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link
frameworks for LLM apps. -
https://github.com/joaomdmoura/crewAI
framework for orchestrating role-playing, autonomous AI agents. -
https://github.com/microsoft/autogen
framework for multiple agents. -
https://github.com/huggingface/smolagents
framework for multiple agents. -
https://github.com/run-llama/llama_index
framework for your LLM application. -
https://github.com/langchain-ai/langchain
framework for LLM apps. -
Enhances:
- https://github.com/stanfordnlp/dspy
framework for algorithmically optimizing LM prompts and weights. - https://github.com/kyegomez/tree-of-thoughts
Tree of Thoughts (ToT) is a powerful and flexible algorithm that significantly advances model reasoning by up to 70%. - https://github.com/togethercomputer/MoA
Get multiple LLMs to collaborate
- https://github.com/stanfordnlp/dspy
-
https://github.com/BerriAI/litellm
Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, etc.].- Or, use Perplexity, TogetherAI, You etc.
-
https://github.com/eth-sri/lmql
programming language for large language models (LLMs) based on a superset of Python -
https://github.com/embedchain/embedchain` Embedchain is an Open Source Framework for personalizing LLM responses`.
-
https://github.com/vanna-ai/vanna
Auto SQL. -
https://github.com/OpenDevin/OpenDevin
Auto software engineer -
https://github.com/OpenInterpreter/open-interpreter
Auto code interpreter -
https://github.com/wandb/openui
auto UI. -
https://github.com/princeton-nlp/SWE-agent
Auto bug fix on git repo. -
https://github.com/ShishirPatil/gorilla
Auto API call -
https://github.com/VinciGit00/Scrapegraph-ai
Auto webscrape. See parsera as well. -
https://github.com/Pythagora-io/gpt-pilot
A coding Copilot -
https://github.com/microsoft/graphrag
Auto graph rag -
https://github.com/infiniflow/ragflow
Auto graph rag
- https://github.com/outlines-dev/outlines
unstructured -> structured - https://github.com/PrefectHQ/marvin
unstructured -> structured - https://github.com/guidance-ai/guidance
unstructured -> structured - https://github.com/guardrails-ai/guardrails
unstructured -> structured - https://github.com/jackmpcollins/magentic
unstructured -> structured - https://github.com/bespokelabsai/curator
Make data.
- https://github.com/browser-use/browser-use
- https://github.com/mendableai/firecrawl
Website html -> markdown. - https://github.com/apify/crawlee-python
Website html -> markdown. - https://github.com/McGill-NLP/webllama
Browse internet with agents - https://github.com/nilsherzig/LLocalSearch
locally running search aggregator using LLM Agents.
- https://github.com/llm-workflow-engine/llm-workflow-engine
LLM Workflow Engine (LWE) is a Power CLI and Workflow manager for LLMs. - https://github.com/iyaja/llama-fs
self-organizing file manager. It automatically renames and organizes your files based on their contents and well-known conventions (e.g., time). - https://github.com/BasedHardware/OpenGlass
convert glasses to ai glasses - https://github.com/microsoft/PubSec-Info-Assistant
Information Assistant Accelerator - https://github.com/goldmansachs/gs-quant
- chainlit: https://github.com/Chainlit/cookbook
- https://github.com/wandb/openui
aims to make the process fun, fast, and flexible. It's also a tool we're using at W&B to test and prototype our next generation tooling for building powerful applications on top of LLM's. - https://github.com/oobabooga/text-generation-webui
A Gradio web UI for Large Language Models.. - https://github.com/google/mesop
Next streamlit
- https://github.com/bigscience-workshop/petals
LLM on private swarm - https://github.com/h2oai/h2o-llmstudio
framework and no-code GUI designed for fine-tuning state-of-the-art large language models (LLMs). - https://github.com/hiyouga/LLaMA-Factory
Finetune - https://github.com/microsoft/DeepSpeed
training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales. - https://github.com/TensorOpsAI/LLMstudio
Prompt Engineering at your fingertips - https://lmstudio.ai/
Discover, download, and run local LLMs - https://github.com/janhq/cortex.cpp
Cortex is a Local AI API Platform that is used to run and customize LLMs. - https://github.com/zylon-ai/private-gpt
local llm - https://github.com/ggerganov/llama.cpp
Inference of Meta's LLaMA model (and others) in pure C/C++ - https://github.com/ollama/ollama
Get up and running with large language models locally. - https://github.com/Mozilla-Ocho/llamafile
reduce LLM to one file (focuses on CPU, uses llamacpp backed) - https://github.com/vllm-project/vllm
host on gpu - https://github.com/SJTU-IPADS/PowerInfer
Fast Large Language Model Serving with a Consumer-grade GPU. - https://github.com/bentoml/OpenLLM
Self-Hosting Large Language Models Made Easy. - https://github.com/mudler/LocalAI
Open Source OpenAI alternative - https://github.com/nomic-ai/gpt4all
Open-source large language models that run locally on your CPU and nearly any GPU.
- https://github.com/google/gemma.cpp
lightweight, standalone C++ inference engine for the Gemma foundation models from Google. - https://github.com/OpenBMB/MiniCPM-V
A GPT-4V Level MLLM for Single Image
- https://github.com/xai-org/grok-1
- https://github.com/Shubhamsaboo/awesome-llm-apps
- https://github.com/Hannibal046/Awesome-LLM
curated list of papers about large language models, especially relating to ChatGPT. It also contains frameworks for LLM training, tools to deploy LLM, courses and tutorials about LLM and all publicly available LLM checkpoints and APIs. - https://github.com/openai/openai-cookbook
Example code and guides for accomplishing common tasks with the OpenAI API. - https://ai.azure.com/explore/
- https://github.com/f/awesome-chatgpt-prompts
- https://github.com/erikbern/ann-benchmarks
- https://debarghyadas.com/writes/github