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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

Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain

[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"

📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG

An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.

Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.

"RAG-Anything: All-in-One RAG Framework"

Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.

Unified framework for building enterprise RAG pipelines with small, specialized models

💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows

Retrieval and Retrieval-augmented LLMs

Agent S: an open agentic framework that uses computers like a human

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

~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.

AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation

Harness LLMs with Multi-Agent Programming

⚡FlashRAG: A Python Toolkit for Efficient RAG Research (WWW2025 Resource)

Fast, Accurate, Lightweight Python library to make State of the Art Embedding

All-in-one platform for search, recommendations, RAG, and analytics offered via API

The code used to train and run inference with the ColVision models, e.g. ColPali, ColQwen2, and ColSmol.

Comprehensive resources on Generative AI, including a detailed roadmap, projects, use cases, interview preparation, and coding preparation.

WFGY is heading toward WFGY 5.0 Polaris Protocol, a major open-source release for AI reasoning, RAG, agents, and real-world workflows. Includes Problem Map, Global Debug Card, WFGY 4.0, and the CFV Easter Egg.

🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL

The collaborative spreadsheet for AI. Chain cells into powerful pipelines, experiment with prompts and models, and evaluate LLM responses in real-time. Work together seamlessly to build and iterate on AI applications.

RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in different LLMs, embeddings, and vector stores, and now includes seamless MCP integration to connect external tools and data sources.

RAG evaluation without the need for "golden answers"

An enterprise-grade AI retriever designed to streamline AI integration into your applications, ensuring cutting-edge accuracy.

Chat with PDF files with source highlights

Persistent memory for Claude Code — 41 neuroscience papers, 26 biological mechanisms with paper-bearing per-mechanism ablation evidence (E1 v3). LongMemEval R@10 98.4% / MRR 0.9124 (n=500). LoCoMo R@10 94.2% / MRR 0.8278 (n=1986). BEAM-10M +33.4% over flat retrieval. PostgreSQL + pgvector. Verified via 31-row two-benchmark ablation campaign.

Open-source, self-hosted knowledge backend for AI agents — hybrid search (vector + keyword), MCP server, 5 connectors, Docker-ready