LLM Frameworks

Ollama — a leading open-source project in the AI/LLM ecosystem.

LangChain — a leading open-source project in the AI/LLM ecosystem.

LlamaIndex — a leading open-source project in the AI/LLM ecosystem.

🚀🚀 「大模型」2小时完全从0训练26M的小参数GPT！🌏 Train a 26M-parameter GPT from scratch in just 2h!

Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.

Build resilient language agents as graphs.

Python scraper based on AI

A lightweight, powerful framework for multi-agent workflows

Pocket Flow: Codebase to Tutorial

Pocket Flow: 100-line LLM framework. Let Agents build Agents!

KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs. It is used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It can effectively overcome the shortcomings of the traditional RAG vector similarity calculation model.

Cybersecurity AI (CAI), the framework for AI Security

AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework

Official release of InternLM series (InternLM, InternLM2, InternLM2.5, InternLM3).

⚙️🦀 Build modular and scalable LLM Applications in Rust

OpenCompass is an LLM evaluation platform, supporting a wide range of models (Llama3, Mistral, InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets.

Enchanted is iOS and macOS app for chatting with private self hosted language models such as Llama2, Mistral or Vicuna using Ollama.

Building AI agents, atomically

A visual playground for agentic workflows: Iterate over your agents 10x faster

Low-latency AI engine for mobile devices & wearables

RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry

AdalFlow: The library to build & auto-optimize LLM applications.

A course of learning LLM inference serving on Apple Silicon for systems engineers: build a tiny vLLM + Qwen.

Easiest and laziest way for building multi-agent LLMs applications.