_handler` to the agent invocation. ([Example Notebook](/guides/cookbook/integration_langgraph)).
+Instrumenting LangGraph follows the same pattern. Simply pass the `langfuse_handler` to the agent invocation. ([Example Notebook](/integrations/frameworks/langgraph)).
 </Callout>
 
 ```python /config={"callbacks": [langfuse_handler]}/
@@ -199,19 +199,9 @@ After executing the application, navigate to your [Langfuse Trace Table](https:/
 import { FileCode, GitPullRequestArrow } from "lucide-react";
 
 <Cards num={2}>
-  <Card
-    title="LangChain (Python)"
-    href="/guides/cookbook/integration_langchain"
-    icon={<FileCode />}
-  />
-  <Card
-    title="LangChain (JS/TS)"
-    href="/guides/cookbook/js_integration_langchain"
-    icon={<FileCode />}
-  />
   <Card
     title="LangGraph (Python)"
-    href="/guides/cookbook/integration_langgraph"
+    href="/integrations/frameworks/langgraph"
     icon={<FileCode />}
   />
   <Card
@@ -282,8 +272,6 @@ def process_user_query(user_input: str):
 answer = process_user_query("What is the capital of France?")
 ```
 
-See the [Langchain + decorator observability cookbook](/guides/cookbook/example_decorator_openai_langchain) for an example of this in action.
-
 **Using context managers:**
 
 ```python /with langfuse.start_as_current_observation(as_type="span", name="multi-step-process") as root_span:/ /prep_span.update/ /root_span.set_trace_io/ /post_span.update/
diff --git a/content/guides/cookbook/integration_langgraph.mdx b/content/integrations/frameworks/langgraph.mdx
similarity index 95%
rename from content/guides/cookbook/integration_langgraph.mdx
rename to content/integrations/frameworks/langgraph.mdx
index 5cfc729153..fdda0ac1cb 100644
--- a/content/guides/cookbook/integration_langgraph.mdx
+++ b/content/integrations/frameworks/langgraph.mdx
@@ -1,13 +1,14 @@
 ---
 source: Jupyter Notebook
 title: Open Source Observability for LangGraph
+sidebarTitle: LangGraph
+logo: /images/integrations/langchain_icon.png
 description: Learn how to use Langfuse for open source observability/tracing in your LangGraph application (Python).
 category: Integrations
 ---
 
 # Cookbook: LangGraph Integration
 
-
 ## What is LangGraph?
 
 [LangGraph](https://langchain-ai.github.io/langgraph/) is an open-source framework by the LangChain team for building complex, stateful, multi-agent applications using large language models (LLMs). LangGraph includes built-in persistence to save and resume state, which enables error recovery and human-in-the-loop workflows.
@@ -18,12 +19,11 @@ This cookbook demonstrates how [Langfuse](https://langfuse.com/docs) helps to de
 
 **By the end of this cookbook, you will be able to:**
 
-
-*   Automatically trace LangGraph application via the Langfuse integration
-*   Add clear trace names, run names, tags, and metadata so traces are easier to find in the Langfuse dashboard
-*   Monitor advanced multi-agent setups
-*   Add scores (like user feedback) to existing LangGraph traces
-*   Manage your prompts used in LangGraph with Langfuse
+- Automatically trace LangGraph application via the Langfuse integration
+- Add clear trace names, run names, tags, and metadata so traces are easier to find in the Langfuse dashboard
+- Monitor advanced multi-agent setups
+- Add scores (like user feedback) to existing LangGraph traces
+- Manage your prompts used in LangGraph with Langfuse
 
 This cookbook is structured as a trace-quality progression: basic trace, named and filterable trace, managed prompt trace, multi-agent traces, nested agents in one trace, and finally a scored trace. Each graph invocation creates a new trace; the scoring example attaches feedback to an existing trace instead of creating another one.
 
@@ -36,12 +36,10 @@ _**Note:** You need to run at least Python 3.11 ([GitHub Issue](https://github.c
 
 </Callout>
 
-
 ```python
 %pip install langfuse langchain langgraph langchain_openai langchain_community
 ```
 
-
 ```python
 import os
 
@@ -58,7 +56,6 @@ os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...");
 
 With the environment variables set, we can now initialize the Langfuse client. get_client() initializes the Langfuse client using the credentials provided in the environment variables.
 
-
 ```python
 from langfuse import get_client
 
@@ -75,8 +72,8 @@ else:
 
 **What we will do in this section:**
 
-*   Build a support chatbot in LangGraph that can answer common questions
-*   Tracing the chatbot's input and output using Langfuse
+- Build a support chatbot in LangGraph that can answer common questions
+- Tracing the chatbot's input and output using Langfuse
 
 We will start with a basic chatbot trace, then improve it with better naming and filtering context in the next example.
 
@@ -84,7 +81,6 @@ We will start with a basic chatbot trace, then improve it with better naming and
 
 Start by creating a `StateGraph`. A `StateGraph` object defines our chatbot's structure as a state machine. We will add nodes to represent the LLM and functions the chatbot can call, and edges to specify how the bot transitions between these functions.
 
-
 ```python
 from typing import Annotated
 
@@ -125,7 +121,6 @@ graph = graph_builder.compile()
 
 Now, we will add the [Langfuse callback handler for LangChain](https://langfuse.com/integrations/frameworks/langchain) to trace the steps of our application: `config={"callbacks": [langfuse_handler]}`. This is the minimal setup and may create generic names such as `LangGraph` in the dashboard.
 
-
 ```python
 from langfuse.langchain import CallbackHandler
 
@@ -141,7 +136,6 @@ for s in graph.stream({"messages": [HumanMessage(content = "What is Langfuse?")]
 
 You can visualize the graph using the `get_graph` method along with a "draw" method
 
-
 ```python
 from IPython.display import Image, display
 display(Image(graph.get_graph().draw_mermaid_png()))
@@ -163,7 +157,6 @@ graph TD;
 
 For production applications, add a clear trace name, run name, tags, user/session identifiers, and metadata. This example reuses the chatbot from Example 1, but makes the resulting trace easier to find in the Langfuse dashboard and easier to group with related traces.
 
-
 ```python
 from langfuse import propagate_attributes
 
@@ -188,7 +181,6 @@ with propagate_attributes(
 
 After running Examples 1 and 2, you will see two traces: one basic automatic trace and one named trace. In the dashboard, use the Trace Name column to find `LangGraph Simple Chatbot` and the Name column to find `handle-chatbot-message`.
 
-
 ![Trace view of chat app in Langfuse](https://langfuse.com/images/cookbook/integration-langgraph/integration_langgraph_chatapp_trace.png)
 
 ## Example 3: Use a managed prompt in the chatbot trace
@@ -197,14 +189,12 @@ This example keeps the same simple chatbot question from Examples 1 and 2, but f
 
 Langfuse prompt management is basically a Prompt CMS (Content Management System). Alternatively, you can also edit and version the prompt in the Langfuse UI.
 
-*   `Name` that identifies the prompt in Langfuse Prompt Management
-*   Prompt with prompt template incl. `{{input variables}}`
-*   `labels` to include `production` to immediately use prompt as the default
+- `Name` that identifies the prompt in Langfuse Prompt Management
+- Prompt with prompt template incl. `{{input variables}}`
+- `labels` to include `production` to immediately use prompt as the default
 
 In this example, we create a system prompt for an assistant that answers every user message in Spanish.
 
-
-
 ```python
 from langfuse import get_client
 
@@ -221,10 +211,8 @@ langfuse.create_prompt(
 
 Use the utility method `.get_langchain_prompt()` to transform the Langfuse prompt into a string that can be used in Langchain.
 
-
 **Context:** Langfuse declares input variables in prompt templates using double brackets (`{{input variable}}`). Langchain uses single brackets for declaring input variables in PromptTemplates (`{input variable}`). The utility method `.get_langchain_prompt()` replaces the double brackets with single brackets. In this example, however, we don't use any variables in our prompt.
 
-
 ```python
 # Get current production version of prompt and transform the Langfuse prompt into a string that can be used in Langchain
 langfuse_system_prompt = langfuse.get_prompt("spanish_answer_system-prompt")
@@ -235,7 +223,6 @@ print(langchain_system_prompt)
 
 Now we can use the new system prompt string to update our assistant.
 
-
 ```python
 from typing import Annotated
 from langchain_openai import ChatOpenAI
@@ -267,7 +254,6 @@ graph_builder.set_finish_point("chatbot")
 graph = graph_builder.compile()
 ```
 
-
 ```python
 from langfuse import propagate_attributes
 from langfuse.langchain import CallbackHandler
@@ -296,15 +282,13 @@ with propagate_attributes(
 
 After running Example 3, use the Trace Name column to find `LangGraph Managed Prompt Chatbot` and the Name column to find `answer-with-managed-prompt`.
 
-
 ## Example 4: Multi-agent traces with supervisor routing
 
 **What we will do in this section**:
 
-*   Build 2 executing agents: One research agent using the LangChain WikipediaAPIWrapper to search Wikipedia and one that uses a custom tool to get the current time.
-*   Build an agent supervisor to help delegate the user questions to one of the two agents
-*   Add Langfuse handler as callback to trace the steps of the supervisor and executing agents
-
+- Build 2 executing agents: One research agent using the LangChain WikipediaAPIWrapper to search Wikipedia and one that uses a custom tool to get the current time.
+- Build an agent supervisor to help delegate the user questions to one of the two agents
+- Add Langfuse handler as callback to trace the steps of the supervisor and executing agents
 
 ```python
 %pip install langfuse langgraph langchain langchain_openai langchain_experimental pandas wikipedia
@@ -314,7 +298,6 @@ After running Example 3, use the Trace Name column to find `LangGraph Managed Pr
 
 For this example, you build an agent to do wikipedia research, and one agent to tell you the current time. Define the tools they will use below:
 
-
 ```python
 from typing import Annotated
 
@@ -345,7 +328,6 @@ def datetime_tool() -> str:
 
 Define a helper function below to simplify adding new agent worker nodes.
 
-
 ```python
 from langchain.agents import create_agent
 from langchain_core.messages import BaseMessage, HumanMessage
@@ -368,7 +350,6 @@ def agent_node(state, agent, name):
 
 It will use function calling to choose the next worker node OR finish processing.
 
-
 ```python
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 
@@ -425,7 +406,6 @@ supervisor_chain = prompt | llm.with_structured_output(function_def)
 
 Now we are ready to start building the graph. Below, define the state and worker nodes using the function we just defined. Then we connect all the edges in the graph.
 
-
 ```python
 import functools
 import operator
@@ -476,7 +456,6 @@ graph_2 = workflow.compile()
 
 You can visualize the graph using the `get_graph` method along with a "draw" method
 
-
 ```python
 from IPython.display import Image, display
 display(Image(graph_2.get_graph().draw_mermaid_png()))
@@ -504,7 +483,6 @@ graph TD;
 
 Add [Langfuse handler](https://langfuse.com/integrations/frameworks/langchain) as callback: `config={"callbacks": [langfuse_handler]}`. This section runs the graph twice, so you will see two traces in Langfuse: one for the research question and one for the current-time question.
 
-
 ```python
 from langfuse import propagate_attributes
 from langfuse.langchain import CallbackHandler
@@ -530,7 +508,6 @@ with propagate_attributes(
         print("----")
 ```
 
-
 ```python
 with propagate_attributes(
     trace_name="LangGraph Time Agent",
@@ -567,7 +544,6 @@ There are setups where one LangGraph agent uses one or multiple other LangGraph
 
 First, we generate a trace_id that can be used for both agents to group the agent executions together in one Langfuse trace.
 
-
 ```python
 from langfuse import get_client, Langfuse, propagate_attributes
 from langfuse.langchain import CallbackHandler
@@ -583,7 +559,6 @@ langfuse_handler = CallbackHandler()
 
 Next, we set up the sub-agent.
 
-
 ```python
 from typing import Annotated
 from langchain_openai import ChatOpenAI
@@ -610,7 +585,6 @@ sub_agent = graph_builder.compile()
 
 Then, we set the tool that uses the research-sub-agent to answer questions.
 
-
 ```python
 from langchain_core.tools import tool
 
@@ -638,8 +612,7 @@ def langgraph_research(question):
   return response["messages"][-1].content
 ```
 
-Set up a second simple LangGraph agent that uses the new `langgraph_research`. 
-
+Set up a second simple LangGraph agent that uses the new `langgraph_research`.
 
 ```python
 from langgraph.prebuilt import create_react_agent
@@ -653,7 +626,6 @@ main_agent = create_react_agent(
 )
 ```
 
-
 ```python
 user_question = "What is Langfuse?"
 
@@ -700,7 +672,6 @@ In the example below, we attach user feedback to the `LangGraph Nested Agent` tr
 
 **→ Learn more about [Custom Scores in Langfuse](https://langfuse.com/docs/scores/custom).**
 
-
 ```python
 from langfuse import get_client
 
@@ -728,10 +699,8 @@ Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/trac
 
 You can add Langfuse as callback when using [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server/)
 
-
 When using the LangGraph Server, the LangGraph Server handles graph invocation automatically. Therefore, you should add the Langfuse callback when declaring the graph.
 
-
 ```python
 from typing import Annotated
 
diff --git a/content/integrations/frameworks/langserve.md b/content/integrations/frameworks/langserve.md
deleted file mode 100644
index eebb8daaab..0000000000
--- a/content/integrations/frameworks/langserve.md
+++ /dev/null
@@ -1,180 +0,0 @@
----
-title: Langserve Integration
-description: This guide shows how to natively integrate Langfuse with LangChain's Langserve for observability, metrics, evals, prompt management, playground, datasets.
-category: Integrations
-sidebarTitle: Langserve
-logo: /images/integrations/langchain_icon.png
----
-
-# Cookbook: Langserve Integration
-
-[Langserve](https://python.langchain.com/docs/langserve/) (Python)
-
-> LangServe helps developers deploy LangChain runnables and chains as a REST API.
->
-> This library is integrated with FastAPI and uses pydantic for data validation.
->
-> In addition, it provides a client that can be used to call into runnables deployed on a server. A JavaScript client is available in LangChain.js.
-
-This cookbook demonstrates how to trace applications deployed via Langserve with Langfuse (using the [LangChain integration](https://langfuse.com/integrations/frameworks/langchain)). We'll run both the server and the client in this notebook.
-
-## Setup
-
-```python
-%pip install fastapi sse_starlette httpx langserve langfuse langchain-openai langchain --upgrade
-```
-
-```python
-import os
-
-# Get keys for your project from the project settings page
-# https://cloud.langfuse.com
-os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "");
-os.environ.setdefault("LANGFUSE_SECRET_KEY", "");
-os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
-# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
-
-# Your openai key
-os.environ.setdefault("OPENAI_API_KEY", "");
-```
-
-## Simple LLM Call Example
-
-Initialize the Langfuse `CallbackHandler` (authenticated via the environment variables set above) and configure the LLM with it as callback handler. Add to Fastapi via Langserve's `add_routes()`.
-
-```python
-from langchain_openai import ChatOpenAI
-from langchain_core.runnables.config import RunnableConfig
-from langfuse import get_client
-from langfuse.langchain import CallbackHandler
-from fastapi import FastAPI
-from langserve import add_routes
-
-# Initialize Langfuse CallbackHandler for Langchain (tracing)
-langfuse_handler = CallbackHandler()
-
-# Tests the SDK connection with the server
-get_client().auth_check()
-
-llm = ChatOpenAI()
-
-config = RunnableConfig(callbacks=[langfuse_handler])
-
-llm_with_langfuse = llm.with_config(config)
-
-# Setup server
-app = FastAPI()
-
-# Add Langserve route
-add_routes(
-    app,
-    llm_with_langfuse,
-    path="/test-simple-llm-call",
-)
-```
-
-_Note: We use TestClient in this example to be able to run the server in a notebook_
-
-```python
-from fastapi.testclient import TestClient
-
-# Initialize TestClient
-client = TestClient(app)
-
-# Test simple route
-response = client.post("/test-simple-llm-call/invoke", json={"input": "Tell me a joke?"})
-```
-
-Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/5f32e2e7-9508-4280-b47b-e0356bc3c81e
-
-![Trace of Langserve Simple LLM Call](https://langfuse.com/images/cookbook/integration_langserve_simple.png)
-
-## LCEL example
-
-```python
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-from langserve import add_routes
-
-# Create Chain
-prompt = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
-
-chain = prompt | llm | StrOutputParser()
-
-# Add new route
-add_routes(
-    app,
-    chain.with_config(config),
-    path="/test-chain",
-)
-
-# Test chain route
-response = client.post("/test-chain/invoke", json={"input": {"topic": "Berlin"}})
-```
-
-Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/261d1006-74ff-4b67-8baf-afdfc827aee2
-
-![Trace of Langserve LCEL Example](https://langfuse.com/images/cookbook/integration_langserve_chain.png)
-
-## Agent Example
-
-```python
-from langchain_core.tools import tool
-from langchain_core.utils.function_calling import convert_to_openai_tool
-from langchain.agents.format_scratchpad.openai_tools import (
-    format_to_openai_tool_messages,
-)
-from langchain.agents import AgentExecutor
-from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser
-from langserve.pydantic_v1 import BaseModel
-from langchain_core.prompts import MessagesPlaceholder
-
-class Input(BaseModel):
-    input: str
-
-prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", "You are a helpful assistant."),
-        ("user", "{input}"),
-        MessagesPlaceholder(variable_name="agent_scratchpad"),
-    ]
-)
-
-@tool
-def word_length(word: str) -> int:
-    """Returns a counter word"""
-    return len(word)
-
-tools = [word_length]
-
-llm_with_tools = llm.bind(tools=[convert_to_openai_tool(tool) for tool in tools])
-
-agent = (
-    {
-        "input": lambda x: x["input"],
-        "agent_scratchpad": lambda x: format_to_openai_tool_messages(
-            x["intermediate_steps"]
-        ),
-    }
-    | prompt
-    | llm_with_tools
-    | OpenAIToolsAgentOutputParser()
-)
-agent_executor = AgentExecutor(agent=agent, tools=tools)
-
-agent_config = RunnableConfig({"run_name": "agent"}, callbacks=[langfuse_handler])
-
-add_routes(
-    app,
-    agent_executor.with_types(input_type=Input).with_config(
-        agent_config
-    ),
-    path="/test-agent",
-)
-
-response = client.post("/test-agent/invoke", json={"input": {"input": "How long is Leonardo DiCaprios last name?"}})
-```
-
-Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/ed1d57f9-2f35-4e72-8150-b061f21840a7
-
-![Trace of Langserve Agent example](https://langfuse.com/images/cookbook/integration_langserve_agent.png)
diff --git a/content/integrations/frameworks/meta.json b/content/integrations/frameworks/meta.json
index e5873c8d4e..179c00505b 100644
--- a/content/integrations/frameworks/meta.json
+++ b/content/integrations/frameworks/meta.json
@@ -18,7 +18,7 @@
     "koog",
     "langchain-deepagents",
     "langchain",
-    "langserve",
+    "langgraph",
     "litellm-sdk",
     "livekit",
     "llamaindex-workflows",
diff --git a/content/integrations/gateways/litellm.mdx b/content/integrations/gateways/litellm.mdx
index 6200349b32..d2220359d9 100644
--- a/content/integrations/gateways/litellm.mdx
+++ b/content/integrations/gateways/litellm.mdx
@@ -20,7 +20,7 @@ There are three ways to integrate LiteLLM with Langfuse:
 
 1. Sending logs via the LiteLLM Proxy to capture all LLM calls going through the proxy.
 2. Using the LiteLLM SDK to capture LLM calls directly.
-3. Using any compatible framework (such as the OpenAI or LangChain SDK) to capture LLM calls.
+3. Using a compatible client SDK (such as the Langfuse OpenAI SDK wrapper) to capture LLM calls in your application code — see [below](#openai-sdk-wrapper).
 
 <Callout type="info">
   This integration is for the LiteLLM Proxy. If you are looking for the LiteLLM
@@ -111,6 +111,146 @@ You can add additional Langfuse attributes to the requests in order to group req
 </Tab>
 </Tabs>
 
+## Trace calls client-side with the OpenAI SDK wrapper [#openai-sdk-wrapper]
+
+Instead of (or in addition to) logging from the proxy, you can trace calls in your application code with the Langfuse OpenAI SDK wrapper ([Python](/integrations/model-providers/openai-py), [JS/TS](/integrations/model-providers/openai-js)). Since the proxy exposes an OpenAI-compatible API, the wrapper captures every call routed through it as a generation in Langfuse, and you can group multiple calls into a single trace.
+
+<Tabs items={["Python", "JS/TS"]}>
+<Tab>
+
+Install the dependencies and set your Langfuse credentials:
+
+```bash
+pip install langfuse openai
+```
+
+```python
+import os
+
+# Get keys for your project from the project settings page: https://cloud.langfuse.com
+os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..."
+os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..."
+os.environ["LANGFUSE_BASE_URL"] = "https://cloud.langfuse.com"  # 🇪🇺 EU region
+# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
+```
+
+Point the wrapped OpenAI client at the proxy to log a single call:
+
+```python
+from langfuse.openai import openai
+
+# LiteLLM Proxy runs on http://0.0.0.0:4000 by default
+client = openai.OpenAI(base_url="http://0.0.0.0:4000")
+
+completion = client.chat.completions.create(
+    model="gpt-5.1",
+    name="calculator",  # optional name of the generation in Langfuse
+    messages=[
+        {"role": "system", "content": "You are a very accurate calculator."},
+        {"role": "user", "content": "1 + 1 = "},
+    ],
+)
+print(completion.choices[0].message.content)
+```
+
+Use the `@observe()` decorator to group multiple proxy calls into a single trace, for example when your application mixes LLM calls with retrieval or API calls:
+
+```python
+from langfuse import observe
+from langfuse.openai import openai
+
+client = openai.OpenAI(base_url="http://0.0.0.0:4000")
+
+@observe()
+def calculate():
+    results = []
+    for task in ["1 + 1 = ", "2 + 3 = "]:
+        completion = client.chat.completions.create(
+            model="gpt-5.1",
+            messages=[
+                {"role": "system", "content": "You are a very accurate calculator."},
+                {"role": "user", "content": task},
+            ],
+        )
+        results.append(completion.choices[0].message.content)
+    return results
+
+calculate()
+```
+
+</Tab>
+<Tab>
+
+Install the dependencies and set your Langfuse credentials:
+
+```bash
+npm install openai @langfuse/openai @langfuse/tracing @langfuse/otel @opentelemetry/sdk-node
+```
+
+```bash filename=".env"
+LANGFUSE_SECRET_KEY = "sk-lf-..."
+LANGFUSE_PUBLIC_KEY = "pk-lf-..."
+LANGFUSE_BASE_URL = "https://cloud.langfuse.com" # 🇪🇺 EU region
+# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
+```
+
+Initialize the OpenTelemetry SDK with the Langfuse span processor:
+
+```typescript
+import { NodeSDK } from "@opentelemetry/sdk-node";
+import { LangfuseSpanProcessor } from "@langfuse/otel";
+
+const sdk = new NodeSDK({
+  spanProcessors: [new LangfuseSpanProcessor()],
+});
+
+sdk.start();
+```
+
+Wrap the OpenAI client with `observeOpenAI` and point it at the proxy to log a single call:
+
+```typescript
+import { OpenAI } from "openai";
+import { observeOpenAI } from "@langfuse/openai";
+
+// LiteLLM Proxy runs on http://0.0.0.0:4000 by default
+const client = observeOpenAI(new OpenAI({ baseURL: "http://0.0.0.0:4000" }));
+
+const completion = await client.chat.completions.create({
+  model: "gpt-5.1",
+  messages: [
+    { role: "system", content: "You are a very accurate calculator." },
+    { role: "user", content: "1 + 1 = " },
+  ],
+});
+console.log(completion.choices[0].message.content);
+```
+
+Use `startActiveObservation` to group multiple proxy calls into a single trace:
+
+```typescript
+import { OpenAI } from "openai";
+import { observeOpenAI } from "@langfuse/openai";
+import { startActiveObservation } from "@langfuse/tracing";
+
+const client = observeOpenAI(new OpenAI({ baseURL: "http://0.0.0.0:4000" }));
+
+await startActiveObservation("user-request", async () => {
+  for (const task of ["1 + 1 = ", "2 + 3 = "]) {
+    await client.chat.completions.create({
+      model: "gpt-5.1",
+      messages: [
+        { role: "system", content: "You are a very accurate calculator." },
+        { role: "user", content: task },
+      ],
+    });
+  }
+});
+```
+
+</Tab>
+</Tabs>
+
 import LitellmAbout from "@/components-mdx/litellm-about.mdx";
 
 <LitellmAbout />
diff --git a/content/integrations/model-providers/amazon-bedrock.md b/content/integrations/model-providers/amazon-bedrock.md
index 1842cf2923..a5610bcebc 100644
--- a/content/integrations/model-providers/amazon-bedrock.md
+++ b/content/integrations/model-providers/amazon-bedrock.md
@@ -1,6 +1,7 @@
 ---
 title: "Amazon Bedrock Integration"
 sidebarTitle: Amazon Bedrock
+category: Integrations
 seoTitle: Open Source Observability and Metrics for Amazon Bedrock
 description: Open source observability for Amazon Bedrock applications and the Bedrock SDK.
 logo: /images/integrations/bedrock_icon.png
diff --git a/content/integrations/model-providers/databricks.mdx b/content/integrations/model-providers/databricks.mdx
index 8d580756c4..0563e1f2fb 100644
--- a/content/integrations/model-providers/databricks.mdx
+++ b/content/integrations/model-providers/databricks.mdx
@@ -13,17 +13,136 @@ logo: /images/integrations/databricks_icon.svg
 
 ## Tracing and Observability
 
-Integrating Databricks with Langfuse enables you to trace your applications built with Databricks, experiment with prompts in the Langfuse Playground and benchmark your models through rigorous evaluations.
+Databricks serving endpoints expose an OpenAI-compatible API, so you can trace them with Langfuse in three ways: via the OpenAI SDK, via LangChain, or via LlamaIndex.
 
-import { FileCode, BookOpen } from "lucide-react";
+For all three approaches, configure your Langfuse and Databricks credentials as environment variables:
 
-<Cards>
-  <Card
-    title="Example Notebook"
-    href="/guides/cookbook/integration_databricks"
-    icon={<FileCode />}
-  />
-</Cards>
+```python
+import os
+
+# Get keys for your project from the project settings page: https://cloud.langfuse.com
+os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..."
+os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..."
+os.environ["LANGFUSE_BASE_URL"] = "https://cloud.langfuse.com"  # 🇪🇺 EU region
+# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
+
+os.environ["DATABRICKS_TOKEN"] = "dapi-..."  # Databricks personal access token
+os.environ["DATABRICKS_HOST"] = "https://dbc-XXXXX-XXXX.cloud.databricks.com"  # Databricks workspace URL
+```
+
+<Tabs items={["OpenAI SDK", "LangChain", "LlamaIndex"]}>
+<Tab>
+
+Databricks endpoints act as a drop-in replacement for the OpenAI API. The `langfuse.openai` client automatically traces your requests to Langfuse. For more examples, see the [OpenAI integration docs](/integrations/model-providers/openai-py).
+
+```bash
+pip install langfuse openai
+```
+
+```python
+import os
+from langfuse.openai import OpenAI
+
+# Create an OpenAI-like client pointing to Databricks
+client = OpenAI(
+    api_key=os.environ.get("DATABRICKS_TOKEN"),
+    base_url=f"{os.environ.get('DATABRICKS_HOST')}/serving-endpoints",
+)
+
+response = client.chat.completions.create(
+    messages=[
+        {"role": "system", "content": "You are an AI assistant."},
+        {"role": "user", "content": "What is Databricks?"},
+    ],
+    model="mistral-7b",  # Adjust based on your Databricks serving endpoint name
+    max_tokens=256,
+)
+print(response.choices[0].message.content)
+```
+
+![Databricks example trace in Langfuse](/images/docs/databricks/databricks-example-trace-openai-sdk.png)
+
+_[Link to public trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/029b2344-e2a2-4c52-8d04-bd71f70c1120?timestamp=2025-03-06T14%3A45%3A04.141Z)_
+
+</Tab>
+<Tab>
+
+The [`ChatDatabricks`](https://python.langchain.com/docs/integrations/chat/databricks/) class wraps your Databricks Model Serving endpoint; the Langfuse `CallbackHandler` collects the trace data. For more examples, see the [LangChain integration docs](/integrations/frameworks/langchain).
+
+```bash
+pip install langfuse databricks-langchain
+```
+
+```python
+from databricks_langchain import ChatDatabricks
+from langfuse.langchain import CallbackHandler
+
+# Initialize Langfuse CallbackHandler for LangChain (tracing)
+langfuse_handler = CallbackHandler()
+
+chat_model = ChatDatabricks(
+    endpoint="mistral-7b",  # Your Databricks Model Serving endpoint name
+    temperature=0.1,
+    max_tokens=256,
+)
+
+messages = [
+    ("system", "You are a chatbot that can answer questions about Databricks."),
+    ("user", "What is Databricks Model Serving?"),
+]
+
+chat_model.invoke(messages, config={"callbacks": [langfuse_handler]})
+```
+
+![Databricks example trace in Langfuse](/images/docs/databricks/databricks-example-trace-langchain.png)
+
+_[Link to public trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/a55411bb-4bb4-435c-b922-e446683888ff?timestamp=2025-03-06T14%3A57%3A59.273Z)_
+
+</Tab>
+<Tab>
+
+If you use [LlamaIndex](https://github.com/run-llama/llama_index), you can replace the default LLM with a Databricks endpoint and trace calls via the OpenInference LlamaIndex instrumentation. For more examples, see the [LlamaIndex integration docs](/integrations/frameworks/llamaindex).
+
+```bash
+pip install langfuse llama-index llama-index-llms-databricks openinference-instrumentation-llama-index
+```
+
+```python
+import os
+from langfuse import get_client
+from llama_index.core.llms import ChatMessage
+from llama_index.llms.databricks import Databricks
+from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
+
+langfuse = get_client()
+
+# Initialize LlamaIndex instrumentation
+LlamaIndexInstrumentor().instrument()
+
+# Create a Databricks LLM instance
+llm = Databricks(
+    model="mistral-7b",  # Your Databricks serving endpoint name
+    api_key=os.environ.get("DATABRICKS_TOKEN"),
+    api_base=f"{os.environ.get('DATABRICKS_HOST')}/serving-endpoints/",
+)
+
+messages = [
+    ChatMessage(role="system", content="You are a helpful assistant."),
+    ChatMessage(role="user", content="What is Databricks?"),
+]
+
+response = llm.chat(messages)
+print(response)
+```
+
+![Databricks example LlamaIndex trace in Langfuse](/images/docs/databricks/databricks-example-llamaindex-trace.png)
+
+_[Link to public trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/589a858e-9678-4624-bcb6-2e0266ecb1b3?timestamp=2025-03-06T15%3A10%3A02.467Z&observation=dd7b6235-6c92-4c9b-a966-872bc281c060)_
+
+</Tab>
+</Tabs>
+
+import { BookOpen } from "lucide-react";
 
 ## Playground & Evaluations
 
diff --git a/content/integrations/model-providers/openai-assistants-api.md b/content/integrations/model-providers/openai-assistants-api.md
index 28c01b562d..3b2431febc 100644
--- a/content/integrations/model-providers/openai-assistants-api.md
+++ b/content/integrations/model-providers/openai-assistants-api.md
@@ -4,11 +4,12 @@ sidebarTitle: OpenAI Assistants API
 description: Use of Langfuse decorator to trace calls made to openai assistants
 category: Integrations
 logo: /images/integrations/openai_icon.svg
-logoAppearance: dark
 ---
 
 # Cookbook: Observability for OpenAI Assistants API with Langfuse
 
+> **Note:** OpenAI has deprecated the Assistants API and will shut it down on August 26, 2026. For new projects, use the [Responses API](https://platform.openai.com/docs/guides/migrate-to-responses) instead.
+
 This cookbook demonstrates how to use the Langfuse [`observe` decorator](https://langfuse.com/docs/observability/sdk/instrumentation#observe-wrapper) to trace calls made to the [OpenAI Assistants API](https://platform.openai.com/docs/assistants/overview). It covers creating an assistant, running it on a thread, and observing the execution with [Langfuse tracing](https://langfuse.com/docs/tracing).
 
 Note: The native [OpenAI SDK wrapper](https://langfuse.com/integrations/model-providers/openai-py) does not support tracing of the OpenAI assistants API, you need to instrument it via the decorator as shown in this notebook.
diff --git a/content/integrations/model-providers/openai-js.mdx b/content/integrations/model-providers/openai-js.mdx
index 22bc70e90c..9fd5af8c89 100644
--- a/content/integrations/model-providers/openai-js.mdx
+++ b/content/integrations/model-providers/openai-js.mdx
@@ -90,18 +90,6 @@ Done!✨ You now have full observability of your OpenAI calls in Langfuse.
 
 </Steps>
 
-Check out the notebook for end-to-end examples of the integration:
-
-import { FileCode } from "lucide-react";
-
-<Cards>
-  <Card
-    title="Example notebook"
-    href="/guides/cookbook/js_integration_openai"
-    icon={<FileCode />}
-  />
-</Cards>
-
 ## Troubleshooting
 
 ### Queuing and batching of events
@@ -162,6 +150,68 @@ const res = await observeOpenAI(new OpenAI(), {
   mistakenly having multiple clients running.
 </Callout>
 
+### Group multiple generations into a single trace
+
+Use `propagateAttributes` to apply trace attributes (name, tags, metadata) to all observations created inside its callback, and the `startActiveObservation` context manager to group multiple OpenAI generations under a single root span. Input and output are set on the root observation.
+
+```typescript
+import { startActiveObservation, propagateAttributes } from "@langfuse/tracing";
+import OpenAI from "openai";
+import { observeOpenAI } from "@langfuse/openai";
+
+const country = "Germany";
+
+// Propagate trace attributes to all observations created inside the callback
+await propagateAttributes(
+  {
+    traceName: "City poem generator",
+    tags: ["updated"],
+    metadata: { env: "development" },
+  },
+  async () => {
+    await startActiveObservation("user-request", async (span) => {
+      // Generations created inside the callback are automatically nested under the active span
+      const capital = (
+        await observeOpenAI(new OpenAI(), {
+          generationName: "get-capital",
+        }).chat.completions.create({
+          model: "gpt-4o",
+          messages: [
+            { role: "system", content: "What is the capital of the country?" },
+            { role: "user", content: country },
+          ],
+        })
+      ).choices[0].message.content;
+
+      const poem = (
+        await observeOpenAI(new OpenAI(), {
+          generationName: "generate-poem",
+        }).chat.completions.create({
+          model: "gpt-4o",
+          messages: [
+            {
+              role: "system",
+              content: "You are a poet. Create a poem about this city.",
+            },
+            { role: "user", content: capital },
+          ],
+        })
+      ).choices[0].message.content;
+
+      // Set input and output on the root observation
+      span.update({
+        input: country,
+        output: poem,
+      });
+    });
+  },
+);
+```
+
+![Langfuse Trace](/images/cookbook/example-js-sdk/js_integration_openai_grouped.png)
+
+[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/e0a2f2b374a58cf9b4548240f879fc71?timestamp=2025-08-25T14:39:41.599Z&display=details)
+
 ### Link to Langfuse prompts
 
 With [Langfuse Prompt management](/docs/prompt-management/get-started) you can effectively manage and version your prompts. You can link your OpenAI generations to a prompt by passing the `langfusePrompt` property to the `observeOpenAI` function.
diff --git a/content/integrations/model-providers/openai-py.mdx b/content/integrations/model-providers/openai-py.mdx
index 773d5e777e..37f7a0cd2a 100644
--- a/content/integrations/model-providers/openai-py.mdx
+++ b/content/integrations/model-providers/openai-py.mdx
@@ -107,18 +107,6 @@ get_client().auth_check()
 
 _No changes required._
 
-Check out the notebook for end-to-end examples of the integration:
-
-import { FileCode } from "lucide-react";
-
-<Cards>
-  <Card
-    title="Example notebook"
-    href="/guides/cookbook/integration_openai_sdk"
-    icon={<FileCode />}
-  />
-</Cards>
-
 </Steps>
 
 ## Troubleshooting
@@ -200,7 +188,7 @@ export LANGFUSE_TRACING_ENABLED=false
 
 ## Advanced usage
 
-### Custom trace properties
+### Custom trace properties [#custom-trace-properties]
 
 You can add the following properties to the openai method:
 
@@ -380,7 +368,7 @@ get_client().flush()
 
 Since OpenAI beta APIs are changing frequently across versions, we fully support only the stable APIs in the OpenAI SDK. If you are using a beta API, you can still use the Langfuse SDK by wrapping the OpenAI SDK manually with the `@observe()` [decorator](/docs/sdk/python/decorators).
 
-#### Structured Output
+#### Structured Output [#structured-output]
 
 For **structured output parsing**, you have two fully instrumented options depending on your `openai` Python SDK version:
 
diff --git a/content/integrations/model-providers/xai-grok.mdx b/content/integrations/model-providers/xai-grok.mdx
index 527879c838..952a67e9b0 100644
--- a/content/integrations/model-providers/xai-grok.mdx
+++ b/content/integrations/model-providers/xai-grok.mdx
@@ -84,7 +84,7 @@ You can enhance your Grok traces:
 - Use [Langfuse Prompt Management](https://langfuse.com/docs/prompts/get-started) and link prompts to traces
 - Add [score](https://langfuse.com/docs/scores/custom) to traces
 
-Visit the [OpenAI SDK cookbook](https://langfuse.com/guides/cookbook/integration_openai_sdk) to see more examples on passing additional parameters.
+Visit the [OpenAI SDK integration docs](/integrations/model-providers/openai-py#custom-trace-properties) to see more examples on passing additional parameters.
 Find out more about Langfuse Evaluations and Prompt Management in the [Langfuse documentation](https://langfuse.com/docs).
 
 ## Step 6: See Traces in Langfuse
diff --git a/content/integrations/other/meta.json b/content/integrations/other/meta.json
index a90bb469f1..33491ca259 100644
--- a/content/integrations/other/meta.json
+++ b/content/integrations/other/meta.json
@@ -11,7 +11,11 @@
     "librechat",
     "mcp-use",
     "milvus",
+    "mlflow",
     "openclaw",
+    "openinference",
+    "openlit",
+    "openllmetry",
     "parallel-ai",
     "promptfoo",
     "slack",
diff --git a/content/guides/cookbook/otel_integration_mlflow.md b/content/integrations/other/mlflow.md
similarity index 98%
rename from content/guides/cookbook/otel_integration_mlflow.md
rename to content/integrations/other/mlflow.md
index 878b127686..ca5b4bdd54 100644
--- a/content/guides/cookbook/otel_integration_mlflow.md
+++ b/content/integrations/other/mlflow.md
@@ -1,5 +1,7 @@
 ---
 title: MLflow Integration via OpenTelemetry
+sidebarTitle: MLflow
+logo: /images/integrations/opentelemetry_icon.svg
 description: Example cookbook for the MLflow Langfuse integration using OpenTelemetry.
 category: Integrations
 ---
@@ -12,7 +14,6 @@ Langfuse is an [OpenTelemetry backend](https://langfuse.com/docs/opentelemetry/g
 
 Install the necessary Python packages: `openai`, `langfuse`, and `mlflow`. These will allow you to interact with OpenAI as well as setup the instrumentation for tracing.
 
-
 ```python
 %pip install mlflow openai opentelemetry-exporter-otlp-proto-http langfuse --upgrade
 ```
@@ -21,7 +22,6 @@ Install the necessary Python packages: `openai`, `langfuse`, and `mlflow`. These
 
 Before sending any requests, you need to configure your credentials and endpoints. First, set up the Langfuse authentication by providing your public and secret keys. Then, configure the OpenTelemetry exporter endpoint and headers to point to Langfuse's backend. You should also specify your OpenAI API key.
 
-
 ```python
 import os
 import base64
@@ -41,7 +41,6 @@ os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...");
 
 Configure `tracer_provider` and add a span processor to export traces to Langfuse. `OTLPSpanExporter()` uses the endpoint and headers from the environment variables.
 
-
 ```python
 from opentelemetry.sdk.trace import TracerProvider
 from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
@@ -58,12 +57,11 @@ trace.set_tracer_provider(trace_provider)
 tracer = trace.get_tracer(__name__)
 ```
 
-*Explanation:* This block configures the necessary environment variables. The Langfuse keys are combined and base64 encoded to form an authentication token that is then set in the OTLP headers. Additionally, the OpenTelemetry endpoint is provided to direct trace data to Langfuse's backend.
+_Explanation:_ This block configures the necessary environment variables. The Langfuse keys are combined and base64 encoded to form an authentication token that is then set in the OTLP headers. Additionally, the OpenTelemetry endpoint is provided to direct trace data to Langfuse's backend.
 
 ## Step 3: Initialize Instrumentation
 
-With the environment set up, import the needed libraries and initialize MLflow instrumentation. Have a look at all available instrumentation modules [here](https://mlflow.org/docs/latest/tracing/integrations/). 
-
+With the environment set up, import the needed libraries and initialize MLflow instrumentation. Have a look at all available instrumentation modules [here](https://mlflow.org/docs/latest/tracing/integrations/).
 
 ```python
 import mlflow
@@ -76,7 +74,6 @@ mlflow.openai.autolog()
 
 For this example, we will make a simple chat completion request to the OpenAI Chat API. This will generate trace data that you can later view in the Langfuse dashboard.
 
-
 ```python
 import openai
 
@@ -90,18 +87,12 @@ openai.OpenAI().chat.completions.create(
 )
 ```
 
-
-
-
     ChatCompletion(id='chatcmpl-BD7XRulRhULb7NjGfeOkhN3BB9zud', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content="I don't have real-time data access to provide current weather conditions. I recommend checking a reliable weather website or a weather app for the most accurate and up-to-date information.", refusal=None, role='assistant', annotations=[], audio=None, function_call=None, tool_calls=None))], created=1742466941, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier='default', system_fingerprint='fp_b8bc95a0ac', usage=CompletionUsage(completion_tokens=35, prompt_tokens=23, total_tokens=58, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0)))
 
-
-
 ## Step 5: Pass Additional Attributes (Optional)
 
 Opentelemetry lets you attach a set of attributes to all spans by setting [`set_attribute`](https://opentelemetry.io/docs/languages/python/instrumentation/#add-attributes-to-a-span). This allows you to set properties like a Langfuse Session ID, to group traces into Langfuse Sessions or a User ID, to assign traces to a specific user. You can find a list of all supported attributes in the [here](/docs/opentelemetry/get-started#property-mapping).
 
-
 ```python
 import openai
 
@@ -133,27 +124,26 @@ with tracer.start_as_current_span("OpenAI-Trace") as span:
 
 
     Enhanced observability in Large Language Models (LLMs) plays a crucial role in AI debugging by providing deeper insights into the behavior, performance, and decision-making processes of these models. Here are some key ways in which improved observability aids in debugging:
-    
+
     1. **Monitoring Performance Metrics**: Enhanced observability allows for real-time tracking of various performance metrics such as accuracy, response time, and model drift. By continuously monitoring these indicators, developers can quickly identify when an LLM deviates from expected behavior, enabling timely interventions.
-    
+
     2. **Traceability of Inputs and Outputs**: Improved observability enables the logging and tracking of inputs and outputs, which helps in understanding how specific inputs influence model outputs. This traceability allows engineers to pinpoint the source of unexpected behavior or errors in the model's responses.
-    
+
     3. **Contextual Analysis**: By incorporating contextual information, observability tools can provide insights into the model's processing of input data, including how it interprets the context or nuances. Understanding the reasoning behind a model's output helps identify when and why it may misinterpret queries or generate inappropriate responses.
-    
+
     4. **Understanding Model Decisions**: Enhanced observability often involves explainable AI techniques such as feature importance analysis and attention visualization. These tools help in understanding which parts of the input were most influential in the model's decision-making process, thus revealing biases or weaknesses in the model.
-    
+
     5. **Anomaly Detection**: By establishing baselines for normal model behavior, enhanced observability systems can detect anomalies in responses. This capability helps in identifying potential issues that may not be apparent through traditional testing methods or user feedback alone.
-    
+
     6. **Testing and Validation**: Enhanced observability can support ongoing testing and validation of LLMs by providing insights into how the model performs across different scenarios and datasets. This testing can help identify gaps in training data or areas where the model may require additional fine-tuning.
-    
+
     7. **Feedback Loops**: Observability facilitates the creation of feedback loops where user interactions and model performance data can be used to continuously improve the model. This iterative process helps rectify shortcomings and refine the model's outputs over time.
-    
+
     8. **Collaboration and Communication**: Enhanced observability provides a shared understanding of model behavior among teams. This improved visibility fosters better communication between data scientists, engineers, and stakeholders, leading to more informed decision-making regarding model improvements and debugging efforts.
-    
+
     9. **Compliance and Ethical Considerations**: With enhanced observability, it becomes easier to assess models against compliance and ethical standards. Debugging efforts can include the identification of biases or harmful outputs, ensuring the model adheres to ethical AI principles.
-    
-    In summary, enhanced observability in LLMs significantly improves AI debugging by providing comprehensive insights into model behavior, facilitating the identification of issues, and enabling more effective interventions. This ultimately leads to the development of more reliable, robust, and ethical AI systems.
 
+    In summary, enhanced observability in LLMs significantly improves AI debugging by providing comprehensive insights into model behavior, facilitating the identification of issues, and enabling more effective interventions. This ultimately leads to the development of more reliable, robust, and ethical AI systems.
 
 ## Step 6: See Traces in Langfuse
 
diff --git a/content/guides/cookbook/otel_integration_arize.md b/content/integrations/other/openinference.md
similarity index 98%
rename from content/guides/cookbook/otel_integration_arize.md
rename to content/integrations/other/openinference.md
index d4e9325abf..b44a90007a 100644
--- a/content/guides/cookbook/otel_integration_arize.md
+++ b/content/integrations/other/openinference.md
@@ -1,17 +1,18 @@
 ---
 title: Tracing using the OpenInference SDK
+sidebarTitle: OpenInference (Arize)
+logo: /images/integrations/opentelemetry_icon.svg
 description: Example cookbook on using the OpenInference SDK by Arize to trace your application to Langfuse.
 ---
 
 # Tracing using the OpenInference SDK
 
-Langfuse offers an [OpenTelemetry compatible SDK](https://langfuse.com/docs/sdk/python/sdk-v3). With the OpenInference instrumentation library, you can log traces from multiple other frameworks to Langfuse. Below is an example of tracing OpenAI to Langfuse, you can find a full list of supported frameworks [here](https://docs.arize.com/phoenix/tracing/integrations-tracing). To make this example work with other frameworks, you just need to change the instrumentor to match the framework. 
+Langfuse offers an [OpenTelemetry compatible SDK](https://langfuse.com/docs/sdk/python/sdk-v3). With the OpenInference instrumentation library, you can log traces from multiple other frameworks to Langfuse. Below is an example of tracing OpenAI to Langfuse, you can find a full list of supported frameworks [here](https://docs.arize.com/phoenix/tracing/integrations-tracing). To make this example work with other frameworks, you just need to change the instrumentor to match the framework.
 
 ## Step 1: Install Dependencies
 
 Install the necessary Python packages to enable OpenTelemetry tracing, openinference instrumentation, and the OpenAI SDK for making LLM requests.
 
-
 ```python
 %pip install langfuse openai openinference-instrumentation-openai --upgrade
 ```
@@ -22,7 +23,6 @@ Set your Langfuse API keys for the basic auth header. Get your Langfuse API keys
 
 Also, add your `OPENAI_API_KEY` as an environment variable.
 
-
 ```python
 import os
 
@@ -38,12 +38,11 @@ os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...");
 
 With the environment variables set, we can now initialize the Langfuse client. `get_client()` initializes the Langfuse client using the credentials provided in the environment variables.
 
-
 ```python
 from langfuse import get_client
- 
+
 langfuse = get_client()
- 
+
 # Verify connection
 if langfuse.auth_check():
     print("Langfuse client is authenticated and ready!")
@@ -53,12 +52,10 @@ else:
 
     Langfuse client is authenticated and ready!
 
-
 ## Step 3: Initialize Instrumentation
 
 We use the `OpenAIInstrumentor` to instrument the OpenAI SDK. You can replace this with any of the frameworks supported [here](https://docs.arize.com/phoenix/tracing/integrations-tracing)
 
-
 ```python
 from openinference.instrumentation.openai import OpenAIInstrumentor
 
@@ -69,7 +66,6 @@ OpenAIInstrumentor().instrument()
 
 With instrumentation enabled, every OpenAI API call will now be traced. The following example sends a chat completion request to illustrate the integration.
 
-
 ```python
 import openai
 response = openai.OpenAI().chat.completions.create(
@@ -85,29 +81,28 @@ print(response.choices[0].message.content)
 ```
 
     Enhanced observability in large language models (LLMs) plays a crucial role in improving AI debugging by providing deeper insights into model performance, behavior, and internal mechanics. Here are several ways in which enhanced observability can improve AI debugging:
-    
+
     1. **Detailed Monitoring**: Enhanced observability allows developers to monitor various metrics related to model performance in real time, such as accuracy, precision, recall, and F1 scores across different tasks. This helps identify specific areas where the model is underperforming.
-    
+
     2. **Data Drift Detection**: Observability tools can monitor incoming data distributions and detect drift over time. If the data the model encounters changes significantly from the training data, this can lead to degraded performance. Detecting data drift enables timely interventions, such as model retraining or adjustment.
-    
+
     3. **Error Analysis**: Enhanced observability tools can assist in logging and categorizing errors made by the model, such as misclassifications or inappropriate responses. By analyzing these errors, developers can pinpoint patterns and root causes, guiding debugging efforts more effectively.
-    
+
     4. **Model Interpretability**: Observability can be coupled with interpretability tools that provide insights into how a model makes decisions. By understanding the features or tokens that lead to certain outputs, developers can identify whether the model is relying on spurious correlations or misjudgments in particular contexts.
-    
+
     5. **Traceability**: With enhanced observability, it's possible to track the inputs and outputs of the model dynamically. This feature can help reproduce issues and understand the circumstances under which certain errors occur, facilitating quicker resolutions.
-    
+
     6. **User Feedback Integration**: Integrating user feedback into observability systems can provide qualitative insights into model performance. When users provide feedback on model outputs, it can highlight areas of misunderstanding or repeated errors that quantitative metrics may not reveal.
-    
+
     7. **Performance Variability Monitoring**: Tracking performance variability across different populations or contexts can uncover biases or inconsistencies in model behavior. By identifying and understanding these variances, developers can address fairness and ethical concerns more efficiently.
-    
+
     8. **Version Control and Experimentation**: Observability can provide a framework for tracking experiments with different model versions. This allows developers to understand how changes in training data, model architecture, or hyperparameters affect performance, making it easier to identify optimal configurations.
-    
+
     9. **Logging Contextual Information**: By capturing contextual information during model execution (e.g., user queries, environmental variables), developers can better understand the conditions that lead to specific outputs, making debugging more straightforward.
-    
+
     10. **Alerts and Anomaly Detection**: With enhanced observability, systems can automatically alert developers to anomalies in performance, enabling them to investigate and resolve issues proactively, rather than reactively waiting for user reports or performance drops.
-    
-    In conclusion, enhanced observability fosters a more proactive and informed debugging process for large language models. By improving transparency and understanding of the model's behavior, developers can diagnose issues more efficiently, refine model performance, and ultimately build more robust AI solutions.
 
+    In conclusion, enhanced observability fosters a more proactive and informed debugging process for large language models. By improving transparency and understanding of the model's behavior, developers can diagnose issues more efficiently, refine model performance, and ultimately build more robust AI solutions.
 
 ## Step 5: View the Traces in Langfuse
 
diff --git a/content/guides/cookbook/otel_integration_openlit.md b/content/integrations/other/openlit.md
similarity index 98%
rename from content/guides/cookbook/otel_integration_openlit.md
rename to content/integrations/other/openlit.md
index 24c32b247d..5bf40ef656 100644
--- a/content/guides/cookbook/otel_integration_openlit.md
+++ b/content/integrations/other/openlit.md
@@ -1,5 +1,7 @@
 ---
 title: OpenLIT Integration via OpenTelemetry
+sidebarTitle: OpenLIT
+logo: /images/integrations/opentelemetry_icon.svg
 description: Example cookbook for the OpenLIT Langfuse integration using OpenTelemetry.
 category: Integrations
 ---
@@ -12,7 +14,6 @@ Langfuse is an [OpenTelemetry backend](https://langfuse.com/docs/opentelemetry/g
 
 Install the necessary Python packages: `openai`, `langfuse`, and `openlit`. These will allow you to interact with OpenAI as well as setup the instrumentation for tracing.
 
-
 ```python
 %pip install openai langfuse openlit --upgrade
 ```
@@ -21,7 +22,6 @@ Install the necessary Python packages: `openai`, `langfuse`, and `openlit`. Thes
 
 Before sending any requests, you need to configure your credentials and endpoints. First, set up the Langfuse authentication by providing your public and secret keys. Then, configure the OpenTelemetry exporter endpoint and headers to point to Langfuse's backend. You should also specify your OpenAI API key.
 
-
 ```python
 import os
 
@@ -37,12 +37,11 @@ os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...");
 
 With the environment variables set, we can now initialize the Langfuse client. `get_client()` initializes the Langfuse client using the credentials provided in the environment variables.
 
-
 ```python
 from langfuse import get_client
- 
+
 langfuse = get_client()
- 
+
 # Verify connection
 if langfuse.auth_check():
     print("Langfuse client is authenticated and ready!")
@@ -54,7 +53,6 @@ else:
 
 With the environment set up, import the needed libraries and initialize OpenLIT instrumentation. We set `tracer=tracer` to use the tracer we created in the previous step.
 
-
 ```python
 import openlit
 
@@ -66,7 +64,6 @@ openlit.init(disable_batch=True)
 
 For this example, we will make a simple chat completion request to the OpenAI Chat API. This will generate trace data that you can later view in the Langfuse dashboard.
 
-
 ```python
 from openai import OpenAI
 
diff --git a/content/guides/cookbook/otel_integration_openllmetry.md b/content/integrations/other/openllmetry.md
similarity index 98%
rename from content/guides/cookbook/otel_integration_openllmetry.md
rename to content/integrations/other/openllmetry.md
index c29b6c6bec..75403d2cb3 100644
--- a/content/guides/cookbook/otel_integration_openllmetry.md
+++ b/content/integrations/other/openllmetry.md
@@ -1,5 +1,7 @@
 ---
-title: "OpenLLMetry Integration via OpenTelemetry"
+title: OpenLLMetry Integration via OpenTelemetry
+sidebarTitle: OpenLLMetry
+logo: /images/integrations/opentelemetry_icon.svg
 description: Example cookbook for the OpenLLMetry Langfuse integration using OpenTelemetry.
 category: Integrations
 ---
@@ -14,7 +16,6 @@ Langfuse provides a backend built on OpenTelemetry for ingesting trace data, and
 
 Begin by installing the necessary Python packages. In this example, we need the `openai` library to interact with OpenAI’s API and `traceloop-sdk` for enabling OpenLLMetry instrumentation.
 
-
 ```python
 %pip install openai traceloop-sdk langfuse
 ```
@@ -23,7 +24,6 @@ Begin by installing the necessary Python packages. In this example, we need the
 
 Before sending any requests, configure your environment with the necessary credentials and endpoints. Here, we set up Langfuse authentication by combining your public and secret keys into a Base64-encoded token. We also specify the Langfuse endpoint based on your desired [data region](https://langfuse.com/security/data-regions) (EU, US, Japan, or HIPAA) and provide your OpenAI API key.
 
-
 ```python
 import os
 import base64
@@ -49,12 +49,11 @@ os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...");
 
 With the environment variables set, we can now initialize the Langfuse client. `get_client()` initializes the Langfuse client using the credentials provided in the environment variables.
 
-
 ```python
 from langfuse import get_client
- 
+
 langfuse = get_client()
- 
+
 # Verify connection
 if langfuse.auth_check():
     print("Langfuse client is authenticated and ready!")
@@ -64,11 +63,9 @@ else:
 
     Langfuse client is authenticated and ready!
 
-
 ## Step 3: Initialize Instrumentation
 
-Next, initialize the OpenLLMetry instrumentation using the `traceloop-sdk`. Using `disable_batch=True` is recommended if you run this code in a notebook as traces are sent immediately without waiting for batching. 
-
+Next, initialize the OpenLLMetry instrumentation using the `traceloop-sdk`. Using `disable_batch=True` is recommended if you run this code in a notebook as traces are sent immediately without waiting for batching.
 
 ```python
 from traceloop.sdk import Traceloop
@@ -83,12 +80,10 @@ Traceloop.init(disable_batch=True,
 
     ERROR:root:Error initializing redis instrumentor: No module named 'opentelemetry.instrumentation.redis'
 
-
 ## Step 4: Execute a Sample LLM Request
 
 With instrumentation enabled, every OpenAI API call will now be traced. The following example sends a chat completion request to illustrate the integration.
 
-
 ```python
 from openai import OpenAI
 
@@ -109,7 +104,6 @@ print(chat_completion)
 
     ChatCompletion(id='chatcmpl-BjRWj0Gn9A1PdPYslJ9rDNW730I97', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content="LLM observability refers to the practices, tools, and methodologies used to monitor, analyze, and understand the behavior and performance of Large Language Models (LLMs) in real time. As organizations increasingly rely on LLMs for various applications—such as natural language processing, chatbots, content generation, and more—ensuring their reliability, accuracy, and ethical alignment has become critical.\n\nKey components of LLM observability include:\n\n1. **Monitoring Performance**: Tracking metrics such as response time, resource utilization, and throughput to ensure that the model operates efficiently under load.\n\n2. **Quality Analysis**: Evaluating the quality of the model's outputs through various means, including user feedback, automated evaluation metrics, and comparison to ground truth data.\n\n3. **Behavior Analysis**: Analyzing the model's behavior in different contexts to identify biases, unintentional outputs, or other anomalies. This includes examining edge cases where the model might fail or produce unexpected results.\n\n4. **Debugging Tools**: Implementing tools that help trace issues or problems back to specific inputs, configurations, or model parameters that may be causing suboptimal performance.\n\n5. **Data Drift Detection**: Monitoring the input data for changes over time that could affect the model's performance, such as shifts in language use, terminology, or user behavior.\n\n6. **Feedback Loops**: Establishing mechanisms for continuous feedback from users and incorporating that information back into the model development lifecycle for fine-tuning and improvements.\n\n7. **Compliance and Safety**: Ensuring that the model adheres to ethical standards and legal requirements, especially regarding data usage and content generation.\n\nEffective observability can help organizations better manage their LLM deployments, minimize risks, and enhance the overall user experience by ensuring that models perform accurately and reliably in a wide range of scenarios.", refusal=None, role='assistant', annotations=[], audio=None, function_call=None, tool_calls=None))], created=1750170273, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier='default', system_fingerprint='fp_34a54ae93c', usage=CompletionUsage(completion_tokens=368, prompt_tokens=14, total_tokens=382, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0)))
 
-
 ## Step 5: View the Trace in Langfuse
 
 After running the above code, you can review the generated trace in your Langfuse dashboard:
diff --git a/content/library/index.mdx b/content/library/index.mdx
index f9bb2efb90..bceb53ada1 100644
--- a/content/library/index.mdx
+++ b/content/library/index.mdx
@@ -26,7 +26,7 @@ description: A collection of resources, blog posts, and talks to learn more abou
 ## LLMOps
 
 - **What We Learned from a Year of Building with LLMs**, _by Eugene Yan, Bryan Bischof, Charles Frye, Hamel Husain, Jason Liu and Shreya Shankar_, [Part 1](https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/), [Part 2](https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-ii/), [Part 3](https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-iii-strategy/)
-- **Traceability and Observability in Multi-Step LLM Systems**, [webinar](/guides/videos/webinar-observability-llm-systems) _by Marc Klingen_
+- **Traceability and Observability in Multi-Step LLM Systems**, [webinar](/resources/engineering/webinar-observability-llm-systems) _by Marc Klingen_
 - **Data Flywheels for LLM Applications**, [blog post](https://www.sh-reya.com/blog/ai-engineering-flywheel/), _by Shreya Shankar_
 - **Latency optimization**, [cookbook](https://platform.openai.com/docs/guides/latency-optimization), _by OpenAI_
 - **The OSS LLMOps Stack**, [page](https://oss-llmops-stack.com) _by LiteLLM and Langfuse_
diff --git a/content/marketing/enterprise.mdx b/content/marketing/enterprise.mdx
index 72e8a1f9d6..54cd4df09e 100644
--- a/content/marketing/enterprise.mdx
+++ b/content/marketing/enterprise.mdx
@@ -112,7 +112,7 @@ export function TalkToUsButton() {
 
 ### Webinars
 
-- [Traces as the core of the LLMOps workflow (webinar + slides)](/guides/videos/webinar-observability-llm-systems)
+- [Traces as the core of the LLMOps workflow (webinar + slides)](/resources/engineering/webinar-observability-llm-systems)
 - [Continuous Agent Evals with Langfuse and AWS Bedrock AgentCore](https://www.youtube.com/watch?v=api-Z4TfDvM)
 
 ### Resellers and managed Langfuse deployments
diff --git a/content/guides/cookbook/langfuse_sdk_performance_test.md b/content/resources/engineering/langfuse-sdk-performance-test.md
similarity index 98%
rename from content/guides/cookbook/langfuse_sdk_performance_test.md
rename to content/resources/engineering/langfuse-sdk-performance-test.md
index 32aeac3d7f..8b91a5559e 100644
--- a/content/guides/cookbook/langfuse_sdk_performance_test.md
+++ b/content/resources/engineering/langfuse-sdk-performance-test.md
@@ -1,6 +1,6 @@
 ---
 title: Langfuse SDK Performance Test
-category: SDKs
+description: Benchmark of the latency impact of the Langfuse Python SDK and its OpenAI, Langchain, and LlamaIndex integrations.
 ---
 
 # Langfuse SDK Performance Test
@@ -8,23 +8,23 @@ category: SDKs
 Langfuse shall have a minimal impact on latency. This is achieved by running almost entirely in the background and by batching all requests to the Langfuse API.
 
 Coverage of this performance test:
+
 - Langfuse SDK: start_observation(), start_as_current_observation(), @observe decorator
 - Langchain Integration
 - OpenAI Integration
 - LlamaIndex Integration
 
 Limitations:
+
 - We test integrations using OpenAI's hosted models, making the experiment less controlled but actual latency of the integrations impact more realistic.
 - The timing outputs in this notebook are from an older run on a specific machine and network. Absolute numbers will vary, re-run the notebook to benchmark your own environment.
 
 ## Setup
 
-
 ```python
 %pip install langfuse --upgrade
 ```
 
-
 ```python
 import os
 
@@ -38,14 +38,12 @@ os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪
 os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...");
 ```
 
-
 ```python
 from langfuse import get_client
 
 langfuse = get_client()
 ```
 
-
 ```python
 import pandas as pd
 import timeit
@@ -67,14 +65,12 @@ def time_func(func, runs=100):
 
 `start_observation()`, manually creating and ending a root span
 
-
 ```python
 time_func(lambda: langfuse.start_observation(name="perf-span").end())
 ```
 
 `start_observation(as_type="generation")`, nested as a child observation
 
-
 ```python
 root_span = langfuse.start_observation(name="perf-root-span")
 
@@ -85,7 +81,6 @@ root_span.end()
 
 `start_as_current_observation()`, context manager that sets the active observation
 
-
 ```python
 def traced_operation():
     with langfuse.start_as_current_observation(as_type="span", name="perf-span"):
@@ -96,7 +91,6 @@ time_func(traced_operation)
 
 `@observe` decorator, automatically capturing timings, inputs and outputs
 
-
 ```python
 from langfuse import observe
 
@@ -111,12 +105,10 @@ time_func(observed_function)
 
 Docs: https://langfuse.com/integrations/frameworks/langchain
 
-
 ```python
 %pip install langchain langchain-openai --upgrade
 ```
 
-
 ```python
 from langchain_openai import ChatOpenAI
 from langchain.prompts import ChatPromptTemplate
@@ -127,7 +119,6 @@ model = ChatOpenAI(max_tokens=10)
 chain = prompt | model | StrOutputParser()
 ```
 
-
 ```python
 from langfuse.langchain import CallbackHandler
 langfuse_handler = CallbackHandler()
@@ -135,15 +126,11 @@ langfuse_handler = CallbackHandler()
 
 ### Benchmark without Langfuse
 
-
 ```python
 langchain_stats_no_langfuse = time_func(lambda: chain.invoke({"person":"Paul Graham"}))
 langchain_stats_no_langfuse
 ```
 
-
-
-
     count         100.000000
     mean (sec)      0.529463
     std (sec)       0.685193
@@ -154,19 +141,13 @@ langchain_stats_no_langfuse
     max (sec)       7.107237
     dtype: float64
 
-
-
 ### With Langfuse Tracing
 
-
 ```python
 langchain_stats_with_langfuse = time_func(lambda: chain.invoke({"person":"Paul Graham"}, {"callbacks":[langfuse_handler]}))
 langchain_stats_with_langfuse
 ```
 
-
-
-
     count         100.000000
     mean (sec)      0.618286
     std (sec)       0.165149
@@ -177,25 +158,20 @@ langchain_stats_with_langfuse
     max (sec)       1.838614
     dtype: float64
 
-
-
 ## OpenAI Integration
 
 Docs: https://langfuse.com/integrations/model-providers/openai-py
 
-
 ```python
 %pip install langfuse openai --upgrade --quiet
 ```
 
-
 ```python
 import openai
 ```
 
 ### Benchmark without Langfuse
 
-
 ```python
 time_func(lambda: openai.chat.completions.create(
   model="gpt-3.5-turbo",
@@ -206,9 +182,6 @@ time_func(lambda: openai.chat.completions.create(
 ))
 ```
 
-
-
-
     count         100.000000
     mean (sec)      0.524097
     std (sec)       0.220446
@@ -219,16 +192,12 @@ time_func(lambda: openai.chat.completions.create(
     max (sec)       1.789671
     dtype: float64
 
-
-
 ### With Langfuse Tracing
 
-
 ```python
 from langfuse.openai import openai
 ```
 
-
 ```python
 time_func(lambda: openai.chat.completions.create(
   model="gpt-3.5-turbo",
@@ -239,9 +208,6 @@ time_func(lambda: openai.chat.completions.create(
 ))
 ```
 
-
-
-
     count         100.000000
     mean (sec)      0.515243
     std (sec)       0.286902
@@ -252,20 +218,16 @@ time_func(lambda: openai.chat.completions.create(
     max (sec)       2.613779
     dtype: float64
 
-
-
 ## LlamaIndex Integration
 
 Docs: https://langfuse.com/integrations/frameworks/llamaindex
 
-
 ```python
 %pip install llama-index openinference-instrumentation-llama-index --upgrade --quiet
 ```
 
 Sample documents
 
-
 ```python
 from llama_index.core import Document
 
@@ -281,7 +243,6 @@ Throughout his career, Silverstein has been celebrated for his diverse range of
 
 Index
 
-
 ```python
 # Example index construction + LLM query
 from llama_index.core import VectorStoreIndex
@@ -289,9 +250,6 @@ from llama_index.core import VectorStoreIndex
 time_func(lambda: VectorStoreIndex.from_documents([doc1,doc2]))
 ```
 
-
-
-
     count         100.000000
     mean (sec)      0.171673
     std (sec)       0.058332
@@ -302,19 +260,13 @@ time_func(lambda: VectorStoreIndex.from_documents([doc1,doc2]))
     max (sec)       0.459417
     dtype: float64
 
-
-
 Query
 
-
 ```python
 index = VectorStoreIndex.from_documents([doc1,doc2])
 time_func(lambda: index.as_query_engine().query("What did he do growing up?"))
 ```
 
-
-
-
     count         100.000000
     mean (sec)      0.795817
     std (sec)       0.338263
@@ -325,11 +277,8 @@ time_func(lambda: index.as_query_engine().query("What did he do growing up?"))
     max (sec)       3.495263
     dtype: float64
 
-
-
 ### With Langfuse Tracing
 
-
 ```python
 from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
 
@@ -339,14 +288,12 @@ LlamaIndexInstrumentor().instrument()
 
 Index
 
-
 ```python
 time_func(lambda: VectorStoreIndex.from_documents([doc1,doc2]))
 ```
 
 Query
 
-
 ```python
 index = VectorStoreIndex.from_documents([doc1,doc2])
 time_func(lambda: index.as_query_engine().query("What did he do growing up?"))
diff --git a/content/resources/engineering/meta.json b/content/resources/engineering/meta.json
index a06e11a28f..4640d1e274 100644
--- a/content/resources/engineering/meta.json
+++ b/content/resources/engineering/meta.json
@@ -30,6 +30,9 @@
     "prompt-cicd",
     "golden-dataset-evaluation",
     "chatbot-intent-analytics",
-    "langfuse-for-product-teams"
+    "langfuse-for-product-teams",
+    "langfuse-sdk-performance-test",
+    "prompt-management-performance-benchmark",
+    "webinar-observability-llm-systems"
   ]
 }
diff --git a/content/guides/cookbook/prompt_management_performance_benchmark.md b/content/resources/engineering/prompt-management-performance-benchmark.md
similarity index 98%
rename from content/guides/cookbook/prompt_management_performance_benchmark.md
rename to content/resources/engineering/prompt-management-performance-benchmark.md
index 4097b6a027..a0fffb3d7a 100644
--- a/content/guides/cookbook/prompt_management_performance_benchmark.md
+++ b/content/resources/engineering/prompt-management-performance-benchmark.md
@@ -1,7 +1,6 @@
 ---
 title: Prompt Management Performance Benchmark
 description: Performance benchmark on Langfuse Prompt Management measuring latency of retrieving and compiling prompts.
-category: Prompt Management
 ---
 
 # Langfuse Prompt Management Performance Test
@@ -14,12 +13,10 @@ The test accounts for network latency, so absolute values may vary based on geog
 
 The test requires a prompt named `perf-test` to be set up in the authenticated project.
 
-
 ```python
 %pip install langfuse
 ```
 
-
 ```python
 import os
 
@@ -31,7 +28,6 @@ os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪
 # Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
 ```
 
-
 ```python
 import time
 import pandas as pd
@@ -46,7 +42,6 @@ langfuse = Langfuse()
 assert langfuse.auth_check(), "Langfuse client not initialized – check your environment variables."
 ```
 
-
 ```python
 N_RUNS = 1_000
 prompt_name = "perf-test"
@@ -62,7 +57,6 @@ for _ in tqdm(range(N_RUNS), desc="Benchmarking"):
 durations_series = pd.Series(durations, name="seconds")
 ```
 
-
 ```python
 stats = durations_series.describe(percentiles=[0.25, 0.5, 0.75, 0.99])
 stats
@@ -82,7 +76,6 @@ min         0.032702 sec
 max         0.409609 sec
 ```
 
-
 ```python
 plt.figure(figsize=(8,4))
 plt.hist(durations_series, bins=30)
diff --git a/content/guides/videos/webinar-observability-llm-systems.mdx b/content/resources/engineering/webinar-observability-llm-systems.mdx
similarity index 100%
rename from content/guides/videos/webinar-observability-llm-systems.mdx
rename to content/resources/engineering/webinar-observability-llm-systems.mdx
diff --git a/cookbook/_routes.json b/cookbook/_routes.json
index 4803e99e59..98690a97b8 100644
--- a/cookbook/_routes.json
+++ b/cookbook/_routes.json
@@ -4,20 +4,10 @@
     "docsPath": null,
     "isGuide": true
   },
-  {
-    "notebook": "integration_langchain.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
-  {
-    "notebook": "integration_langserve.ipynb",
-    "docsPath": "integrations/frameworks/langserve",
-    "isGuide": true
-  },
   {
     "notebook": "integration_langgraph.ipynb",
-    "docsPath": null,
-    "isGuide": true
+    "docsPath": "integrations/frameworks/langgraph",
+    "isGuide": false
   },
   {
     "notebook": "integration_langchain_deepagents.ipynb",
@@ -29,75 +19,20 @@
     "docsPath": null,
     "isGuide": true
   },
-  {
-    "notebook": "js_integration_langchain.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
-  {
-    "notebook": "integration_openai_sdk.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
   {
     "notebook": "integration_openai_assistants.ipynb",
     "docsPath": "integrations/model-providers/openai-assistants-api",
-    "isGuide": true
-  },
-  {
-    "notebook": "integration_openai_structured_output.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
-  {
-    "notebook": "js_integration_openai.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
-  {
-    "notebook": "integration_llama-index-callback.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
-  {
-    "notebook": "integration_llama-index_instrumentation.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
-  {
-    "notebook": "integration_llama-index_milvus-lite.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
-  {
-    "notebook": "evaluation_with_langchain.ipynb",
-    "docsPath": null,
-    "isGuide": true
+    "isGuide": false
   },
   {
     "notebook": "example_pydantic_ai_mcp_agent_evaluation.ipynb",
     "docsPath": null,
     "isGuide": true
   },
-  {
-    "notebook": "integration_azure_openai_langchain.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
   {
     "notebook": "langfuse_sdk_performance_test.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
-  {
-    "notebook": "integration_litellm_proxy.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
-  {
-    "notebook": "js_integration_litellm_proxy.ipynb",
-    "docsPath": null,
-    "isGuide": true
+    "docsPath": "resources/engineering/langfuse-sdk-performance-test",
+    "isGuide": false
   },
   {
     "notebook": "evaluation_of_rag_with_ragas.ipynb",
@@ -119,16 +54,6 @@
     "docsPath": null,
     "isGuide": true
   },
-  {
-    "notebook": "evaluation_with_uptrain.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
-  {
-    "notebook": "example_decorator_openai_langchain.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
   {
     "notebook": "integration_instructor.ipynb",
     "docsPath": "integrations/frameworks/instructor",
@@ -159,11 +84,6 @@
     "docsPath": "integrations/frameworks/dspy",
     "isGuide": false
   },
-  {
-    "notebook": "integration_llama_index_posthog_mistral.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
   {
     "notebook": "example_intent_classification_pipeline.ipynb",
     "docsPath": null,
@@ -221,13 +141,13 @@
   },
   {
     "notebook": "otel_integration_openllmetry.ipynb",
-    "docsPath": null,
-    "isGuide": true
+    "docsPath": "integrations/other/openllmetry",
+    "isGuide": false
   },
   {
     "notebook": "otel_integration_openlit.ipynb",
-    "docsPath": null,
-    "isGuide": true
+    "docsPath": "integrations/other/openlit",
+    "isGuide": false
   },
   {
     "notebook": "integration_beeai.ipynb",
@@ -257,12 +177,12 @@
   {
     "notebook": "integration_llamaindex_workflows.ipynb",
     "docsPath": "integrations/frameworks/llamaindex-workflows",
-    "isGuide": true
+    "isGuide": false
   },
   {
     "notebook": "otel_integration_arize.ipynb",
-    "docsPath": null,
-    "isGuide": true
+    "docsPath": "integrations/other/openinference",
+    "isGuide": false
   },
   {
     "notebook": "integration_firecrawl.ipynb",
@@ -289,11 +209,6 @@
     "docsPath": "integrations/model-providers/fireworks-ai",
     "isGuide": false
   },
-  {
-    "notebook": "integration_databricks.ipynb",
-    "docsPath": null,
-    "isGuide": true
-  },
   {
     "notebook": "integration_novitaai.ipynb",
     "docsPath": "integrations/model-providers/novitaai",
@@ -311,8 +226,8 @@
   },
   {
     "notebook": "otel_integration_mlflow.ipynb",
-    "docsPath": null,
-    "isGuide": true
+    "docsPath": "integrations/other/mlflow",
+    "isGuide": false
   },
   {
     "notebook": "example_langgraph_agents.ipynb",
@@ -352,27 +267,27 @@
   {
     "notebook": "integration_llama_index.ipynb",
     "docsPath": "integrations/frameworks/llamaindex",
-    "isGuide": true
+    "isGuide": false
   },
   {
     "notebook": "integration_amazon_bedrock.ipynb",
     "docsPath": "integrations/model-providers/amazon-bedrock",
-    "isGuide": true
+    "isGuide": false
   },
   {
     "notebook": "prompt_management_performance_benchmark.ipynb",
-    "docsPath": null,
-    "isGuide": true
+    "docsPath": "resources/engineering/prompt-management-performance-benchmark",
+    "isGuide": false
   },
   {
     "notebook": "integration_anthropic.ipynb",
     "docsPath": "integrations/model-providers/anthropic",
-    "isGuide": true
+    "isGuide": false
   },
   {
     "notebook": "js_integration_anthropic.ipynb",
     "docsPath": "integrations/model-providers/anthropic-js",
-    "isGuide": true
+    "isGuide": false
   },
   {
     "notebook": "integration_byteplus.ipynb",
diff --git a/cookbook/evaluation_with_langchain.ipynb b/cookbook/evaluation_with_langchain.ipynb
deleted file mode 100644
index b58036443d..0000000000
--- a/cookbook/evaluation_with_langchain.ipynb
+++ /dev/null
@@ -1,376 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "SWL354n0DECo"
-      },
-      "source": [
-        "---\n",
-        "description: Cookbook that demonstrates how to run Langchain evaluations on data in Langfuse.\n",
-        "category: Evaluation\n",
-        "---\n",
-        "\n",
-        "# Run Langchain Evaluations on data in Langfuse\n",
-        "\n",
-        "This cookbook shows how model-based evaluations can be used to automate the evaluation of production completions in Langfuse. This example uses Langchain and is adaptable to other libraries. Which library is the best to use depends heavily on the use case.\n",
-        "\n",
-        "This cookbook follows three steps:\n",
-        "1. Fetch production `generations` stored in Langfuse\n",
-        "2. Evaluate these `generations` using Langchain\n",
-        "3. Ingest results back into Langfuse as `scores`\n",
-        "\n",
-        "\n",
-        "----\n",
-        "Not using Langfuse yet? [Get started](https://langfuse.com/docs/get-started) by capturing LLM events."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "WbfTYaTkEu3G"
-      },
-      "source": [
-        "### Setup\n",
-        "\n",
-        "First you need to install Langfuse and Langchain via pip and then set the environment variables."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Qclwxd9LRPAL"
-      },
-      "outputs": [],
-      "source": [
-        "%pip install langfuse langchain langchain-classic langchain-openai --upgrade"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "pUptRFgoRQJW"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "# Get keys for your project from the project settings page: https://cloud.langfuse.com\n",
-        "os.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-...\");\n",
-        "os.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"sk-lf-...\");\n",
-        "os.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n",
-        "# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n",
-        "\n",
-        "# Your openai key\n",
-        "os.environ.setdefault(\"OPENAI_API_KEY\", \"sk-proj-...\");"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "CQhmQQpLRa1K"
-      },
-      "outputs": [],
-      "source": [
-        "os.environ['EVAL_MODEL'] = \"gpt-3.5-turbo-instruct\"\n",
-        "\n",
-        "# Langchain Eval types\n",
-        "EVAL_TYPES={\n",
-        "    \"hallucination\": True,\n",
-        "    \"conciseness\": True,\n",
-        "    \"relevance\": True,\n",
-        "    \"coherence\": True,\n",
-        "    \"harmfulness\": True,\n",
-        "    \"maliciousness\": True,\n",
-        "    \"helpfulness\": True,\n",
-        "    \"controversiality\": True,\n",
-        "    \"misogyny\": True,\n",
-        "    \"criminality\": True,\n",
-        "    \"insensitivity\": True\n",
-        "}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Yiwrz1-mavJ4"
-      },
-      "source": [
-        "Initialize the Langfuse Python SDK, more information [here](https://langfuse.com/docs/sdk/python#1-installation)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "id": "8viV4KT5RMjA"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Langfuse client is authenticated and ready!\n"
-          ]
-        }
-      ],
-      "source": [
-        "from langfuse import get_client\n",
-        " \n",
-        "langfuse = get_client()\n",
-        " \n",
-        "# Verify connection\n",
-        "if langfuse.auth_check():\n",
-        "    print(\"Langfuse client is authenticated and ready!\")\n",
-        "else:\n",
-        "    print(\"Authentication failed. Please check your credentials and host.\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bjMZ1VLhF2Vv"
-      },
-      "source": [
-        "### Fetching data\n",
-        "\n",
-        "Load all `generations` from Langfuse filtered by `name`, in this case `OpenAI`. Names are used in Langfuse to identify different types of generations within an application. Change it to the name you want to evaluate.\n",
-        "\n",
-        "Checkout [docs](https://langfuse.com/docs/sdk/python#generation) on how to set the name when ingesting an LLM Generation."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "id": "3r3jOEX0RvXi"
-      },
-      "outputs": [],
-      "source": [
-        "def fetch_all_pages(name=None, user_id = None, limit=50):\n",
-        "    page = 1\n",
-        "    all_data = []\n",
-        "\n",
-        "    while True:\n",
-        "        response = langfuse.api.trace.list(name=name, limit=limit, user_id=user_id, page=page)\n",
-        "        if not response.data:\n",
-        "            break\n",
-        "\n",
-        "        all_data.extend(response.data)\n",
-        "        page += 1\n",
-        "\n",
-        "    return all_data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 35,
-      "metadata": {
-        "id": "cAnLShvjBDBU"
-      },
-      "outputs": [],
-      "source": [
-        "generations = fetch_all_pages(user_id='user_123')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 40,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "'adb5ba6beab14984ab89006ee09e9cd6'"
-            ]
-          },
-          "execution_count": 40,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "generations[0].id"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hYM6UG_dGbb6"
-      },
-      "source": [
-        "### Set up evaluation functions\n",
-        "\n",
-        "In this section, we define functions to set up the Langchain eval based on the entries in `EVAL_TYPES`. Hallucinations require their own function. More on the Langchain evals can be found [here](https://python.langchain.com/docs/guides/evaluation/string/criteria_eval_chain)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 37,
-      "metadata": {
-        "id": "7NijTmslvyK8"
-      },
-      "outputs": [],
-      "source": [
-        "from langchain_classic.evaluation import load_evaluator\n",
-        "from langchain_openai import OpenAI\n",
-        "from langchain_classic.evaluation.criteria import LabeledCriteriaEvalChain\n",
-        "\n",
-        "def get_evaluator_for_key(key: str):\n",
-        "  llm = OpenAI(temperature=0, model=os.environ.get('EVAL_MODEL'))\n",
-        "  return load_evaluator(\"criteria\", criteria=key, llm=llm)\n",
-        "\n",
-        "def get_hallucination_eval():\n",
-        "  criteria = {\n",
-        "    \"hallucination\": (\n",
-        "      \"Does this submission contain information\"\n",
-        "      \" not present in the input or reference?\"\n",
-        "    ),\n",
-        "  }\n",
-        "  llm = OpenAI(temperature=0, model=os.environ.get('EVAL_MODEL'))\n",
-        "\n",
-        "  return LabeledCriteriaEvalChain.from_llm(\n",
-        "      llm=llm,\n",
-        "      criteria=criteria,\n",
-        "  )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tzZZfztGdrIQ"
-      },
-      "source": [
-        "### Execute evaluation\n",
-        "\n",
-        "Below, we execute the evaluation for each `Generation` loaded above. Each score is ingested into Langfuse via [`langfuse.score()`](https://langfuse.com/docs/scores).\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "qMa2OEtqvyGg"
-      },
-      "outputs": [],
-      "source": [
-        "def execute_eval_and_score():\n",
-        "\n",
-        "  for generation in generations:\n",
-        "    criteria = [key for key, value in EVAL_TYPES.items() if value and key != \"hallucination\"]\n",
-        "\n",
-        "    for criterion in criteria:\n",
-        "      eval_result = get_evaluator_for_key(criterion).evaluate_strings(\n",
-        "          prediction=generation.output,\n",
-        "          input=generation.input,\n",
-        "      )\n",
-        "      print(eval_result)\n",
-        "\n",
-        "      langfuse.create_score(name=criterion, trace_id=generation.id, observation_id=generation.id, value=eval_result[\"score\"], comment=eval_result['reasoning'])\n",
-        "\n",
-        "execute_eval_and_score()\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 28,
-      "metadata": {
-        "id": "YcTF-z8eeL0a"
-      },
-      "outputs": [],
-      "source": [
-        "# hallucination\n",
-        "\n",
-        "def eval_hallucination():\n",
-        "\n",
-        "  chain = get_hallucination_eval()\n",
-        "\n",
-        "  for generation in generations:\n",
-        "    eval_result = chain.evaluate_strings(\n",
-        "      prediction=generation.output,\n",
-        "      input=generation.input,\n",
-        "      reference=generation.input\n",
-        "    )\n",
-        "    print(eval_result)\n",
-        "    if eval_result is not None and eval_result[\"score\"] is not None and eval_result[\"reasoning\"] is not None:\n",
-        "      langfuse.create_score(name='hallucination', trace_id=generation.id, observation_id=generation.id, value=eval_result[\"score\"], comment=eval_result['reasoning'])\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 29,
-      "metadata": {
-        "id": "n4zeFEKlfjQ-"
-      },
-      "outputs": [],
-      "source": [
-        "if EVAL_TYPES.get(\"hallucination\") == True:\n",
-        "  eval_hallucination()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 30,
-      "metadata": {
-        "id": "-ROOd8d8rdl6"
-      },
-      "outputs": [],
-      "source": [
-        "# SDK is async, make sure to await all requests\n",
-        "langfuse.flush()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MsKpVyYdavJ5"
-      },
-      "source": [
-        "### See Scores in Langfuse\n",
-        "\n",
-        " In the Langfuse UI, you can filter Traces by `Scores` and look into the details for each. Check out Langfuse Analytics to understand the impact of new prompt versions or application releases on these scores.\n",
-        "\n",
-        "![Image of Trace](https://langfuse.com/images/docs/trace-conciseness-score.jpg)\n",
-        "_Example trace with conciseness score_\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "CkeLD_ciVD2w"
-      },
-      "source": [
-        "## Get in touch\n",
-        "\n",
-        "Looking for a specific way to score your production data in Langfuse? Join the [Discord](https://langfuse.com/discord) and discuss your use case!"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": ".venv",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.13.2"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
diff --git a/cookbook/evaluation_with_uptrain.ipynb b/cookbook/evaluation_with_uptrain.ipynb
deleted file mode 100644
index 050e420a4c..0000000000
--- a/cookbook/evaluation_with_uptrain.ipynb
+++ /dev/null
@@ -1,674 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hIDdGw6hepiF"
-      },
-      "source": [
-        "---\n",
-        "description: This notebook demonstrates how to run UpTrain's evaluation metrics on the traces generated by Langfuse.\n",
-        "category: Evaluation\n",
-        "---\n",
-        "\n",
-        "# Evaluate Langfuse LLM Traces with UpTrain\n",
-        "\n",
-        "UpTrain's open-source library offers a series of evaluation metrics to assess LLM applications.\n",
-        "\n",
-        "This notebook demonstrates how to run UpTrain's evaluation metrics on the traces generated by Langfuse. In Langfuse you can then monitor these scores over time or use them to compare different experiments."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Setup\n",
-        "\n",
-        "You can get your Langfuse API keys [here](https://cloud.langfuse.com/) and OpenAI API key [here](https://platform.openai.com/api-keys)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%pip install langfuse datasets uptrain litellm openai rouge_score --upgrade"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "R1nkKfEUepiH"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "# Get keys for your project from the project settings page: https://cloud.langfuse.com\n",
-        "os.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-...\");\n",
-        "os.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"sk-lf-...\");\n",
-        "os.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n",
-        "# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n",
-        "\n",
-        "# Your openai key\n",
-        "os.environ.setdefault(\"OPENAI_API_KEY\", \"sk-proj-...\");"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6lfVmRQlepiI"
-      },
-      "source": [
-        "## Sample Dataset\n",
-        "\n",
-        "We use this dataset to represent [traces](https://langfuse.com/docs/tracing) that you have logged to Langfuse. In a production environment, you would use your own data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "N-wEyOQzepiI",
-        "outputId": "48b3948c-22f1-4802-d5e7-2f3c6eb9689d"
-      },
-      "outputs": [],
-      "source": [
-        "data = [\n",
-        "    {\n",
-        "        \"question\": \"What are the symptoms of a heart attack?\",\n",
-        "        \"context\": \"A heart attack, or myocardial infarction, occurs when the blood supply to the heart muscle is blocked. Chest pain is a good symptom of heart attack, though there are many others.\",\n",
-        "        \"response\": \"Symptoms of a heart attack may include chest pain or discomfort, shortness of breath, nausea, lightheadedness, and pain or discomfort in one or both arms, the jaw, neck, or back.\"\n",
-        "    },\n",
-        "    {\n",
-        "        \"question\": \"Can stress cause physical health problems?\",\n",
-        "        \"context\": \"Stress is the body's response to challenges or threats. Yes, chronic stress can contribute to various physical health problems, including cardiovascular issues.\",\n",
-        "        \"response\": \"Yes, chronic stress can contribute to various physical health problems, including cardiovascular issues, and a weakened immune system.\"\n",
-        "    },\n",
-        "    {\n",
-        "        'question': \"What are the symptoms of a heart attack?\",\n",
-        "        'context': \"A heart attack, or myocardial infarction, occurs when the blood supply to the heart muscle is blocked. Symptoms of a heart attack may include chest pain or discomfort, shortness of breath and nausea.\",\n",
-        "        'response': \"Heart attack symptoms are usually just indigestion and can be relieved with antacids.\"\n",
-        "    },\n",
-        "    {\n",
-        "        'question': \"Can stress cause physical health problems?\",\n",
-        "        'context': \"Stress is the body's response to challenges or threats. Yes, chronic stress can contribute to various physical health problems, including cardiovascular issues.\",\n",
-        "        'response': \"Stress is not real, it is just imaginary!\"\n",
-        "    }\n",
-        "]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Run Evaluations using UpTrain\n",
-        "\n",
-        "We have used the following 3 metrics from UpTrain's open-source library:\n",
-        "\n",
-        "1. [Context Relevance](https://docs.uptrain.ai/predefined-evaluations/context-awareness/context-relevance): Evaluates how relevant the retrieved context is to the question specified.\n",
-        "\n",
-        "2. [Factual Accuracy](https://docs.uptrain.ai/predefined-evaluations/context-awareness/factual-accuracy): Evaluates whether the response generated is factually correct and grounded by the provided context.\n",
-        "\n",
-        "3. [Response Completeness](https://docs.uptrain.ai/predefined-evaluations/response-quality/response-completeness): Evaluates whether the response has answered all the aspects of the question specified.\n",
-        "\n",
-        "You can look at the complete list of UpTrain's supported metrics [here](https://docs.uptrain.ai/predefined-evaluations/overview)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\u001b[32m2025-06-17 10:43:14.568\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36muptrain.operators.language.llm\u001b[0m:\u001b[36mfetch_responses\u001b[0m:\u001b[36m268\u001b[0m - \u001b[33m\u001b[1mDetected a running event loop, scheduling requests in a separate thread.\u001b[0m\n",
-            "100%|██████████| 4/4 [00:01<00:00,  2.85it/s]\n",
-            "/Users/jannik/Documents/GitHub/langfuse-docs/.venv/lib/python3.13/site-packages/uptrain/operators/language/llm.py:271: RuntimeWarning: coroutine 'LLMMulticlient.async_fetch_responses' was never awaited\n",
-            "  with ThreadPoolExecutor(max_workers=1) as executor:\n",
-            "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n",
-            "\u001b[32m2025-06-17 10:43:15.996\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36muptrain.operators.language.llm\u001b[0m:\u001b[36mfetch_responses\u001b[0m:\u001b[36m268\u001b[0m - \u001b[33m\u001b[1mDetected a running event loop, scheduling requests in a separate thread.\u001b[0m\n",
-            "100%|██████████| 4/4 [00:01<00:00,  2.74it/s]\n",
-            "\u001b[32m2025-06-17 10:43:17.464\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36muptrain.operators.language.llm\u001b[0m:\u001b[36mfetch_responses\u001b[0m:\u001b[36m268\u001b[0m - \u001b[33m\u001b[1mDetected a running event loop, scheduling requests in a separate thread.\u001b[0m\n",
-            "100%|██████████| 4/4 [00:03<00:00,  1.19it/s]\n",
-            "\u001b[32m2025-06-17 10:43:20.860\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36muptrain.operators.language.llm\u001b[0m:\u001b[36mfetch_responses\u001b[0m:\u001b[36m268\u001b[0m - \u001b[33m\u001b[1mDetected a running event loop, scheduling requests in a separate thread.\u001b[0m\n",
-            "100%|██████████| 4/4 [00:01<00:00,  3.13it/s]\n",
-            "\u001b[32m2025-06-17 10:43:22.148\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36muptrain.framework.evalllm\u001b[0m:\u001b[36mevaluate\u001b[0m:\u001b[36m376\u001b[0m - \u001b[1mLocal server not running, start the server to log data and visualize in the dashboard!\u001b[0m\n"
-          ]
-        }
-      ],
-      "source": [
-        "from uptrain import EvalLLM, Evals\n",
-        "import json\n",
-        "import pandas as pd\n",
-        "\n",
-        "eval_llm = EvalLLM(openai_api_key=os.environ[\"OPENAI_API_KEY\"])\n",
-        "\n",
-        "res = eval_llm.evaluate(\n",
-        "    data = data,\n",
-        "    checks = [Evals.CONTEXT_RELEVANCE, Evals.FACTUAL_ACCURACY, Evals.RESPONSE_COMPLETENESS]\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Using Langfuse\n",
-        "\n",
-        "There are two main ways to run evaluations:\n",
-        "\n",
-        "1. Score each Trace (in development): This means you will run the UpTrain evaluations for each trace item.\n",
-        "\n",
-        "2. Score in Batches (in production): In this method we will simulate fetching production traces on a periodic basis to score them using the UpTrain evaluators. Often, you'll want to sample the traces instead of scoring all of them to control evaluation costs."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Development: Score each trace while it's created"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Langfuse client is authenticated and ready!\n"
-          ]
-        }
-      ],
-      "source": [
-        "from langfuse import get_client\n",
-        " \n",
-        "langfuse = get_client()\n",
-        " \n",
-        "# Verify connection\n",
-        "if langfuse.auth_check():\n",
-        "    print(\"Langfuse client is authenticated and ready!\")\n",
-        "else:\n",
-        "    print(\"Authentication failed. Please check your credentials and host.\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We mock the instrumentation of your application by using the sample dataset. See the [quickstart](https://langfuse.com/docs/get-started) to integrate Langfuse with your application."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# start a new trace when you get a question\n",
-        "question = data[0]['question']\n",
-        "context = data[0]['context']\n",
-        "response = data[0]['response']\n",
-        "\n",
-        "with langfuse.start_as_current_observation(as_type=\"span\", name=\"uptrain trace\") as trace:\n",
-        "    # Store trace_id for later use\n",
-        "    trace_id = trace.trace_id\n",
-        "    \n",
-        "    # retrieve the relevant chunks\n",
-        "    # chunks = get_similar_chunks(question)\n",
-        "    \n",
-        "    # pass it as span\n",
-        "    with trace.start_as_current_observation(\n",
-        "        name=\"retrieval\", \n",
-        "        input={'question': question}, \n",
-        "        output={'context': context}\n",
-        "    ):\n",
-        "        pass\n",
-        "\n",
-        "    # use llm to generate a answer with the chunks\n",
-        "    # answer = get_response_from_llm(question, chunks)\n",
-        "    \n",
-        "    with trace.start_as_current_observation(\n",
-        "        name=\"generation\", \n",
-        "        input={'question': question, 'context': context}, \n",
-        "        output={'response': response}\n",
-        "    ):\n",
-        "        pass"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We reuse the scores previously calculated for the traces in the sample dataset. In development, you would run the UpTrain evaluations for the single trace as it's created."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "langfuse.create_score(name='context_relevance', value=res[0]['score_context_relevance'], trace_id=trace_id)\n",
-        "langfuse.create_score(name='factual_accuracy', value=res[0]['score_factual_accuracy'], trace_id=trace_id)\n",
-        "langfuse.create_score(name='response_completeness', value=res[0]['score_response_completeness'], trace_id=trace_id)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![UpTrain Evals on a single trace in Langfuse](https://langfuse.com/images/cookbook/uptrain-trace.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Production: Add scores to traces in batches"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To simulate a production environment, we will log our sample dataset to Langfuse."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "for interaction in data:\n",
-        "    with langfuse.start_as_current_observation(as_type=\"span\", name=\"uptrain batch\") as trace:\n",
-        "        with trace.start_as_current_observation(\n",
-        "            name=\"retrieval\",\n",
-        "            input={'question': interaction['question']},\n",
-        "            output={'context': interaction['context']}\n",
-        "        ):\n",
-        "            pass\n",
-        "        \n",
-        "        with trace.start_as_current_observation(\n",
-        "            name=\"generation\",\n",
-        "            input={'question': interaction['question'], 'context': interaction['context']},\n",
-        "            output={'response': interaction['response']}\n",
-        "        ):\n",
-        "            pass\n",
-        " \n",
-        "# await that Langfuse SDK has processed all events before trying to retrieve it in the next step\n",
-        "langfuse.flush()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can now retrieve the traces like regular production data and evaluate them using UpTrain."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def get_traces(name=None, limit=10000, user_id=None):\n",
-        "    all_data = []\n",
-        "    page = 1\n",
-        " \n",
-        "    while True:\n",
-        "        response = langfuse.api.trace.list(\n",
-        "            name=name, page=page, user_id=user_id, order_by=None\n",
-        "        )\n",
-        "        if not response.data:\n",
-        "            break\n",
-        "        page += 1\n",
-        "        all_data.extend(response.data)\n",
-        "        if len(all_data) > limit:\n",
-        "            break\n",
-        " \n",
-        "    return all_data[:limit]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Optional: create a random sample to reduce evaluation costs."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from random import sample\n",
-        " \n",
-        "NUM_TRACES_TO_SAMPLE = 4\n",
-        "traces = get_traces(name=\"uptrain batch\")\n",
-        "traces_sample = sample(traces, NUM_TRACES_TO_SAMPLE)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Convert the data into a dataset to be used for evaluation with UpTrain."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "evaluation_batch = {\n",
-        "    \"question\": [],\n",
-        "    \"context\": [],\n",
-        "    \"response\": [],\n",
-        "    \"trace_id\": [],\n",
-        "}\n",
-        " \n",
-        "for t in traces_sample:\n",
-        "    observations = [langfuse.api.observations.get(o) for o in t.observations]\n",
-        "    for o in observations:\n",
-        "        if o.name == 'retrieval':\n",
-        "            question = o.input['question']\n",
-        "            context = o.output['context']\n",
-        "        if o.name=='generation':\n",
-        "            answer = o.output['response']\n",
-        "    evaluation_batch['question'].append(question)\n",
-        "    evaluation_batch['context'].append(context)\n",
-        "    evaluation_batch['response'].append(response)\n",
-        "    evaluation_batch['trace_id'].append(t.id)\n",
-        "\n",
-        "\n",
-        "data = [dict(zip(evaluation_batch,t)) for t in zip(*evaluation_batch.values())]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Evaluate the batch using UpTrain."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 19,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\u001b[32m2025-06-17 10:46:35.647\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36muptrain.operators.language.llm\u001b[0m:\u001b[36mfetch_responses\u001b[0m:\u001b[36m268\u001b[0m - \u001b[33m\u001b[1mDetected a running event loop, scheduling requests in a separate thread.\u001b[0m\n",
-            "100%|██████████| 4/4 [00:01<00:00,  3.06it/s]\n",
-            "/Users/jannik/Documents/GitHub/langfuse-docs/.venv/lib/python3.13/site-packages/uptrain/operators/language/llm.py:271: RuntimeWarning: coroutine 'LLMMulticlient.async_fetch_responses' was never awaited\n",
-            "  with ThreadPoolExecutor(max_workers=1) as executor:\n",
-            "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n",
-            "\u001b[32m2025-06-17 10:46:36.963\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36muptrain.operators.language.llm\u001b[0m:\u001b[36mfetch_responses\u001b[0m:\u001b[36m268\u001b[0m - \u001b[33m\u001b[1mDetected a running event loop, scheduling requests in a separate thread.\u001b[0m\n",
-            "100%|██████████| 4/4 [00:02<00:00,  1.88it/s]\n",
-            "\u001b[32m2025-06-17 10:46:39.097\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36muptrain.operators.language.llm\u001b[0m:\u001b[36mfetch_responses\u001b[0m:\u001b[36m268\u001b[0m - \u001b[33m\u001b[1mDetected a running event loop, scheduling requests in a separate thread.\u001b[0m\n",
-            "100%|██████████| 4/4 [00:03<00:00,  1.12it/s]\n",
-            "\u001b[32m2025-06-17 10:46:42.703\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36muptrain.operators.language.llm\u001b[0m:\u001b[36mfetch_responses\u001b[0m:\u001b[36m268\u001b[0m - \u001b[33m\u001b[1mDetected a running event loop, scheduling requests in a separate thread.\u001b[0m\n",
-            "100%|██████████| 4/4 [00:01<00:00,  3.87it/s]\n",
-            "\u001b[32m2025-06-17 10:46:43.749\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36muptrain.framework.evalllm\u001b[0m:\u001b[36mevaluate\u001b[0m:\u001b[36m376\u001b[0m - \u001b[1mLocal server not running, start the server to log data and visualize in the dashboard!\u001b[0m\n"
-          ]
-        }
-      ],
-      "source": [
-        "\n",
-        "res = eval_llm.evaluate(\n",
-        "    data = data,\n",
-        "    checks = [Evals.CONTEXT_RELEVANCE, Evals.FACTUAL_ACCURACY, Evals.RESPONSE_COMPLETENESS]\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Add the `trace_id` back to the dataset as it was omitted in the previous step to be compatible with UpTrain."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 20,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>question</th>\n",
-              "      <th>context</th>\n",
-              "      <th>response</th>\n",
-              "      <th>trace_id</th>\n",
-              "      <th>score_context_relevance</th>\n",
-              "      <th>explanation_context_relevance</th>\n",
-              "      <th>score_factual_accuracy</th>\n",
-              "      <th>explanation_factual_accuracy</th>\n",
-              "      <th>score_response_completeness</th>\n",
-              "      <th>explanation_response_completeness</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>Can stress cause physical health problems?</td>\n",
-              "      <td>Stress is the body's response to challenges or...</td>\n",
-              "      <td>Symptoms of a heart attack may include chest p...</td>\n",
-              "      <td>a105ba2b-337f-4af7-a367-663df325b44d</td>\n",
-              "      <td>0.5</td>\n",
-              "      <td>{\\n    \"Reasoning\": \"The given context can giv...</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>{\\n    \"Result\": [\\n        {\\n            \"Fa...</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>{\\n    \"Reasoning\": \"The given response does n...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>What are the symptoms of a heart attack?</td>\n",
-              "      <td>A heart attack, or myocardial infarction, occu...</td>\n",
-              "      <td>Symptoms of a heart attack may include chest p...</td>\n",
-              "      <td>66730079-4f83-40ff-9eb6-2fbf07b79bf1</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>{\\n    \"Reasoning\": \"The given context provide...</td>\n",
-              "      <td>0.6</td>\n",
-              "      <td>{\\n    \"Result\": [\\n        {\\n            \"Fa...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>{\\n    \"Reasoning\": \"The given response is com...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>Can stress cause physical health problems?</td>\n",
-              "      <td>Stress is the body's response to challenges or...</td>\n",
-              "      <td>Symptoms of a heart attack may include chest p...</td>\n",
-              "      <td>7206b436f865f4a5fe892f2b5ec4cbe6</td>\n",
-              "      <td>0.5</td>\n",
-              "      <td>{\\n    \"Reasoning\": \"The given context can giv...</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>{\\n    \"Result\": [\\n        {\\n            \"Fa...</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>{\\n    \"Reasoning\": \"The given response does n...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>Can stress cause physical health problems?</td>\n",
-              "      <td>Stress is the body's response to challenges or...</td>\n",
-              "      <td>Symptoms of a heart attack may include chest p...</td>\n",
-              "      <td>ec78d1929443997d1dbbdef2822b37dd</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>{\\n    \"Reasoning\": \"The given context can ans...</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>{\\n    \"Result\": [\\n        {\\n            \"Fa...</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>{\\n    \"Reasoning\": \"The given response does n...</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                                     question  \\\n",
-              "0  Can stress cause physical health problems?   \n",
-              "1    What are the symptoms of a heart attack?   \n",
-              "2  Can stress cause physical health problems?   \n",
-              "3  Can stress cause physical health problems?   \n",
-              "\n",
-              "                                             context  \\\n",
-              "0  Stress is the body's response to challenges or...   \n",
-              "1  A heart attack, or myocardial infarction, occu...   \n",
-              "2  Stress is the body's response to challenges or...   \n",
-              "3  Stress is the body's response to challenges or...   \n",
-              "\n",
-              "                                            response  \\\n",
-              "0  Symptoms of a heart attack may include chest p...   \n",
-              "1  Symptoms of a heart attack may include chest p...   \n",
-              "2  Symptoms of a heart attack may include chest p...   \n",
-              "3  Symptoms of a heart attack may include chest p...   \n",
-              "\n",
-              "                               trace_id  score_context_relevance  \\\n",
-              "0  a105ba2b-337f-4af7-a367-663df325b44d                      0.5   \n",
-              "1  66730079-4f83-40ff-9eb6-2fbf07b79bf1                      1.0   \n",
-              "2      7206b436f865f4a5fe892f2b5ec4cbe6                      0.5   \n",
-              "3      ec78d1929443997d1dbbdef2822b37dd                      1.0   \n",
-              "\n",
-              "                       explanation_context_relevance  score_factual_accuracy  \\\n",
-              "0  {\\n    \"Reasoning\": \"The given context can giv...                     0.0   \n",
-              "1  {\\n    \"Reasoning\": \"The given context provide...                     0.6   \n",
-              "2  {\\n    \"Reasoning\": \"The given context can giv...                     0.0   \n",
-              "3  {\\n    \"Reasoning\": \"The given context can ans...                     0.0   \n",
-              "\n",
-              "                        explanation_factual_accuracy  \\\n",
-              "0  {\\n    \"Result\": [\\n        {\\n            \"Fa...   \n",
-              "1  {\\n    \"Result\": [\\n        {\\n            \"Fa...   \n",
-              "2  {\\n    \"Result\": [\\n        {\\n            \"Fa...   \n",
-              "3  {\\n    \"Result\": [\\n        {\\n            \"Fa...   \n",
-              "\n",
-              "   score_response_completeness  \\\n",
-              "0                          0.0   \n",
-              "1                          1.0   \n",
-              "2                          0.0   \n",
-              "3                          0.0   \n",
-              "\n",
-              "                   explanation_response_completeness  \n",
-              "0  {\\n    \"Reasoning\": \"The given response does n...  \n",
-              "1  {\\n    \"Reasoning\": \"The given response is com...  \n",
-              "2  {\\n    \"Reasoning\": \"The given response does n...  \n",
-              "3  {\\n    \"Reasoning\": \"The given response does n...  "
-            ]
-          },
-          "execution_count": 20,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df = pd.DataFrame(res)\n",
-        "\n",
-        "# add the langfuse trace_id to the result dataframe\n",
-        "df[\"trace_id\"] = [d['trace_id'] for d in data]\n",
-        "\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now that we have the evaluations, we can add them back to the traces in Langfuse as [scores](https://langfuse.com/docs/scores)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 21,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "for _, row in df.iterrows():\n",
-        "    for metric_name in [\"context_relevance\", \"factual_accuracy\",\"response_completeness\"]:\n",
-        "        langfuse.create_score(\n",
-        "            name=metric_name,\n",
-        "            value=row[\"score_\"+metric_name],\n",
-        "            trace_id=row[\"trace_id\"]\n",
-        "        )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In Langfuse, you can now see the scores for each trace and monitor them over time."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![UpTrain Evals on a list of traces in Langfuse](https://langfuse.com/images/cookbook/uptrain-batch.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": []
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": ".venv",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.13.2"
-    },
-    "orig_nbformat": 4
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
diff --git a/cookbook/example_data_migration-jp.ipynb b/cookbook/example_data_migration-jp.ipynb
index 3ce3e0146d..b6109ea27e 100644
--- a/cookbook/example_data_migration-jp.ipynb
+++ b/cookbook/example_data_migration-jp.ipynb
@@ -5,10 +5,12 @@
       "id": "c3013a69",
       "metadata": {},
       "source": [
-        "<!-- NOTEBOOK_METADATA title: \"Langfuseプロジェクト間のデータ移行 (Python SDK v4)\" sidebarTitle: \"データ移行スクリプト\" description: \"Python SDK v4を使って、Langfuseプロジェクト間でオブザベーション、スコア、プロンプト、データセット、カスタムモデル、スコア設定を移行します。\" -->\n",
+        "<!-- NOTEBOOK_METADATA title: \"Langfuseプロジェクト間のデータ移行 (Python SDK v4)\" sidebarTitle: \"データ移行スクリプト\" description: \"Python SDK v4を使って、Langfuseプロジェクト間でオブザベーション、スコア、プロンプト、データセット、カスタムモデル、スコア設定を移行します。\" category: \"Examples\" -->\n",
         "\n",
         "# Langfuseプロジェクト間のデータ移行 (Python SDK v4)\n",
         "\n",
+        "このクックブックは[英語](/guides/cookbook/example_data_migration)でも利用できます。\n",
+        "\n",
         "このノートブックでは、**Langfuse Python SDK v4** を使って、あるLangfuseプロジェクトから別のLangfuseプロジェクトへデータを移行します。\n",
         "\n",
         "<!-- CALLOUT_START type: \"warning\" -->\n",
diff --git a/cookbook/example_data_migration.ipynb b/cookbook/example_data_migration.ipynb
index b998181dbe..986bc5bcd6 100644
--- a/cookbook/example_data_migration.ipynb
+++ b/cookbook/example_data_migration.ipynb
@@ -5,10 +5,12 @@
       "id": "c3013a69",
       "metadata": {},
       "source": [
-        "<!-- NOTEBOOK_METADATA title: \"Migrating Data Between Langfuse Projects (Python SDK v4)\" sidebarTitle: \"Data Migration Script\" description: \"Migrate observations, scores, prompts, datasets, custom models, and score configs between Langfuse projects using the Python SDK v4.\" -->\n",
+        "<!-- NOTEBOOK_METADATA title: \"Migrating Data Between Langfuse Projects (Python SDK v4)\" sidebarTitle: \"Data Migration Script\" description: \"Migrate observations, scores, prompts, datasets, custom models, and score configs between Langfuse projects using the Python SDK v4.\" category: \"Examples\" -->\n",
         "\n",
         "# Migrating Data Between Langfuse Projects (Python SDK v4)\n",
         "\n",
+        "This cookbook is also available in [Japanese](/guides/cookbook/example_data_migration-jp).\n",
+        "\n",
         "This notebook migrates data from one Langfuse project to another using the **Langfuse Python SDK v4**.\n",
         "\n",
         "<!-- CALLOUT_START type: \"warning\" -->\n",
diff --git a/cookbook/example_decorator_openai_langchain.ipynb b/cookbook/example_decorator_openai_langchain.ipynb
deleted file mode 100644
index 76d861c2f2..0000000000
--- a/cookbook/example_decorator_openai_langchain.ipynb
+++ /dev/null
@@ -1,255 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "description: LLM Rap Battle traced using the Langfuse Decorator, OpenAI & Langchain Integration\n",
-    "category: Integrations\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZqZ8AN39K6kG"
-   },
-   "source": [
-    "# Example: Langfuse Decorator + OpenAI Integration + Langchain Integration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "pla_RO_7bP3u"
-   },
-   "outputs": [],
-   "source": [
-    "%pip install langfuse openai langchain_openai langchain --upgrade"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "id": "LQ4UnVpDbP3v"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "# Get keys for your project from the project settings page: https://cloud.langfuse.com\n",
-    "os.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-...\");\n",
-    "os.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"sk-lf-...\");\n",
-    "os.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n",
-    "# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n",
-    "\n",
-    "# Your openai key\n",
-    "os.environ.setdefault(\"OPENAI_API_KEY\", \"sk-proj-...\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "pJXWPL1Xg_HO"
-   },
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "id": "anEKybkhlwju"
-   },
-   "outputs": [],
-   "source": [
-    "import random\n",
-    "from operator import itemgetter\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema import StrOutputParser\n",
-    "from langfuse import observe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "itxfY1Cug9iE"
-   },
-   "outputs": [],
-   "source": [
-    "from langfuse import observe, get_client, propagate_attributes\n",
-    "langfuse = get_client()\n",
-    "\n",
-    "# import openai\n",
-    "from langfuse.openai import openai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Om5dYvIwg14h"
-   },
-   "source": [
-    "## Example: LLM Rap Battle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "id": "UNOk7cElboz1"
-   },
-   "outputs": [],
-   "source": [
-    "@observe()\n",
-    "def get_random_rap_topic():\n",
-    "  topics = [\n",
-    "      \"OSS software\",\n",
-    "      \"artificial general intelligence\"\n",
-    "  ]\n",
-    "  return random.choice(topics)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "id": "O-QmAjTGfX-Q"
-   },
-   "outputs": [],
-   "source": [
-    "from langfuse.langchain import CallbackHandler\n",
-    "\n",
-    "@observe()\n",
-    "def summarize_rap_langchain(rap):\n",
-    "\n",
-    "    # Initialize the Langfuse handler\n",
-    "    langfuse_handler = CallbackHandler()\n",
-    "\n",
-    "    # Create chain\n",
-    "    prompt = ChatPromptTemplate.from_template(\"Summarrize this rap: {rap}\")\n",
-    "    model = ChatOpenAI()\n",
-    "    chain = prompt | model | StrOutputParser()\n",
-    "\n",
-    "    # Pass handler to invoke\n",
-    "    summary = chain.invoke(\n",
-    "        {\"rap\": rap},\n",
-    "        config={\"callbacks\":[langfuse_handler]}\n",
-    "    )\n",
-    "\n",
-    "    return summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "MXl9E_mNcWw_"
-   },
-   "outputs": [],
-   "source": [
-    "@observe()\n",
-    "def rap_battle(turns: int = 5):\n",
-    "  topic = get_random_rap_topic()\n",
-    "\n",
-    "  print(f\"Topic: {topic}\")\n",
-    "\n",
-    "  # Propagate attributes to all child observations\n",
-    "  with propagate_attributes(\n",
-    "      metadata={\"topic\":topic},\n",
-    "      tags=[\"Launch Week 1\"]\n",
-    "  ):\n",
-    "\n",
-    "    messages = [\n",
-    "        {\"role\": \"system\", \"content\": \"We are all rap artist. When it is our turn, we drop a fresh line.\"},\n",
-    "        {\"role\": \"user\", \"content\": f\"Kick it off, today's topic is {topic}, here's the mic...\"}\n",
-    "    ]\n",
-    "\n",
-    "    for turn in range(turns):\n",
-    "        completion = openai.chat.completions.create(\n",
-    "          model=\"gpt-4o\",\n",
-    "          messages=messages,\n",
-    "        )\n",
-    "        rap_line = completion.choices[0].message.content\n",
-    "        messages.append({\"role\": \"assistant\", \"content\": rap_line})\n",
-    "        print(f\"\\nRap {turn}: {rap_line}\")\n",
-    "\n",
-    "    summary = summarize_rap_langchain([message['content'] for message in messages])\n",
-    "\n",
-    "  return summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "id": "Vg8bc0mzd7uG"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Topic: artificial general intelligence\n",
-      "\n",
-      "Rap 0: I'm kickin' it with AGI, the future's in my sights,  \n",
-      "Machines getting smarter, like they got new insights,  \n",
-      "Beyond narrow AI, it's a brand new domain,  \n",
-      "Computers thinkin' freely, sharpenin' their brain.  \n",
-      "\n",
-      "Rap 1: Flippin' through algorithms, a digital symphony,  \n",
-      "These code lines are flowin', create new epiphanies,  \n",
-      "Boundless like the universe, we're breakin' every mold,  \n",
-      "With AGI in the lab, we're changin' stories untold.  \n",
-      "\n",
-      "Rap 2: Pixels turn to poetry with intelligence so bold,  \n",
-      "Machines learnin' feelings, understanding what’s unsaid,  \n",
-      "They sift through data waves, pickin' up the threads,  \n",
-      "Craftin' neural pathways where human thoughts are spread.  \n",
-      "\n",
-      "Rap 3: Mathematics in motion, like a symphony of thought,  \n",
-      "Explorin' every crevice, solutions we sought,  \n",
-      "From zeroes and ones to infinite potential,  \n",
-      "In the realm of AGI, we're only experimental.  \n",
-      "\n",
-      "Summary: The rap is about artificial general intelligence (AGI) and how it is changing the future. The artists discuss the capabilities of machines becoming smarter and thinking freely, breaking boundaries and creating new epiphanies. They describe AGI as creating new possibilities and understanding human thoughts, with infinite potential and still in an experimental stage.\n"
-     ]
-    }
-   ],
-   "source": [
-    "rap_summary = rap_battle(turns=4)\n",
-    "print(\"\\nSummary: \" + rap_summary)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/cookbook/example_evaluating_openai_agents.ipynb b/cookbook/example_evaluating_openai_agents.ipynb
index 54c14a6615..ec5439f2e6 100644
--- a/cookbook/example_evaluating_openai_agents.ipynb
+++ b/cookbook/example_evaluating_openai_agents.ipynb
@@ -9,7 +9,7 @@
     "---\n",
     "title: Example - Tracing and Evaluation for the OpenAI-Agents SDK\n",
     "description: This guide shows how to evaluate the OpenAI-Agents SDK with Langfuse using online and offline evaluation methods. \n",
-    "category: Integrations\n",
+    "category: Evaluation\n",
     "---\n",
     "\n",
     "# Evaluation for the OpenAI-Agents SDK\n",
diff --git a/cookbook/example_intent_classification_pipeline.ipynb b/cookbook/example_intent_classification_pipeline.ipynb
index 72110d1cca..706815fb3a 100644
--- a/cookbook/example_intent_classification_pipeline.ipynb
+++ b/cookbook/example_intent_classification_pipeline.ipynb
@@ -10,7 +10,7 @@
         "---\n",
         "title: Guide - Building an intent classification pipeline\n",
         "description: Build an intent classification pipeline to understand how users are using your LLM application and how performance differs by intent.\n",
-        "category: Classification\n",
+        "category: Examples\n",
         "---\n",
         "\n",
         "# LLM Application Intent Classification\n",
@@ -786,7 +786,7 @@
         "\n",
         "Observations in Langfuse are immutable, so tags cannot be edited after an observation was created. To tag an existing trace, add a small additional observation with the predicted intent as a tag to that trace. Tags across all observations of a trace are aggregated on the trace level.\n",
         "\n",
-        "> **Note:** `trace_context.trace_id` must be a W3C-format trace ID (32 lowercase hex characters), which is what current Langfuse SDKs generate. Traces created with older SDK versions may use UUID-format IDs (with dashes) \u2014 the tagging observation cannot be attached to those. If you work with historical traces, filter for W3C-format IDs, e.g. `traces_df[traces_df[\"trace_id\"].str.fullmatch(r\"[0-9a-f]{32}\")]`."
+        "> **Note:** `trace_context.trace_id` must be a W3C-format trace ID (32 lowercase hex characters), which is what current Langfuse SDKs generate. Traces created with older SDK versions may use UUID-format IDs (with dashes) — the tagging observation cannot be attached to those. If you work with historical traces, filter for W3C-format IDs, e.g. `traces_df[traces_df[\"trace_id\"].str.fullmatch(r\"[0-9a-f]{32}\")]`."
       ]
     },
     {
diff --git a/cookbook/example_langgraph_agents.ipynb b/cookbook/example_langgraph_agents.ipynb
index 05d1e63b83..47896ea106 100644
--- a/cookbook/example_langgraph_agents.ipynb
+++ b/cookbook/example_langgraph_agents.ipynb
@@ -6,7 +6,7 @@
         "id": "qsOnSYBqfoSL"
       },
       "source": [
-        "<!-- NOTEBOOK_METADATA source: \"Jupyter Notebook\" title: \"Example - Trace and Evaluate LangGraph Agents\" description: \"This guide shows how to evaluate LangGraph Agents with Langfuse using online and offline evaluation methods.\" category: \"Integrations\" -->\n",
+        "<!-- NOTEBOOK_METADATA source: \"Jupyter Notebook\" title: \"Example - Trace and Evaluate LangGraph Agents\" description: \"This guide shows how to evaluate LangGraph Agents with Langfuse using online and offline evaluation methods.\" category: \"Evaluation\" -->\n",
         "\n",
         "# Evaluate LangGraph Agents\n",
         "\n",
diff --git a/cookbook/example_llm_security_monitoring.ipynb b/cookbook/example_llm_security_monitoring.ipynb
index 9e2cecaf8f..c0fa3229af 100644
--- a/cookbook/example_llm_security_monitoring.ipynb
+++ b/cookbook/example_llm_security_monitoring.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "---\n",
         "description: Overview of common security problems facing LLM-based applications and how to use Langfuse to trace, prevent, and evaluate security risks.\n",
-        "category: Security\n",
+        "category: Examples\n",
         "---\n",
         "\n",
         "# Example: Monitoring LLM Security\n",
diff --git a/cookbook/integration_amazon_bedrock.ipynb b/cookbook/integration_amazon_bedrock.ipynb
index 1193f1433f..2ea374d775 100644
--- a/cookbook/integration_amazon_bedrock.ipynb
+++ b/cookbook/integration_amazon_bedrock.ipynb
@@ -8,6 +8,7 @@
       "source": [
         "---\n",
         "sidebarTitle: Amazon Bedrock\n",
+        "category: Integrations\n",
         "seoTitle: Open Source Observability and Metrics for Amazon Bedrock\n",
         "description: Open source observability for Amazon Bedrock applications and the Bedrock SDK.\n",
         "logo: /images/integrations/bedrock_icon.png\n",
diff --git a/cookbook/integration_azure_openai_langchain.ipynb b/cookbook/integration_azure_openai_langchain.ipynb
deleted file mode 100644
index 54d03507f7..0000000000
--- a/cookbook/integration_azure_openai_langchain.ipynb
+++ /dev/null
@@ -1,243 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "KlceIPalN3QR"
-   },
-   "source": "---\ndescription: This cookbook demonstrates use of Langfuse with Azure OpenAI and Langchain for prompt versioning and evaluations.\ncategory: Integrations\n---"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "mqBspBzuRk9C"
-   },
-   "source": "# Langfuse Tracing and Prompt Management for Azure OpenAI and Langchain\n\nThis cookbook demonstrates use of Langfuse with Azure OpenAI and Langchain for prompt versioning and evaluations."
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "AbSpd5EiZouE"
-   },
-   "source": "## Setup"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "YNyU6IzCZouE"
-   },
-   "outputs": [],
-   "source": "%pip install --quiet langfuse langchain langchain-openai --upgrade"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": "import os\n\n# get keys for your project from https://cloud.langfuse.com\nos.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-***\");\nos.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"sk-lf-***\");\nos.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n\n# your azure openai configuration\nos.environ.setdefault(\"AZURE_OPENAI_ENDPOINT\", \"your Azure OpenAI endpoint\");\nos.environ.setdefault(\"AZURE_OPENAI_API_KEY\", \"your Azure OpenAI API key\");\nos.environ.setdefault(\"OPENAI_API_TYPE\", \"azure\");\nos.environ.setdefault(\"OPENAI_API_VERSION\", \"2023-09-01-preview\");"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll use the native Langfuse integration for Langchain. Learn more it in the [documentation](https://langfuse.com/integrations/frameworks/langchain)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "divRadPqZouF"
-   },
-   "outputs": [],
-   "source": "from langfuse import get_client\nfrom langfuse.langchain import CallbackHandler\n\n# Initialize Langfuse CallbackHandler for Langchain (tracing)\nlangfuse_handler = CallbackHandler()\n\n# optional, verify your Langfuse credentials\nget_client().auth_check()"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "FzsO2Ci4EjJS"
-   },
-   "source": [
-    "Langchain imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "L9u5QMVCbPtR"
-   },
-   "outputs": [],
-   "source": "from langchain_openai import AzureChatOpenAI\nfrom langchain_core.prompts import (\n    ChatPromptTemplate,\n    SystemMessagePromptTemplate,\n    HumanMessagePromptTemplate,\n)"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simple example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": "from langchain_openai import AzureChatOpenAI\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langfuse.langchain import CallbackHandler\n\nlangfuse_handler = CallbackHandler()\n\nprompt = ChatPromptTemplate.from_template(\"what is the city {person} is from?\")\nmodel = AzureChatOpenAI(\n    deployment_name=\"gpt-4o\",\n    model_name=\"gpt-4o\",\n)\nchain = prompt | model\n\nchain.invoke({\"person\": \"Satya Nadella\"}, config={\"callbacks\":[langfuse_handler]})"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**✨ Done. Go to the Langfuse Dashboard to explore the trace of this run.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "KAW-Gt4mN3QV"
-   },
-   "source": [
-    "## Example using Langfuse Prompt Management and Langchain\n",
-    "\n",
-    "Learn more about Langfuse Prompt Management in the [docs](https://langfuse.com/docs/prompts)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": "# Initialize the Langfuse Client\nfrom langfuse import get_client\nlangfuse = get_client()\n\ntemplate = \"\"\"\nYou are an AI assistant travel assistant that provides vacation recommendations to users. \nYou should also be able to provide information about the weather, local customs, and travel restrictions. \n\"\"\"\n\n# Push the prompt to Langfuse and immediately promote it to production\nlangfuse.create_prompt(\n    name=\"travel_consultant\",\n    prompt=template,\n    labels=[\"production\"],\n)"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In your production environment, you can then fetch the production version of the prompt. The Langfuse client caches the prompt to improve performance. You can configure this behavior via a custom TTL or disable it completely."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the prompt from Langfuse, cache it for 5 minutes\n",
-    "langfuse_prompt = langfuse.get_prompt(\"travel_consultant\", cache_ttl_seconds=300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We do not use the native Langfuse `prompt.compile()` but use the raw `prompt.prompt` as Langchain will insert the prompt variables (if any)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system_message_prompt = SystemMessagePromptTemplate.from_template(langfuse_prompt.prompt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": "llm = AzureChatOpenAI(\n    deployment_name=\"gpt-4o\",\n    model_name=\"gpt-4o\",\n)\n\nhuman_message_prompt = HumanMessagePromptTemplate.from_template(\"{text}\")\nchat_prompt = ChatPromptTemplate.from_messages(\n    [system_message_prompt, human_message_prompt]\n)\nchain = chat_prompt | llm\n\nresult = chain.invoke(\n    {\"text\": \"Where should I go on vacation in December for warm weather and beaches?\"},\n    config={\"callbacks\": [langfuse_handler]},\n)\n\nprint(result.content)"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Multiple Langchain runs in same Langfuse trace"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Langchain setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": "from langchain_openai import AzureChatOpenAI\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.output_parsers import StrOutputParser\nfrom operator import itemgetter\n\nprompt1 = ChatPromptTemplate.from_template(\n    \"What {type} is easiest to learn but hardest to master? Give a step by step approach of your thoughts, ending in your answer\"\n)\nprompt2 = ChatPromptTemplate.from_template(\n    \"How {type} can be learned in 21 days? respond in {language}\"\n)\nmodel = AzureChatOpenAI(\n    deployment_name=\"gpt-4o\",\n    model_name=\"gpt-4o\",\n)\nchain1 = prompt1 | model | StrOutputParser()\nchain2 = (\n    {\"type\": chain1, \"language\": itemgetter(\"language\")}\n    | prompt2\n    | model\n    | StrOutputParser()\n)"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": "Run the chain multiple times within the same Langfuse trace by wrapping the invocations in an enclosing span (created via `start_as_current_observation`). Trace attributes such as `user_id` are set via `propagate_attributes`."
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": "from langfuse import get_client, propagate_attributes\nfrom langfuse.langchain import CallbackHandler\n\nlangfuse = get_client()\nlangfuse_handler = CallbackHandler()\n\n# Create an enclosing span to group both runs into a single trace\nwith langfuse.start_as_current_observation(\n    as_type=\"span\", name=\"chain_of_thought_example\"\n) as root_span:\n    with propagate_attributes(user_id=\"user-1234\"):\n        # First run\n        chain2.invoke(\n            {\"type\": \"business\", \"language\": \"german\"},\n            config={\"callbacks\": [langfuse_handler]},\n        )\n\n        # Second run\n        chain2.invoke(\n            {\"type\": \"business\", \"language\": \"english\"},\n            config={\"callbacks\": [langfuse_handler]},\n        )"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "AxQlUOmVPEwz"
-   },
-   "source": [
-    "## Adding scores\n",
-    "\n",
-    "When evaluating traces of your LLM application in Langfuse, you need to add [scores](https://langfuse.com/docs/scores) to the trace. For simplicity, we'll add a mocked score. Check out the docs for more information on complex score types."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": "Get the `trace_id` from the enclosing span of the previous run. Inside an active observation, you can also get it via `langfuse.get_current_trace_id()`."
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": "trace_id = root_span.trace_id"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "PudCopwEPFgh"
-   },
-   "outputs": [],
-   "source": "# Add score to the trace via the Langfuse Python Client\nlangfuse.create_score(\n    trace_id=trace_id,\n    name=\"user-explicit-feedback\",\n    value=1,\n    comment=\"I like how personalized the response is\",\n)"
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/cookbook/integration_databricks.ipynb b/cookbook/integration_databricks.ipynb
deleted file mode 100644
index 0a6d58e520..0000000000
--- a/cookbook/integration_databricks.ipynb
+++ /dev/null
@@ -1,358 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<!-- NOTEBOOK_METADATA source: \"⚠️ Jupyter Notebook\" title: \"Integrate Databricks with Langfuse\" sidebarTitle: \"Databricks\" logo: \"/images/integrations/databricks_icon.svg\" description: \"Learn how to monitor and trace Databricks models with Langfuse to improve and debug your AI applications.\" category: \"Integrations\" -->"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Observability for Databricks Models with Langfuse\n",
-        "\n",
-        "Databricks provides a powerful platform for hosting and serving large language models. By combining Databricks' serving endpoints with **Langfuse**, you can trace, monitor, and analyze your AI workloads in development and production.\n",
-        "\n",
-        "This notebook demonstrates **three** different ways to use Databricks models with Langfuse:\n",
-        "1. **OpenAI SDK:** Use Databricks model endpoints via the OpenAI SDK.\n",
-        "2. **LangChain:** Integrate with the Databricks LLM interface in a LangChain pipeline.\n",
-        "3. **LlamaIndex:** Use Databricks endpoints within LlamaIndex.\n",
-        "\n",
-        "> **What is Databricks Model Serving?**  \n",
-        "Databricks Model Serving allows you to serve large-scale models in a production environment, with automatic scaling and a robust infrastructure. It also enables you to fine-tune LLMs on your private data, ensuring your models can leverage proprietary information while maintaining data privacy.\n",
-        "\n",
-        "> **What is Langfuse?**  \n",
-        "[Langfuse](https://langfuse.com) is an open source platform for LLM observability and monitoring. It helps you trace and monitor your AI applications by capturing metadata, prompt details, token usage, latency, and more.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 1. Install Dependencies\n",
-        "\n",
-        "Before you begin, install the necessary packages in your Python environment:\n",
-        "\n",
-        "- **openai**: Needed to call Databricks endpoints via the OpenAI SDK.\n",
-        "- **databricks-langchain**: Needed to call Databricks endpoints via an \"OpenAI-like\" interface.\n",
-        "- **llama-index** and **llama-index-llms-databricks**: For using Databricks endpoints within LlamaIndex.\n",
-        "- **langfuse**: Required for sending trace data to the Langfuse platform.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%pip install openai langfuse databricks-langchain llama-index llama-index-llms-databricks openinference-instrumentation-llama-index"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 2. Set Up Environment Variables\n",
-        "\n",
-        "Configure your **Langfuse** credentials and **Databricks** credentials as environment variables. Replace the dummy keys below with the real ones from your respective accounts.\n",
-        "\n",
-        " - `LANGFUSE_PUBLIC_KEY` / `LANGFUSE_SECRET_KEY`: From your Langfuse Project Settings.\n",
-        " - `LANGFUSE_BASE_URL`: `https://cloud.langfuse.com` (EU), `https://us.cloud.langfuse.com` (US), `https://jp.cloud.langfuse.com` (Japan), or `https://hipaa.cloud.langfuse.com` (HIPAA).\n",
-        " - `DATABRICKS_TOKEN`: Your Databricks personal access token.\n",
-        " - `DATABRICKS_HOST`: Your Databricks workspace URL (e.g., `https://dbc-xxxxxxx.cloud.databricks.com`).\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "# Example environment variables (replace with your actual keys/tokens)\n",
-        "os.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-...\");  # your public key\n",
-        "os.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"sk-lf-...\");  # your secret key\n",
-        "os.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\");  # Other regions: https://us.cloud.langfuse.com (US), https://jp.cloud.langfuse.com (Japan), https://hipaa.cloud.langfuse.com (HIPAA)\n",
-        "\n",
-        "os.environ.setdefault(\"DATABRICKS_TOKEN\", \"dapi-...\");  # Databricks personal access token\n",
-        "os.environ.setdefault(\"DATABRICKS_HOST\", \"https://dbc-XXXXX-XXXX.cloud.databricks.com\");"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Approach 1: Using Databricks Models via the OpenAI SDK\n",
-        "\n",
-        "Databricks endpoints can act as a drop-in replacement for the OpenAI API. This makes it easy to integrate with existing code that relies on the `openai` library. Under the hood, `langfuse.openai.OpenAI` automatically traces your requests to Langfuse.\n",
-        "\n",
-        "### Steps\n",
-        "1. Import the `OpenAI` client from `langfuse.openai`.\n",
-        "2. Create a client, setting `api_key` to your Databricks token and `base_url` to your Databricks workspace endpoints.\n",
-        "3. Use the client’s `chat.completions.create()` method to send a prompt.\n",
-        "4. See the trace in your Langfuse dashboard.\n",
-        "\n",
-        "**Note:** For more examples on tracing OpenAI with Langfuse see the [OpenAI integration docs](https://langfuse.com/integrations/model-providers/openai-py)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Langfuse OpenAI client\n",
-        "from langfuse.openai import OpenAI\n",
-        "\n",
-        "# Retrieve the environment variables\n",
-        "databricks_token = os.environ.get(\"DATABRICKS_TOKEN\")\n",
-        "databricks_host = os.environ.get(\"DATABRICKS_HOST\")\n",
-        "\n",
-        "# Create an OpenAI-like client pointing to Databricks\n",
-        "client = OpenAI(\n",
-        "    api_key=databricks_token,  # Databricks personal access token\n",
-        "    base_url=f\"{databricks_host}/serving-endpoints\",  # your Databricks workspace\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "response = client.chat.completions.create(\n",
-        "    messages=[\n",
-        "        {\"role\": \"system\", \"content\": \"You are an AI assistant.\"},\n",
-        "        {\"role\": \"user\", \"content\": \"What is Databricks?\"}\n",
-        "    ],\n",
-        "    model=\"mistral-7b\", # Adjust based on your Databricks serving endpoint name\n",
-        "    max_tokens=256\n",
-        ")\n",
-        "\n",
-        "# Print out the response from the model\n",
-        "print(response.choices[0].message.content)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once the request completes, **log in to your Langfuse dashboard** and look for the new trace. You will see details like the prompt, response, latency, token usage, etc.\n",
-        "\n",
-        "![Databricks example trace in Langfuse](/images/docs/databricks/databricks-example-trace-openai-sdk.png)\n",
-        "\n",
-        "_[Link to public trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/029b2344-e2a2-4c52-8d04-bd71f70c1120?timestamp=2025-03-06T14%3A45%3A04.141Z)_ \n",
-        "\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Approach 2: Using LangChain\n",
-        "\n",
-        "Databricks models can also be used via LangChain. The [`ChatDatabricks`](https://python.langchain.com/docs/integrations/chat/databricks/) class wraps your Databricks Model Serving endpoint.\n",
-        "\n",
-        "### Steps\n",
-        "1. Set `DATABRICKS_HOST` as an environment variable.\n",
-        "2. Initialize a Langfuse `CallbackHandler` that automatically collects trace data.\n",
-        "3. Use `ChatDatabricks` with your endpoint name, temperature, or other parameters.\n",
-        "4. Invoke the model with messages and pass in the Langfuse callback handler.\n",
-        "5. See the trace in your Langfuse dashboard.\n",
-        "\n",
-        "**Note:** For more examples on tracing LangChain with Langfuse see the [LangChain integration docs](https://langfuse.com/integrations/frameworks/langchain)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "# Get keys for your project from the project settings page: https://cloud.langfuse.com\n",
-        "os.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-...\");\n",
-        "os.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"sk-lf-...\");\n",
-        "os.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n",
-        "# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langfuse.langchain import CallbackHandler\n",
-        " \n",
-        "# Initialize Langfuse CallbackHandler for Langchain (tracing)\n",
-        "langfuse_handler = CallbackHandler()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from databricks_langchain import ChatDatabricks\n",
-        "\n",
-        "chat_model = ChatDatabricks(\n",
-        "    endpoint=\"mistral-7b\",   # Your Databricks Model Serving endpoint name\n",
-        "    temperature=0.1,\n",
-        "    max_tokens=256,\n",
-        "    # Other parameters can be added here\n",
-        ")\n",
-        "\n",
-        "# Build a prompt as a list of system/user messages\n",
-        "messages = [\n",
-        "    (\"system\", \"You are a chatbot that can answer questions about Databricks.\"),\n",
-        "    (\"user\", \"What is Databricks Model Serving?\")\n",
-        "]\n",
-        "\n",
-        "# Invoke the model using LangChain's .invoke() method\n",
-        "chat_model.invoke(messages, config={\"callbacks\": [langfuse_handler]})"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "After running the code, open your Langfuse dashboard to see the recorded conversation.\n",
-        "\n",
-        "![Databricks example trace in Langfuse](https://langfuse.com/images/docs/databricks/databricks-example-trace-langchain.png)\n",
-        "\n",
-        "_[Link to public trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/a55411bb-4bb4-435c-b922-e446683888ff?timestamp=2025-03-06T14%3A57%3A59.273Z)_ \n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Approach 3: Using LlamaIndex\n",
-        "\n",
-        "If you use [LlamaIndex](https://github.com/jerryjliu/llama_index) for data ingestion, indexing, or retrieval-augmented generation, you can replace the default LLM with a Databricks endpoint.\n",
-        "\n",
-        "### Steps\n",
-        "1. Import `Databricks` from `llama_index.llms.databricks`.\n",
-        "2. Initialize a `Databricks` LLM with your endpoint name and Databricks credentials.\n",
-        "3. Use `LlamaIndexInstrumentor` from `langfuse.llama_index` to enable automatic tracing.\n",
-        "4. Invoke the LLM with a chat request.\n",
-        "5. See the trace in your Langfuse dashboard.\n",
-        "\n",
-        "**Note:** For more examples on tracing LlamaIndex with Langfuse see the [LlamaIndex integration docs](https://langfuse.com/integrations/frameworks/llamaindex).\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from llama_index.llms.databricks import Databricks\n",
-        "\n",
-        "# Create a Databricks LLM instance\n",
-        "llm = Databricks(\n",
-        "    model=\"mistral-7b\",  # Your Databricks serving endpoint name\n",
-        "    api_key=os.environ.get(\"DATABRICKS_TOKEN\"),\n",
-        "    api_base=f\"{os.environ.get('DATABRICKS_HOST')}/serving-endpoints/\"\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "from langfuse import get_client\n",
-        "from openinference.instrumentation.llama_index import LlamaIndexInstrumentor\n",
-        " \n",
-        "# Get keys for your project from the project settings page: https://cloud.langfuse.com\n",
-        "os.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-...\");\n",
-        "os.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"sk-lf-...\");\n",
-        "os.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n",
-        "# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n",
-        " \n",
-        "langfuse = get_client()\n",
-        "\n",
-        "# Initialize LlamaIndex instrumentation\n",
-        "LlamaIndexInstrumentor().instrument()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from llama_index.core.llms import ChatMessage\n",
-        "\n",
-        "messages = [\n",
-        "    ChatMessage(role=\"system\", content=\"You are a helpful assistant.\"),\n",
-        "    ChatMessage(role=\"user\", content=\"What is Databricks?\")\n",
-        "]\n",
-        "\n",
-        "response = llm.chat(messages)\n",
-        "print(response)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can now log into Langfuse to view your LlamaIndex calls, with details on prompts, token usage, completion data, and more.\n",
-        "\n",
-        "![Databricks example LlamaIndex trace in Langfuse](https://langfuse.com/images/docs/databricks/databricks-example-llamaindex-trace.png)\n",
-        "\n",
-        "_[Link to public trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/589a858e-9678-4624-bcb6-2e0266ecb1b3?timestamp=2025-03-06T15%3A10%3A02.467Z&observation=dd7b6235-6c92-4c9b-a966-872bc281c060)_ \n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<!-- MARKDOWN_COMPONENT name: \"LearnMore\" path: \"@/components-mdx/integration-learn-more.mdx\" -->"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next Steps\n",
-        "- See how to use Databricks models in the Langfuse Playground and for LLM-as-a-Judge evaluations [here](https://langfuse.com/integrations/model-providers/databricks).\n",
-        "- Explore the [Databricks documentation](https://docs.databricks.com/aws/en/machine-learning/model-serving/manage-serving-endpoints) for advanced model serving configurations.\n",
-        "- Learn more about [Langfuse tracing features](https://langfuse.com/docs) to track your entire application flow.\n",
-        "- Try out Langfuse [Prompt Management](https://langfuse.com/docs/prompts/get-started) or set up [LLM-as-a-Judge evaluations](https://langfuse.com/docs/scores/model-based-evals).\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": ".venv",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.13.2"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}
diff --git a/cookbook/integration_langchain.ipynb b/cookbook/integration_langchain.ipynb
deleted file mode 100644
index d8bbc7f108..0000000000
--- a/cookbook/integration_langchain.ipynb
+++ /dev/null
@@ -1,387 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "KlceIPalN3QR"
-   },
-   "source": [
-    "---\n",
-    "description: Cookbook with examples of the Langfuse Integration for Langchain (Python).\n",
-    "category: Integrations\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "mqBspBzuRk9C"
-   },
-   "source": [
-    "<!-- NOTEBOOK_METADATA source: \"⚠️ Jupyter Notebook\" title: \"Cookbook: Langchain Integration (Python)\" sidebarTitle: \"Cookbook: Langchain Integration (Python)\" description: \"Cookbook with examples of the Langfuse Integration for Langchain (Python).\" category: \"Integrations\" -->\n",
-    "\n",
-    "# Cookbook: Langchain Integration (Python)\n",
-    "\n",
-    "<a href=\"https://langfuse.com/guides/cookbook/integration_langchain\"><img className=\"inline\" alt=\"Python\" src=\"https://img.shields.io/badge/Python-d4d4d8?style=flat&logo=python&logoColor=white\" /></a> <a href=\"https://langfuse.com/guides/cookbook/js_integration_langchain\"><img className=\"inline\" alt=\"JS/TS\" src=\"https://img.shields.io/badge/JS/TS-F7DF1E?style=flat&logo=javascript&logoColor=black\" /></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "x1oaA7XYGOfX"
-   },
-   "source": [
-    "This is a cookbook with examples of the Langfuse Integration for Langchain (Python).\n",
-    "\n",
-    "Follow the [integration guide](https://langfuse.com/integrations/frameworks/langchain) to add this integration to your Langchain project. The integration also supports Langchain JS."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "AbSpd5EiZouE"
-   },
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "YNyU6IzCZouE",
-    "outputId": "234c71fb-f822-4b48-f4c0-94efe5f79305"
-   },
-   "outputs": [],
-   "source": [
-    "%pip install langfuse langchain langchain-classic langchain_openai langchain_community --upgrade"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "OpE57ujJZouE"
-   },
-   "source": [
-    "Initialize the Langfuse client with your API keys from the project settings in the Langfuse UI and add them to your environment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "dEdF-668ZouF"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "# Get keys for your project from the project settings page: https://cloud.langfuse.com\n",
-    "os.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-...\");\n",
-    "os.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"sk-lf-...\");\n",
-    "os.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n",
-    "# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n",
-    "\n",
-    "# Your openai key\n",
-    "os.environ.setdefault(\"OPENAI_API_KEY\", \"sk-proj-..\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "id": "divRadPqZouF"
-   },
-   "outputs": [],
-   "source": [
-    "from langfuse.langchain import CallbackHandler\n",
-    " \n",
-    "# Initialize Langfuse CallbackHandler for Langchain (tracing)\n",
-    "langfuse_handler = CallbackHandler()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "1ZXRf2FZXEXV"
-   },
-   "source": [
-    "## Examples"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "QvRWPsZ-NoAr"
-   },
-   "source": [
-    "### Sequential Chain in Langchain Expression Language (LCEL)\n",
-    "\n",
-    "![Trace of Langchain LCEL](https://langfuse.com/images/cookbook/integration_langchain/langchain_LCEL.png)\n",
-    "\n",
-    "[Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/dbe646b2b67957d22e8780c429b2d20f?timestamp=2025-06-11T09%3A09%3A58.823Z&display=details)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 35
-    },
-    "id": "3-HEia6gNoAr",
-    "outputId": "ab223b99-4719-420c-ffe7-5444e4b67806"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Barack Obama es de la ciudad de Chicago, Illinois, en los Estados Unidos.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from operator import itemgetter\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "langfuse_handler = CallbackHandler()\n",
-    "\n",
-    "prompt1 = ChatPromptTemplate.from_template(\"what is the city {person} is from?\")\n",
-    "prompt2 = ChatPromptTemplate.from_template(\n",
-    "    \"what country is the city {city} in? respond in {language}\"\n",
-    ")\n",
-    "model = ChatOpenAI()\n",
-    "chain1 = prompt1 | model | StrOutputParser()\n",
-    "chain2 = (\n",
-    "    {\"city\": chain1, \"language\": itemgetter(\"language\")}\n",
-    "    | prompt2\n",
-    "    | model\n",
-    "    | StrOutputParser()\n",
-    ")\n",
-    "\n",
-    "chain2.invoke({\"person\": \"obama\", \"language\": \"spanish\"}, config={\"callbacks\":[langfuse_handler]})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "NlBSpILFXEXV"
-   },
-   "source": [
-    "#### Runnable methods\n",
-    "\n",
-    "Runnables are units of work that can be invoked, batched, streamed, transformed and composed.\n",
-    "\n",
-    "The examples below show how to use the following methods with Langfuse:\n",
-    "\n",
-    "- invoke/ainvoke: Transforms a single input into an output.\n",
-    "- batch/abatch: Efficiently transforms multiple inputs into outputs.\n",
-    "- stream/astream: Streams output from a single input as it’s produced."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "Y8N8pybGXEXV",
-    "outputId": "df6d23fc-ed65-4fd2-d0d6-3b9f97e3a497"
-   },
-   "outputs": [],
-   "source": [
-    "# Async Invoke\n",
-    "await chain2.ainvoke({\"person\": \"biden\", \"language\": \"german\"}, config={\"callbacks\":[langfuse_handler]})\n",
-    "\n",
-    "# Batch\n",
-    "chain2.batch([{\"person\": \"elon musk\", \"language\": \"english\"}, {\"person\": \"mark zuckerberg\", \"language\": \"english\"}], config={\"callbacks\":[langfuse_handler]})\n",
-    "\n",
-    "# Async Batch\n",
-    "await chain2.abatch([{\"person\": \"jeff bezos\", \"language\": \"english\"}, {\"person\": \"tim cook\", \"language\": \"english\"}], config={\"callbacks\":[langfuse_handler]})\n",
-    "\n",
-    "# Stream\n",
-    "for chunk in chain2.stream({\"person\": \"steve jobs\", \"language\": \"english\"}, config={\"callbacks\":[langfuse_handler]}):\n",
-    "    print(\"Streaming chunk:\", chunk)\n",
-    "\n",
-    "# Async Stream\n",
-    "async for chunk in chain2.astream({\"person\": \"bill gates\", \"language\": \"english\"}, config={\"callbacks\":[langfuse_handler]}):\n",
-    "    print(\"Async Streaming chunk:\", chunk)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "FP5avhNb3TBH"
-   },
-   "source": [
-    "### RetrievalQA\n",
-    "\n",
-    "![Trace of Langchain QA Retrieval in Langfuse](https://langfuse.com/images/cookbook/integration_langchain/langchain_qa_retrieval.png)\n",
-    "\n",
-    "[Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/84e1ac07dedbce3b2a236b6ece6950d9?timestamp=2025-06-11T09:29:10.248Z&display=details)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "id": "wjiWEkRUFzCf"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ.setdefault(\"SERPAPI_API_KEY\", \"...\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "p0CgEPSlEpkC",
-    "outputId": "36c800af-025d-407e-eca1-c215bba62cd2"
-   },
-   "outputs": [],
-   "source": [
-    "%pip install unstructured selenium langchain-chroma --upgrade"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 438
-    },
-    "id": "kHDVa-Ssb-KT",
-    "outputId": "efab8170-76b9-412e-c086-365a16f065a9"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'query': 'What did the president say about Ketanji Brown Jackson',\n",
-       " 'result': \" The president nominated her to serve on the United States Supreme Court and praised her as one of the nation's top legal minds who will continue the legacy of retiring Justice Stephen Breyer.\"}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_community.document_loaders import SeleniumURLLoader\n",
-    "from langchain_chroma import Chroma\n",
-    "from langchain_text_splitters import CharacterTextSplitter\n",
-    "from langchain_openai import OpenAI, OpenAIEmbeddings\n",
-    "from langchain_classic.chains import RetrievalQA\n",
-    "\n",
-    "langfuse_handler = CallbackHandler()\n",
-    "\n",
-    "urls = [\n",
-    "    \"https://raw.githubusercontent.com/langfuse/langfuse-docs/main/public/state_of_the_union.txt\",\n",
-    "]\n",
-    "loader = SeleniumURLLoader(urls=urls)\n",
-    "llm = OpenAI()\n",
-    "documents = loader.load()\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "texts = text_splitter.split_documents(documents)\n",
-    "embeddings = OpenAIEmbeddings()\n",
-    "docsearch = Chroma.from_documents(texts, embeddings)\n",
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "chain = RetrievalQA.from_chain_type(\n",
-    "    llm,\n",
-    "    retriever=docsearch.as_retriever(search_kwargs={\"k\": 1}),\n",
-    ")\n",
-    "\n",
-    "chain.invoke(query, config={\"callbacks\":[langfuse_handler]})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "OIxwkX9p1ZR7"
-   },
-   "source": [
-    "### AzureOpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "b43rIMig1ZR7"
-   },
-   "outputs": [],
-   "source": [
-    "os.environ.setdefault(\"AZURE_OPENAI_ENDPOINT\", \"<Azure OpenAI endpoint>\");\n",
-    "os.environ.setdefault(\"AZURE_OPENAI_API_KEY\", \"<Azure OpenAI API key>\");\n",
-    "os.environ.setdefault(\"OPENAI_API_TYPE\", \"azure\");\n",
-    "os.environ.setdefault(\"OPENAI_API_VERSION\", \"2023-09-01-preview\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "_lLdPwnr1ZR7"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_openai import AzureChatOpenAI\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langfuse.langchain import CallbackHandler\n",
-    " \n",
-    "# Initialize Langfuse CallbackHandler for Langchain (tracing)\n",
-    "langfuse_handler = CallbackHandler()\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_template(\"what is the city {person} is from?\")\n",
-    "model = AzureChatOpenAI(\n",
-    "    deployment_name=\"gpt-4o\",\n",
-    "    model_name=\"gpt-4o\",\n",
-    ")\n",
-    "chain = prompt | model\n",
-    "\n",
-    "chain.invoke({\"person\": \"Satya Nadella\"}, config={\"callbacks\":[langfuse_handler]})"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/cookbook/integration_langgraph.ipynb b/cookbook/integration_langgraph.ipynb
index ba4f34a608..bd35c79642 100644
--- a/cookbook/integration_langgraph.ipynb
+++ b/cookbook/integration_langgraph.ipynb
@@ -6,7 +6,7 @@
     "id": "MPJsVa1O4TuL"
    },
    "source": [
-    "<!-- NOTEBOOK_METADATA source: \"Jupyter Notebook\" title: \"Open Source Observability for LangGraph\" description: \"Learn how to use Langfuse for open source observability/tracing in your LangGraph application (Python).\" category: \"Integrations\" -->\n",
+    "<!-- NOTEBOOK_METADATA source: \"Jupyter Notebook\" title: \"Open Source Observability for LangGraph\" sidebarTitle: \"LangGraph\" logo: \"/images/integrations/langchain_icon.png\" description: \"Learn how to use Langfuse for open source observability/tracing in your LangGraph application (Python).\" category: \"Integrations\" -->\n",
     "\n",
     "# Cookbook: LangGraph Integration\n"
    ]
diff --git a/cookbook/integration_langserve.ipynb b/cookbook/integration_langserve.ipynb
deleted file mode 100644
index 62e342d910..0000000000
--- a/cookbook/integration_langserve.ipynb
+++ /dev/null
@@ -1,246 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "DAQz4b8eMk3b"
-   },
-   "source": "---\ntitle: Langserve Integration\ndescription: This guide shows how to natively integrate Langfuse with LangChain's Langserve for observability, metrics, evals, prompt management, playground, datasets.\ncategory: Integrations\nsidebarTitle: Langserve\nlogo: /images/integrations/langchain_icon.png\n---\n\n# Cookbook: Langserve Integration"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6f6yUZG2kuff"
-   },
-   "source": [
-    "[Langserve](https://python.langchain.com/docs/langserve/) (Python)\n",
-    "> LangServe helps developers deploy LangChain runnables and chains as a REST API.\n",
-    ">\n",
-    "> This library is integrated with FastAPI and uses pydantic for data validation.\n",
-    ">\n",
-    "> In addition, it provides a client that can be used to call into runnables deployed on a server. A JavaScript client is available in LangChain.js.\n",
-    "\n",
-    "This cookbook demonstrates how to trace applications deployed via Langserve with Langfuse (using the [LangChain integration](https://langfuse.com/integrations/frameworks/langchain)). We'll run both the server and the client in this notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "RhFKjAOYk6d-"
-   },
-   "source": "## Setup"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "KK5uk9JBNFYC",
-    "outputId": "e7703ceb-a126-4bdd-f030-147abd9a50db"
-   },
-   "outputs": [],
-   "source": "%pip install fastapi sse_starlette httpx langserve langfuse langchain-openai langchain --upgrade"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "lnNfBrDy2a8B"
-   },
-   "outputs": [],
-   "source": "import os\n\n# Get keys for your project from the project settings page\n# https://cloud.langfuse.com\nos.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"\");\nos.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"\");\nos.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n\n# Your openai key\nos.environ.setdefault(\"OPENAI_API_KEY\", \"\");"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "V9Xap-l62ptn"
-   },
-   "source": "## Simple LLM Call Example\n\nInitialize the Langfuse `CallbackHandler` (authenticated via the environment variables set above) and configure the LLM with it as callback handler. Add to Fastapi via Langserve's `add_routes()`."
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "QCUxy1f820dn"
-   },
-   "outputs": [],
-   "source": "from langchain_openai import ChatOpenAI\nfrom langchain_core.runnables.config import RunnableConfig\nfrom langfuse import get_client\nfrom langfuse.langchain import CallbackHandler\nfrom fastapi import FastAPI\nfrom langserve import add_routes\n\n# Initialize Langfuse CallbackHandler for Langchain (tracing)\nlangfuse_handler = CallbackHandler()\n\n# Tests the SDK connection with the server\nget_client().auth_check()\n\nllm = ChatOpenAI()\n\nconfig = RunnableConfig(callbacks=[langfuse_handler])\n\nllm_with_langfuse = llm.with_config(config)\n\n# Setup server\napp = FastAPI()\n\n# Add Langserve route\nadd_routes(\n    app,\n    llm_with_langfuse,\n    path=\"/test-simple-llm-call\",\n)"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "oHlJtl2AdNpH"
-   },
-   "source": [
-    "\n",
-    "\n",
-    "*Note: We use TestClient in this example to be able to run the server in a notebook*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "kekipGdp4_tk",
-    "outputId": "1fb7ec39-0559-4bbb-f417-0cdcabec9fe4"
-   },
-   "outputs": [],
-   "source": [
-    "from fastapi.testclient import TestClient\n",
-    "\n",
-    "# Initialize TestClient\n",
-    "client = TestClient(app)\n",
-    "\n",
-    "# Test simple route\n",
-    "response = client.post(\"/test-simple-llm-call/invoke\", json={\"input\": \"Tell me a joke?\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "UgHaaxS_cJT1"
-   },
-   "source": [
-    "Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/5f32e2e7-9508-4280-b47b-e0356bc3c81e\n",
-    "\n",
-    "![Trace of Langserve Simple LLM Call](https://langfuse.com/images/cookbook/integration_langserve_simple.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "eIQsCJQF9rhI"
-   },
-   "source": [
-    "## LCEL example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "2D2t0h1j-X6c",
-    "outputId": "58591cfb-2927-4179-c54f-9aa671129c69"
-   },
-   "outputs": [],
-   "source": "from langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.output_parsers import StrOutputParser\nfrom langserve import add_routes\n\n# Create Chain\nprompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n\nchain = prompt | llm | StrOutputParser()\n\n# Add new route\nadd_routes(\n    app,\n    chain.with_config(config),\n    path=\"/test-chain\",\n)\n\n# Test chain route\nresponse = client.post(\"/test-chain/invoke\", json={\"input\": {\"topic\": \"Berlin\"}})"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "sd1xjUVHcPAX"
-   },
-   "source": [
-    "Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/261d1006-74ff-4b67-8baf-afdfc827aee2\n",
-    "\n",
-    "![Trace of Langserve LCEL Example](https://langfuse.com/images/cookbook/integration_langserve_chain.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "39MTCpb9OsY_"
-   },
-   "source": [
-    "## Agent Example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "OLqZMRrR4gjO"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_core.tools import tool\n",
-    "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
-    "from langchain.agents.format_scratchpad.openai_tools import (\n",
-    "    format_to_openai_tool_messages,\n",
-    ")\n",
-    "from langchain.agents import AgentExecutor\n",
-    "from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser\n",
-    "from langserve.pydantic_v1 import BaseModel\n",
-    "from langchain_core.prompts import MessagesPlaceholder\n",
-    "\n",
-    "class Input(BaseModel):\n",
-    "    input: str\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You are a helpful assistant.\"),\n",
-    "        (\"user\", \"{input}\"),\n",
-    "        MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "@tool\n",
-    "def word_length(word: str) -> int:\n",
-    "    \"\"\"Returns a counter word\"\"\"\n",
-    "    return len(word)\n",
-    "\n",
-    "tools = [word_length]\n",
-    "\n",
-    "llm_with_tools = llm.bind(tools=[convert_to_openai_tool(tool) for tool in tools])\n",
-    "\n",
-    "agent = (\n",
-    "    {\n",
-    "        \"input\": lambda x: x[\"input\"],\n",
-    "        \"agent_scratchpad\": lambda x: format_to_openai_tool_messages(\n",
-    "            x[\"intermediate_steps\"]\n",
-    "        ),\n",
-    "    }\n",
-    "    | prompt\n",
-    "    | llm_with_tools\n",
-    "    | OpenAIToolsAgentOutputParser()\n",
-    ")\n",
-    "agent_executor = AgentExecutor(agent=agent, tools=tools)\n",
-    "\n",
-    "agent_config = RunnableConfig({\"run_name\": \"agent\"}, callbacks=[langfuse_handler])\n",
-    "\n",
-    "add_routes(\n",
-    "    app,\n",
-    "    agent_executor.with_types(input_type=Input).with_config(\n",
-    "        agent_config\n",
-    "    ),\n",
-    "    path=\"/test-agent\",\n",
-    ")\n",
-    "\n",
-    "response = client.post(\"/test-agent/invoke\", json={\"input\": {\"input\": \"How long is Leonardo DiCaprios last name?\"}})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "1caM-ElUcTdE"
-   },
-   "source": [
-    "Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/ed1d57f9-2f35-4e72-8150-b061f21840a7\n",
-    "\n",
-    "![Trace of Langserve Agent example](https://langfuse.com/images/cookbook/integration_langserve_agent.png)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/cookbook/integration_litellm_proxy.ipynb b/cookbook/integration_litellm_proxy.ipynb
deleted file mode 100644
index 94c9b70653..0000000000
--- a/cookbook/integration_litellm_proxy.ipynb
+++ /dev/null
@@ -1,308 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "5b26fc84-994d-4e93-9e57-6a427561ac86",
-   "metadata": {},
-   "source": [
-    "<!-- NOTEBOOK_METADATA source: \"⚠️ Jupyter Notebook\" title: \"Cookbook - LiteLLM (Proxy) + Langfuse OpenAI Integration + Python Decorator\" sidebarTitle: \"Cookbook - LiteLLM (Proxy) + Langfuse OpenAI Integration + Python Decorator\" description: \"The stack to use any of 100+ models in Python without having to change your code and with full observability.\" category: \"Integrations\" -->\n",
-    "\n",
-    "# Cookbook: LiteLLM (Proxy) + Langfuse OpenAI Integration + `@observe` Decorator\n",
-    "\n",
-    "<a href=\"https://langfuse.com/guides/cookbook/integration_litellm_proxy\"><img className=\"inline\" alt=\"Python\" src=\"https://img.shields.io/badge/Python-d4d4d8?style=flat&logo=python&logoColor=white\" /></a> <a href=\"https://langfuse.com/guides/cookbook/js_integration_litellm_proxy\"><img className=\"inline\" alt=\"JS/TS\" src=\"https://img.shields.io/badge/JS/TS-F7DF1E?style=flat&logo=javascript&logoColor=black\" /></a>\n",
-    "\n",
-    "We want to share a stack that's commonly used by the Langfuse community to quickly experiment with 100+ models from different providers without changing code. This stack includes:\n",
-    "- [**LiteLLM Proxy**](https://docs.litellm.ai/docs/) ([GitHub](https://github.com/BerriAI/litellm)) which standardizes 100+ model provider APIs on the OpenAI API schema. It removes the complexity of direct API calls by centralizing interactions with these APIs through a single endpoint. You can also self-host the LiteLLM Proxy as it is open-source.\n",
-    "- **Langfuse OpenAI SDK Wrapper** ([Python](https://langfuse.com/integrations/model-providers/openai-py), [JS](https://langfuse.com/integrations/model-providers/openai-js)) to natively instrument calls to all these 100+ models via the OpenAI SDK. This automatically captures token counts, latencies, streaming response times (time to first token), api errors, and more.\n",
-    "- **Langfuse**: OSS LLM Observability, full overview [here](https://langfuse.com/docs).\n",
-    "\n",
-    "\n",
-    "This cookbook is an end-to-end guide to set up and use this stack. As we'll use Python in this example, we will also use the `@observe` decorator to create nested traces. More on this below.\n",
-    "\n",
-    "Let's dive right in!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ee92c44-cc7c-4bdb-9153-0ddc8c7c07b0",
-   "metadata": {},
-   "source": [
-    "## Install dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "74e39484-8901-4c98-95ec-911ea9f170b9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install \"litellm[proxy]\" langfuse openai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fcc213d5-116e-4a99-95b0-423bbb6bfb75",
-   "metadata": {},
-   "source": [
-    "## Setup environment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c69298dc-6c55-46e4-801f-dd74f47a8675",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "# Get keys for your project from the project settings page: https://cloud.langfuse.com\n",
-    "os.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-...\");\n",
-    "os.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"sk-lf-...\");\n",
-    "os.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n",
-    "# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n",
-    "\n",
-    "# Your openai key\n",
-    "os.environ.setdefault(\"OPENAI_API_KEY\", \"sk-proj-\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e5eade6e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from litellm import completion\n",
-    "from langfuse import observe, get_client\n",
-    "\n",
-    "langfuse = get_client()\n",
-    "\n",
-    "@observe()\n",
-    "def fn():\n",
-    "  # set custom langfuse trace params and generation params\n",
-    "  response = completion(\n",
-    "    model=\"gpt-3.5-turbo\",\n",
-    "    messages=[\n",
-    "      {\"role\": \"user\", \"content\": \"Hi 👋 - i'm openai\"}\n",
-    "    ],\n",
-    "    metadata={\n",
-    "        \"existing_trace_id\": langfuse.get_current_trace_id(),   # set langfuse trace ID\n",
-    "        \"parent_observation_id\": langfuse.get_current_observation_id(),\n",
-    "    },\n",
-    "  )\n",
-    " \n",
-    "  print(response)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d92f6fdd-48f1-4f40-bf58-17be90b8e3b5",
-   "metadata": {},
-   "source": [
-    "## Setup Lite LLM Proxy\n",
-    "\n",
-    "In this example, we'll use GPT-3.5-turbo directly from OpenAI, and llama3 and mistral via the Ollama on our local machine.\n",
-    "\n",
-    "**Steps**\n",
-    "\n",
-    "1. Create a `litellm_config.yaml` to configure which models are available ([docs](https://litellm.vercel.app/docs/proxy/configs)). We'll use gpt-3.5-turbo, and llama3 and mistral via Ollama in this example. Make sure to replace `<openai_key>` with your OpenAI API key.\n",
-    "    ```yaml\n",
-    "    model_list:\n",
-    "      - model_name: gpt-3.5-turbo\n",
-    "        litellm_params:\n",
-    "          model: gpt-3.5-turbo\n",
-    "          api_key: <openai_key>\n",
-    "      - model_name: ollama/llama3\n",
-    "        litellm_params:\n",
-    "          model: ollama/llama3\n",
-    "      - model_name: ollama/mistral\n",
-    "        litellm_params:\n",
-    "          model: ollama/mistral\n",
-    "    ```\n",
-    "2. Ensure that you installed Ollama and have pulled the llama3 (8b) and mistral (7b) models: `ollama pull llama3 && ollama pull mistral`\n",
-    "3. Run the following cli command to start the proxy: `litellm --config litellm_config.yaml`\n",
-    "\n",
-    "The Lite LLM Proxy should be now running on http://0.0.0.0:4000\n",
-    "\n",
-    "To verify the connection you can run `litellm --test`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "042146c5-679b-4259-a55c-56aaf51bdfd7",
-   "metadata": {},
-   "source": [
-    "## Log single LLM Call via Langfuse OpenAI Wrapper\n",
-    "The Langfuse SDK offers a wrapper function around the OpenAI SDK, automatically logging all OpenAI calls as generations to Langfuse.\n",
-    "\n",
-    "For more details, please refer to our [documentation](https://langfuse.com/integrations/model-providers/openai-py)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aeb4761a-7387-4375-aab0-da02c32f1415",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langfuse.openai import openai\n",
-    "\n",
-    "# Set PROXY_URL to the url of your lite_llm_proxy (by default: http://0.0.0.0:4000)\n",
-    "PROXY_URL=\"http://0.0.0.0:4000\"\n",
-    "\n",
-    "system_prompt = \"You are a very accurate calculator. You output only the result of the calculation.\"\n",
-    "\n",
-    "# Configure the OpenAI client to use the LiteLLM proxy\n",
-    "client = openai.OpenAI(base_url=PROXY_URL)\n",
-    "\n",
-    "gpt_completion = client.chat.completions.create(\n",
-    "  model=\"gpt-3.5-turbo\",\n",
-    "  name=\"gpt-3.5\", # optional name of the generation in langfuse\n",
-    "  messages=[\n",
-    "      {\"role\": \"system\", \"content\": system_prompt},\n",
-    "      {\"role\": \"user\", \"content\": \"1 + 1 = \"}],\n",
-    ")\n",
-    "print(gpt_completion.choices[0].message.content)\n",
-    "\n",
-    "llama_completion = client.chat.completions.create(\n",
-    "  model=\"ollama/llama3\",\n",
-    "  name=\"llama3\", # optional name of the generation in langfuse\n",
-    "  messages=[\n",
-    "      {\"role\": \"system\", \"content\": system_prompt},\n",
-    "      {\"role\": \"user\", \"content\": \"3 + 3 = \"}],\n",
-    ")\n",
-    "print(llama_completion.choices[0].message.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "31fe883d-88eb-4ebf-8870-99e9c60104f7",
-   "metadata": {},
-   "source": [
-    "Public trace links for the following examples:\n",
-    "- [GPT-3.5-turbo](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/a4e67d7d-d9cb-455b-9795-3ad41f39431e?observation=81006513-82b1-4ae4-bb98-7e1bc6c009a7)\n",
-    "- [llama3](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/22fdce4a-4d74-4af3-9746-7bafaa45247c?observation=b9b30b5d-7fbf-40b4-acd9-4fdc1776cc87)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4144dcf-3478-4e3f-bed2-a394e8ccc253",
-   "metadata": {},
-   "source": [
-    "## Trace nested LLM Calls via Langfuse OpenAI Wrapper and `@observe` decorator\n",
-    "\n",
-    "Via the Langfuse `@observe()` decorator we can automatically capture execution details of any python function such as inputs, outputs, timings, and more. The decorator simplifies achieving in-depth observability in your applications with minimal code, especially when non-LLM calls are involved for knowledge retrieval (RAG) or api calls (agents).\n",
-    "\n",
-    "For more details on how to utilize this decorator and customize your tracing, refer to our [documentation](https://langfuse.com/docs/sdk/python/decorators).\n",
-    "\n",
-    "Let's have a look at a simple example which uses all three models we have set up in the LiteLLM Proxy:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1aeaa015-6b6b-4ac8-8e07-55e1783a398e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langfuse import observe\n",
-    "from langfuse.openai import openai\n",
-    "\n",
-    "@observe()\n",
-    "def rap_battle(topic: str):\n",
-    "    client = openai.OpenAI(\n",
-    "        base_url=PROXY_URL,\n",
-    "    )\n",
-    "\n",
-    "    messages = [\n",
-    "        {\"role\": \"system\", \"content\": \"You are a rap artist. Drop a fresh line.\"},\n",
-    "        {\"role\": \"user\", \"content\": \"Kick it off, today's topic is {topic}, here's the mic...\"}\n",
-    "    ]\n",
-    "\n",
-    "    # First model (gpt-3.5-turbo) starts the rap\n",
-    "    gpt_completion = client.chat.completions.create(\n",
-    "        model=\"gpt-3.5-turbo\",\n",
-    "        name=\"rap-gpt-3.5-turbo\", # add custom name to Langfuse observation\n",
-    "        messages=messages,\n",
-    "    )\n",
-    "    first_rap = gpt_completion.choices[0].message.content\n",
-    "    messages.append({\"role\": \"assistant\", \"content\": first_rap})\n",
-    "    print(\"Rap 1:\", first_rap)\n",
-    "\n",
-    "    # Second model (ollama/llama3) responds\n",
-    "    llama_completion = client.chat.completions.create(\n",
-    "        model=\"ollama/llama3\",\n",
-    "        name=\"rap-llama3\",\n",
-    "        messages=messages,\n",
-    "    )\n",
-    "    second_rap = llama_completion.choices[0].message.content\n",
-    "    messages.append({\"role\": \"assistant\", \"content\": second_rap})\n",
-    "    print(\"Rap 2:\", second_rap)\n",
-    "\n",
-    "    # Third model (ollama/mistral) adds the final touch\n",
-    "    mistral_completion = client.chat.completions.create(\n",
-    "        model=\"ollama/mistral\",\n",
-    "        name=\"rap-mistral\",\n",
-    "        messages=messages,\n",
-    "    )\n",
-    "    third_rap = mistral_completion.choices[0].message.content\n",
-    "    messages.append({\"role\": \"assistant\", \"content\": third_rap})\n",
-    "    print(\"Rap 3:\", third_rap)\n",
-    "    \n",
-    "    return messages\n",
-    "\n",
-    "# Call the function\n",
-    "rap_battle(\"typography\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91e4d553-5efc-4b94-a1c2-0637133f376b",
-   "metadata": {},
-   "source": [
-    "[Public trace](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/7b1af1be-8096-474e-a2fc-081538ca333c)\n",
-    "\n",
-    "![Public Trace](https://langfuse.com/images/cookbook/integration_litellm_proxy_trace.gif)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "51eb9581",
-   "metadata": {},
-   "source": [
-    "## Learn more\n",
-    "\n",
-    "Check out the docs to learn more about all components of this stack:\n",
-    "- [LiteLLM Proxy](https://docs.litellm.ai/docs/)\n",
-    "- [Langfuse OpenAI SDK Wrapper](https://langfuse.com/integrations/model-providers/openai-py)\n",
-    "- [Langfuse @observe() decorator](https://langfuse.com/docs/sdk/python/decorators)\n",
-    "- [Langfuse](https://langfuse.com/docs)\n",
-    "\n",
-    "If you do not want to capture traces via the OpenAI SDK Wrapper, you can also directly log requests from the LiteLLM Proxy to Langfuse. For more details, refer to the [LiteLLM Docs](https://litellm.vercel.app/docs/proxy/logging#logging-proxy-inputoutput---langfuse)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/cookbook/integration_llama-index-callback.ipynb b/cookbook/integration_llama-index-callback.ipynb
deleted file mode 100644
index e1624a1515..0000000000
--- a/cookbook/integration_llama-index-callback.ipynb
+++ /dev/null
@@ -1,229 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "---\n",
-        "description: Example cookbook for the LlamaIndex Langfuse integration.\n",
-        "category: Integrations\n",
-        "---"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Cookbook LlamaIndex Integration"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This is a simple cookbook that demonstrates how to trace a LlamaIndex application with Langfuse via the [LlamaIndex Langfuse integration](https://langfuse.com/integrations/frameworks/llamaindex). It uses a very simple Index and Query together with the [OpenInference LlamaIndex instrumentation](https://docs.arize.com/phoenix/tracing/integrations-tracing/llamaindex), which automatically exports OpenTelemetry (OTel) spans to Langfuse."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "**Any feedback?** Let us know on Discord or GitHub. We'd love to hear your thoughts."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Make sure you have `llama-index`, `langfuse`, and `openinference-instrumentation-llama-index` installed."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "vscode": {
-          "languageId": "shellscript"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "%pip install llama-index langfuse openinference-instrumentation-llama-index --upgrade"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Set your Langfuse API keys as environment variables. Get your API keys from the [Langfuse project settings](https://cloud.langfuse.com). This example uses OpenAI for embeddings and chat completions, so you also need to set your OpenAI key."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "# Get keys for your project from the project settings page: https://cloud.langfuse.com\n",
-        "\n",
-        "os.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-...\");\n",
-        "os.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"sk-lf-...\");\n",
-        "os.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n",
-        "# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n",
-        "\n",
-        "# Your OpenAI key\n",
-        "os.environ.setdefault(\"OPENAI_API_KEY\", \"sk-proj-...\");"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "With the environment variables set, we can initialize the Langfuse client and register the [OpenInference LlamaIndex instrumentation](https://docs.arize.com/phoenix/tracing/integrations-tracing/llamaindex). This third-party instrumentation automatically captures LlamaIndex operations and exports OpenTelemetry (OTel) spans to Langfuse."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langfuse import get_client\n",
-        "from openinference.instrumentation.llama_index import LlamaIndexInstrumentor\n",
-        "\n",
-        "langfuse = get_client()\n",
-        "\n",
-        "# Verify connection\n",
-        "if langfuse.auth_check():\n",
-        "    print(\"Langfuse client is authenticated and ready!\")\n",
-        "else:\n",
-        "    print(\"Authentication failed. Please check your credentials and host.\")\n",
-        "\n",
-        "# Initialize LlamaIndex instrumentation\n",
-        "LlamaIndexInstrumentor().instrument()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Index"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Example context, thx ChatGPT\n",
-        "from llama_index.core import Document\n",
-        "\n",
-        "doc1 = Document(text=\"\"\"\n",
-        "Maxwell \"Max\" Silverstein, a lauded movie director, screenwriter, and producer, was born on October 25, 1978, in Boston, Massachusetts. A film enthusiast from a young age, his journey began with home movies shot on a Super 8 camera. His passion led him to the University of Southern California (USC), majoring in Film Production. Eventually, he started his career as an assistant director at Paramount Pictures. Silverstein's directorial debut, “Doors Unseen,” a psychological thriller, earned him recognition at the Sundance Film Festival and marked the beginning of a successful directing career.\n",
-        "\"\"\")\n",
-        "doc2 = Document(text=\"\"\"\n",
-        "Throughout his career, Silverstein has been celebrated for his diverse range of filmography and unique narrative technique. He masterfully blends suspense, human emotion, and subtle humor in his storylines. Among his notable works are \"Fleeting Echoes,\" \"Halcyon Dusk,\" and the Academy Award-winning sci-fi epic, \"Event Horizon's Brink.\" His contribution to cinema revolves around examining human nature, the complexity of relationships, and probing reality and perception. Off-camera, he is a dedicated philanthropist living in Los Angeles with his wife and two children.\n",
-        "\"\"\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Example index construction + LLM query\n",
-        "from llama_index.core import VectorStoreIndex\n",
-        "\n",
-        "index = VectorStoreIndex.from_documents([doc1,doc2])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Query"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Query\n",
-        "response = index.as_query_engine().query(\"What did he do growing up?\")\n",
-        "print(response)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Chat\n",
-        "response = index.as_chat_engine().chat(\"What did he do growing up?\")\n",
-        "print(response)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explore traces in Langfuse"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# As we want to immediately see the result in Langfuse, we flush the Langfuse client\n",
-        "langfuse.flush()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Done! ✨ You see traces of your index and query in your Langfuse project.\n",
-        "\n",
-        "![Example Trace in Langfuse](https://langfuse.com/images/cookbook/integration_llama-index/llama-index-example-trace.png)\n",
-        "\n",
-        "[Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/12ea412956f99347b0503c1144acd0ec?timestamp=2025-06-05T15:45:52.971Z&display=details)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n",
-        "## Interested in more advanced features?\n",
-        "\n",
-        "See the full [integration docs](https://langfuse.com/integrations/frameworks/llamaindex) to learn more about advanced features and how to use them:\n",
-        "\n",
-        "- Interoperability with Langfuse Python SDK and other integrations\n",
-        "- Add custom metadata and attributes to the traces"
-      ]
-    }
-  ],
-  "metadata": {
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
diff --git a/cookbook/integration_llama-index_instrumentation.ipynb b/cookbook/integration_llama-index_instrumentation.ipynb
deleted file mode 100644
index 4b0c679e0b..0000000000
--- a/cookbook/integration_llama-index_instrumentation.ipynb
+++ /dev/null
@@ -1,273 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tXvsQZYL03aE"
-      },
-      "source": [
-        "---\n",
-        "description: Example cookbook for the LlamaIndex Langfuse integration using the instrumentation module of LlamaIndex.\n",
-        "category: Integrations\n",
-        "---\n",
-        "\n",
-        "# Cookbook LlamaIndex Integration (Instrumentation Module)\n",
-        "\n",
-        "This is a simple cookbook that demonstrates how to trace LlamaIndex applications with Langfuse based on the [instrumentation module](https://docs.llamaindex.ai/en/stable/module_guides/observability/instrumentation/) of LlamaIndex. It uses the [OpenInference LlamaIndex instrumentation](https://docs.arize.com/phoenix/tracing/integrations-tracing/llamaindex), which builds on LlamaIndex's instrumentation module and exports OpenTelemetry (OTel) spans to Langfuse. See the [LlamaIndex integration docs](https://langfuse.com/integrations/frameworks/llamaindex) for more details."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Setup\n",
-        "\n",
-        "Make sure you have `llama-index`, `langfuse`, and `openinference-instrumentation-llama-index` installed."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "fhOqgxXv0-aL"
-      },
-      "outputs": [],
-      "source": [
-        "%pip install langfuse openinference-instrumentation-llama-index llama-index --upgrade"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Initialize the integration. Get your API keys from the Langfuse project settings. This example uses OpenAI for embeddings and chat completions. You can also use any other model supported by LlamaIndex."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "wFliXlaT1DlW"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "# Get keys for your project from the project settings page\n",
-        "# https://cloud.langfuse.com\n",
-        "os.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"\");\n",
-        "os.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"\");\n",
-        "os.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n",
-        "# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n",
-        "\n",
-        "# Your openai key\n",
-        "os.environ.setdefault(\"OPENAI_API_KEY\", \"\");"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Initialize the Langfuse client and register the OpenInference `LlamaIndexInstrumentor`. The instrumentor hooks into LlamaIndex's instrumentation module and exports OTel spans to Langfuse:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "tc_Si9Rs01zc"
-      },
-      "outputs": [],
-      "source": [
-        "from langfuse import get_client\n",
-        "from openinference.instrumentation.llama_index import LlamaIndexInstrumentor\n",
-        "\n",
-        "langfuse = get_client()\n",
-        "\n",
-        "# Verify connection\n",
-        "if langfuse.auth_check():\n",
-        "    print(\"Langfuse client is authenticated and ready!\")\n",
-        "else:\n",
-        "    print(\"Authentication failed. Please check your credentials and host.\")\n",
-        "\n",
-        "# Initialize LlamaIndex instrumentation\n",
-        "LlamaIndexInstrumentor().instrument()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "x7w2hVGQ13zK"
-      },
-      "source": [
-        "## Index"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "id": "qydmtcE91zDM"
-      },
-      "outputs": [],
-      "source": [
-        "# Example context, thx ChatGPT\n",
-        "from llama_index.core import Document\n",
-        "\n",
-        "doc1 = Document(text=\"\"\"\n",
-        "Maxwell \"Max\" Silverstein, a lauded movie director, screenwriter, and producer, was born on October 25, 1978, in Boston, Massachusetts. A film enthusiast from a young age, his journey began with home movies shot on a Super 8 camera. His passion led him to the University of Southern California (USC), majoring in Film Production. Eventually, he started his career as an assistant director at Paramount Pictures. Silverstein's directorial debut, “Doors Unseen,” a psychological thriller, earned him recognition at the Sundance Film Festival and marked the beginning of a successful directing career.\n",
-        "\"\"\")\n",
-        "doc2 = Document(text=\"\"\"\n",
-        "Throughout his career, Silverstein has been celebrated for his diverse range of filmography and unique narrative technique. He masterfully blends suspense, human emotion, and subtle humor in his storylines. Among his notable works are \"Fleeting Echoes,\" \"Halcyon Dusk,\" and the Academy Award-winning sci-fi epic, \"Event Horizon's Brink.\" His contribution to cinema revolves around examining human nature, the complexity of relationships, and probing reality and perception. Off-camera, he is a dedicated philanthropist living in Los Angeles with his wife and two children.\n",
-        "\"\"\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "id": "OykJH7_416Iy"
-      },
-      "outputs": [],
-      "source": [
-        "# Example index construction + LLM query\n",
-        "from llama_index.core import VectorStoreIndex\n",
-        "\n",
-        "index = VectorStoreIndex.from_documents([doc1,doc2])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "h62UxCTY179q"
-      },
-      "source": [
-        "## Query"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "L79u8D8K16jC",
-        "outputId": "0016cd35-82aa-4e24-e8ee-b7919356c68c"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "He made home movies using a Super 8 camera.\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Query\n",
-        "response = index.as_query_engine().query(\"What did he do growing up?\")\n",
-        "print(response)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1KxscR7-2NFn"
-      },
-      "source": [
-        "Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/d933c7cc-20bf-4db3-810d-bab1c8d9a2a1"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "DRVa80vs19tB",
-        "outputId": "d52dee9e-6876-4152-8518-e2177c322e4c"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "He made home movies using a Super 8 camera growing up.\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Chat\n",
-        "response = index.as_chat_engine().chat(\"What did he do growing up?\")\n",
-        "print(response)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "z5Imz4gX2S5y"
-      },
-      "source": [
-        "Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/4e285b8f-9789-4cf0-a8b4-45473ac420f1\n",
-        "\n",
-        "![LlamaIndex Chat Engine Trace in Langfuse (via instrumentation module)](https://langfuse.com/images/cookbook/integration_llama-index_instrumentation_chatengine_trace.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Custom trace properties"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can set custom trace attributes such as `user_id`, `session_id`, or `tags` with the `propagate_attributes` context manager. To group multiple operations into a single trace and score it, wrap them in a root observation via `langfuse.start_as_current_observation` and use `score_trace` on the root span."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langfuse import propagate_attributes\n",
-        "\n",
-        "with langfuse.start_as_current_observation(as_type=\"span\", name=\"llama-index-query\") as span:\n",
-        "    with propagate_attributes(user_id=\"my-user\", session_id=\"my-session\"):\n",
-        "        response = index.as_query_engine().query(\"What did he do growing up?\")\n",
-        "    span.update(output=str(response))\n",
-        "\n",
-        "    # Score the trace, e.g. based on user feedback\n",
-        "    span.score_trace(name=\"my-score\", value=0.5)\n",
-        "\n",
-        "langfuse.flush()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/6f554d6b-a2bc-4fba-904f-aa54de2897ca"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
diff --git a/cookbook/integration_llama-index_milvus-lite.ipynb b/cookbook/integration_llama-index_milvus-lite.ipynb
deleted file mode 100644
index 3f9b1903c5..0000000000
--- a/cookbook/integration_llama-index_milvus-lite.ipynb
+++ /dev/null
@@ -1,233 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!-- NOTEBOOK_METADATA source: \"⚠️ Jupyter Notebook\" title: \"# Cookbook: LlamaIndex & Milvus Integration\" description: \"Example cookbook for the LlamaIndex, Milvus and Langfuse integration.\" category: \"Integrations\" -->"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cookbook: LlamaIndex & Milvus Integration"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is a simple cookbook that demonstrates how to use the [LlamaIndex Langfuse integration](https://langfuse.com/integrations/frameworks/llamaindex). It uses Milvus Lite to store the documents and Query.\n",
-    "\n",
-    "[Milvus Lite](https://github.com/milvus-io/milvus-lite/) is the lightweight version of Milvus, an open-source vector database that powers AI applications with vector embeddings and similarity search.\n",
-    "\n",
-    "Traces are captured with the [OpenInference LlamaIndex instrumentation](https://docs.arize.com/phoenix/tracing/integrations-tracing/llamaindex), which automatically exports OpenTelemetry (OTel) spans to Langfuse."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%pip install llama-index langfuse openinference-instrumentation-llama-index llama-index-vector-stores-milvus --upgrade"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initialize the integration. Get your API keys from the [Langfuse project settings](https://cloud.langfuse.com) and set them as environment variables. This example uses OpenAI for embeddings and chat completions, so you also need to specify your OpenAI key in an environment variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "# Get keys for your project from the project settings page\n",
-    "# https://cloud.langfuse.com\n",
-    "os.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"\");\n",
-    "os.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"\");\n",
-    "os.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n",
-    "# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n",
-    "\n",
-    "# Your openai key\n",
-    "os.environ.setdefault(\"OPENAI_API_KEY\", \"\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langfuse import get_client\n",
-    "from openinference.instrumentation.llama_index import LlamaIndexInstrumentor\n",
-    "\n",
-    "langfuse = get_client()\n",
-    "\n",
-    "# Verify connection\n",
-    "if langfuse.auth_check():\n",
-    "    print(\"Langfuse client is authenticated and ready!\")\n",
-    "else:\n",
-    "    print(\"Authentication failed. Please check your credentials and host.\")\n",
-    "\n",
-    "# Initialize LlamaIndex instrumentation\n",
-    "LlamaIndexInstrumentor().instrument()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Index using Milvus Lite"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_index.core import Document\n",
-    "\n",
-    "doc1 = Document(text=\"\"\"\n",
-    "Maxwell \"Max\" Silverstein, a lauded movie director, screenwriter, and producer, was born on October 25, 1978, in Boston, Massachusetts. A film enthusiast from a young age, his journey began with home movies shot on a Super 8 camera. His passion led him to the University of Southern California (USC), majoring in Film Production. Eventually, he started his career as an assistant director at Paramount Pictures. Silverstein's directorial debut, “Doors Unseen,” a psychological thriller, earned him recognition at the Sundance Film Festival and marked the beginning of a successful directing career.\n",
-    "\"\"\")\n",
-    "doc2 = Document(text=\"\"\"\n",
-    "Throughout his career, Silverstein has been celebrated for his diverse range of filmography and unique narrative technique. He masterfully blends suspense, human emotion, and subtle humor in his storylines. Among his notable works are \"Fleeting Echoes,\" \"Halcyon Dusk,\" and the Academy Award-winning sci-fi epic, \"Event Horizon's Brink.\" His contribution to cinema revolves around examining human nature, the complexity of relationships, and probing reality and perception. Off-camera, he is a dedicated philanthropist living in Los Angeles with his wife and two children.\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Example index construction + LLM query\n",
-    "\n",
-    "from llama_index.core import VectorStoreIndex\n",
-    "from llama_index.core import StorageContext\n",
-    "from llama_index.vector_stores.milvus import MilvusVectorStore\n",
-    "\n",
-    "\n",
-    "vector_store = MilvusVectorStore(\n",
-    "    uri=\"tmp/milvus_demo.db\", dim=1536, overwrite=False\n",
-    ")\n",
-    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
-    "\n",
-    "index = VectorStoreIndex.from_documents(\n",
-    "    [doc1,doc2], storage_context=storage_context\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Query"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Query\n",
-    "response = index.as_query_engine().query(\"What did he do growing up?\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Chat\n",
-    "response = index.as_chat_engine().chat(\"What did he do growing up?\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Explore traces in Langfuse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# As we want to immediately see the result in Langfuse, we flush the Langfuse client\n",
-    "langfuse.flush()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Done! ✨ You see traces of your index and query in your Langfuse project.\n",
-    "\n",
-    "![Example Trace in Langfuse](https://langfuse.com/images/cookbook/integration_llama-index/llama-index-example-trace.png)\n",
-    "\n",
-    "[Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/12ea412956f99347b0503c1144acd0ec?timestamp=2025-06-05T15:45:52.971Z&display=details)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Interested in more advanced features?\n",
-    "\n",
-    "See the full [integration docs](https://langfuse.com/integrations/frameworks/llamaindex) to learn more about advanced features and how to use them:\n",
-    "\n",
-    "- Interoperability with Langfuse Python SDK and other integrations\n",
-    "- Add custom metadata and attributes to the traces"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/cookbook/integration_llama_index_posthog_mistral.ipynb b/cookbook/integration_llama_index_posthog_mistral.ipynb
deleted file mode 100644
index 6839d1638b..0000000000
--- a/cookbook/integration_llama_index_posthog_mistral.ipynb
+++ /dev/null
@@ -1,423 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "title: Monitoring LlamaIndex applications with PostHog and Langfuse\n",
-    "description: Learn how to build and monitor a RAG application using LlamaIndex with Mistral, Langfuse, and PostHog. This step-by-step guide covers setup, integration, and analytics for optimizing LLM performance.\n",
-    "category: Integrations\n",
-    "---\n",
-    "\n",
-    "# Analyze and Debug LlamaIndex Applications with PostHog and Langfuse\n",
-    "\n",
-    "In this cookbook, we show you how to build a RAG application with [LlamaIndex](https://www.llamaindex.ai/), observe the steps with [Langfuse](https://langfuse.com/), and analyze the data in [PostHog](https://posthog.com/).\n",
-    "\n",
-    "## What is Langfuse?\n",
-    "[Langfuse](https://langfuse.com/) is an open-source AI engineering platform designed to help engineers understand and optimize user interactions with their language model applications. It provides tools for tracking, debugging, and improving LLM performance in real-world use cases. Langfuse is available both as a managed [cloud solution](https://langfuse.com/cloud) and for [local or self-hosted](https://langfuse.com/docs/deployment/feature-overview) deployments.\n",
-    "\n",
-    "## What is PostHog?\n",
-    "[PostHog](https://posthog.com/) is a popular choice for product analytics. Combining Langfuse's LLM analytics with PostHog's product analytics makes it easy to:\n",
-    "\n",
-    "- **Analyze User Engagement**: Determine how often users interact with specific LLM features and understand their overall activity patterns.\n",
-    "- **Correlate Feedback with Behavior**: See how user feedback captured in Langfuse correlates with user behavior in PostHog.\n",
-    "- **Monitor LLM Performance**: Track and analyze metrics such as model cost, latency, and user feedback to optimize LLM performance.\n",
-    "\n",
-    "## What is LlamaIndex?\n",
-    "LlamaIndex [(GitHub)](https://github.com/run-llama/llama_index) is a data framework designed to connect LLMs with external data sources. It helps structure, index, and query data effectively. This makes it easier for developers to build advanced LLM applications.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## How to Build a Simple RAG Application with LlamaIndex and Mistral\n",
-    "\n",
-    "In this tutorial, we demonstrate how to create a chat application that provides answers to questions about hedgehog care. LlamaIndex is used to vectorize a [hedgehog care guide](https://www.pro-igel.de/downloads/merkblaetter_engl/wildtier_engl.pdf) with the [Mistral 8x22B model](https://docs.mistral.ai/getting-started/models/). All model generations are then traced using Langfuse's [LlamaIndex integration](https://langfuse.com/integrations/frameworks/llamaindex).\n",
-    "\n",
-    "Finally, the [PostHog integration](https://langfuse.com/docs/analytics/posthog) allows you to view detailed analytics about your hedgehog application directly in PostHog.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 1: Set up LlamaIndex and Mistral\n",
-    "\n",
-    "First, we set our Mistral API key as an environment variable. If you haven't already, [sign up for a Mistral account](https://console.mistral.ai/). Then [subscribe](https://console.mistral.ai/billing/) to a free trial or billing plan, after which you'll be able to [generate an API key](https://console.mistral.ai/api-keys/) (💡 You can use any other model supported by LlamaIndex; we just use Mistral in this cookbook).\n",
-    "\n",
-    "Then, we use LlamaIndex to initialize both a Mistral language model and an embedding model. We then set these models in the LlamaIndex `Settings` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install llama-index llama-index-llms-mistralai llama-index-embeddings-mistralai nest_asyncio --upgrade"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set the Mistral API key\n",
-    "import os\n",
-    "\n",
-    "os.environ.setdefault(\"MISTRAL_API_KEY\", \"...\");\n",
-    "\n",
-    "# Ensures that sync and async code can be used together without issues\n",
-    "import nest_asyncio\n",
-    "\n",
-    "nest_asyncio.apply()\n",
-    "\n",
-    "# Import and set up llama index\n",
-    "from llama_index.llms.mistralai import MistralAI\n",
-    "from llama_index.embeddings.mistralai import MistralAIEmbedding\n",
-    "from llama_index.core import Settings\n",
-    "\n",
-    "# Define your LLM and embedding model\n",
-    "llm = MistralAI(model=\"open-mixtral-8x22b\", temperature=0.1)\n",
-    "embed_model = MistralAIEmbedding(model_name=\"mistral-embed\")\n",
-    "\n",
-    "# Set the LLM and embedding model in the Settings object\n",
-    "Settings.llm = llm\n",
-    "Settings.embed_model = embed_model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 2: Initialize Langfuse\n",
-    "\n",
-    "Next, we initialize the Langfuse client. [Sign up](https://cloud.langfuse.com/auth/sign-up) for Langfuse if you haven't already. Copy your [API keys](https://langfuse.com/faq/all/where-are-langfuse-api-keys) from your project settings and add them to your environment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install langfuse openinference-instrumentation-llama-index wget"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "# Get keys for your project from the project settings page: https://cloud.langfuse.com\n",
-    "os.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-...\");\n",
-    "os.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"sk-lf-...\");\n",
-    "os.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n",
-    "# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the environment variables set, we can now initialize the Langfuse client. get_client() initializes the Langfuse client using the credentials provided in the environment variables."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Langfuse client is authenticated and ready!\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langfuse import get_client\n",
-    " \n",
-    "langfuse = get_client()\n",
-    " \n",
-    "# Verify connection\n",
-    "if langfuse.auth_check():\n",
-    "    print(\"Langfuse client is authenticated and ready!\")\n",
-    "else:\n",
-    "    print(\"Authentication failed. Please check your credentials and host.\")\n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we initialize the [OpenInference LlamaIndex instrumentation](https://docs.arize.com/phoenix/tracing/integrations-tracing/llamaindex). This third-party instrumentation automatically captures LlamaIndex operations and exports OpenTelemetry (OTel) spans to Langfuse.\n",
-    "\n",
-    "Find out more about the Langfuse's LlamaIndex integration [here](https://langfuse.com/integrations/frameworks/llamaindex)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from openinference.instrumentation.llama_index import LlamaIndexInstrumentor\n",
-    " \n",
-    "# Initialize LlamaIndex instrumentation\n",
-    "LlamaIndexInstrumentor().instrument()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 3: Download data\n",
-    "\n",
-    "We download the file we want to use for RAG. In this example, we use a [hedgehog care guide](https://www.pro-igel.de/downloads/merkblaetter_engl/wildtier_engl.pdf) pdf file to enable the language model to answer questions about caring for hedgehogs 🦔."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'./hedgehog (1).pdf'"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import wget\n",
-    "\n",
-    "url = \"https://www.pro-igel.de/downloads/merkblaetter_engl/wildtier_engl.pdf\"\n",
-    "wget.download(url, \"./hedgehog.pdf\")   # saves as ./hedgehog.pdf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we load the pdf using the LlamaIndex [`SimpleDirectoryReader`](https://docs.llamaindex.ai/en/stable/module_guides/loading/simpledirectoryreader/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_index.core import SimpleDirectoryReader\n",
-    "\n",
-    "hedgehog_docs = SimpleDirectoryReader(\n",
-    "    input_files=[\"./hedgehog.pdf\"]\n",
-    ").load_data()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 4: Build RAG on the hedgehog doc\n",
-    "\n",
-    "Next, we create vector embeddings of the hedgehog document using [`VectorStoreIndex`](https://docs.llamaindex.ai/en/stable/module_guides/indexing/vector_store_index/) and then convert it into a [queryable engine](https://docs.llamaindex.ai/en/stable/module_guides/deploying/query_engine/) to retrieve information based on queries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_index.core import VectorStoreIndex\n",
-    "\n",
-    "hedgehog_index = VectorStoreIndex.from_documents(hedgehog_docs)\n",
-    "hedgehog_query_engine = hedgehog_index.as_query_engine(similarity_top_k=5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, to put everything together, we query the engine and print a response:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hedgehogs that may require help include young hedgehogs in need of assistance during autumn, those in need of care, orphaned hoglets, and hedgehogs in need of rehabilitation before release. Additionally, hedgehogs facing dangers such as poison, pesticides, and hazards in built-up areas may also need assistance.\n"
-     ]
-    }
-   ],
-   "source": [
-    "response = hedgehog_query_engine.query(\"Which hedgehogs require help?\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "All steps of the LLM chain are now tracked in Langfuse.\n",
-    "\n",
-    "Example trace in Langfuse: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/367db23d-5b03-446b-bc73-36e289596c00"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Example trace in the Langfuse UI](https://langfuse.com/images/cookbook/example-posthog-llamaindex-mistral/trace-posthog-llamaindex-miostral.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 5: (Optional) Implement user feedback to see how your application is performing\n",
-    "\n",
-    "To monitor the quality of your hedgehog chat application, you can use [Langfuse Scores](https://langfuse.com/docs/scores/overview) to store user feedback (e.g. thumps up/down or comments). These scores can then be analyzed in PostHog.\n",
-    "\n",
-    "Scores are used to evaluate single observations or entire traces. You can create them via the annotation workflow in the Langfuse UI, run model-based evaluation or ingest via the SDK as we do it in this example.\n",
-    "\n",
-    "To get the context of the current observation, we use the [`observe()` decorator](https://langfuse.com/docs/sdk/python/decorators) and apply it to the hedgehog_helper() function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Based on the provided context, there is no information regarding keeping hedgehogs as pets. The text primarily discusses the biology, behavior, and protection of wild hedgehogs. It is important to note that laws and regulations regarding the keeping of wild animals as pets can vary greatly, so it is always best to consult with local wildlife authorities or experts.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langfuse import observe, get_client\n",
-    "\n",
-    "langfuse = get_client()\n",
-    "\n",
-    "# Langfuse observe() decorator to automatically create a trace for the top-level function and spans for any nested functions.\n",
-    "@observe()\n",
-    "def hedgehog_helper(user_message):\n",
-    "    response = hedgehog_query_engine.query(user_message)\n",
-    "    trace_id = langfuse.get_current_trace_id()\n",
-    "\n",
-    "    print(response)\n",
-    "\n",
-    "    return trace_id\n",
-    "\n",
-    "trace_id = hedgehog_helper(\"Can I keep the hedgehog as a pet?\")\n",
-    "\n",
-    "# Score the trace, e.g. to add user feedback using the trace_id\n",
-    "langfuse.create_score(\n",
-    "    trace_id=trace_id,\n",
-    "    name=\"user-explicit-feedback\",\n",
-    "    value=0.9,\n",
-    "    data_type=\"NUMERIC\",  # optional, inferred if not provided\n",
-    "    comment=\"Good to know!\",  # optional\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 6: See your data in PostHog\n",
-    "\n",
-    "Finally, we connect PostHog to our Langfuse account. Below is a summary of the steps to take (or see the [docs](https://posthog.com/docs/ai-engineering/langfuse-posthog) for full details):\n",
-    "\n",
-    "1. [Sign up](https://us.posthog.com/) for your free PostHog account if you haven't already\n",
-    "2. Copy both your project API key and host from your [project settings](https://us.posthog.com/project/settings/project-details).\n",
-    "3. In your [Langfuse dashboard](https://langfuse.com/cloud), click on **Settings** and scroll down to the **Integrations** section to find the PostHog integration.\n",
-    "4. Click **Configure** and paste in your PostHog host and project API key (you can find these in your [PostHog project settings](https://us.posthog.com/settings/project)).\n",
-    "5. Click **Enabled** and then **Save**.\n",
-    "\n",
-    " Langfuse will then begin exporting your data to PostHog once a day.\n",
-    "\n",
-    "**Using the Langfuse dashboard template:**\n",
-    "\n",
-    "Once you've installed the integration, [dashboard templates](https://posthog.com/docs/ai-engineering/langfuse-posthog#using-the-langfuse-dashboard-template) help you quickly set up relevant insights.\n",
-    "\n",
-    "For our hedgehog chat application, we are using the template dashboard shown below. This enables you to analyze model cost, user feedback, and latency in PostHog.\n",
-    "\n",
-    "To create your own dashboard from a template:\n",
-    "\n",
-    "1. Go to the [dashboard](https://us.posthog.com/dashboard) tab in PostHog.\n",
-    "2. Click the **New dashboard** button in the top right.\n",
-    "3. Select **LLM metrics – Langfuse** from the list of templates."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Posthog Dashboard showing user feedback and number of traces](https://langfuse.com/images/cookbook/example-posthog-llamaindex-mistral/dashboard-posthog-1.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Posthog Dashboard showing latency and costs](https://langfuse.com/images/cookbook/example-posthog-llamaindex-mistral/dashboard-posthog-2.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Feedback\n",
-    "\n",
-    "If you have any feedback or requests, please create a GitHub [Issue](https://github.com/orgs/langfuse/discussions) or share your idea with the community on [Discord](https://discord.langfuse.com/)."
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/cookbook/integration_openai_assistants.ipynb b/cookbook/integration_openai_assistants.ipynb
index b69a84ad31..6f1df2a5c9 100644
--- a/cookbook/integration_openai_assistants.ipynb
+++ b/cookbook/integration_openai_assistants.ipynb
@@ -18,7 +18,27 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# Cookbook: Observability for OpenAI Assistants API with Langfuse\n\nThis cookbook demonstrates how to use the Langfuse [`observe` decorator](https://langfuse.com/docs/observability/sdk/instrumentation#observe-wrapper) to trace calls made to the [OpenAI Assistants API](https://platform.openai.com/docs/assistants/overview). It covers creating an assistant, running it on a thread, and observing the execution with [Langfuse tracing](https://langfuse.com/docs/tracing).\n\nNote: The native [OpenAI SDK wrapper](https://langfuse.com/integrations/model-providers/openai-py) does not support tracing of the OpenAI assistants API, you need to instrument it via the decorator as shown in this notebook.\n\n## What is the Assistants API?\n\nThe Assistants API from OpenAI allows developers to build AI assistants that can utilize multiple tools and data sources in parallel, such as code interpreters, file search, and custom tools created by calling functions. These assistants can access OpenAI's language models like GPT-4 with specific prompts, maintain persistent conversation histories, and process various file formats like text, images, and spreadsheets. Developers can fine-tune the language models on their own data and control aspects like output randomness. The API provides a framework for creating AI applications that combine language understanding with external tools and data.\n\n## Example Trace Output\n\n![OpenAI Assistants Trace in Langfuse](https://langfuse.com/images/docs/openai-assistants-trace.png)\n\n## Setup\n\nInstall the required packages:"
+   "source": [
+    "# Cookbook: Observability for OpenAI Assistants API with Langfuse\n",
+    "\n",
+    "> **Note:** OpenAI has deprecated the Assistants API and will shut it down on August 26, 2026. For new projects, use the [Responses API](https://platform.openai.com/docs/guides/migrate-to-responses) instead.\n",
+    "\n",
+    "This cookbook demonstrates how to use the Langfuse [`observe` decorator](https://langfuse.com/docs/observability/sdk/instrumentation#observe-wrapper) to trace calls made to the [OpenAI Assistants API](https://platform.openai.com/docs/assistants/overview). It covers creating an assistant, running it on a thread, and observing the execution with [Langfuse tracing](https://langfuse.com/docs/tracing).\n",
+    "\n",
+    "Note: The native [OpenAI SDK wrapper](https://langfuse.com/integrations/model-providers/openai-py) does not support tracing of the OpenAI assistants API, you need to instrument it via the decorator as shown in this notebook.\n",
+    "\n",
+    "## What is the Assistants API?\n",
+    "\n",
+    "The Assistants API from OpenAI allows developers to build AI assistants that can utilize multiple tools and data sources in parallel, such as code interpreters, file search, and custom tools created by calling functions. These assistants can access OpenAI's language models like GPT-4 with specific prompts, maintain persistent conversation histories, and process various file formats like text, images, and spreadsheets. Developers can fine-tune the language models on their own data and control aspects like output randomness. The API provides a framework for creating AI applications that combine language understanding with external tools and data.\n",
+    "\n",
+    "## Example Trace Output\n",
+    "\n",
+    "![OpenAI Assistants Trace in Langfuse](https://langfuse.com/images/docs/openai-assistants-trace.png)\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "Install the required packages:"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/cookbook/integration_openai_sdk.ipynb b/cookbook/integration_openai_sdk.ipynb
deleted file mode 100644
index 29ddd37e5f..0000000000
--- a/cookbook/integration_openai_sdk.ipynb
+++ /dev/null
@@ -1,682 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "mfMAzJYcirtK"
-   },
-   "source": [
-    "<!-- NOTEBOOK_METADATA source: \"⚠️ Jupyter Notebook\" title: \"Cookbook - OpenAI Integration (Python)\" sidebarTitle: \"Cookbook - OpenAI Integration (Python)\" description: \"Drop-in replacement of OpenAI SDK to get full observability in Langfuse by changing only the import\" category: \"Integrations\" -->\n",
-    "\n",
-    "# Cookbook: OpenAI Integration (Python)\n",
-    "\n",
-    "<a href=\"https://langfuse.com/guides/cookbook/integration_openai_sdk\"><img className=\"inline\" alt=\"Python\" src=\"https://img.shields.io/badge/Python-d4d4d8?style=flat&logo=python&logoColor=white\" /></a> <a href=\"https://langfuse.com/guides/cookbook/js_integration_openai\"><img className=\"inline\" alt=\"JS/TS\" src=\"https://img.shields.io/badge/JS/TS-F7DF1E?style=flat&logo=javascript&logoColor=black\" /></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "B0A389k2irtK"
-   },
-   "source": [
-    "This is a cookbook with examples of the Langfuse Integration for OpenAI (Python).\n",
-    "\n",
-    "Follow the [integration guide](https://langfuse.com/integrations/model-providers/openai-py) to add this integration to your OpenAI project."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Uq04G_FSWjF-"
-   },
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XYoil3FcOIQt"
-   },
-   "source": [
-    "The integration is compatible with OpenAI SDK versions `>=0.27.8`. It supports async functions and streaming for OpenAI SDK versions `>=1.0.0`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "hVOOiBtUPtOO"
-   },
-   "outputs": [],
-   "source": [
-    "%pip install langfuse openai --upgrade"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "7-s-hY3PPupC"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "# Get keys for your project from the project settings page: https://cloud.langfuse.com\n",
-    "os.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-...\");\n",
-    "os.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"sk-lf-...\");\n",
-    "os.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n",
-    "# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n",
-    "\n",
-    "# Your openai key\n",
-    "os.environ.setdefault(\"OPENAI_API_KEY\", \"sk-proj-...\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "ldSEJ0bAP4sj"
-   },
-   "outputs": [],
-   "source": [
-    "# instead of: import openai\n",
-    "from langfuse.openai import openai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2ovnAAdbaLmD"
-   },
-   "source": [
-    "## Examples\n",
-    "\n",
-    "### Chat completion (text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "id": "c8RhokKUP9I0"
-   },
-   "outputs": [],
-   "source": [
-    "completion = openai.chat.completions.create(\n",
-    "  name=\"test-chat\",\n",
-    "  model=\"gpt-4o\",\n",
-    "  messages=[\n",
-    "      {\"role\": \"system\", \"content\": \"You are a very accurate calculator. You output only the result of the calculation.\"},\n",
-    "      {\"role\": \"user\", \"content\": \"1 + 1 = \"}],\n",
-    "  temperature=0,\n",
-    "  metadata={\"someMetadataKey\": \"someValue\"},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Link to trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/3e9d8ae6670747cb60b1434b97c3bfdb?timestamp=2025-11-13T14%3A28%3A51.831Z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "SAqxBgOqKTzO"
-   },
-   "source": [
-    "### Chat completion (image)\n",
-    "\n",
-    "Simple example using the OpenAI vision's functionality. Images may be passed in the `user` messages. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "completion = openai.chat.completions.create(\n",
-    "  name=\"test-url-image\",\n",
-    "  model=\"gpt-4o-mini\", # GPT-4o, GPT-4o mini, and GPT-4 Turbo have vision capabilities\n",
-    "  messages=[\n",
-    "      {\"role\": \"system\", \"content\": \"You are an AI trained to describe and interpret images. Describe the main objects and actions in the image.\"},\n",
-    "      {\"role\": \"user\", \"content\": [\n",
-    "        {\"type\": \"text\", \"text\": \"What’s depicted in this image?\"},\n",
-    "        {\n",
-    "          \"type\": \"image_url\",\n",
-    "          \"image_url\": {\n",
-    "            \"url\": \"https://static.langfuse.com/langfuse-dev/langfuse-example-image.jpeg\",\n",
-    "          },\n",
-    "        },\n",
-    "      ],\n",
-    "    }\n",
-    "  ],\n",
-    "  temperature=0,\n",
-    "  metadata={\"someMetadataKey\": \"someValue\"},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "M4iJpqYQirtM"
-   },
-   "source": [
-    "Go to https://cloud.langfuse.com or your own instance to see your generation.\n",
-    "\n",
-    "![Chat completion](https://langfuse.com/images/docs/multi-modal-trace.png)\n",
-    "\n",
-    "[Link to trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/b1dc51c45bc87df7d4b292717da072af?timestamp=2025-11-13T14%3A28%3A58.703Z&observation=e6af2e5e6f815256)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Chat completion (streaming)\n",
-    "\n",
-    "Simple example using the OpenAI streaming functionality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "id": "b9gRlb2rKTaA"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Why don't scientists trust atoms?\n",
-      "\n",
-      "Because they make up everything!None"
-     ]
-    }
-   ],
-   "source": [
-    "completion = openai.chat.completions.create(\n",
-    "  name=\"test-chat\",\n",
-    "  model=\"gpt-4o\",\n",
-    "  messages=[\n",
-    "      {\"role\": \"system\", \"content\": \"You are a professional comedian.\"},\n",
-    "      {\"role\": \"user\", \"content\": \"Tell me a joke.\"}],\n",
-    "  temperature=0,\n",
-    "  metadata={\"someMetadataKey\": \"someValue\"},\n",
-    "  stream=True\n",
-    ")\n",
-    "\n",
-    "for chunk in completion:\n",
-    "  print(chunk.choices[0].delta.content, end=\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "F2pvm0qLKg7Q"
-   },
-   "source": [
-    "### Chat completion (async)\n",
-    "\n",
-    "Simple example using the OpenAI async client. It takes the Langfuse configurations either from the environment variables or from the attributes on the `openai` module."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "id": "Hggwggv_MKpV"
-   },
-   "outputs": [],
-   "source": [
-    "from langfuse.openai import AsyncOpenAI\n",
-    "\n",
-    "async_client = AsyncOpenAI()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "id": "ZIUKD8Z3KmvQ"
-   },
-   "outputs": [],
-   "source": [
-    "completion = await async_client.chat.completions.create(\n",
-    "  name=\"test-chat\",\n",
-    "  model=\"gpt-4o\",\n",
-    "  messages=[\n",
-    "      {\"role\": \"system\", \"content\": \"You are a very accurate calculator. You output only the result of the calculation.\"},\n",
-    "      {\"role\": \"user\", \"content\": \"1 + 100 = \"}],\n",
-    "  temperature=0,\n",
-    "  metadata={\"someMetadataKey\": \"someValue\"},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "M4iJpqYQirtM"
-   },
-   "source": [
-    "Go to https://cloud.langfuse.com or your own instance to see your generation.\n",
-    "\n",
-    "![Chat completion](https://langfuse.com/images/docs/openai-chat.png)\n",
-    "\n",
-    "[Link to trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/00d7d6cd4278b6003fc79337cd6d4394?timestamp=2025-11-13T14%3A33%3A14.490Z&observation=79d778c36b344d7e)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ky7CtCNzaSrn"
-   },
-   "source": [
-    "### Functions\n",
-    "\n",
-    "Simple example using Pydantic to generate the function schema."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "jJfBdHowaRgs"
-   },
-   "outputs": [],
-   "source": [
-    "%pip install pydantic --upgrade"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "id": "2gA-zGk7VYYp"
-   },
-   "outputs": [],
-   "source": [
-    "from typing import List\n",
-    "from pydantic import BaseModel\n",
-    "\n",
-    "class StepByStepAIResponse(BaseModel):\n",
-    "    title: str\n",
-    "    steps: List[str]\n",
-    "schema = StepByStepAIResponse.model_json_schema() # returns a dict like JSON schema"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "ORtNcN4-afDC"
-   },
-   "outputs": [],
-   "source": [
-    "from openai import OpenAI\n",
-    "import json\n",
-    "\n",
-    "client = OpenAI()\n",
-    "\n",
-    "response = client.chat.completions.create(\n",
-    "    model=\"gpt-5\",\n",
-    "    messages=[\n",
-    "        {\"role\": \"user\", \"content\": \"Explain how to assemble a PC\"}\n",
-    "    ],\n",
-    "    tools=[\n",
-    "        {\n",
-    "            \"type\": \"function\",\n",
-    "            \"function\": {\n",
-    "                \"name\": \"get_answer_for_user_query\",\n",
-    "                \"description\": \"Get user answer in a series of steps\",\n",
-    "                \"parameters\": StepByStepAIResponse.model_json_schema()\n",
-    "            }\n",
-    "        }\n",
-    "    ],\n",
-    "    tool_choice={\"type\": \"function\", \"function\": {\"name\": \"get_answer_for_user_query\"}}\n",
-    ")\n",
-    "\n",
-    "tool_call = response.choices[0].message.tool_calls[0]\n",
-    "args = json.loads(tool_call.function.arguments)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "qurrm-Ntp24O"
-   },
-   "source": [
-    "Go to https://cloud.langfuse.com or your own instance to see your generation.\n",
-    "\n",
-    "![Function](https://langfuse.com/images/docs/openai-function.png)\n",
-    "\n",
-    "[Link to trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/cbbd69af7e00a939da55cbba80b2313e?timestamp=2025-11-13T14%3A38%3A08.304Z)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Langfuse Features (User, Tags, Metadata, Session)\n",
-    "\n",
-    "You can access additional Langfuse features by adding the relevant attributes to the OpenAI request. The Langfuse integration will parse these attributes. See [docs](https://langfuse.com/integrations/model-providers/openai-py#custom-trace-properties) for details on all available features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langfuse.openai import openai\n",
-    " \n",
-    "completion = openai.chat.completions.create(\n",
-    "  name=\"test-chat\",\n",
-    "  model=\"gpt-3.5-turbo\",\n",
-    "  messages=[\n",
-    "    {\"role\": \"system\", \"content\": \"You are a calculator.\"},\n",
-    "    {\"role\": \"user\", \"content\": \"1 + 1 = \"}],\n",
-    "  temperature=0,\n",
-    "  metadata={\n",
-    "    \"langfuse_tags\": [\"tag-1\", \"tag-2\"],\n",
-    "    \"langfuse_user_id\": \"user-123\",\n",
-    "    \"langfuse_session_id\": \"session-123\",\n",
-    "    \"langfuse_metadata\": {\"key\": \"value\"}\n",
-    "  }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/286c5c70-b077-4826-a493-36c510362a5a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## AzureOpenAI\n",
-    "\n",
-    "The integration also works with the `AzureOpenAI` and `AsyncAzureOpenAI` classes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "AZURE_OPENAI_KEY=\"\"\n",
-    "AZURE_ENDPOINT=\"\"\n",
-    "AZURE_DEPLOYMENT_NAME=\"cookbook-gpt-4o-mini\" # example deployment name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# instead of: from openai import AzureOpenAI\n",
-    "from langfuse.openai import AzureOpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "client = AzureOpenAI(\n",
-    "    api_key=AZURE_OPENAI_KEY,  \n",
-    "    api_version=\"2023-03-15-preview\",\n",
-    "    azure_endpoint=AZURE_ENDPOINT\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "client.chat.completions.create(\n",
-    "  name=\"test-chat-azure-openai\",\n",
-    "  model=AZURE_DEPLOYMENT_NAME, # deployment name\n",
-    "  messages=[\n",
-    "      {\"role\": \"system\", \"content\": \"You are a very accurate calculator. You output only the result of the calculation.\"},\n",
-    "      {\"role\": \"user\", \"content\": \"1 + 1 = \"}],\n",
-    "  temperature=0,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Example trace: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/7ceb3ee3-0f2a-4f36-ad11-87ff636efd1e"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Su1OaQq3rPPh"
-   },
-   "source": [
-    "## Group multiple generations into a single trace\n",
-    "\n",
-    "Many applications require more than one OpenAI call. The `@observe()` decorator allows you to nest all LLM calls of a single API invocation into the same `trace` in Langfuse."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "zMDVxzS1ltWU"
-   },
-   "outputs": [],
-   "source": [
-    "from langfuse.openai import openai\n",
-    "from langfuse import observe\n",
-    "\n",
-    "@observe() # decorator to automatically create trace and nest generations\n",
-    "def main(country: str, user_id: str, **kwargs) -> str:\n",
-    "    # nested generation 1: use openai to get capital of country\n",
-    "    capital = openai.chat.completions.create(\n",
-    "      name=\"geography-teacher\",\n",
-    "      model=\"gpt-4o\",\n",
-    "      messages=[\n",
-    "          {\"role\": \"system\", \"content\": \"You are a Geography teacher helping students learn the capitals of countries. Output only the capital when being asked.\"},\n",
-    "          {\"role\": \"user\", \"content\": country}],\n",
-    "      temperature=0,\n",
-    "    ).choices[0].message.content\n",
-    "\n",
-    "    # nested generation 2: use openai to write poem on capital\n",
-    "    poem = openai.chat.completions.create(\n",
-    "      name=\"poet\",\n",
-    "      model=\"gpt-4o\",\n",
-    "      messages=[\n",
-    "          {\"role\": \"system\", \"content\": \"You are a poet. Create a poem about a city.\"},\n",
-    "          {\"role\": \"user\", \"content\": capital}],\n",
-    "      temperature=1,\n",
-    "      max_tokens=200,\n",
-    "    ).choices[0].message.content\n",
-    "\n",
-    "    return poem\n",
-    "\n",
-    "# run main function and let Langfuse decorator do the rest\n",
-    "print(main(\"Bulgaria\", \"admin\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Ehx2NZuIrPPh"
-   },
-   "source": [
-    "![Trace with multiple OpenAI calls](https://langfuse.com/images/docs/openai-trace-grouped.png)\n",
-    "\n",
-    "[Link to trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/db22d0a442216b485abd83cc9df6d9ee?timestamp=2025-11-13T15:05:05.559Z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "-HeMqTWgK4xL"
-   },
-   "source": [
-    "## Fully featured: Interoperability with Langfuse SDK\n",
-    "\n",
-    "The `trace` is a core object in Langfuse and you can add rich metadata to it. See [Python SDK docs](https://langfuse.com/docs/sdk/python#traces-1) for full documentation on this.\n",
-    "\n",
-    "Some of the functionality enabled by custom traces:\n",
-    "- custom name to identify a specific trace-type\n",
-    "- user-level tracking\n",
-    "- experiment tracking via versions and releases\n",
-    "- custom metadata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "28to65wpK4xL"
-   },
-   "outputs": [],
-   "source": [
-    "from langfuse.openai import openai\n",
-    "from langfuse import observe, get_client, propagate_attributes\n",
-    "langfuse = get_client()\n",
-    "\n",
-    "@observe() # decorator to automatically create trace and nest generations\n",
-    "def main(country: str, user_id: str, **kwargs) -> str:\n",
-    "\n",
-    "    # Propagate attributes to all child observations\n",
-    "    with propagate_attributes(\n",
-    "        session_id=\"1234\",\n",
-    "        user_id=user_id,\n",
-    "        tags=[\"tag1\", \"tag2\"],\n",
-    "        metadata = {\"env\": \"development\"}\n",
-    "    ):\n",
-    "\n",
-    "      # nested generation 1: use openai to get capital of country\n",
-    "      capital = openai.chat.completions.create(\n",
-    "        name=\"geography-teacher\",\n",
-    "        model=\"gpt-4o\",\n",
-    "        messages=[\n",
-    "            {\"role\": \"system\", \"content\": \"You are a Geography teacher helping students learn the capitals of countries. Output only the capital when being asked.\"},\n",
-    "            {\"role\": \"user\", \"content\": country}],\n",
-    "        temperature=0,\n",
-    "      ).choices[0].message.content\n",
-    "\n",
-    "      # nested generation 2: use openai to write poem on capital\n",
-    "      poem = openai.chat.completions.create(\n",
-    "        name=\"poet\",\n",
-    "        model=\"gpt-4o\",\n",
-    "        messages=[\n",
-    "            {\"role\": \"system\", \"content\": \"You are a poet. Create a poem about a city.\"},\n",
-    "            {\"role\": \"user\", \"content\": capital}],\n",
-    "        temperature=1,\n",
-    "        max_tokens=200,\n",
-    "      ).choices[0].message.content\n",
-    "\n",
-    "    return poem\n",
-    "\n",
-    "# create random trace_id, could also use existing id from your application, e.g. conversation id\n",
-    "trace_id = langfuse.create_trace_id()\n",
-    "\n",
-    "# run main function, set your own id, and let Langfuse decorator do the rest\n",
-    "print(main(\"Bulgaria\", \"admin\", langfuse_observation_id=trace_id))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Link to trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/0d73de5c936a1fce6c63bd5b20a7c0c2?timestamp=2025-11-13T15:13:27.879Z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "O3jxed-VrPPi"
-   },
-   "source": [
-    "## Programmatically add scores"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "uMO6tn53rPPi"
-   },
-   "source": [
-    "You can add [scores](https://langfuse.com/docs/scores) to the trace, to e.g. record user feedback or some programmatic evaluation. Scores are used throughout Langfuse to filter traces and on the dashboard. See the docs on scores for more details.\n",
-    "\n",
-    "The score is associated to the trace using the `trace_id`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "id": "J0argbJhrPPi"
-   },
-   "outputs": [],
-   "source": [
-    "from langfuse import observe, get_client\n",
-    "langfuse = get_client()\n",
-    "\n",
-    "@observe() # decorator to automatically create trace and nest generations\n",
-    "def main():\n",
-    "    # get trace_id of current trace\n",
-    "    trace_id = langfuse.get_current_trace_id()\n",
-    "\n",
-    "    # rest of your application ...\n",
-    "\n",
-    "    return \"res\", trace_id\n",
-    "\n",
-    "# execute the main function to generate a trace\n",
-    "_, trace_id = main()\n",
-    "\n",
-    "# Score the trace from outside the trace context\n",
-    "langfuse.create_score(\n",
-    "    trace_id=trace_id,\n",
-    "    name=\"my-score-name\",\n",
-    "    value=1\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/cookbook/integration_openai_structured_output.ipynb b/cookbook/integration_openai_structured_output.ipynb
deleted file mode 100644
index 835b27ae11..0000000000
--- a/cookbook/integration_openai_structured_output.ipynb
+++ /dev/null
@@ -1,392 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "m13Z9rJHCicu"
-   },
-   "source": [
-    "---\n",
-    "title: Observe OpenAI Structured Outputs with Langfuse\n",
-    "description: Learn how to use Langfuse to monitor OpenAI Structured Outputs\n",
-    "category: Integrations\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "yIyq5cb2GsrX"
-   },
-   "source": [
-    "# Cookbook: Trace OpenAI Structured Outputs with Langfuse\n",
-    "\n",
-    "In this cookbook you will learn how to use Langfuse to monitor OpenAI Structured Outputs.\n",
-    "\n",
-    "## What are structured outputs?\n",
-    "Generating structured data from unstructured inputs is a core AI use case today. Structured outputs make especially chained LLM calls, UI component generation, and model-based evaluation more reliable. [Structured Outputs](https://openai.com/index/introducing-structured-outputs-in-the-api/) is a new capability of the OpenAI API that builds upon JSON mode and function calling to enforce a strict schema in a model output.\n",
-    "\n",
-    "## How to trace structured output in Langfuse?\n",
-    "If you use the OpenAI Python SDK, you can use the [Langfuse drop-in replacement](https://langfuse.com/integrations/model-providers/openai-py) to get full logging by changing only the import. With that, you can monitor the structured output generated by OpenAI in Langfuse.\n",
-    "\n",
-    "```diff\n",
-    "- import openai\n",
-    "+ from langfuse.openai import openai\n",
-    "\n",
-    "Alternative imports:\n",
-    "+ from langfuse.openai import OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI\n",
-    "```\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "hH5klZnTTzuq"
-   },
-   "source": [
-    "## Step 1: Initialize Langfuse\n",
-    "Initialize the Langfuse client with your [API keys](https://langfuse.com/faq/all/where-are-langfuse-api-keys) from the project settings in the Langfuse UI and add them to your environment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "collapsed": true,
-    "id": "n11_vChnEdrg",
-    "outputId": "9c5c1aad-380d-4950-ae30-7d033deef0a9"
-   },
-   "outputs": [],
-   "source": [
-    "%pip install langfuse openai --upgrade"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "id": "79Cu3eLlEOCA"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "# Get keys for your project from the project settings page\n",
-    "# https://cloud.langfuse.com\n",
-    "os.environ.setdefault(\"LANGFUSE_PUBLIC_KEY\", \"\");\n",
-    "os.environ.setdefault(\"LANGFUSE_SECRET_KEY\", \"\");\n",
-    "os.environ.setdefault(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\"); # 🇪🇺 EU region\n",
-    "# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n",
-    "\n",
-    "# Your openai key\n",
-    "os.environ.setdefault(\"OPENAI_API_KEY\", \"\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "eK8oTvWHMaTx"
-   },
-   "source": [
-    "## Step 2: Math tutor example\n",
-    "\n",
-    "In this example, we'll build a math tutoring tool that outputs steps to solve a math problem as an array of structured objects.\n",
-    "\n",
-    "This setup is useful for applications where each step needs to be displayed separately, allowing users to progress through the solution at their own pace.\n",
-    "\n",
-    "(Example taken from [OpenAI cookbook](https://cookbook.openai.com/examples/structured_outputs_intro))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": "**Note on `parse` vs `response_format`:** The Langfuse OpenAI integration instruments both the stable `client.chat.completions.parse(...)` (available in `openai>=1.92.0`) and the legacy `client.beta.chat.completions.parse(...)` (for older SDK versions, where `parse` is re-routed to the stable method on newer SDKs). Both paths support Langfuse-specific attributes such as `name`, `metadata`, `langfuse_session_id`, etc. The `response_format` approach with `client.chat.completions.create` shown below works on all SDK versions and is useful if you cannot upgrade `openai`. For an example using the `parse` helper with a Pydantic model, see the section further down."
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "id": "8IZjc893Ef1c"
-   },
-   "outputs": [],
-   "source": [
-    "# Use the Langfuse drop-in replacement to get full logging by changing only the import.\n",
-    "# With that, you can monitor the structured output generated by OpenAI in Langfuse.\n",
-    "from langfuse.openai import OpenAI\n",
-    "import json\n",
-    "\n",
-    "openai_model = \"gpt-4o-2024-08-06\"\n",
-    "client = OpenAI()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "YV7nfCKqV-FS"
-   },
-   "source": [
-    "In the `response_format` parameter you can now supply a JSON Schema via `json_schema`. When using `response_format` with `strict: true`, the model's output will adhere to the provided schema.\n",
-    "\n",
-    "Function calling remains similar, but with the new parameter `strict: true`, you can now ensure that the schema provided for the functions is strictly followed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "id": "iKWRHCjVF55-"
-   },
-   "outputs": [],
-   "source": [
-    "math_tutor_prompt = '''\n",
-    "    You are a helpful math tutor. You will be provided with a math problem,\n",
-    "    and your goal will be to output a step by step solution, along with a final answer.\n",
-    "    For each step, just provide the output as an equation use the explanation field to detail the reasoning.\n",
-    "'''\n",
-    "\n",
-    "def get_math_solution(question):\n",
-    "    response = client.chat.completions.create(\n",
-    "    model = openai_model,\n",
-    "    messages=[\n",
-    "        {\n",
-    "            \"role\": \"system\",\n",
-    "            \"content\": math_tutor_prompt\n",
-    "        },\n",
-    "        {\n",
-    "            \"role\": \"user\",\n",
-    "            \"content\": question\n",
-    "        }\n",
-    "    ],\n",
-    "    response_format={\n",
-    "        \"type\": \"json_schema\",\n",
-    "        \"json_schema\": {\n",
-    "            \"name\": \"math_reasoning\",\n",
-    "            \"schema\": {\n",
-    "                \"type\": \"object\",\n",
-    "                \"properties\": {\n",
-    "                    \"steps\": {\n",
-    "                        \"type\": \"array\",\n",
-    "                        \"items\": {\n",
-    "                            \"type\": \"object\",\n",
-    "                            \"properties\": {\n",
-    "                                \"explanation\": {\"type\": \"string\"},\n",
-    "                                \"output\": {\"type\": \"string\"}\n",
-    "                            },\n",
-    "                            \"required\": [\"explanation\", \"output\"],\n",
-    "                            \"additionalProperties\": False\n",
-    "                        }\n",
-    "                    },\n",
-    "                    \"final_answer\": {\"type\": \"string\"}\n",
-    "                },\n",
-    "                \"required\": [\"steps\", \"final_answer\"],\n",
-    "                \"additionalProperties\": False\n",
-    "            },\n",
-    "            \"strict\": True\n",
-    "        }\n",
-    "    }\n",
-    "    )\n",
-    "\n",
-    "    return response.choices[0].message"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "Qn9N-utqINy_",
-    "outputId": "2510cbb4-1784-4fcf-c7f5-ead1de58abfe"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\"steps\":[{\"explanation\":\"We need to isolate the term with the variable, 8x. So, we start by subtracting 7 from both sides to remove the constant term on the left side.\",\"output\":\"8x + 7 - 7 = -23 - 7\"},{\"explanation\":\"The +7 and -7 on the left side cancel each other out, leaving us with 8x. The right side simplifies to -30.\",\"output\":\"8x = -30\"},{\"explanation\":\"To solve for x, divide both sides of the equation by 8, which is the coefficient of x.\",\"output\":\"x = -30 / 8\"},{\"explanation\":\"Simplify the fraction -30/8 by finding the greatest common divisor, which is 2.\",\"output\":\"x = -15 / 4\"}],\"final_answer\":\"x = -15/4\"}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Testing with an example question\n",
-    "question = \"how can I solve 8x + 7 = -23\"\n",
-    "\n",
-    "result = get_math_solution(question)\n",
-    "\n",
-    "print(result.content)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 456
-    },
-    "id": "mAwEts-hCA73",
-    "outputId": "9101c8ff-7b83-4ae6-f6c0-0da6a45ab251"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Step 1: We need to isolate the term with the variable, 8x. So, we start by subtracting 7 from both sides to remove the constant term on the left side.\n",
-      "\n",
-      "8x + 7 - 7 = -23 - 7\n",
-      "\n",
-      "\n",
-      "Step 2: The +7 and -7 on the left side cancel each other out, leaving us with 8x. The right side simplifies to -30.\n",
-      "\n",
-      "8x = -30\n",
-      "\n",
-      "\n",
-      "Step 3: To solve for x, divide both sides of the equation by 8, which is the coefficient of x.\n",
-      "\n",
-      "x = -30 / 8\n",
-      "\n",
-      "\n",
-      "Step 4: Simplify the fraction -30/8 by finding the greatest common divisor, which is 2.\n",
-      "\n",
-      "x = -15 / 4\n",
-      "\n",
-      "\n",
-      "Final answer:\n",
-      "\n",
-      "\n",
-      "x = -15/4\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print results step by step\n",
-    "\n",
-    "result = json.loads(result.content)\n",
-    "steps = result['steps']\n",
-    "final_answer = result['final_answer']\n",
-    "for i in range(len(steps)):\n",
-    "    print(f\"Step {i+1}: {steps[i]['explanation']}\\n\")\n",
-    "    print(steps[i]['output'])\n",
-    "    print(\"\\n\")\n",
-    "\n",
-    "print(\"Final answer:\\n\\n\")\n",
-    "print(final_answer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "KBoP_eEfLGMb"
-   },
-   "source": [
-    "## Step 3: See your trace in Langfuse\n",
-    "\n",
-    "You can now see the trace and the JSON schema in Langfuse.\n",
-    "\n",
-    "[Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/3ecc3849-66c9-4eaf-b26b-bde26b7eebed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "xhY0etZVU33V"
-   },
-   "source": [
-    "![View example trace in the Langfuse UI](https://langfuse.com/images/cookbook/integration-openai-structured-outputs-tracing.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": "## Alternative: Using the SDK `parse` helper\n\nOpenAI also offers a `parse` helper that lets you pass a Pydantic model directly via `response_format`. As of [`openai-python` v1.92.0](https://github.com/openai/openai-python/releases/tag/v1.92.0), `parse` is part of the stable API at `client.chat.completions.parse(...)` (it previously lived under `client.beta.chat.completions.parse`). Langfuse instruments both paths, so you can set Langfuse attributes (`name`, `metadata`, `langfuse_session_id`, …) on either one. Prefer the stable path in new code:"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": "from pydantic import BaseModel\n\nclass MathReasoning(BaseModel):\n    class Step(BaseModel):\n        explanation: str\n        output: str\n\n    steps: list[Step]\n    final_answer: str\n\ndef get_math_solution(question: str):\n    # Stable API on openai>=1.92.0. On older SDKs, use client.beta.chat.completions.parse instead.\n    response = client.chat.completions.parse(\n        model=openai_model,\n        messages=[\n            {\"role\": \"system\", \"content\": math_tutor_prompt},\n            {\"role\": \"user\", \"content\": question},\n        ],\n        response_format=MathReasoning,\n        name=\"math-tutor-parse\",\n    )\n\n    return response.choices[0].message"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Step(explanation='To isolate the term with the variable on one side of the equation, start by subtracting 7 from both sides.', output='8x = -23 - 7'), Step(explanation='Combine like terms on the right side to simplify the equation.', output='8x = -30'), Step(explanation='Divide both sides by 8 to solve for x.', output='x = -30 / 8'), Step(explanation='Simplify the fraction by dividing both the numerator and the denominator by their greatest common divisor, which is 2.', output='x = -15 / 4')]\n",
-      "Final answer:\n",
-      "x = -15/4\n"
-     ]
-    }
-   ],
-   "source": [
-    "result = get_math_solution(question).parsed\n",
-    "\n",
-    "print(result.steps)\n",
-    "print(\"Final answer:\")\n",
-    "print(result.final_answer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## See your trace in Langfuse\n",
-    "\n",
-    "You can now see the trace and your supplied Pydantic model in Langfuse.\n",
-    "\n",
-    "[Example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/59c4376a-c8eb-4ecb-8780-2f028b87e7eb)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![View example trace in the Langfuse UI](https://langfuse.com/images/cookbook/integration_openai_structured_outputs_tracing_parse.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "UFWiSfqtEdJO"
-   },
-   "source": [
-    "## Feedback\n",
-    "\n",
-    "If you have any feedback or requests, please create a GitHub [Issue](https://langfuse.com/issue) or share your idea with the community on [Discord](https://langfuse.com/discord)."
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/cookbook/js_integration_langchain.ipynb b/cookbook/js_integration_langchain.ipynb
deleted file mode 100644
index 15228dd982..0000000000
--- a/cookbook/js_integration_langchain.ipynb
+++ /dev/null
@@ -1,202 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!-- NOTEBOOK_METADATA source: \"⚠️ Jupyter Notebook\" title: \"Cookbook - Langchain Integration (JS/TS)\" sidebarTitle: \"Langchain Integration (JS/TS)\" description: \"Cookbook with examples of the Langfuse Integration for Langchain (JS/TS).\" category: \"Examples\" -->\n",
-    "\n",
-    "# Cookbook: Langchain Integration (JS/TS)\n",
-    "\n",
-    "<a href=\"https://langfuse.com/guides/cookbook/integration_langchain\"><img className=\"inline\" alt=\"Python\" src=\"https://img.shields.io/badge/Python-3776AB?style=flat&logo=python&logoColor=white\" /></a> <a href=\"https://langfuse.com/guides/cookbook/js_integration_langchain\"><img className=\"inline\" alt=\"JS/TS\" src=\"https://img.shields.io/badge/JS/TS-d4d4d8?style=flat&logo=javascript&logoColor=white\" /></a>\n",
-    "\n",
-    "This is a cookbook with examples of the Langfuse integration for Langchain (JS/TS).\n",
-    "\n",
-    "Follow the [integration guide](https://langfuse.com/integrations/frameworks/langchain) to add this integration to your Langchain project."
-   ],
-   "id": "ec41aeab-9670-478d-a093-438eeb39883f"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!-- STEPS_START -->\n",
-    "## Set Up Environment\n",
-    "\n",
-    "Get your Langfuse API keys by signing up for [Langfuse Cloud](https://langfuse.com/cloud) or [self-hosting Langfuse](https://langfuse.com/self-hosting). You’ll also need your OpenAI API key.\n",
-    "\n",
-    "> **Note**: This cookbook uses **Deno.js** for execution, which requires different syntax for importing packages and setting environment variables. For Node.js applications, the setup process is similar but uses standard `npm` packages and `process.env`."
-   ],
-   "id": "433cd090-63d3-450c-ae95-8c16239cdff8"
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [
-    "// Langfuse authentication keys\n",
-    "Deno.env.set(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-***\");\n",
-    "Deno.env.set(\"LANGFUSE_SECRET_KEY\", \"sk-lf-***\");\n",
-    "\n",
-    "// Langfuse host configuration\n",
-    "// Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n",
-    "Deno.env.set(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\")\n",
-    "\n",
-    "// Set environment variables using Deno-specific syntax\n",
-    "Deno.env.set(\"OPENAI_API_KEY\", \"sk-proj-***\");"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "id": "2e512873"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the environment variables set, we can now initialize the `langfuseSpanProcessor` which is passed to the main OpenTelemetry SDK that orchestrates tracing."
-   ],
-   "id": "6ab1310a"
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [
-    "// Import required dependencies\n",
-    "import 'npm:dotenv/config';\n",
-    "import { NodeSDK } from \"npm:@opentelemetry/sdk-node\";\n",
-    "import { LangfuseSpanProcessor } from \"npm:@langfuse/otel\";\n",
-    " \n",
-    "// Export the processor to be able to flush it later\n",
-    "// This is important for ensuring all spans are sent to Langfuse\n",
-    "export const langfuseSpanProcessor = new LangfuseSpanProcessor({\n",
-    "    publicKey: process.env.LANGFUSE_PUBLIC_KEY!,\n",
-    "    secretKey: process.env.LANGFUSE_SECRET_KEY!,\n",
-    "    baseUrl: process.env.LANGFUSE_BASE_URL ?? 'https://cloud.langfuse.com', // Default to cloud if not specified\n",
-    "    environment: process.env.NODE_ENV ?? 'development', // Default to development if not specified\n",
-    "  });\n",
-    " \n",
-    "// Initialize the OpenTelemetry SDK with our Langfuse processor\n",
-    "const sdk = new NodeSDK({\n",
-    "  spanProcessors: [langfuseSpanProcessor],\n",
-    "});\n",
-    " \n",
-    "// Start the SDK to begin collecting telemetry\n",
-    "// The warning about crypto module is expected in Deno and doesn't affect basic tracing functionality. Media upload features will be disabled, but all core tracing works normally\n",
-    "sdk.start();"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "id": "f356ebc8"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2: Instantiate the CallbackHandler\n",
-    "\n",
-    "Instantiate the CallbackHandler and pass it to your chain’s `.invoke()` or `.stream()` method in the callbacks array. All operations within the chain will be traced as nested observations."
-   ],
-   "id": "7f633097-159f-4d3a-9db4-f01f76b95990"
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [
-    "import { CallbackHandler } from \"npm:@langfuse/langchain\";\n",
-    " \n",
-    "const langfuseHandler = new CallbackHandler({\n",
-    "  sessionId: \"user-session-123\",\n",
-    "  userId: \"user-abc\",\n",
-    "  tags: [\"langchain-test\"],\n",
-    "});"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "id": "d93516c9-ec31-4ddf-9fcb-fbbdb6274d77"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3: Langchain Example\n",
-    "\n",
-    "We create a simple agent that can answer questions and call tools. The agent will use OpenAI GPT-5-mini as its language model, a basic weather function as a tool, and a simple prompt to guide its behavior. All tools and model calls are traced in Langfuse."
-   ],
-   "id": "59615d2d-179e-4234-848f-41db0a6ddd85"
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [
-    "import { createAgent, tool } from \"npm:langchain\";\n",
-    "import * as z from \"npm:zod\";\n",
-    "\n",
-    "const getWeather = tool(\n",
-    "  (input) => `It's always sunny in ${input.city}!`,\n",
-    "  {\n",
-    "    name: \"get_weather\",\n",
-    "    description: \"Get the weather for a given city\",\n",
-    "    schema: z.object({\n",
-    "      city: z.string().describe(\"The city to get the weather for\"),\n",
-    "    }),\n",
-    "  }\n",
-    ");\n",
-    "\n",
-    "const agent = createAgent({\n",
-    "  model: \"openai:gpt-5-mini\",\n",
-    "  tools: [getWeather],\n",
-    "});"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "id": "06374c27"
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [
-    "console.log(\n",
-    "    await agent.invoke(\n",
-    "        { messages: [{ role: \"user\", content: \"What's the weather in San Francisco?\" }] }, \n",
-    "        { callbacks: [langfuseHandler] }\n",
-    "    )\n",
-    ");"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "id": "ca21dbfc"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4: Explore the trace in Langfuse\n",
-    "\n",
-    "In the Langfuse interface, you can see a detailed trace of all steps in the Langchain application.\n",
-    "\n",
-    "![Langfuse Trace](https://langfuse.com/images/cookbook/example-js-sdk/js_integration_langchain_trace.png)\n",
-    "\n",
-    "[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/2c1581dd9cecdafb6ca091b83d7ea99a?timestamp=2025-10-30T08:45:41.319Z)\n",
-    "<!-- STEPS_END -->"
-   ],
-   "id": "5b8f65da"
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Deno",
-   "language": "typescript",
-   "name": "deno"
-  },
-  "language_info": {
-   "codemirror_mode": "typescript",
-   "file_extension": ".ts",
-   "mimetype": "text/x.typescript",
-   "name": "typescript",
-   "nbconvert_exporter": "script",
-   "pygments_lexer": "typescript",
-   "version": "5.6.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/cookbook/js_integration_litellm_proxy.ipynb b/cookbook/js_integration_litellm_proxy.ipynb
deleted file mode 100644
index 0eadc5ca86..0000000000
--- a/cookbook/js_integration_litellm_proxy.ipynb
+++ /dev/null
@@ -1,246 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!-- NOTEBOOK_METADATA source: \"⚠️ Jupyter Notebook\" title: \"Cookbook - LiteLLM (Proxy) + Langfuse OpenAI Integration (JS/TS)\" sidebarTitle: \"LiteLLM (Proxy) + Langfuse OpenAI Integration (JS/TS)\" description: \"The stack to use any of 100+ models in your JS/TS application without having to change your code and with full observability.\" category: \"Examples\" -->\n",
-    "\n",
-    "# Cookbook: LiteLLM (Proxy) + Langfuse OpenAI Integration (JS/TS)\n",
-    "\n",
-    "<a href=\"https://langfuse.com/guides/cookbook/integration_litellm_proxy\"><img className=\"inline\" alt=\"Python\" src=\"https://img.shields.io/badge/Python-3776AB?style=flat&logo=python&logoColor=white\" /></a> <a href=\"https://langfuse.com/guides/cookbook/js_integration_litellm_proxy\"><img className=\"inline\" alt=\"JS/TS\" src=\"https://img.shields.io/badge/JS/TS-d4d4d8?style=flat&logo=javascript&logoColor=white\" /></a>\n",
-    "\n",
-    "This notebook demonstrates how to use the following stack to experiment with 100+ LLMs from different providers without changing code:\n",
-    "\n",
-    "- [**LiteLLM Proxy**](https://docs.litellm.ai/docs/) ([GitHub](https://github.com/BerriAI/litellm)): Standardizes 100+ model provider APIs on the OpenAI API schema.\n",
-    "- **Langfuse OpenAI SDK Wrapper** ([JS/TS](https://langfuse.com/integrations/model-providers/openai-js)): Natively instruments calls to 100+ models via the OpenAI SDK.\n",
-    "- **Langfuse**: OSS LLM Observability, full overview [here](https://langfuse.com/docs).\n",
-    "\n",
-    "> **Note**: This cookbook uses **Deno.js** for execution, which requires different syntax for importing packages and setting environment variables. For Node.js applications, the setup process is similar but uses standard `npm` packages and `process.env`.\n",
-    "\n",
-    "<!-- STEPS_START -->\n",
-    "## Set Up Environment\n",
-    "\n",
-    "Get your Langfuse API keys by signing up for [Langfuse Cloud](https://langfuse.com/cloud) or [self-hosting Langfuse](https://langfuse.com/self-hosting). You’ll also need your OpenAI API key.\n",
-    "\n",
-    "> **Note**: This cookbook uses **Deno.js** for execution, which requires different syntax for importing packages and setting environment variables. For Node.js applications, the setup process is similar but uses standard `npm` packages and `process.env`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "// Langfuse authentication keys\n",
-    "Deno.env.set(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-***\");\n",
-    "Deno.env.set(\"LANGFUSE_SECRET_KEY\", \"sk-lf-***\");\n",
-    "\n",
-    "// Langfuse host configuration\n",
-    "// Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n",
-    "Deno.env.set(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\")\n",
-    "\n",
-    "// Set environment variables using Deno-specific syntax\n",
-    "Deno.env.set(\"OPENAI_API_KEY\", \"sk-proj-***\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the environment variables set, we can now initialize the `langfuseSpanProcessor` which is passed to the main OpenTelemetry SDK that orchestrates tracing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "// Import required dependencies\n",
-    "import 'npm:dotenv/config';\n",
-    "import { NodeSDK } from \"npm:@opentelemetry/sdk-node\";\n",
-    "import { LangfuseSpanProcessor } from \"npm:@langfuse/otel\";\n",
-    " \n",
-    "// Export the processor to be able to flush it later\n",
-    "// This is important for ensuring all spans are sent to Langfuse\n",
-    "export const langfuseSpanProcessor = new LangfuseSpanProcessor({\n",
-    "    publicKey: process.env.LANGFUSE_PUBLIC_KEY!,\n",
-    "    secretKey: process.env.LANGFUSE_SECRET_KEY!,\n",
-    "    baseUrl: process.env.LANGFUSE_BASE_URL ?? 'https://cloud.langfuse.com', // Default to cloud if not specified\n",
-    "    environment: process.env.NODE_ENV ?? 'development', // Default to development if not specified\n",
-    "  });\n",
-    " \n",
-    "// Initialize the OpenTelemetry SDK with our Langfuse processor\n",
-    "const sdk = new NodeSDK({\n",
-    "  spanProcessors: [langfuseSpanProcessor],\n",
-    "});\n",
-    " \n",
-    "// Start the SDK to begin collecting telemetry\n",
-    "// The warning about crypto module is expected in Deno and doesn't affect basic tracing functionality. Media upload features will be disabled, but all core tracing works normally\n",
-    "sdk.start();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup Lite LLM Proxy\n",
-    "\n",
-    "In this example, we'll use llama3 via the Ollama on our local machine.\n",
-    "\n",
-    "**Steps**\n",
-    "\n",
-    "1. Create a `litellm_config.yaml` to configure which models are available ([docs](https://litellm.vercel.app/docs/proxy/configs)). We'll use llama3 via Ollama in this example. \n",
-    "1. Ensure that you installed Ollama and have pulled the llama3 (8b) model: `ollama pull llama3`\n",
-    "2. Run the following cli command to start the proxy: `litellm --config litellm_config.yaml`\n",
-    "\n",
-    "The Lite LLM Proxy should be now running on http://0.0.0.0:4000\n",
-    "\n",
-    "To verify the connection you can run `litellm --test`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Log single LLM Call via Langfuse OpenAI Wrapper\n",
-    "\n",
-    "The Langfuse SDK offers a wrapper function around the OpenAI SDK, automatically logging all OpenAI calls as generations to Langfuse. We wrap the client for each call separately in order to be able to pass a name. For more details, please refer to our [documentation](https://langfuse.com/integrations/model-providers/openai-js)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6\n"
-     ]
-    }
-   ],
-   "source": [
-    "import { OpenAI } from \"npm:openai\";\n",
-    "import { observeOpenAI } from \"npm:@langfuse/openai\";\n",
-    "\n",
-    "const PROXY_URL = \"http://0.0.0.0:4000\";\n",
-    "const client = observeOpenAI(new OpenAI({baseURL: PROXY_URL}));\n",
-    "\n",
-    "const systemPrompt = \"You are a very accurate calculator. You output only the result of the calculation.\";\n",
-    "\n",
-    "const llamaCompletion = await client.chat.completions.create({\n",
-    "  model: \"ollama/llama3\",\n",
-    "  messages: [\n",
-    "    {role: \"system\", content: systemPrompt},\n",
-    "    {role: \"user\", content: \"3 + 3 = \"}\n",
-    "  ],\n",
-    "}); \n",
-    "console.log(llamaCompletion.choices[0].message.content);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/0b78e39b9a36195b7373a3c4536ad574?timestamp=2025-08-25T15%3A07%3A24.014Z&display=details&observation=fa33232a3cc957e1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Trace nested LLM Calls using Langfuse JS SDK\n",
-    "\n",
-    "Use the context manager of the Langfuse TypeScript SDK to group multiple LiteLLM generations together and update the top level span."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import { startActiveObservation, startObservation } from \"npm:@langfuse/tracing\";\n",
-    "import { observeOpenAI } from \"npm:@langfuse/openai\";\n",
-    "\n",
-    "const client = observeOpenAI(new OpenAI({baseURL: PROXY_URL}));\n",
-    "\n",
-    "const systemPrompt = \"You are a very accurate calculator. You output only the result of the calculation.\";\n",
-    " \n",
-    "await startActiveObservation(\"user-request\", async (span) => {\n",
-    "\n",
-    "  await client.chat.completions.create({\n",
-    "    model: \"ollama/llama3\",\n",
-    "    messages: [\n",
-    "      {role: \"system\", content: systemPrompt},\n",
-    "      {role: \"user\", content: \"3 + 3 = \"}\n",
-    "    ],\n",
-    "  }); \n",
-    "\n",
-    "  await client.chat.completions.create({\n",
-    "    model: \"ollama/llama3\",\n",
-    "    messages: [\n",
-    "      {role: \"system\", content: systemPrompt},\n",
-    "      {role: \"user\", content: \"1 - 1 = \"}\n",
-    "    ],\n",
-    "  }); \n",
-    "\n",
-    "  await client.chat.completions.create({\n",
-    "    model: \"ollama/llama3\",\n",
-    "    messages: [\n",
-    "      {role: \"system\", content: systemPrompt},\n",
-    "      {role: \"user\", content: \"2 + 3 = \"}\n",
-    "    ],\n",
-    "  }); \n",
-    "});"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## View traces in Langfuse\n",
-    "\n",
-    "![Public Trace](https://langfuse.com/images/cookbook/example-js-sdk/integration_litellm_proxy_trace.png)\n",
-    "\n",
-    "[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/1aab1c70319ce53e2604801a5e464293?timestamp=2025-08-25T15%3A52%3A19.900Z&display=details&observation=b0f019915d463967)\n",
-    "<!-- STEPS_END -->"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Learn more\n",
-    "\n",
-    "Check out the docs to learn more about all components of this stack:\n",
-    "- [LiteLLM Proxy](https://docs.litellm.ai/docs/)\n",
-    "- [Langfuse OpenAI SDK Wrapper](https://langfuse.com/integrations/model-providers/openai-js)\n",
-    "- [Langfuse](https://langfuse.com/docs)\n",
-    "\n",
-    "If you do not want to capture traces via the OpenAI SDK Wrapper, you can also directly log requests from the LiteLLM Proxy to Langfuse. For more details, refer to the [LiteLLM Docs](https://litellm.vercel.app/docs/proxy/logging#logging-proxy-inputoutput---langfuse)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Deno",
-   "language": "typescript",
-   "name": "deno"
-  },
-  "language_info": {
-   "codemirror_mode": "typescript",
-   "file_extension": ".ts",
-   "mimetype": "text/x.typescript",
-   "name": "typescript",
-   "nbconvert_exporter": "script",
-   "pygments_lexer": "typescript",
-   "version": "5.6.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/cookbook/js_integration_openai.ipynb b/cookbook/js_integration_openai.ipynb
deleted file mode 100644
index d5a262e0ce..0000000000
--- a/cookbook/js_integration_openai.ipynb
+++ /dev/null
@@ -1,378 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0bf4e4bd-070f-42d2-8aeb-427fd8b509cf",
-   "metadata": {},
-   "source": [
-    "<!-- NOTEBOOK_METADATA source: \"⚠️ Jupyter Notebook\" title: \"Cookbook - OpenAI Integration (JS/TS)\" sidebarTitle: \"OpenAI Integration (JS/TS)\" description: \"Learn how to use Langfuse for open source observability/tracing of the OpenAI JS SDK by adding `observeOpenAI`.\" category: \"Examples\" -->\n",
-    "\n",
-    "# Cookbook: OpenAI Integration (JS/TS)\n",
-    "\n",
-    "<a href=\"https://langfuse.com/guides/cookbook/integration_openai_sdk\"><img className=\"inline\" alt=\"Python\" src=\"https://img.shields.io/badge/Python-3776AB?style=flat&logo=python&logoColor=white\" /></a> <a href=\"https://langfuse.com/guides/cookbook/js_integration_openai\"><img className=\"inline\" alt=\"JS/TS\" src=\"https://img.shields.io/badge/JS/TS-d4d4d8?style=flat&logo=javascript&logoColor=white\" /></a>\n",
-    "\n",
-    "This cookbook provides examples of the Langfuse Integration for OpenAI (JS/TS). Follow the [integration guide](https://langfuse.com/integrations/model-providers/openai-js) to add this integration to your OpenAI project."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f5ecf9cd-490a-4f85-803a-b2155677f581",
-   "metadata": {},
-   "source": [
-    "<!-- STEPS_START -->\n",
-    "## Set Up Environment\n",
-    "\n",
-    "Get your Langfuse API keys by signing up for [Langfuse Cloud](https://langfuse.com/cloud) or [self-hosting Langfuse](https://langfuse.com/self-hosting). You’ll also need your OpenAI API key.\n",
-    "\n",
-    "\n",
-    "> **Note**: This cookbook uses **Deno.js** for execution, which requires different syntax for importing packages and setting environment variables. For Node.js applications, the setup process is similar but uses standard `npm` packages and `process.env`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a7cf83d5-7a20-4ae1-a5be-4a8f276a213d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "// Langfuse authentication keys\n",
-    "Deno.env.set(\"LANGFUSE_PUBLIC_KEY\", \"pk-lf-***\");\n",
-    "Deno.env.set(\"LANGFUSE_SECRET_KEY\", \"sk-lf-***\");\n",
-    "\n",
-    "// Langfuse host configuration\n",
-    "// Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com\n",
-    "Deno.env.set(\"LANGFUSE_BASE_URL\", \"https://cloud.langfuse.com\")\n",
-    "\n",
-    "// Set environment variables using Deno-specific syntax\n",
-    "Deno.env.set(\"OPENAI_API_KEY\", \"sk-proj-***\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "290c354d",
-   "metadata": {},
-   "source": [
-    "With the environment variables set, we can now initialize the `langfuseSpanProcessor` which is passed to the main OpenTelemetry SDK that orchestrates tracing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ad25652c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "// Import required dependencies\n",
-    "import 'npm:dotenv/config';\n",
-    "import { NodeSDK } from \"npm:@opentelemetry/sdk-node\";\n",
-    "import { LangfuseSpanProcessor } from \"npm:@langfuse/otel\";\n",
-    " \n",
-    "// Export the processor to be able to flush it later\n",
-    "// This is important for ensuring all spans are sent to Langfuse\n",
-    "export const langfuseSpanProcessor = new LangfuseSpanProcessor({\n",
-    "    publicKey: process.env.LANGFUSE_PUBLIC_KEY!,\n",
-    "    secretKey: process.env.LANGFUSE_SECRET_KEY!,\n",
-    "    baseUrl: process.env.LANGFUSE_BASE_URL ?? 'https://cloud.langfuse.com', // Default to cloud if not specified\n",
-    "    environment: process.env.NODE_ENV ?? 'development', // Default to development if not specified\n",
-    "  });\n",
-    " \n",
-    "// Initialize the OpenTelemetry SDK with our Langfuse processor\n",
-    "const sdk = new NodeSDK({\n",
-    "  spanProcessors: [langfuseSpanProcessor],\n",
-    "});\n",
-    " \n",
-    "// Start the SDK to begin collecting telemetry\n",
-    "// The warning about crypto module is expected in Deno and doesn't affect basic tracing functionality. Media upload features will be disabled, but all core tracing works normally\n",
-    "sdk.start();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "90e2da1c-ea23-493e-9c74-d212fcf7b2ec",
-   "metadata": {},
-   "source": [
-    "## Example 1: Chat Completion"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "db6bdd9b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import OpenAI from \"npm:openai\";\n",
-    "import { observeOpenAI } from \"npm:@langfuse/openai\";"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "d0db9215-4fa9-4e3a-aeb5-bddf48192ea4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "// Initialize OpenAI client with observerOpenAI wrapper\n",
-    "const openai = observeOpenAI(new OpenAI());"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b1a00305-0209-4de7-83c5-b87953833a43",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import OpenAI from \"npm:openai\";\n",
-    "import { observeOpenAI } from \"npm:@langfuse/openai\";\n",
-    "\n",
-    "// Configured via environment variables, see above\n",
-    "const openai = observeOpenAI(new OpenAI());\n",
-    "\n",
-    "const completion = await openai.chat.completions.create({\n",
-    "  model: 'gpt-4o',\n",
-    "  messages: [{ role: \"system\", content: \"Tell me a joke.\" }],\n",
-    "  max_tokens: 100,\n",
-    "});\n",
-    "\n",
-    "console.log(completion.choices[0]?.message.content);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc6d16d9-de98-4f10-8212-fdf43891bf05",
-   "metadata": {},
-   "source": [
-    "![Langfuse Trace](https://langfuse.com/images/cookbook/example-js-sdk/js_integration_openai_simple.png)\n",
-    "\n",
-    "[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/a3568a4051958f944016ad65ffed0fdc?timestamp=2025-08-25T14%3A19%3A17.724Z&display=details&observation=bbb60b9338f261ac)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "34ce01c8-cca2-4e7f-b4ef-15640b89a45a",
-   "metadata": {},
-   "source": [
-    "## Example 2: Chat completion (streaming)\n",
-    "\n",
-    "Simple example using OpenAI streaming, passing custom parameters to rename the generation and add a tag to the trace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "80b727de-67bf-44c5-93bf-2a9c562794ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import OpenAI from \"npm:openai\";\n",
-    "import { observeOpenAI } from \"npm:@langfuse/openai\";\n",
-    "\n",
-    "// Initialize OpenAI SDK with Langfuse\n",
-    "const openaiWithLangfuse = observeOpenAI(new OpenAI(), { generationName: \"OpenAI Stream Trace\", tags: [\"stream\"]} )\n",
-    "\n",
-    "// Call OpenAI\n",
-    "const stream = await openaiWithLangfuse.chat.completions.create({\n",
-    "  model: 'gpt-4o',\n",
-    "  messages: [{ role: \"system\", content: \"Tell me a joke.\" }],\n",
-    "  stream: true,\n",
-    "});\n",
-    "\n",
-    "for await (const chunk of stream) {\n",
-    "    const content = chunk.choices[0]?.delta?.content || '';\n",
-    "    console.log(content);\n",
-    "  }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6c93ac08-70b1-4d10-b402-5a766918199d",
-   "metadata": {},
-   "source": [
-    "[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/5be1f917ea127f535ddf39d84b42bbf9?timestamp=2025-08-25T14:22:44.892Z&display=details)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d75b0944-ab82-4769-9e6c-a5dda0e5ad9b",
-   "metadata": {},
-   "source": [
-    "## Example 3: Add additional metadata and parameters\n",
-    "\n",
-    "You can pass additional trace and generation attributes to the `observeOpenAI` wrapper. Refer to the [integration guide](https://langfuse.com/integrations/model-providers/openai-js) for comprehensive details.\n",
-    "\n",
-    "Example usage:\n",
-    "\n",
-    "- Assigning a custom trace and generation name\n",
-    "- Enabling user-level and session-level tracking\n",
-    "- Adding tags and custom metadata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6dd7c881-6ead-45e8-a118-090defd8f706",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import OpenAI from \"npm:openai\";\n",
-    "import { observeOpenAI } from \"npm:@langfuse/openai\";\n",
-    "\n",
-    "// Initialize OpenAI SDK with Langfuse and custom parameters\n",
-    "const openaiWithLangfuse = observeOpenAI(new OpenAI(), {\n",
-    "    traceName: \"OpenAI Custom Trace\",\n",
-    "    generationName: \"OpenAI Custom Generation\",\n",
-    "    generationMetadata: {env: \"dev\"},\n",
-    "    sessionId: \"session-id\",\n",
-    "    userId: \"user-id\",\n",
-    "    tags: [\"custom\"],\n",
-    "})\n",
-    "\n",
-    "// Call OpenAI\n",
-    "const completion = await openaiWithLangfuse.chat.completions.create({\n",
-    "  model: 'gpt-4o',\n",
-    "  messages: [{ role: \"system\", content: \"Tell me a joke.\" }],\n",
-    "  max_tokens: 100,\n",
-    "});"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e657fc32-c4de-481c-8862-f6168c065f9f",
-   "metadata": {},
-   "source": [
-    "[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/5aba0399-d640-4fc1-8cbc-42ebf688b189?timestamp=2025-08-25T14:23:43.007Z&display=details)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2df27fc5-98ca-404d-bdd4-fec06445360a",
-   "metadata": {},
-   "source": [
-    "## Example 4: Function Calling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "f47eaa4f-e30d-40d4-a264-15796f3fd74c",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "20degC\n"
-     ]
-    }
-   ],
-   "source": [
-    "import OpenAI from \"npm:openai\";\n",
-    "import { observeOpenAI } from \"npm:@langfuse/openai\";\n",
-    "\n",
-    "// Initialize OpenAI SDK with Langfuse\n",
-    "const openaiWithLangfuse = observeOpenAI(new OpenAI(), { generationName: \"OpenAI FunctionCall Trace\", tags: [\"function\"]} )\n",
-    "\n",
-    "// Define custom function\n",
-    "async function getWeather(location: string) {\n",
-    "  if (location === \"Berlin\")\n",
-    "    {return \"20degC\"}\n",
-    "  else \n",
-    "    {return \"unknown\"}\n",
-    "}\n",
-    "\n",
-    "// Create function specification required for OpenAI API\n",
-    "const functions = [{\n",
-    "    type: \"function\",\n",
-    "    function: {\n",
-    "        name: \"getWeather\",\n",
-    "        description: \"Get the current weather in a given location\",\n",
-    "        parameters: {\n",
-    "            type: \"object\",\n",
-    "            properties: {\n",
-    "                location: {\n",
-    "                    type: \"string\",\n",
-    "                    description: \"The city, e.g. San Francisco\",\n",
-    "                },\n",
-    "            },\n",
-    "            required: [\"location\"],\n",
-    "        },\n",
-    "    },\n",
-    "}]\n",
-    "\n",
-    "// Call OpenAI\n",
-    "const res = await openaiWithLangfuse.chat.completions.create({\n",
-    "    model: 'gpt-4o',\n",
-    "    messages: [{ role: 'user', content: \"What's the weather like in Berlin today\"}],\n",
-    "    tool_choice: \"auto\",\n",
-    "    tools: functions,\n",
-    "})\n",
-    "\n",
-    "const tool_call = res.choices[0].message.tool_calls;\n",
-    "if (tool_call[0].function.name === \"getWeather\") {\n",
-    "    const argsStr = tool_call[0].function.arguments;\n",
-    "    const args = JSON.parse(argsStr); \n",
-    "    const answer = await getWeather(args[\"location\"]);\n",
-    "    console.log(answer);\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "448017d3-b925-483f-b16b-e5c735d7e0c6",
-   "metadata": {},
-   "source": [
-    "[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/8efcae3385d3ecff4c6f5a8ef9ca8e60?timestamp=2025-08-25T14%3A24%3A59.978Z&display=details&observation=e3f0e48874fdfa83)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a84acbae-90c9-4a46-9ad4-130d64406318",
-   "metadata": {},
-   "source": [
-    "## Example 5: Group multiple generations into a single trace\n",
-    "\n",
-    "Use `propagateAttributes` to apply trace attributes (name, tags, metadata) to all observations created inside its callback, and the `startActiveObservation` context manager to group two OpenAI generations under a single root span. Input and output are set on the root observation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "14f73952",
-   "metadata": {},
-   "outputs": [],
-   "source": "import { startActiveObservation, propagateAttributes } from \"npm:@langfuse/tracing\";\nimport OpenAI from \"npm:openai\";\nimport { observeOpenAI } from \"npm:@langfuse/openai\";\n\nconst country = \"Germany\";\n\n// Propagate trace attributes to all observations created inside the callback\nawait propagateAttributes(\n  {\n    traceName: \"City poem generator\",\n    tags: [\"updated\"],\n    metadata: { env: \"development\" },\n  },\n  async () => {\n    await startActiveObservation(\"user-request\", async (span) => {\n      // Generations created inside the callback are automatically nested under the active span\n      const capital = (\n        await observeOpenAI(new OpenAI(), {\n          generationName: \"get-capital\",\n        }).chat.completions.create({\n          model: \"gpt-4o\",\n          messages: [\n            { role: \"system\", content: \"What is the capital of the country?\" },\n            { role: \"user\", content: country },\n          ],\n        })\n      ).choices[0].message.content;\n\n      const poem = (\n        await observeOpenAI(new OpenAI(), {\n          generationName: \"generate-poem\",\n        }).chat.completions.create({\n          model: \"gpt-4o\",\n          messages: [\n            {\n              role: \"system\",\n              content: \"You are a poet. Create a poem about this city.\",\n            },\n            { role: \"user\", content: capital },\n          ],\n        })\n      ).choices[0].message.content;\n\n      // Set input and output on the root observation\n      span.update({\n        input: country,\n        output: poem,\n      });\n    });\n  },\n);\n\n// Ensure all spans are flushed to Langfuse\nawait langfuseSpanProcessor.forceFlush();"
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f54b2361-9df6-4160-95a5-1f3eab7af9bd",
-   "metadata": {},
-   "source": [
-    "![Langfuse Trace](https://langfuse.com/images/cookbook/example-js-sdk/js_integration_openai_grouped.png)\n",
-    "\n",
-    "[Public trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/e0a2f2b374a58cf9b4548240f879fc71?timestamp=2025-08-25T14:39:41.599Z&display=details)\n",
-    "<!-- STEPS_END -->"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Deno",
-   "language": "typescript",
-   "name": "deno"
-  },
-  "language_info": {
-   "codemirror_mode": "typescript",
-   "file_extension": ".ts",
-   "mimetype": "text/x.typescript",
-   "name": "typescript",
-   "nbconvert_exporter": "script",
-   "pygments_lexer": "typescript",
-   "version": "5.6.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/cookbook/js_prompt_management_langchain.ipynb b/cookbook/js_prompt_management_langchain.ipynb
index ee377f1d1f..619ab6f959 100644
--- a/cookbook/js_prompt_management_langchain.ipynb
+++ b/cookbook/js_prompt_management_langchain.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!-- NOTEBOOK_METADATA source: \"⚠️ Jupyter Notebook\" title: \"Example - Langfuse Prompt Management with Langchain (JS)\" sidebarTitle: \"Prompt Management with Langchain (JS)\" description: \"Example how to version control and manage prompts with Langfuse Prompt Management and Langchain JS.\" category: \"Examples\" -->\n",
+    "<!-- NOTEBOOK_METADATA source: \"⚠️ Jupyter Notebook\" title: \"Example - Langfuse Prompt Management with Langchain (JS)\" sidebarTitle: \"Prompt Management with Langchain (JS)\" description: \"Example how to version control and manage prompts with Langfuse Prompt Management and Langchain JS.\" category: \"Prompt Management\" -->\n",
     "\n",
     "# Example: Langfuse Prompt Management with Langchain (JS)\n",
     "\n",
diff --git a/cookbook/langfuse_sdk_performance_test.ipynb b/cookbook/langfuse_sdk_performance_test.ipynb
index d7ae3e8919..9dd451ac31 100644
--- a/cookbook/langfuse_sdk_performance_test.ipynb
+++ b/cookbook/langfuse_sdk_performance_test.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "---\n",
         "title: Langfuse SDK Performance Test\n",
-        "category: SDKs\n",
+        "description: Benchmark of the latency impact of the Langfuse Python SDK and its OpenAI, Langchain, and LlamaIndex integrations.\n",
         "---\n",
         "\n",
         "# Langfuse SDK Performance Test\n",
diff --git a/cookbook/otel_integration_arize.ipynb b/cookbook/otel_integration_arize.ipynb
index a88e3014c5..67a4f8cef1 100644
--- a/cookbook/otel_integration_arize.ipynb
+++ b/cookbook/otel_integration_arize.ipynb
@@ -6,6 +6,8 @@
    "source": [
     "---\n",
     "title: Tracing using the OpenInference SDK\n",
+    "sidebarTitle: OpenInference (Arize)\n",
+    "logo: /images/integrations/opentelemetry_icon.svg\n",
     "description: Example cookbook on using the OpenInference SDK by Arize to trace your application to Langfuse.\n",
     "---\n",
     "\n",
diff --git a/cookbook/otel_integration_mlflow.ipynb b/cookbook/otel_integration_mlflow.ipynb
index 1ded8b1e94..4dd2654aa5 100644
--- a/cookbook/otel_integration_mlflow.ipynb
+++ b/cookbook/otel_integration_mlflow.ipynb
@@ -6,6 +6,8 @@
       "source": [
         "---\n",
         "title: MLflow Integration via OpenTelemetry\n",
+        "sidebarTitle: MLflow\n",
+        "logo: /images/integrations/opentelemetry_icon.svg\n",
         "description: Example cookbook for the MLflow Langfuse integration using OpenTelemetry.\n",
         "category: Integrations\n",
         "---\n",
diff --git a/cookbook/otel_integration_openlit.ipynb b/cookbook/otel_integration_openlit.ipynb
index 3a3e59c171..40fe00933a 100644
--- a/cookbook/otel_integration_openlit.ipynb
+++ b/cookbook/otel_integration_openlit.ipynb
@@ -6,6 +6,8 @@
       "source": [
         "---\n",
         "title: OpenLIT Integration via OpenTelemetry\n",
+        "sidebarTitle: OpenLIT\n",
+        "logo: /images/integrations/opentelemetry_icon.svg\n",
         "description: Example cookbook for the OpenLIT Langfuse integration using OpenTelemetry.\n",
         "category: Integrations\n",
         "---\n",
diff --git a/cookbook/otel_integration_openllmetry.ipynb b/cookbook/otel_integration_openllmetry.ipynb
index 43e821fc99..dbb8312ba3 100644
--- a/cookbook/otel_integration_openllmetry.ipynb
+++ b/cookbook/otel_integration_openllmetry.ipynb
@@ -7,6 +7,9 @@
       },
       "source": [
         "---\n",
+        "title: OpenLLMetry Integration via OpenTelemetry\n",
+        "sidebarTitle: OpenLLMetry\n",
+        "logo: /images/integrations/opentelemetry_icon.svg\n",
         "description: Example cookbook for the OpenLLMetry Langfuse integration using OpenTelemetry.\n",
         "category: Integrations\n",
         "---\n",
diff --git a/cookbook/prompt_management_performance_benchmark.ipynb b/cookbook/prompt_management_performance_benchmark.ipynb
index 6d41b9a176..12fe975d83 100644
--- a/cookbook/prompt_management_performance_benchmark.ipynb
+++ b/cookbook/prompt_management_performance_benchmark.ipynb
@@ -9,7 +9,6 @@
         "---\n",
         "title: Prompt Management Performance Benchmark\n",
         "description: Performance benchmark on Langfuse Prompt Management measuring latency of retrieving and compiling prompts.\n",
-        "category: Prompt Management\n",
         "---\n",
         "\n",
         "# Langfuse Prompt Management Performance Test\n",
diff --git a/lib/redirects.js b/lib/redirects.js
index a50605a820..8180c427a3 100644
--- a/lib/redirects.js
+++ b/lib/redirects.js
@@ -18,6 +18,11 @@ const nonPermanentRedirects = [
     "/loom-gpt4-PR",
     "https://www.loom.com/share/5c044ca77be44ff7821967834dd70cba",
   ],
+  // 2023 webinar moved from /guides/videos to engineering resources
+  [
+    "/guides/videos/webinar-observability-llm-systems",
+    "/resources/engineering/webinar-observability-llm-systems",
+  ],
   [
     "/terms",
     "https://clickhouse.com/legal/clickhouse-general-terms-and-conditions",
@@ -196,7 +201,7 @@ const nonPermanentRedirects = [
   ],
   [
     "/docs/scores/model-based-evals/langchain",
-    "/cookbook/evaluation_with_langchain",
+    "/guides/cookbook/example_external_evaluation_pipelines",
   ],
   ["/docs/scores/getting-started", "/docs/scores/overview"],
   ["/experimentation", "/docs/datasets/overview"],
@@ -217,7 +222,7 @@ const nonPermanentRedirects = [
   // (/docs/prompts/get-started is itself redirected to /docs/prompt-management/get-started).
   ["/docs/prompts", "/docs/prompt-management/get-started"],
   ["/changelog/2024-03-03-posthog-integration", "/docs/analytics/posthog"],
-  ["/guides/videos/2-min", "/guides/videos/introducing-langfuse-2.0"],
+  ["/guides/videos/2-min", "/guides/videos"],
   ["/tos", "/terms"],
   ["/docs/export-and-fine-tuning", "/docs/query-traces"],
   [
@@ -428,7 +433,7 @@ const integrationsSection202507 = [
   ],
   [
     "/docs/integrations/langchain/example-python-langserve",
-    "/integrations/frameworks/langserve",
+    "/integrations/frameworks/langchain",
   ],
   ["/docs/integrations/mastra", "/integrations/frameworks/mastra"],
   [
@@ -587,15 +592,15 @@ const integrationsSection202507 = [
   // These don't have integration pages but are available as cookbooks
   [
     "/docs/integrations/langchain/example-python",
-    "/guides/cookbook/integration_langchain",
+    "/integrations/frameworks/langchain",
   ],
   [
     "/docs/integrations/langchain/example-python-langgraph",
-    "/guides/cookbook/integration_langgraph",
+    "/integrations/frameworks/langgraph",
   ],
   [
     "/docs/integrations/langchain/example-javascript",
-    "/guides/cookbook/js_integration_langchain",
+    "/integrations/frameworks/langchain",
   ],
   [
     "/docs/integrations/langchain/example-langgraph-agents",
@@ -603,23 +608,23 @@ const integrationsSection202507 = [
   ],
   [
     "/docs/integrations/openai/python/examples",
-    "/guides/cookbook/integration_openai_sdk",
+    "/integrations/model-providers/openai-py",
   ],
   [
     "/docs/integrations/openai/python/structured-outputs",
-    "/guides/cookbook/integration_openai_structured_output",
+    "/integrations/model-providers/openai-py",
   ],
   [
     "/docs/integrations/openai/js/examples",
-    "/guides/cookbook/js_integration_openai",
+    "/integrations/model-providers/openai-js",
   ],
   [
     "/docs/integrations/llama-index/deprecated/example-python",
-    "/guides/cookbook/integration_llama-index-callback",
+    "/integrations/frameworks/llamaindex",
   ],
   [
     "/docs/integrations/llama-index/deprecated/example-python-instrumentation-module",
-    "/guides/cookbook/integration_llama-index_instrumentation",
+    "/integrations/frameworks/llamaindex",
   ],
   [
     "/docs/integrations/haystack/example-python",
@@ -627,11 +632,11 @@ const integrationsSection202507 = [
   ],
   [
     "/docs/integrations/litellm/example-proxy-python",
-    "/guides/cookbook/integration_litellm_proxy",
+    "/integrations/gateways/litellm",
   ],
   [
     "/docs/integrations/litellm/example-proxy-js",
-    "/guides/cookbook/js_integration_litellm_proxy",
+    "/integrations/gateways/litellm",
   ],
   [
     "/docs/integrations/mirascope/example-python",
@@ -639,7 +644,7 @@ const integrationsSection202507 = [
   ],
   [
     "/docs/integrations/databricks/example-python",
-    "/guides/cookbook/integration_databricks",
+    "/integrations/model-providers/databricks",
   ],
   [
     "/docs/integrations/openaiagentssdk/example-evaluating-openai-agents",
@@ -688,21 +693,12 @@ const mainDocsReorder202507 = [
     "/docs/model-usage-and-cost",
     "/docs/observability/features/token-and-cost-tracking",
   ],
-  [
-    "/docs/opentelemetry/example-arize",
-    "/guides/cookbook/otel_integration_arize",
-  ],
-  [
-    "/docs/opentelemetry/example-mlflow",
-    "/guides/cookbook/otel_integration_mlflow",
-  ],
-  [
-    "/docs/opentelemetry/example-openlit",
-    "/guides/cookbook/otel_integration_openlit",
-  ],
+  ["/docs/opentelemetry/example-arize", "/integrations/other/openinference"],
+  ["/docs/opentelemetry/example-mlflow", "/integrations/other/mlflow"],
+  ["/docs/opentelemetry/example-openlit", "/integrations/other/openlit"],
   [
     "/docs/opentelemetry/example-openllmetry",
-    "/guides/cookbook/otel_integration_openllmetry",
+    "/integrations/other/openllmetry",
   ],
   ["/docs/opentelemetry/get-started", "/integrations/native/opentelemetry"],
   ["/docs/playground", "/docs/prompt-management/features/playground"],
@@ -1388,7 +1384,7 @@ const brokenLinksCleanup202602 = [
   ["/docs/data-export", "/docs/api-and-data-platform/overview"],
   [
     "/docs/integrations/llama-index/example-python-instrumentation-module",
-    "/guides/cookbook/integration_llama-index_instrumentation",
+    "/integrations/frameworks/llamaindex",
   ],
   [
     "/docs/sdk/typescript/example-vercel-ai",
@@ -1603,6 +1599,158 @@ const brokenLinksCleanup202608 = [
   ],
 ];
 
+// Guides section cleanup (2026-08): outdated cookbooks and videos removed from
+// /guides, integration cookbooks moved to /integrations, benchmarks moved to
+// /resources/engineering.
+const guidesCleanup202608 = [
+  // LangServe is discontinued (folded into LangChain)
+  [
+    "/guides/cookbook/integration_langserve",
+    "/integrations/frameworks/langchain",
+  ],
+  ["/integrations/frameworks/langserve", "/integrations/frameworks/langchain"],
+  // Companion cookbooks merged into their integration reference pages
+  [
+    "/guides/cookbook/integration_langchain",
+    "/integrations/frameworks/langchain",
+  ],
+  [
+    "/guides/cookbook/js_integration_langchain",
+    "/integrations/frameworks/langchain",
+  ],
+  [
+    "/guides/cookbook/integration_openai_sdk",
+    "/integrations/model-providers/openai-py",
+  ],
+  [
+    "/guides/cookbook/js_integration_openai",
+    "/integrations/model-providers/openai-js",
+  ],
+  [
+    "/guides/cookbook/integration_openai_structured_output",
+    "/integrations/model-providers/openai-py",
+  ],
+  [
+    "/guides/cookbook/integration_azure_openai_langchain",
+    "/integrations/frameworks/langchain",
+  ],
+  [
+    "/guides/cookbook/integration_litellm_proxy",
+    "/integrations/gateways/litellm",
+  ],
+  [
+    "/guides/cookbook/js_integration_litellm_proxy",
+    "/integrations/gateways/litellm",
+  ],
+  [
+    "/guides/cookbook/integration_databricks",
+    "/integrations/model-providers/databricks",
+  ],
+  // Dual-published integration cookbooks now live only at their /integrations URL
+  [
+    "/guides/cookbook/integration_amazon_bedrock",
+    "/integrations/model-providers/amazon-bedrock",
+  ],
+  [
+    "/guides/cookbook/integration_anthropic",
+    "/integrations/model-providers/anthropic",
+  ],
+  [
+    "/guides/cookbook/js_integration_anthropic",
+    "/integrations/model-providers/anthropic-js",
+  ],
+  // LangGraph cookbook promoted to the LangGraph integration page
+  [
+    "/guides/cookbook/integration_langgraph",
+    "/integrations/frameworks/langgraph",
+  ],
+  // Duplicate/outdated LlamaIndex cookbooks consolidated into the integration page
+  [
+    "/guides/cookbook/integration_llama-index-callback",
+    "/integrations/frameworks/llamaindex",
+  ],
+  [
+    "/guides/cookbook/integration_llama-index_instrumentation",
+    "/integrations/frameworks/llamaindex",
+  ],
+  [
+    "/guides/cookbook/integration_llama_index",
+    "/integrations/frameworks/llamaindex",
+  ],
+  [
+    "/guides/cookbook/integration_llamaindex_workflows",
+    "/integrations/frameworks/llamaindex-workflows",
+  ],
+  [
+    "/guides/cookbook/integration_llama-index_milvus-lite",
+    "/integrations/other/milvus",
+  ],
+  [
+    "/guides/cookbook/integration_llama_index_posthog_mistral",
+    "/integrations/analytics/posthog",
+  ],
+  [
+    "/guides/cookbook/integration_openai_assistants",
+    "/integrations/model-providers/openai-assistants-api",
+  ],
+  // Removed evaluation cookbooks (UpTrain unmaintained, LangChain evals superseded)
+  ["/guides/cookbook/evaluation_with_uptrain", "/docs/evaluation/overview"],
+  [
+    "/guides/cookbook/evaluation_with_langchain",
+    "/guides/cookbook/example_external_evaluation_pipelines",
+  ],
+  // Removed decorator + OpenAI + Langchain interop demo
+  [
+    "/guides/cookbook/example_decorator_openai_langchain",
+    "/docs/observability/sdk/instrumentation",
+  ],
+  // OTel instrumentation-library cookbooks moved to /integrations/other
+  [
+    "/guides/cookbook/otel_integration_arize",
+    "/integrations/other/openinference",
+  ],
+  ["/guides/cookbook/otel_integration_mlflow", "/integrations/other/mlflow"],
+  ["/guides/cookbook/otel_integration_openlit", "/integrations/other/openlit"],
+  [
+    "/guides/cookbook/otel_integration_openllmetry",
+    "/integrations/other/openllmetry",
+  ],
+  // Performance benchmarks moved to engineering resources
+  [
+    "/guides/cookbook/langfuse_sdk_performance_test",
+    "/resources/engineering/langfuse-sdk-performance-test",
+  ],
+  [
+    "/guides/cookbook/prompt_management_performance_benchmark",
+    "/resources/engineering/prompt-management-performance-benchmark",
+  ],
+  // Outdated launch/announcement videos removed from /guides/videos
+  ["/guides/videos/introducing-langfuse-2.0", "/guides/videos"],
+  ["/guides/videos/introducing-datasets-v2", "/guides/videos"],
+  // Video wrapper pages whose video is embedded on the canonical page
+  [
+    "/guides/videos/run-langfuse-locally",
+    "/self-hosting/deployment/docker-compose",
+  ],
+  [
+    "/guides/videos/external-evaluation-pipelines",
+    "/guides/cookbook/example_external_evaluation_pipelines",
+  ],
+  [
+    "/guides/videos/introducing-python-decorator",
+    "/docs/observability/sdk/instrumentation",
+  ],
+  ["/guides/videos/posthog-integration", "/integrations/analytics/posthog"],
+  [
+    "/guides/videos/llm-playground",
+    "/docs/prompt-management/features/playground",
+  ],
+  [
+    "/guides/videos/llm-as-a-judge-eval-on-dataset-experiments",
+    "/docs/evaluation/evaluation-methods/llm-as-a-judge",
+  ],
+];
+
 const permanentRedirects = [
   // llm-evaluation blog consolidation (2026-07): 101 post merged into the evals leader post
   [
@@ -1651,6 +1799,7 @@ const permanentRedirects = [
   ...goodTraceBestPracticesMove202607,
   ...promptMcpPageRemoval202607,
   ...brokenLinksCleanup202608,
+  ...guidesCleanup202608,
 ];
 
 module.exports = { nonPermanentRedirects, permanentRedirects };

From 2aa203866498f4cb67aadcb32138a5b82956be6b Mon Sep 17 00:00:00 2001
From: Lotte Verheyden <48100308+Lotte-Verheyden@users.noreply.github.com>
Date: Fri, 7 Aug 2026 17:36:26 -0700
Subject: [PATCH 2/2] docs: apply review feedback to guides cleanup

Move the Ragas evaluation cookbook to /resources/engineering (redirect
and inbound links retargeted) and keep the video gallery on the /guides
page instead of folding videos into the topic categories.
---
 components/CookbookIndex.tsx                  | 26 +-----
 content/academy/evaluate/index.mdx            |  2 +-
 content/academy/japan/evaluate.mdx            |  2 +-
 content/blog/2025-11-12-evals.mdx             |  2 +-
 .../evaluation-methods/llm-as-a-judge.mdx     |  2 +-
 .../fifteen-questions-langfuse-answered.mdx   |  2 +-
 content/guides/cookbook/meta.json             |  1 -
 content/guides/index.mdx                      | 12 ++-
 .../beginners-guide-to-rag-evaluation.mdx     |  1 -
 .../videos/debug-rag-agent-with-tracing.mdx   |  1 -
 content/guides/videos/headless-langfuse.mdx   |  1 -
 .../videos/replace-vibes-with-evals.mdx       |  1 -
 content/integrations/frameworks/ragas.mdx     |  4 +-
 .../evaluation-of-rag-with-ragas.md}          | 80 +++++--------------
 content/resources/engineering/meta.json       |  1 +
 cookbook/_routes.json                         |  4 +-
 cookbook/evaluation_of_rag_with_ragas.ipynb   |  1 -
 lib/redirects.js                              |  7 +-
 18 files changed, 47 insertions(+), 103 deletions(-)
 rename content/{guides/cookbook/evaluation_of_rag_with_ragas.md => resources/engineering/evaluation-of-rag-with-ragas.md} (98%)

diff --git a/components/CookbookIndex.tsx b/components/CookbookIndex.tsx
index 0a4febc7cf..010daf5869 100644
--- a/components/CookbookIndex.tsx
+++ b/components/CookbookIndex.tsx
@@ -1,27 +1,15 @@
 import { guidesSource } from "@/lib/source";
-import { FileCode, Video, ArrowRight } from "lucide-react";
+import { FileCode, ArrowRight } from "lucide-react";
 import { Cards, Card } from "@/components/docs";
 
 type CookbookPage = ReturnType<typeof guidesSource.getPages>[number];
 
-export const CookbookIndex = ({
-  categories,
-  includeVideos,
-}: {
-  categories?: string[];
-  includeVideos?: boolean;
-}) => (
+export const CookbookIndex = ({ categories }: { categories?: string[] }) => (
   <>
     {Object.entries(
       guidesSource
         .getPages()
-        .filter(
-          (page) =>
-            page.url.startsWith("/guides/cookbook/") ||
-            (includeVideos &&
-              page.url.startsWith("/guides/videos/") &&
-              !!page.data.category),
-        )
+        .filter((page) => page.url.startsWith("/guides/cookbook/"))
         .reduce(
           (acc, page) => {
             const category =
@@ -64,13 +52,7 @@ export const CookbookIndex = ({
                   href={page.url}
                   key={page.url}
                   className="no-underline group"
-                  icon={
-                    page.url.startsWith("/guides/videos/") ? (
-                      <Video className="h-3 w-3" />
-                    ) : (
-                      <FileCode className="h-3 w-3" />
-                    )
-                  }
+                  icon={<FileCode className="h-3 w-3" />}
                 >
                   <div className="flex flex-1 items-center gap-3 w-full justify-between">
                     <span className="flex-1 text-text-secondary group-hover:text-text-primary transition-colors duration-220">
diff --git a/content/academy/evaluate/index.mdx b/content/academy/evaluate/index.mdx
index 2ff55a73d9..6b14978350 100644
--- a/content/academy/evaluate/index.mdx
+++ b/content/academy/evaluate/index.mdx
@@ -165,7 +165,7 @@ If production behavior matches expectations, you can keep scaling with more conf
       lede: "Test whether your judge prompt agrees with how you would label cases, and iterate until it does.",
     },
     {
-      href: "/guides/cookbook/evaluation_of_rag_with_ragas",
+      href: "/resources/engineering/evaluation-of-rag-with-ragas",
       topic: "RAG evaluation with RAGAS",
       lede: "Score retrieval and generation quality for a RAG application.",
     },
diff --git a/content/academy/japan/evaluate.mdx b/content/academy/japan/evaluate.mdx
index 6cff2462cc..ba11512fd3 100644
--- a/content/academy/japan/evaluate.mdx
+++ b/content/academy/japan/evaluate.mdx
@@ -197,7 +197,7 @@ _アップロードされた PDF から構造化フィールド (ベンダー、
   title="その他のガイド"
   guides={[
     {
-      href: "/guides/cookbook/evaluation_of_rag_with_ragas",
+      href: "/resources/engineering/evaluation-of-rag-with-ragas",
       topic: "RAGAS を使った RAG 評価",
       lede: "RAG アプリケーションのリトリーバルと生成の品質を採点します。",
     },
diff --git a/content/blog/2025-11-12-evals.mdx b/content/blog/2025-11-12-evals.mdx
index eb7968a263..ad0ff77e1f 100644
--- a/content/blog/2025-11-12-evals.mdx
+++ b/content/blog/2025-11-12-evals.mdx
@@ -249,7 +249,7 @@ For apps beyond one-shot queries, extend the basics. The methods stay the same;
 
 ### RAG pipelines [#rag]
 
-Because a RAG system has a retrieval step and a generation step, measure them separately: retrieval relevance and precision for the document-fetching stage, then generation-side metrics on the answer. The two metrics that carry most RAG evaluations are **faithfulness** (does the answer stick to the retrieved context?) and **answer relevance** (does the answer actually address the question?). We cover judge design and runnable code for both in [RAG faithfulness evaluation](/resources/engineering/rag-faithfulness-evaluation) and [answer relevance evaluation](/resources/engineering/answer-relevance-evaluation), and when ungrounded output is your main worry, [hallucination detection](/resources/engineering/hallucination-detection) walks through a taxonomy and evaluator setup. Langfuse also integrates with [RAGAS](/guides/cookbook/evaluation_of_rag_with_ragas) for specialized RAG metrics.
+Because a RAG system has a retrieval step and a generation step, measure them separately: retrieval relevance and precision for the document-fetching stage, then generation-side metrics on the answer. The two metrics that carry most RAG evaluations are **faithfulness** (does the answer stick to the retrieved context?) and **answer relevance** (does the answer actually address the question?). We cover judge design and runnable code for both in [RAG faithfulness evaluation](/resources/engineering/rag-faithfulness-evaluation) and [answer relevance evaluation](/resources/engineering/answer-relevance-evaluation), and when ungrounded output is your main worry, [hallucination detection](/resources/engineering/hallucination-detection) walks through a taxonomy and evaluator setup. Langfuse also integrates with [RAGAS](/resources/engineering/evaluation-of-rag-with-ragas) for specialized RAG metrics.
 
 ### AI agents [#agents]
 
diff --git a/content/docs/evaluation/evaluation-methods/llm-as-a-judge.mdx b/content/docs/evaluation/evaluation-methods/llm-as-a-judge.mdx
index 2c67fe91d6..ae254945ed 100644
--- a/content/docs/evaluation/evaluation-methods/llm-as-a-judge.mdx
+++ b/content/docs/evaluation/evaluation-methods/llm-as-a-judge.mdx
@@ -413,7 +413,7 @@ Cost depends on the judge model and the size of the inputs being evaluated. A ty
 <Details>
 <Summary>Can I use LLM-as-a-Judge for RAG evaluation?</Summary>
 
-Yes. LLM-as-a-Judge is particularly effective for RAG pipelines. You can evaluate faithfulness (is the answer grounded in the retrieved context?), relevance (does the answer address the question?), and completeness (does the answer cover all relevant information?). Langfuse also integrates with [RAGAS](/guides/cookbook/evaluation_of_rag_with_ragas) for specialized RAG evaluation metrics.
+Yes. LLM-as-a-Judge is particularly effective for RAG pipelines. You can evaluate faithfulness (is the answer grounded in the retrieved context?), relevance (does the answer address the question?), and completeness (does the answer cover all relevant information?). Langfuse also integrates with [RAGAS](/resources/engineering/evaluation-of-rag-with-ragas) for specialized RAG evaluation metrics.
 
 </Details>
 
diff --git a/content/faq/all/fifteen-questions-langfuse-answered.mdx b/content/faq/all/fifteen-questions-langfuse-answered.mdx
index 374c72c90a..ad81559127 100644
--- a/content/faq/all/fifteen-questions-langfuse-answered.mdx
+++ b/content/faq/all/fifteen-questions-langfuse-answered.mdx
@@ -101,7 +101,7 @@ The answer is yes, Langfuse is an open source alternative to Promptlayer. You ca
 
 ## 15. How can I use RAGAS evaluations?
 
-**The answer is, you can use RAGAS Evaluations with Langfuse.** We have written a tutorial that shows how to [run RAGAS evaluations](https://langfuse.com/guides/cookbook/evaluation_of_rag_with_ragas) on your historical LLM data. Additionally, you can use the RAGAS evaluation prompts and use them with [Langfuse's automated eval service](/docs/evaluation/evaluation-methods/llm-as-a-judge) to run RAGAS evaluations on any new LLM trace.
+**The answer is, you can use RAGAS Evaluations with Langfuse.** We have written a tutorial that shows how to [run RAGAS evaluations](/resources/engineering/evaluation-of-rag-with-ragas) on your historical LLM data. Additionally, you can use the RAGAS evaluation prompts and use them with [Langfuse's automated eval service](/docs/evaluation/evaluation-methods/llm-as-a-judge) to run RAGAS evaluations on any new LLM trace.
 
 ## 16. Can I use Langchain, Langserve and Langgraph with Langfuse Observability and Tracing?
 
diff --git a/content/guides/cookbook/meta.json b/content/guides/cookbook/meta.json
index f83625304c..245977774a 100644
--- a/content/guides/cookbook/meta.json
+++ b/content/guides/cookbook/meta.json
@@ -3,7 +3,6 @@
   "pages": [
     "index",
     "error-analysis-llm-applications",
-    "evaluation_of_rag_with_ragas",
     "example_data_migration",
     "example_evaluating_multi_turn_conversations",
     "example_evaluating_openai_agents",
diff --git a/content/guides/index.mdx b/content/guides/index.mdx
index 4e664cf4e3..523f8505ca 100644
--- a/content/guides/index.mdx
+++ b/content/guides/index.mdx
@@ -127,10 +127,16 @@ Extend your evaluation approach for complex, multi-step applications.
   />
 </TutorialCards>
 
-## All guides [#cookbooks]
+## Cookbooks
 
-Cookbooks and video guides, sorted by topic. The notebook sources (`ipynb`) live in the [`cookbook`](https://github.com/langfuse/langfuse-docs/tree/main/cookbook) directory of the repository.
+Runnable cookbooks, sorted by topic. The notebook sources (`ipynb`) live in the [`cookbook`](https://github.com/langfuse/langfuse-docs/tree/main/cookbook) directory of the repository.
 
 import { CookbookIndex } from "@/components/CookbookIndex";
 
-<CookbookIndex includeVideos />
+<CookbookIndex />
+
+## Video Tutorials
+
+import { VideoIndex } from "@/components/VideoIndex";
+
+<VideoIndex />
diff --git a/content/guides/videos/beginners-guide-to-rag-evaluation.mdx b/content/guides/videos/beginners-guide-to-rag-evaluation.mdx
index 978ea20ee1..1e5d5b29e4 100644
--- a/content/guides/videos/beginners-guide-to-rag-evaluation.mdx
+++ b/content/guides/videos/beginners-guide-to-rag-evaluation.mdx
@@ -2,7 +2,6 @@
 title: "Beginner's Guide to RAG Evaluation with Langfuse and Ragas"
 description: "Webinar by Prof. Tom Yeh on how to evaluate RAG applications with Langfuse and Ragas."
 ogImage: /images/videos/beginners-guide-to-rag-evaluation.jpg
-category: Evaluation
 ---
 
 # Beginner's Guide to RAG Evaluation with Langfuse and Ragas
diff --git a/content/guides/videos/debug-rag-agent-with-tracing.mdx b/content/guides/videos/debug-rag-agent-with-tracing.mdx
index fa874a3fcd..e62eddcbd0 100644
--- a/content/guides/videos/debug-rag-agent-with-tracing.mdx
+++ b/content/guides/videos/debug-rag-agent-with-tracing.mdx
@@ -2,7 +2,6 @@
 title: "Debug a Fan-Out RAG Agent With Tracing"
 description: "A confident, fluent, completely wrong answer from a RAG support agent — and how one Langfuse trace leads straight to the single broken config line behind the bad retrieval, wrong tool, and hallucination."
 ogImage: /images/videos/debug-rag-agent-with-tracing.jpg
-category: Examples
 ---
 
 # Debug a Fan-Out RAG Agent With Tracing
diff --git a/content/guides/videos/headless-langfuse.mdx b/content/guides/videos/headless-langfuse.mdx
index 5f1557770e..a5f1216bdb 100644
--- a/content/guides/videos/headless-langfuse.mdx
+++ b/content/guides/videos/headless-langfuse.mdx
@@ -2,7 +2,6 @@
 title: "Headless Langfuse From Your Coding Agent"
 description: "Instrument, trace, analyze, build a dataset, and run LLM-as-a-judge evaluations without touching the Langfuse UI — driven entirely from your coding agent with the Langfuse Agent Skill."
 ogImage: /images/videos/headless-langfuse.jpg
-category: Examples
 ---
 
 # Headless Langfuse From Your Coding Agent
diff --git a/content/guides/videos/replace-vibes-with-evals.mdx b/content/guides/videos/replace-vibes-with-evals.mdx
index 09a19558c5..4c3355ed1c 100644
--- a/content/guides/videos/replace-vibes-with-evals.mdx
+++ b/content/guides/videos/replace-vibes-with-evals.mdx
@@ -2,7 +2,6 @@
 title: "Replace Vibes With Evals: Build an Evaluation Pipeline"
 description: "Stop shipping AI features on gut feel. Use Langfuse to trace production, run error analysis, capture failures into a dataset, score them with evaluators, and measure every change with experiments before it ships."
 ogImage: /images/videos/replace-vibes-with-evals.jpg
-category: Evaluation
 ---
 
 # Replace Vibes With Evals: Build an Evaluation Pipeline
diff --git a/content/integrations/frameworks/ragas.mdx b/content/integrations/frameworks/ragas.mdx
index 9af2b41506..a5fdf91843 100644
--- a/content/integrations/frameworks/ragas.mdx
+++ b/content/integrations/frameworks/ragas.mdx
@@ -185,7 +185,7 @@ The same metrics run on live traffic. Because Ragas metrics are reference-free,
 2. **Evaluate the batch with Ragas**, either per sample via `single_turn_ascore` or in bulk via `ragas.evaluate`.
 3. **Write the scores back** with `langfuse.create_score(name=..., value=..., trace_id=...)` so each score appears on the trace that produced the answer.
 
-The [Ragas cookbook](/guides/cookbook/evaluation_of_rag_with_ragas) walks through both variants end to end, including scoring individual traces at request time and batch-scoring a periodic sample.
+The [Ragas cookbook](/resources/engineering/evaluation-of-rag-with-ragas) walks through both variants end to end, including scoring individual traces at request time and batch-scoring a periodic sample.
 
 If you want RAG scoring on production traffic without operating an evaluation job at all, Langfuse's managed [LLM-as-a-judge](/docs/evaluation/evaluation-methods/llm-as-a-judge) evaluators run server-side against sampled observations, and the managed evaluator catalog includes evaluators built in partnership with Ragas.
 
@@ -213,7 +213,7 @@ import { FileCode } from "lucide-react";
 <Cards>
   <Card
     title="Cookbook: Evaluation of RAG pipelines with Ragas"
-    href="/guides/cookbook/evaluation_of_rag_with_ragas"
+    href="/resources/engineering/evaluation-of-rag-with-ragas"
     icon={<FileCode />}
   />
 </Cards>
diff --git a/content/guides/cookbook/evaluation_of_rag_with_ragas.md b/content/resources/engineering/evaluation-of-rag-with-ragas.md
similarity index 98%
rename from content/guides/cookbook/evaluation_of_rag_with_ragas.md
rename to content/resources/engineering/evaluation-of-rag-with-ragas.md
index d36f056950..ada1fff734 100644
--- a/content/guides/cookbook/evaluation_of_rag_with_ragas.md
+++ b/content/resources/engineering/evaluation-of-rag-with-ragas.md
@@ -1,12 +1,12 @@
 ---
 title: "Evaluation of RAG pipelines with Ragas"
 description: Use RAGAS to evaluate your RAG pipelines traced with Langfuse to measure the quality of your retrieval and sythesis.
-category: Evaluation
 ---
 
 # Evaluation of RAG pipelines with Ragas
 
 Langfuse offers the feature to score your traces and spans. They can be used in multiple ways across Langfuse:
+
 1. Displayed on trace to provide a quick overview
 2. Segment all execution traces by scores to e.g. find all traces with a low-quality score
 3. Analytics: Detailed score reporting with drill downs into use cases and user segments
@@ -15,7 +15,6 @@ Ragas is an open-source tool that can help you run [Model-Based Evaluation](http
 
 ## The Environment
 
-
 ```python
 import os
 
@@ -29,7 +28,6 @@ os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪
 os.environ.setdefault("OPENAI_API_KEY", "sk-proj-");
 ```
 
-
 ```python
 %pip install langfuse datasets ragas llama_index python-dotenv openai --upgrade
 ```
@@ -39,12 +37,12 @@ os.environ.setdefault("OPENAI_API_KEY", "sk-proj-");
 For this example, we are going to use a dataset that has already been prepared by querying a RAG system and gathering its outputs. See below for instruction on how to fetch your production data from Langfuse.
 
 The dataset contains the following columns
+
 - `question`: list[str] - These are the questions your RAG pipeline will be evaluated on.
 - `answer`: list[str] - The answer generated from the RAG pipeline and given to the user.
 - `contexts`: list[list[str]] - The contexts which were passed into the LLM to answer the question.
 - `ground_truths`: list[list[str]] - The ground truth answer to the questions. However, this can be ignored for online evaluations since we will not have access to ground-truth data in our case.
 
-
 ```python
 from datasets import load_dataset
 
@@ -52,17 +50,13 @@ fiqa_eval = load_dataset("explodinggradients/fiqa", "ragas_eval")['baseline']
 fiqa_eval
 ```
 
-
-
-
     Dataset({
         features: ['question', 'ground_truths', 'answer', 'contexts'],
         num_rows: 30
     })
 
-
-
 ## The Metrics
+
 For going to measure the following aspects of a RAG system. These metric are from the Ragas library:
 
 1. [faithfulness](https://docs.ragas.io/en/latest/concepts/metrics/available_metrics/faithfulness/): This measures the factual consistency of the generated answer against the given context.
@@ -71,7 +65,6 @@ For going to measure the following aspects of a RAG system. These metric are fro
 
 Checkout the [RAGAS documentation](https://docs.ragas.io/en/latest/concepts/metrics/index.html) to know more about these metrics and how they work.
 
-
 ```python
 # import metrics
 from ragas.metrics import (
@@ -90,7 +83,6 @@ metrics = [
 
 Now you have to initialize the metrics with LLMs and Embeddings of your choice. In this example we are going to use OpenAI.
 
-
 ```python
 from ragas.run_config import RunConfig
 from ragas.metrics.base import MetricWithLLM, MetricWithEmbeddings
@@ -107,7 +99,6 @@ def init_ragas_metrics(metrics, llm, embedding):
         metric.init(run_config)
 ```
 
-
 ```python
 from langchain_openai.chat_models import ChatOpenAI
 from langchain_openai.embeddings import OpenAIEmbeddings
@@ -127,7 +118,9 @@ init_ragas_metrics(
 ```
 
 ## The Setup
+
 You can use model-based evaluation with Ragas in 2 ways
+
 1. Score each Trace: This means you will run the evaluations for each trace item. This gives you much better idea since of how each call to your RAG pipelines is performing but can be expensive
 2. Score as Batch: In this method we will take a random sample of traces on a periodic basis and score them. This brings down cost and gives you a rough estimate the performance of your app but can miss out on important samples.
 
@@ -137,30 +130,22 @@ In this cookbook, we'll show you how to setup both.
 
 Lets take a small example of a single trace and see how you can score that with Ragas. First lets load the data
 
-
 ```python
 row = fiqa_eval[0]
 row['question'], row['answer']
 ```
 
-
-
-
     ('How to deposit a cheque issued to an associate in my business into my business account?',
      '\nThe best way to deposit a cheque issued to an associate in your business into your business account is to open a business account with the bank. You will need a state-issued "dba" certificate from the county clerk\'s office as well as an Employer ID Number (EIN) issued by the IRS. Once you have opened the business account, you can have the associate sign the back of the cheque and deposit it into the business account.')
 
-
-
 Now lets init a Langfuse client SDK to instrument you app.
 
-
 ```python
 from langfuse import get_client
 
 langfuse = get_client()
 ```
 
-
 ```python
 # Verify connection
 if langfuse.auth_check():
@@ -171,10 +156,8 @@ else:
 
     Langfuse client is authenticated and ready!
 
-
 Here we are defining a utility function to score your trace with the metrics you chose.
 
-
 ```python
 from ragas.dataset_schema import SingleTurnSample
 
@@ -192,13 +175,13 @@ async def score_with_ragas(query, chunks, answer):
 ```
 
 You compute the score with each request. Below I've outlined a dummy application that does the following steps
+
 1. gets a question from the user
 2. fetch context from the database or vector store that can be used to answer the question from the user
 3. pass the question and the contexts to the LLM to generate the answer
 
 All these step are logged as spans in a single trace in langfuse. You can read more about traces and spans from the [langfuse documentation](https://langfuse.com/docs/tracing).
 
-
 ```python
 # start a new trace when you get a question
 question = row['question']
@@ -208,24 +191,24 @@ answer = row['answer']
 with langfuse.start_as_current_observation(as_type="span", name="rag") as trace:
     # Store trace_id for later use
     trace_id = trace.trace_id
-    
+
     # retrieve the relevant chunks
     # chunks = get_similar_chunks(question)
-    
+
     # pass it as span
     with trace.start_as_current_observation(
-        name="retrieval", 
-        input={'question': question}, 
+        name="retrieval",
+        input={'question': question},
         output={'contexts': contexts}
     ):
         pass
 
     # use llm to generate a answer with the chunks
     # answer = get_response_from_llm(question, chunks)
-    
+
     with trace.start_as_current_observation(
-        name="generation", 
-        input={'question': question, 'contexts': contexts}, 
+        name="generation",
+        input={'question': question, 'contexts': contexts},
         output={'answer': answer}
     ):
         pass
@@ -250,18 +233,15 @@ ragas_scores
      'answer_relevancy': np.float64(0.9825100521118072),
      'llm_context_precision_without_reference': 0.9999999999}
 
-
-
 Once the scores are computed you can add them to the trace in Langfuse:
 
-
 ```python
 # send the scores
 # Use the trace_id stored from the previous cell
 for m in metrics:
     langfuse.create_score(
-        name=m.name, 
-        value=ragas_scores[m.name], 
+        name=m.name,
+        value=ragas_scores[m.name],
         trace_id=trace_id
     )
 ```
@@ -280,14 +260,13 @@ You can run this periodically to keep track of how the scores are changing acros
 
 To create demo data in Langfuse, lets first create ~10 traces with the fiqa dataset.
 
-
 ```python
 # fiqa traces
 for interaction in fiqa_eval.select(range(10, 20)):
     question = interaction['question']
     contexts = interaction['contexts']
     answer = interaction['answer']
-    
+
     with langfuse.start_as_current_observation(as_type="span", name="rag") as trace:
         with trace.start_as_current_observation(
             name="retrieval",
@@ -295,7 +274,7 @@ for interaction in fiqa_eval.select(range(10, 20)):
             output={'contexts': contexts}
         ):
             pass
-        
+
         with trace.start_as_current_observation(
             name="generation",
             input={'question': question, 'contexts': contexts},
@@ -309,7 +288,6 @@ langfuse.flush()
 
 Now that the dataset is uploaded to langfuse you can retrieve it as needed with this handy function.
 
-
 ```python
 def get_traces(name=None, limit=None, user_id=None):
     all_data = []
@@ -329,7 +307,6 @@ def get_traces(name=None, limit=None, user_id=None):
     return all_data[:limit]
 ```
 
-
 ```python
 from random import sample
 
@@ -340,16 +317,10 @@ traces_sample = sample(traces, NUM_TRACES_TO_SAMPLE)
 len(traces_sample)
 ```
 
-
-
-
     3
 
-
-
 Now lets make a batch and score it. Ragas uses huggingface dataset object to build the dataset and run the evaluation. If you run this on your own production data, use the right keys to extract the question, contexts and answer from the trace
 
-
 ```python
 # score on a sample
 from random import sample
@@ -375,7 +346,6 @@ for t in traces_sample:
     evaluation_batch['trace_id'].append(t.id)
 ```
 
-
 ```python
 # run ragas evaluate
 from datasets import Dataset
@@ -386,27 +356,18 @@ ds = Dataset.from_dict(evaluation_batch)
 r = evaluate(ds, metrics=[Faithfulness(), ResponseRelevancy()])
 ```
 
-
     Evaluating:   0%|          | 0/6 [00:00<?, ?it/s]
 
-
 And that is it! You can see the scores over a time period.
 
-
 ```python
 r
 ```
 
-
-
-
     {'faithfulness': 0.5516, 'answer_relevancy': 0.9294}
 
-
-
 You can also push the scores back into Langfuse or use the exported pandas dataframe to run further analysis.
 
-
 ```python
 df = r.to_pandas()
 
@@ -416,9 +377,6 @@ df["trace_id"] = ds["trace_id"]
 df.head()
 ```
 
-
-
-
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -432,6 +390,7 @@ df.head()
     .dataframe thead th {
         text-align: right;
     }
+
 </style>
 <table border="1" class="dataframe">
   <thead>
@@ -477,9 +436,6 @@ df.head()
 </table>
 </div>
 
-
-
-
 ```python
 for _, row in df.iterrows():
     for metric_name in ["faithfulness", "answer_relevancy"]:
diff --git a/content/resources/engineering/meta.json b/content/resources/engineering/meta.json
index 4640d1e274..68518e50be 100644
--- a/content/resources/engineering/meta.json
+++ b/content/resources/engineering/meta.json
@@ -24,6 +24,7 @@
     "evaluating-sessions-conversations",
     "llm-evaluation-strategy",
     "rag-faithfulness-evaluation",
+    "evaluation-of-rag-with-ragas",
     "answer-relevance-evaluation",
     "hallucination-detection",
     "llm-regression-testing",
diff --git a/cookbook/_routes.json b/cookbook/_routes.json
index 98690a97b8..39c11497a6 100644
--- a/cookbook/_routes.json
+++ b/cookbook/_routes.json
@@ -36,8 +36,8 @@
   },
   {
     "notebook": "evaluation_of_rag_with_ragas.ipynb",
-    "docsPath": null,
-    "isGuide": true
+    "docsPath": "resources/engineering/evaluation-of-rag-with-ragas",
+    "isGuide": false
   },
   {
     "notebook": "prompt_management_openai_functions.ipynb",
diff --git a/cookbook/evaluation_of_rag_with_ragas.ipynb b/cookbook/evaluation_of_rag_with_ragas.ipynb
index e92234cda3..e080401caa 100644
--- a/cookbook/evaluation_of_rag_with_ragas.ipynb
+++ b/cookbook/evaluation_of_rag_with_ragas.ipynb
@@ -9,7 +9,6 @@
    "source": [
     "---\n",
     "description: Use RAGAS to evaluate your RAG pipelines traced with Langfuse to measure the quality of your retrieval and sythesis.\n",
-    "category: Evaluation\n",
     "---\n",
     "\n",
     "# Evaluation of RAG pipelines with Ragas\n",
diff --git a/lib/redirects.js b/lib/redirects.js
index 8180c427a3..abc31083df 100644
--- a/lib/redirects.js
+++ b/lib/redirects.js
@@ -197,7 +197,7 @@ const nonPermanentRedirects = [
   ["/docs/scores/model-based-evals/overview", "/docs/scores/model-based-evals"],
   [
     "/docs/scores/model-based-evals/ragas",
-    "/cookbook/evaluation_of_rag_with_ragas",
+    "/resources/engineering/evaluation-of-rag-with-ragas",
   ],
   [
     "/docs/scores/model-based-evals/langchain",
@@ -1715,6 +1715,11 @@ const guidesCleanup202608 = [
     "/guides/cookbook/otel_integration_openllmetry",
     "/integrations/other/openllmetry",
   ],
+  // Ragas evaluation cookbook moved to engineering resources
+  [
+    "/guides/cookbook/evaluation_of_rag_with_ragas",
+    "/resources/engineering/evaluation-of-rag-with-ragas",
+  ],
   // Performance benchmarks moved to engineering resources
   [
     "/guides/cookbook/langfuse_sdk_performance_test",