Stars
Autoswagger by Intruder - detect API auth weaknesses
Everything you need to know to build your own RAG application
Chrome DevTools for coding agents
Context engineering is the new vibe coding - it's the way to actually make AI coding assistants work. Claude Code is the best for this so that's what this repo is centered around, but you can applyโฆ
Low-code programming for event-driven applications
Optimize prompts, code, and more with AI-powered Reflective Text Evolution
LLM agents built for control. Designed for real-world use. Deployed in minutes.
[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)
Paper2Agent is a multi-agent AI system that automatically transforms research papers into interactive AI agents with minimal human input.
Eigent: The World's First Multi-agent Workforce to Unlock Your Exceptional Productivity.
The all-in-one Desktop & Docker AI application with built-in RAG, AI agents, No-code agent builder, MCP compatibility, and more.
"AutoAgent: Fully-Automated and Zero-Code LLM Agent Framework"
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
Open Source Application for Advanced LLM + Diffusion Engineering: interact, train, fine-tune, and evaluate large language models on your own computer.
Open-source platform to build and deploy AI agent workflows.
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
Fine-tuning & Reinforcement Learning for LLMs. ๐ฆฅ Train OpenAI gpt-oss, DeepSeek-R1, Qwen3, Gemma 3, TTS 2x faster with 70% less VRAM.
An open-source, low-code machine learning library in Python
Automatically visualize your pandas dataframe via a single print! ๐ ๐ก
Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.