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deleted file mode 100644
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-.md-grid {
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diff --git a/mkdocs.yml b/mkdocs.yml
deleted file mode 100644
index e0d697adc..000000000
--- a/mkdocs.yml
+++ /dev/null
@@ -1,335 +0,0 @@
-site_name: Strands Agents SDK
-site_description: Documentation for Strands Agents, a simple-to-use, code-first, lightweight library for building AI agents
-site_dir: site
-site_url: https://strandsagents.com
-
-repo_url: https://github.com/strands-agents/sdk-python
-edit_uri: https://github.com/strands-agents/docs/edit/main/docs
-
-theme:
-  name: material
-  custom_dir: overrides
-  logo: assets/logo-light.svg  # Safari doesn't support <style> tags inside SVGs so we need to a light and a dark SVG
-  logo_dark: assets/logo-dark.svg  # Safari doesn't support <style> tags inside SVGs so we need to a light and a dark SVG
-  favicon: assets/logo-auto.svg
-  favicon_png: assets/logo-light.png  # Safari doesn't support SVG favicons
-  palette:
-    # Palette toggle for light mode
-    - media: "(prefers-color-scheme: light)"
-      primary: custom
-      scheme: default
-      toggle:
-        icon: material/brightness-7
-        name: Switch to dark mode
-    # Palette toggle for dark mode
-    - media: "(prefers-color-scheme: dark)"
-      primary: custom
-      scheme: slate
-      toggle:
-        icon: material/brightness-4
-        name: Switch to light mode
-  icon:
-    admonition:
-      code: material/code-json
-  features:
-    - content.action.edit
-    - content.code.copy
-    - content.tabs.link
-    - content.code.select
-    - navigation.indexes
-    - navigation.instant
-    - navigation.instant.prefetch
-    - navigation.instant.progress
-    - navigation.tabs
-    - navigation.tabs.sticky
-    - navigation.sections
-    - navigation.top
-    - search.highlight
-    - content.code.copy
-
-markdown_extensions:
-  - admonition
-  - codehilite
-  - pymdownx.highlight
-  - pymdownx.tabbed:
-      alternate_style: true
-      slugify: !!python/object/apply:pymdownx.slugs.slugify
-        kwds:
-          case: lower
-  - pymdownx.details
-  - pymdownx.emoji:
-      emoji_index: !!python/name:material.extensions.emoji.twemoji
-      emoji_generator: !!python/name:material.extensions.emoji.to_svg
-  - pymdownx.snippets:
-      base_path: ["docs"]
-      check_paths: true
-      dedent_subsections: true
-  - pymdownx.superfences:
-      custom_fences:
-          - name: mermaid
-            class: mermaid
-            format: !!python/name:pymdownx.superfences.fence_code_format
-  - tables
-  - toc:
-      title: On this page
-      permalink: true
-
-extra_css:
-  - stylesheets/extra.css
-
-extra_javascript:
-  # Workaround for site_url breaking mermaid rendering; see the following for more info:
-  # https://github.com/squidfunk/mkdocs-material/issues/3742#issuecomment-1076068038
-  - https://unpkg.com/mermaid@11/dist/mermaid.min.js
-  - assets/auto-redirect.js
-
-nav:
-  - User Guide:
-    - Welcome: README.md
-    - Quickstart:
-      - Getting Started: user-guide/quickstart/overview.md
-      - Python: user-guide/quickstart/python.md
-      - TypeScript: user-guide/quickstart/typescript.md
-    - Concepts:
-      - Agents:
-        - Agent Loop: user-guide/concepts/agents/agent-loop.md
-        - State: user-guide/concepts/agents/state.md
-        - Session Management: user-guide/concepts/agents/session-management.md
-        - Prompts: user-guide/concepts/agents/prompts.md
-        - Retry Strategies: user-guide/concepts/agents/retry-strategies.md
-        - Hooks: user-guide/concepts/agents/hooks.md
-        - Structured Output: user-guide/concepts/agents/structured-output.md
-        - Conversation Management: user-guide/concepts/agents/conversation-management.md
-      - Tools:
-        - Overview: user-guide/concepts/tools/index.md
-        - Creating Custom Tools: user-guide/concepts/tools/custom-tools.md
-        - Model Context Protocol (MCP): user-guide/concepts/tools/mcp-tools.md
-        - Executors: user-guide/concepts/tools/executors.md
-        - Community Tools Package: user-guide/concepts/tools/community-tools-package.md
-      - Plugins:
-        - Overview: user-guide/concepts/plugins/index.md
-        - Steering: user-guide/concepts/plugins/steering.md
-      - Model Providers:
-        - Overview: user-guide/concepts/model-providers/index.md
-        - Amazon Bedrock: user-guide/concepts/model-providers/amazon-bedrock.md
-        - Amazon Nova: user-guide/concepts/model-providers/amazon-nova.md
-        - Anthropic: user-guide/concepts/model-providers/anthropic.md
-        - Gemini: user-guide/concepts/model-providers/gemini.md
-        - LiteLLM: user-guide/concepts/model-providers/litellm.md
-        - llama.cpp: user-guide/concepts/model-providers/llamacpp.md
-        - LlamaAPI: user-guide/concepts/model-providers/llamaapi.md
-        - MistralAI: user-guide/concepts/model-providers/mistral.md
-        - Ollama: user-guide/concepts/model-providers/ollama.md
-        - OpenAI: user-guide/concepts/model-providers/openai.md
-        - SageMaker: user-guide/concepts/model-providers/sagemaker.md
-        - Writer: user-guide/concepts/model-providers/writer.md
-        - Custom Providers: user-guide/concepts/model-providers/custom_model_provider.md
-        - Cohere<sup> community</sup>: user-guide/concepts/model-providers/cohere.md
-        - CLOVA Studio<sup> community</sup>: user-guide/concepts/model-providers/clova-studio.md
-        - FireworksAI<sup> community</sup>: user-guide/concepts/model-providers/fireworksai.md
-        - Nebius Token Factory<sup> community</sup>: user-guide/concepts/model-providers/nebius-token-factory.md
-        - xAI<sup> community</sup>: user-guide/concepts/model-providers/xai.md
-      - Streaming:
-        - Overview: user-guide/concepts/streaming/index.md
-        - Async Iterators: user-guide/concepts/streaming/async-iterators.md
-        - Callback Handlers: user-guide/concepts/streaming/callback-handlers.md
-      - Multi-agent:
-        - Agent2Agent (A2A): user-guide/concepts/multi-agent/agent-to-agent.md
-        - Agents as Tools: user-guide/concepts/multi-agent/agents-as-tools.md
-        - Swarm: user-guide/concepts/multi-agent/swarm.md
-        - Graph: user-guide/concepts/multi-agent/graph.md
-        - Workflow: user-guide/concepts/multi-agent/workflow.md
-        - Multi-agent Patterns: user-guide/concepts/multi-agent/multi-agent-patterns.md
-      - Interrupts: user-guide/concepts/interrupts.md
-      - Bidirectional Streaming:
-        - Quickstart: user-guide/concepts/bidirectional-streaming/quickstart.md
-        - BidiAgent: user-guide/concepts/bidirectional-streaming/agent.md
-        - Models:
-          - Nova Sonic: user-guide/concepts/bidirectional-streaming/models/nova_sonic.md
-          - Gemini Live: user-guide/concepts/bidirectional-streaming/models/gemini_live.md
-          - OpenAI Realtime: user-guide/concepts/bidirectional-streaming/models/openai_realtime.md
-        - IO: user-guide/concepts/bidirectional-streaming/io.md
-        - Events: user-guide/concepts/bidirectional-streaming/events.md
-        - Interruptions: user-guide/concepts/bidirectional-streaming/interruption.md
-        - Hooks: user-guide/concepts/bidirectional-streaming/hooks.md
-        - Session Management: user-guide/concepts/bidirectional-streaming/session-management.md
-      - Experimental:
-        - AgentConfig: user-guide/concepts/experimental/agent-config.md
-    - Safety & Security:
-      - Responsible AI: user-guide/safety-security/responsible-ai.md
-      - Guardrails: user-guide/safety-security/guardrails.md
-      - Prompt Engineering: user-guide/safety-security/prompt-engineering.md
-      - PII Redaction: user-guide/safety-security/pii-redaction.md
-    - Observability & Debugging:
-      - Observability: user-guide/observability-evaluation/observability.md
-      - Metrics: user-guide/observability-evaluation/metrics.md
-      - Traces: user-guide/observability-evaluation/traces.md
-      - Logs: user-guide/observability-evaluation/logs.md
-    - Strands Evals SDK:
-      - Getting Started: user-guide/evals-sdk/quickstart.md
-      - Eval SOP: user-guide/evals-sdk/eval-sop.md
-      - Evaluators:
-        - Overview: user-guide/evals-sdk/evaluators/index.md
-        - Output: user-guide/evals-sdk/evaluators/output_evaluator.md
-        - Trajectory: user-guide/evals-sdk/evaluators/trajectory_evaluator.md
-        - Interactions: user-guide/evals-sdk/evaluators/interactions_evaluator.md
-        - Helpfulness: user-guide/evals-sdk/evaluators/helpfulness_evaluator.md
-        - Faithfulness: user-guide/evals-sdk/evaluators/faithfulness_evaluator.md
-        - Goal Success Rate: user-guide/evals-sdk/evaluators/goal_success_rate_evaluator.md
-        - Tool Selection Accuracy: user-guide/evals-sdk/evaluators/tool_selection_evaluator.md
-        - Tool Parameter Accuracy: user-guide/evals-sdk/evaluators/tool_parameter_evaluator.md
-        - Custom: user-guide/evals-sdk/evaluators/custom_evaluator.md
-      - Experiment Generator: user-guide/evals-sdk/experiment_generator.md
-      - Simulators:
-        - Overview: user-guide/evals-sdk/simulators/index.md
-        - User Simulation: user-guide/evals-sdk/simulators/user_simulation.md
-      - How-To Guides:
-        - Experiment Management: user-guide/evals-sdk/how-to/experiment_management.md
-        - Serialization: user-guide/evals-sdk/how-to/serialization.md
-    - Deploy:
-      - Operating Agents in Production: user-guide/deploy/operating-agents-in-production.md
-      - Amazon Bedrock AgentCore:
-          - Overview: user-guide/deploy/deploy_to_bedrock_agentcore/index.md
-          - Python: user-guide/deploy/deploy_to_bedrock_agentcore/python.md
-          - TypeScript: user-guide/deploy/deploy_to_bedrock_agentcore/typescript.md
-      - AWS Lambda<sup> new</sup>: user-guide/deploy/deploy_to_aws_lambda.md
-      - AWS Fargate: user-guide/deploy/deploy_to_aws_fargate.md
-      - AWS App Runner: user-guide/deploy/deploy_to_aws_apprunner.md
-      - Amazon EKS: user-guide/deploy/deploy_to_amazon_eks.md
-      - Amazon EC2: user-guide/deploy/deploy_to_amazon_ec2.md
-      - Docker:
-        - Overview: user-guide/deploy/deploy_to_docker/index.md
-        - Python: user-guide/deploy/deploy_to_docker/python.md
-        - TypeScript: user-guide/deploy/deploy_to_docker/typescript.md
-      - Kubernetes: user-guide/deploy/deploy_to_kubernetes.md
-      - Terraform: user-guide/deploy/deploy_to_terraform.md
-    - Versioning & Support: user-guide/versioning-and-support.md
-
-  - Examples:
-    - Overview: examples/README.md
-    - CLI Reference Agent Implementation: examples/python/cli-reference-agent.md
-    - Weather Forecaster: examples/python/weather_forecaster.md
-    - Memory Agent: examples/python/memory_agent.md
-    - File Operations: examples/python/file_operations.md
-    - Agents Workflows: examples/python/agents_workflows.md
-    - Knowledge-Base Workflow: examples/python/knowledge_base_agent.md
-    - Structured Output: examples/python/structured_output.md
-    - Multi Agents: examples/python/multi_agent_example/multi_agent_example.md
-    - Cyclic Graph: examples/python/graph_loops_example.md
-    - Meta Tooling: examples/python/meta_tooling.md
-    - MCP: examples/python/mcp_calculator.md
-    - Multi-modal: examples/python/multimodal.md
-
-  - Community:
-      - Community Catalog: community/community-packages.md
-      - Get Featured: community/get-featured.md
-      - Integrations:
-        - AG-UI: community/integrations/ag-ui.md
-      - Model Providers:
-        - Cohere: community/model-providers/cohere.md
-        - CLOVA Studio: community/model-providers/clova-studio.md
-        - Fireworks AI: community/model-providers/fireworksai.md
-        - Nebius Token Factory: community/model-providers/nebius-token-factory.md
-        - NVIDIA NIM: community/model-providers/nvidia-nim.md
-        - SGLang: community/model-providers/sglang.md
-        - vLLM: community/model-providers/vllm.md
-        - MLX: community/model-providers/mlx.md
-        - xAI: community/model-providers/xai.md
-      - Session Managers:
-        - Amazon AgentCore Memory: community/session-managers/agentcore-memory.md
-        - Valkey/Redis: community/session-managers/strands-valkey-session-manager.md
-      - Tool Protocols:
-        - UTCP: community/tools/utcp.md
-      - Tools:
-        - deepgram: community/tools/strands-deepgram.md
-        - hubspot: community/tools/strands-hubspot.md
-        - teams: community/tools/strands-teams.md
-        - telegram: community/tools/strands-telegram.md
-        - telegram-listener: community/tools/strands-telegram-listener.md
-
-  - Contribute ❤️:
-      - Overview: contribute/index.md
-      - Contribution Types:
-        - SDK: contribute/contributing/core-sdk.md
-        - Documentation: contribute/contributing/documentation.md
-        - Feature Proposals: contribute/contributing/feature-proposals.md
-        - Extensions: contribute/contributing/extensions.md
-  - Python API: []
-  - TypeScript API: api-reference/typescript/index.html
-
-exclude_docs: |
-  node_modules
-  .venv
-  _dependencies
-  !.lycheeignore
-
-plugins:
-  - search
-  - privacy
-  - macros:
-      module_name: macros
-  - mike:
-      alias_type: symlink
-      canonical_version: latest
-  - mkdocstrings:
-      handlers:
-        python:
-          options:
-            docstring_style: google
-            show_root_heading: true
-            show_source: true
-  - llmstxt:
-      full_output: llms-full.txt
-      sections:
-        User Guide:
-          - README.md
-          - user-guide/**/*.md
-        Community:
-          - community/**/*.md
-        Examples:
-          - examples/**/*.md
-        Python API:
-          - docs/api-reference/python/**/*.md
-        TypeScript API:
-          - docs/api-reference/typescript/*.html
-
-
-hooks:
-  - build-ts-docs.py
-  - build-py-docs.py
-extra:
-  social:
-    - icon: fontawesome/brands/github
-  version:
-    provider: mike
-  # Variables
-  docs_repo: https://github.com/strands-agents/docs/tree/main
-  sdk_pypi: https://pypi.org/project/strands-agents/
-  sdk_repo: https://github.com/strands-agents/sdk-python/blob/main
-  py_sdk_repo_home: https://github.com/strands-agents/sdk-python/blob/main
-  ts_sdk_repo_home: https://github.com/strands-agents/sdk-typescript/blob/main
-  tools_pypi: https://pypi.org/project/strands-agents-tools/
-  tools_repo: https://github.com/strands-agents/tools/blob/main
-  tools_repo_home: https://github.com/strands-agents/tools
-  agent_builder_pypi: https://pypi.org/project/strands-agents-builder/
-  agent_builder_repo_home: https://github.com/strands-agents/agent-builder
-
-  link_strands_tools: "[`strands-agents-tools`](https://github.com/strands-agents/tools)"
-  link_strands_builder: "[`strands-agents-builder`](https://github.com/strands-agents/agent-builder)"
-  community_contribution_banner: |
-    !!! info "Community Contribution"
-        This is a community-maintained package that is not owned or supported by the Strands team. Validate and review
-        the package before using it in your project.
-
-        Have your own integration? [We'd love to add it here too!](https://github.com/strands-agents/docs/issues/new?assignees=&labels=enhancement&projects=&template=content_addition.yml&title=%5BContent+Addition%5D%3A+)
-
-validation:
-  nav:
-    omitted_files: info
-    not_found: warn
-    absolute_links: warn
-  links:
-    not_found: warn
-    anchors: warn
-    absolute_links: warn
-    unrecognized_links: warn
diff --git a/src/content/docs/user-guide/quickstart.mdx b/src/content/docs/user-guide/quickstart.mdx
deleted file mode 100644
index 1595b297a..000000000
--- a/src/content/docs/user-guide/quickstart.mdx
+++ /dev/null
@@ -1,557 +0,0 @@
----
-title: Quickstart
----
-
-This quickstart guide shows you how to create your first basic Strands agent, add built-in and custom tools to your agent, use different model providers, emit debug logs, and run the agent locally.
-
-After completing this guide you can integrate your agent with a web server, implement concepts like multi-agent, evaluate and improve your agent, along with deploying to production and running at scale.
-
-## Install the SDK
-
-First, ensure that you have Python 3.10+ installed.
-
-We'll create a virtual environment to install the Strands Agents SDK and its dependencies in to.
-
-```bash
-python -m venv .venv
-```
-
-And activate the virtual environment:
-
-* macOS / Linux: `source .venv/bin/activate`
-* Windows (CMD): `.venv\Scripts\activate.bat`
-* Windows (PowerShell): `.venv\Scripts\Activate.ps1`
-
-Next we'll install the `strands-agents` SDK package:
-
-```bash
-pip install strands-agents
-```
-
-The Strands Agents SDK additionally offers the [`strands-agents-tools`](https://pypi.org/project/strands-agents-tools/) ([GitHub](https://github.com/strands-agents/tools)) and [`strands-agents-builder`](https://pypi.org/project/strands-agents-builder/) ([GitHub](https://github.com/strands-agents/agent-builder)) packages for development. The [`strands-agents-tools`](https://pypi.org/project/strands-agents-tools/) package is a community-driven project that provides a set of tools for your agents to use, bridging the gap between large language models and practical applications. The [`strands-agents-builder`](https://pypi.org/project/strands-agents-builder/) package provides an agent that helps you to build your own Strands agents and tools.
-
-
-Let's install those development packages too:
-
-```bash
-pip install strands-agents-tools strands-agents-builder
-```
-
-### Strands MCP Server (Optional)
-
-Strands also provides an MCP (Model Context Protocol) server that can assist you during development. This server gives AI coding assistants in your IDE access to Strands documentation, development prompts, and best practices. You can use it with MCP-compatible clients like Q Developer CLI, Cursor, Claude, Cline, and others to help you:
-
-- Develop custom tools and agents with guided prompts
-- Debug and troubleshoot your Strands implementations
-- Get quick answers about Strands concepts and patterns
-- Design multi-agent systems with Graph or Swarm patterns
-
-To use the MCP server, you'll need [uv](https://github.com/astral-sh/uv) installed on your system. You can install it by following the [official installation instructions](https://github.com/astral-sh/uv#installation).
-
-Once uv is installed, configure the MCP server with your preferred client. For example, to use with Q Developer CLI, add to `~/.aws/amazonq/mcp.json`:
-
-```json
-{
-  "mcpServers": {
-    "strands-agents": {
-      "command": "uvx",
-      "args": ["strands-agents-mcp-server"]
-    }
-  }
-}
-```
-
-See the [MCP server documentation](https://github.com/strands-agents/mcp-server) for setup instructions with other clients.
-
-## Configuring Credentials
-
-Strands supports many different model providers. By default, agents use the Amazon Bedrock model provider with the Claude 4 model. To change the default model, refer to [the Model Providers section](./quickstart/python.md#model-providers).
-
-To use the examples in this guide, you'll need to configure your environment with AWS credentials that have permissions to invoke the Claude 4 model. You can set up your credentials in several ways:
-
-1. **Environment variables**: Set `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and optionally `AWS_SESSION_TOKEN`
-2. **AWS credentials file**: Configure credentials using `aws configure` CLI command
-3. **IAM roles**: If running on AWS services like EC2, ECS, or Lambda, use IAM roles
-4. **Bedrock API keys**: Set the `AWS_BEARER_TOKEN_BEDROCK` environment variable
-
-Make sure your AWS credentials have the necessary permissions to access Amazon Bedrock and invoke the Claude 4 model.
-
-## Project Setup
-
-Now we'll create our Python project where our agent will reside. We'll use this directory structure:
-
-```
-my_agent/
-├── __init__.py
-├── agent.py
-└── requirements.txt
-```
-
-Create the directory: `mkdir my_agent`
-
-Now create `my_agent/requirements.txt` to include the `strands-agents` and `strands-agents-tools` packages as dependencies:
-
-```
-strands-agents>=1.0.0
-strands-agents-tools>=0.2.0
-```
-
-Create the `my_agent/__init__.py` file:
-
-```python
-from . import agent
-```
-
-And finally our `agent.py` file where the goodies are:
-
-```python
-from strands import Agent, tool
-from strands_tools import calculator, current_time
-
-# Define a custom tool as a Python function using the @tool decorator
-@tool
-def letter_counter(word: str, letter: str) -> int:
-    """
-    Count occurrences of a specific letter in a word.
-
-    Args:
-        word (str): The input word to search in
-        letter (str): The specific letter to count
-
-    Returns:
-        int: The number of occurrences of the letter in the word
-    """
-    if not isinstance(word, str) or not isinstance(letter, str):
-        return 0
-
-    if len(letter) != 1:
-        raise ValueError("The 'letter' parameter must be a single character")
-
-    return word.lower().count(letter.lower())
-
-# Create an agent with tools from the community-driven strands-tools package
-# as well as our custom letter_counter tool
-agent = Agent(tools=[calculator, current_time, letter_counter])
-
-# Ask the agent a question that uses the available tools
-message = """
-I have 4 requests:
-
-1. What is the time right now?
-2. Calculate 3111696 / 74088
-3. Tell me how many letter R's are in the word "strawberry" 🍓
-"""
-agent(message)
-```
-
-This basic quickstart agent can perform mathematical calculations, get the current time, run Python code, and count letters in words. The agent automatically determines when to use tools based on the input query and context.
-
-```mermaid
-flowchart LR
-    A[Input & Context] --> Loop
-
-    subgraph Loop[" "]
-        direction TB
-        B["Reasoning (LLM)"] --> C["Tool Selection"]
-        C --> D["Tool Execution"]
-        D --> B
-    end
-
-    Loop --> E[Response]
-```
-
-More details can be found in the [Agent Loop](concepts/agents/agent-loop.md) documentation.
-
-## Running Agents
-
-Our agent is just Python, so we can run it using any mechanism for running Python!
-
-To test our agent we can simply run:
-```bash
-python -u my_agent/agent.py
-```
-
-And that's it! We now have a running agent with powerful tools and abilities in just a few lines of code 🥳.
-
-## Understanding What Agents Did
-
-After running an agent, you can understand what happened during execution through traces and metrics. Every agent invocation returns an [`AgentResult`](@api/python/strands.agent.agent_result#AgentResult) object with comprehensive observability data.
-
-Traces provide detailed insight into the agent's reasoning process. You can access in-memory traces and metrics directly from the [`AgentResult`](@api/python/strands.agent.agent_result#AgentResult), or export them using [OpenTelemetry](observability-evaluation/traces.md) to observability platforms.
-
-:::note[Example result.metrics.get_summary() output]
-
-```python
-result = agent("What is the square root of 144?")
-print(result.metrics.get_summary())
-```
-```python
-{
-  "accumulated_metrics": {
-    "latencyMs": 6253
-  },
-  "accumulated_usage": {
-    "inputTokens": 3921,
-    "outputTokens": 83,
-    "totalTokens": 4004
-  },
-  "average_cycle_time": 0.9406174421310425,
-  "tool_usage": {
-    "calculator": {
-      "execution_stats": {
-        "average_time": 0.008260965347290039,
-        "call_count": 1,
-        "error_count": 0,
-        "success_count": 1,
-        "success_rate": 1.0,
-        "total_time": 0.008260965347290039
-      },
-      "tool_info": {
-        "input_params": {
-          "expression": "sqrt(144)",
-          "mode": "evaluate"
-        },
-        "name": "calculator",
-        "tool_use_id": "tooluse_jR3LAfuASrGil31Ix9V7qQ"
-      }
-    }
-  },
-  "total_cycles": 2,
-  "total_duration": 1.881234884262085,
-  "traces": [
-    {
-      "children": [
-        {
-          "children": [],
-          "duration": 4.476144790649414,
-          "end_time": 1747227039.938964,
-          "id": "c7e86c24-c9d4-4a79-a3a2-f0eaf42b0d19",
-          "message": {
-            "content": [
-              {
-                "text": "I'll calculate the square root of 144 for you."
-              },
-              {
-                "toolUse": {
-                  "input": {
-                    "expression": "sqrt(144)",
-                    "mode": "evaluate"
-                  },
-                  "name": "calculator",
-                  "toolUseId": "tooluse_jR3LAfuASrGil31Ix9V7qQ"
-                }
-              }
-            ],
-            "role": "assistant"
-          },
-          "metadata": {},
-          "name": "stream_messages",
-          "parent_id": "78595347-43b1-4652-b215-39da3c719ec1",
-          "raw_name": null,
-          "start_time": 1747227035.462819
-        },
-        {
-          "children": [],
-          "duration": 0.008296012878417969,
-          "end_time": 1747227039.948415,
-          "id": "4f64ce3d-a21c-4696-aa71-2dd446f71488",
-          "message": {
-            "content": [
-              {
-                "toolResult": {
-                  "content": [
-                    {
-                      "text": "Result: 12"
-                    }
-                  ],
-                  "status": "success",
-                  "toolUseId": "tooluse_jR3LAfuASrGil31Ix9V7qQ"
-                }
-              }
-            ],
-            "role": "user"
-          },
-          "metadata": {
-            "toolUseId": "tooluse_jR3LAfuASrGil31Ix9V7qQ",
-            "tool_name": "calculator"
-          },
-          "name": "Tool: calculator",
-          "parent_id": "78595347-43b1-4652-b215-39da3c719ec1",
-          "raw_name": "calculator - tooluse_jR3LAfuASrGil31Ix9V7qQ",
-          "start_time": 1747227039.940119
-        },
-        {
-          "children": [],
-          "duration": 1.881267786026001,
-          "end_time": 1747227041.8299048,
-          "id": "0261b3a5-89f2-46b2-9b37-13cccb0d7d39",
-          "message": null,
-          "metadata": {},
-          "name": "Recursive call",
-          "parent_id": "78595347-43b1-4652-b215-39da3c719ec1",
-          "raw_name": null,
-          "start_time": 1747227039.948637
-        }
-      ],
-      "duration": null,
-      "end_time": null,
-      "id": "78595347-43b1-4652-b215-39da3c719ec1",
-      "message": null,
-      "metadata": {},
-      "name": "Cycle 1",
-      "parent_id": null,
-      "raw_name": null,
-      "start_time": 1747227035.46276
-    },
-    {
-      "children": [
-        {
-          "children": [],
-          "duration": 1.8811860084533691,
-          "end_time": 1747227041.829879,
-          "id": "1317cfcb-0e87-432e-8665-da5ddfe099cd",
-          "message": {
-            "content": [
-              {
-                "text": "\n\nThe square root of 144 is 12."
-              }
-            ],
-            "role": "assistant"
-          },
-          "metadata": {},
-          "name": "stream_messages",
-          "parent_id": "f482cee9-946c-471a-9bd3-fae23650f317",
-          "raw_name": null,
-          "start_time": 1747227039.948693
-        }
-      ],
-      "duration": 1.881234884262085,
-      "end_time": 1747227041.829896,
-      "id": "f482cee9-946c-471a-9bd3-fae23650f317",
-      "message": null,
-      "metadata": {},
-      "name": "Cycle 2",
-      "parent_id": null,
-      "raw_name": null,
-      "start_time": 1747227039.948661
-    }
-  ]
-}
-```
-:::
-
-This observability data helps you debug agent behavior, optimize performance, and understand the agent's reasoning process. For detailed information, see [Observability](observability-evaluation/observability.md), [Traces](observability-evaluation/traces.md), and [Metrics](observability-evaluation/metrics.md).
-
-
-## Console Output
-
-Agents display their reasoning and responses in real-time to the console by default. You can disable this output by setting `callback_handler=None` when creating your agent:
-
-```python
-agent = Agent(
-    tools=[calculator, current_time, letter_counter],
-    callback_handler=None,
-)
-```
-
-Learn more in the [Callback Handlers](concepts/streaming/callback-handlers.md) documentation.
-
-## Debug Logs
-
-To enable debug logs in our agent, configure the `strands` logger:
-
-```python
-import logging
-from strands import Agent
-
-# Enables Strands debug log level
-logging.getLogger("strands").setLevel(logging.DEBUG)
-
-# Sets the logging format and streams logs to stderr
-logging.basicConfig(
-    format="%(levelname)s | %(name)s | %(message)s",
-    handlers=[logging.StreamHandler()]
-)
-
-agent = Agent()
-
-agent("Hello!")
-```
-
-See the [Logs documentation](observability-evaluation/logs.md) for more information.
-
-## Model Providers
-
-### Identifying a configured model
-
-Strands defaults to the Bedrock model provider using Claude 4 Sonnet. The model your agent is using can be retrieved by accessing [`model.config`](@api/python/strands.models.model#Model.get_config):
-
-```python
-from strands import Agent
-
-agent = Agent()
-
-print(agent.model.config)
-# {'model_id': 'us.anthropic.claude-sonnet-4-20250514-v1:0'}
-```
-
-You can specify a different model in two ways:
-
-1. By passing a string model ID directly to the Agent constructor
-2. By creating a model provider instance with specific configurations
-
-### Using a String Model ID
-
-The simplest way to specify a model is to pass the model ID string directly:
-
-```python
-from strands import Agent
-
-# Create an agent with a specific model by passing the model ID string
-agent = Agent(model="anthropic.claude-sonnet-4-20250514-v1:0")
-```
-
-### Amazon Bedrock (Default)
-
-For more control over model configuration, you can create a model provider instance:
-
-```python
-import boto3
-from strands import Agent
-from strands.models import BedrockModel
-
-# Create a BedrockModel
-bedrock_model = BedrockModel(
-    model_id="anthropic.claude-sonnet-4-20250514-v1:0",
-    region_name="us-west-2",
-    temperature=0.3,
-)
-
-agent = Agent(model=bedrock_model)
-```
-
-For the Amazon Bedrock model provider, see the [Boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html) to configure credentials for your environment. For development, AWS credentials are typically defined in `AWS_` prefixed environment variables or configured with the `aws configure` CLI command.
-
-You will also need to enable model access in Amazon Bedrock for the models that you choose to use with your agents, following the [AWS documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access-modify.html) to enable access.
-
-More details in the [Amazon Bedrock Model Provider](concepts/model-providers/amazon-bedrock.md) documentation.
-
-### Additional Model Providers
-
-Strands Agents supports several other model providers beyond Amazon Bedrock:
-
-- **[Anthropic](concepts/model-providers/anthropic.md)** - Direct API access to Claude models
-- **[Amazon Nova](concepts/model-providers/amazon-nova.md)** - API access to Amazon Nova models
-- **[LiteLLM](concepts/model-providers/litellm.md)** - Unified interface for OpenAI, Mistral, and other providers
-- **[Llama API](concepts/model-providers/llamaapi.md)** - Access to Meta's Llama models
-- **[Mistral](concepts/model-providers/mistral.md)** - Access to Mistral models
-- **[Ollama](concepts/model-providers/ollama.md)** - Run models locally for privacy or offline use
-- **[OpenAI](concepts/model-providers/openai.md)** - Access to OpenAI or OpenAI-compatible models
-- **[Writer](concepts/model-providers/writer.md)** - Access to Palmyra models
-- **[Cohere <sup> community</sup>](../community/model-providers/cohere.md)** - Use Cohere models through an OpenAI compatible interface
-- **[CLOVA Studio<sup> community</sup>](../community/model-providers/clova-studio.md)** - Korean-optimized AI models from Naver Cloud Platform
-- **[FireworksAI<sup> community</sup>](../community/model-providers/fireworksai.md)** - Use FireworksAI models through an OpenAI compatible interface
-- **[OVHcloud AI Endpoints<sup> community</sup>](../community/model-providers/ovhcloud-ai-endpoints.md)** - Use OVHcloud AI Endpoints models with sovereignty, data privacy and GDPR compliance
-- **[Custom Providers](concepts/model-providers/custom_model_provider.md)** - Build your own provider for specialized needs
-
-## Capturing Streamed Data & Events
-
-Strands provides two main approaches to capture streaming events from an agent: async iterators and callback functions.
-
-### Async Iterators
-
-For asynchronous applications (like web servers or APIs), Strands provides an async iterator approach using [`stream_async()`](@api/python/strands.agent.agent#Agent.stream_async). This is particularly useful with async frameworks like FastAPI or Django Channels.
-
-```python
-import asyncio
-from strands import Agent
-from strands_tools import calculator
-
-# Initialize our agent without a callback handler
-agent = Agent(
-    tools=[calculator],
-    callback_handler=None  # Disable default callback handler
-)
-
-# Async function that iterates over streamed agent events
-async def process_streaming_response():
-    prompt = "What is 25 * 48 and explain the calculation"
-
-    # Get an async iterator for the agent's response stream
-    agent_stream = agent.stream_async(prompt)
-
-    # Process events as they arrive
-    async for event in agent_stream:
-        if "data" in event:
-            # Print text chunks as they're generated
-            print(event["data"], end="", flush=True)
-        elif "current_tool_use" in event and event["current_tool_use"].get("name"):
-            # Print tool usage information
-            print(f"\n[Tool use delta for: {event['current_tool_use']['name']}]")
-
-# Run the agent with the async event processing
-asyncio.run(process_streaming_response())
-```
-
-The async iterator yields the same event types as the callback handler callbacks, including text generation events, tool events, and lifecycle events. This approach is ideal for integrating Strands agents with async web frameworks.
-
-See the [Async Iterators](concepts/streaming/async-iterators.md) documentation for full details.
-
-> Note, Strands also offers an [`invoke_async()`](@api/python/strands.agent.agent#Agent.invoke_async) method for non-iterative async invocations.
-
-### Callback Handlers (Callbacks)
-
-We can create a custom callback function (named a [callback handler](concepts/streaming/callback-handlers.md)) that is invoked at various points throughout an agent's lifecycle.
-
-Here is an example that captures streamed data from the agent and logs it instead of printing:
-
-```python
-import logging
-from strands import Agent
-from strands_tools import shell
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger()
-
-# Define a simple callback handler that logs instead of printing
-tool_use_ids = []
-def callback_handler(**kwargs):
-    if "data" in kwargs:
-        # Log the streamed chunks
-        logger.info(f"{kwargs['delta']}")
-    elif "current_tool_use" in kwargs:
-        tool = kwargs["current_tool_use"]
-        if tool["toolUseId"] not in tool_use_ids:
-            # Log the tool use
-            logger.info(f"[Using tool: {tool.get('name')}]")
-            tool_use_ids.append(tool["toolUseId"])
-
-# Create an agent with the callback handler
-agent = Agent(
-    tools=[shell],
-    callback_handler=callback_handler
-)
-
-# Ask the agent a question
-result = agent("What operating system am I using?")
-
-# Print only the last response
-print(f"\n{result}")
-```
-
-The callback handler is called in real-time as the agent thinks, uses tools, and responds.
-
-See the [Callback Handlers](concepts/streaming/callback-handlers.md) documentation for full details.
-
-## Next Steps
-
-Ready to learn more? Check out these resources:
-
-- [Examples](../examples/README.md) - Examples for many use cases, multi-agent systems, autonomous agents, and more
-- [Community Supported Tools](concepts/tools/community-tools-package.md) - The `strands-agents-tools` package provides many powerful example tools for your agents to use during development
-- [Strands Agent Builder](https://github.com/strands-agents/agent-builder) - Use the accompanying `strands-agents-builder` agent builder to harness the power of LLMs to generate your own tools and agents
-- [Agent Loop](concepts/agents/agent-loop.md) - Learn how Strands agents work under the hood
-- [State & Sessions](concepts/agents/state.md) - Understand how agents maintain context and state across a conversation or workflow
-- [Multi-agent](concepts/multi-agent/agents-as-tools.md) - Orchestrate multiple agents together as one system, with each agent completing specialized tasks
-- [Observability & Evaluation](observability-evaluation/observability.md) - Understand how agents make decisions and improve them with data
-- [Operating Agents in Production](deploy/operating-agents-in-production.md) - Taking agents from development to production, operating them responsibly at scale
diff --git a/src/content/docs/user-guide/quickstart/overview.mdx b/src/content/docs/user-guide/quickstart/overview.mdx
index 4f812bb85..fefe8a904 100644
--- a/src/content/docs/user-guide/quickstart/overview.mdx
+++ b/src/content/docs/user-guide/quickstart/overview.mdx
@@ -5,6 +5,7 @@ sidebar:
   label: "Getting Started"
 redirectFrom:
   - docs
+  - docs/user-guide/quickstart
 ---
 
 import { LinkCard, CardGrid } from '@astrojs/starlight/components';
diff --git a/test/known-routes.json b/test/known-routes.json
index 629537b36..e70ee3e95 100644
--- a/test/known-routes.json
+++ b/test/known-routes.json
@@ -156,5 +156,6 @@
   "/docs/user-guide/concepts/model-providers/nebius-token-factory/",
   "/docs/user-guide/concepts/model-providers/fireworksai/",
   "/docs/user-guide/concepts/model-providers/xai/",
-  "/docs/"
+  "/docs/",
+  "/docs/user-guide/quickstart/"
 ]