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Hy Memory (Official Website) is a production-grade dual-system cognitive memory engine and Python SDK (hy-memory) for self-evolving LLM agents, providing specialized memory pipelines across three task scenes: hierarchical long-term cognitive memory for Chat, two-layer fact and…

Are you frustrated with vector database retrieval accuracy for long and complex documents? Vector-based RAG retrieves by semantic similarity. But similarity ≠ relevance — what retrieval actually needs is relevance, and relevance requires reasoning.

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

Enhanced ChatGPT Clone: Features Agents, MCP, Skills, DeepSeek, Anthropic, AWS, OpenAI, Responses API, Azure, Groq, o1, GPT-5, Mistral, OpenRouter, Vertex AI, Gemini, Artifacts, AI model switching, message search, Code Interpreter, langchain, DALL-E-3, OpenAPI Actions…

🔍大模型应用开发实战一：RAG 技术全栈指南，在线阅读地址：

Modern documents increasingly contain diverse multimodal content—text, images, tables, equations, charts, and multimedia—that traditional text-focused RAG systems cannot effectively process.

Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics.

A community-driven hub of 42+ runnable notebooks covering RAG techniques from foundational to cutting-edge - the intuition, the code, and the references to build more accurate, context-rich retrieval systems.

Elasticsearch is a distributed search and analytics engine, scalable data store and vector database optimized for speed and relevance on production-scale workloads. Elasticsearch is the foundation of Elastic's open Stack platform.

AI Engineering is advancing rapidly, and staying at the forefront requires both deep understanding and hands-on experience.

LlamaIndex OSS (by LlamaIndex) is an open-source framework to build agentic applications. Parse is our enterprise platform for agentic OCR, parsing, extraction, indexing and more.

> Note: You can also use pip if you prefer, but uv is recommended for better performance and more reliable dependency management.

> Ingest your enterprise data, extract what matters, build a Context Graph and knowledge graph (KG), and run graph analytics and causal reasoning over all of it, with full decision provenance baked in. Explainable, traceable, and trustworthy by design.

Hypergraph is more powerful. Transform unstructured text into structured knowledge with LLMs. Graphs, hypergraphs, and spatio-temporal extractions — with one command.

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.

[MLsys2026 Best Paper]: RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

A super fast Graph Database uses GraphBLAS under the hood for its sparse adjacency matrix graph representation. Our goal is to provide the best Knowledge Graph for LLM (GraphRAG).

企业级 Agentic RAG 智能体 - 全链路覆盖文档解析、多路检索、意图识别、问题重写、会话记忆、MCP 工具调用与深度思考。面向真实业务场景，从 0 到 1 完整工程实现。

WARNING] > GraphRAG is a research project that explores the functional use of graphs to form a targeted context for question answering.

STORM is a LLM system that writes Wikipedia-like articles from scratch based on Internet search. Co-STORM further enhanced its feature by enabling human to collaborative LLM system to support more aligned and preferred information seeking and knowledge curation.

⭐ If LMCache helps you serve LLMs faster and cheaper, give us a star — it helps more teams discover the project.

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

Learn to build modern AI systems from the ground up through hands-on implementation

Knowhere is a document parsing and retrieval system that turns complex, dirty files into persistent, navigable memory for AI agents—especially across local and offline document collections.

Welcome developers, researchers, and enthusiasts to join the XRAG open-source project!

This framework provides an easy method to compute embeddings for accessing, using, and training state-of-the-art embedding and reranker models. a.

A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.

Our hosted service, Chroma Cloud, powers serverless vector, hybrid, and full-text search. It's extremely fast, cost-effective, scalable and painless. Create a DB and try it out in under 30 seconds with $5 of free credits.

A vector index built on TurboQuant, written in Rust with Python bindings

A no-code RAG server that keeps answers in sync with your documents. Works with 8 vector databases. Built on Pathway.