Top RAG repositories on GitHub
Retrieval-augmented generation pipelines, embeddings, and grounding tooling.
Ranked by stars across 1,530 repositories tagged rag. Refreshed daily.
- 1open-webui/open-webui★ 152,627 · ⑂ 22,336
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
- ollama
- ollama-webui
- llm
- webui
- self-hosted
- llm-ui
- 2langchain-ai/langchain★ 146,746 · ⑂ 24,541
The agent engineering platform.
- ai
- anthropic
- gemini
- langchain
- llm
- openai
- 3Shubhamsaboo/awesome-llm-apps★ 139,148 · ⑂ 20,454
100+ AI Agents, Agent Skills and RAG Apps - Free and Open Source.
- llms
- rag
- python
- agents
- 4Graphify-Labs/graphify★ 119,860 · ⑂ 11,590
Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.
- claude-code
- graphrag
- knowledge-graph
- codex
- openclaw
- skills
- Live search
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- 5thedotmack/claude-mem★ 94,334 · ⑂ 8,330
Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More
- ai
- ai-agents
- ai-memory
- anthropic
- artificial-intelligence
- claude
- 6infiniflow/ragflow★ 91,065 · ⑂ 10,791
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
- ai
- ai-agents
- context-engine
- rag
- retrieval-augmented-generation
- agentic-ai
- 7PaddlePaddle/PaddleOCR★ 89,885 · ⑂ 11,373
Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.
- ocr
- chineseocr
- pdf2markdown
- pp-ocr
- pp-structure
- document-parsing
- 8
- 9dair-ai/Prompt-Engineering-Guide★ 78,503 · ⑂ 8,630
🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
- deep-learning
- prompt-engineering
- openai
- chatgpt
- language-model
- generative-ai
- 10headroomlabs-ai/headroom★ 73,245 · ⑂ 5,636
Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
- agent
- ai
- anthropic
- compression
- context-engineering
- context-window
- Live search
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- 11Mintplex-Labs/anything-llm★ 66,258 · ⑂ 7,370
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
- rag
- localai
- vector-database
- llm
- ai-agents
- multimodal
- 12mem0ai/mem0★ 65,716 · ⑂ 7,717
The Memory Layer for AI Agents - Drop-in memory infrastructure for AI agents and apps. Context that persists. Built for production.
- ai
- chatgpt
- llm
- python
- rag
- long-term-memory
- 13pathwaycom/llm-app★ 58,911 · ⑂ 1,499
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.
- chatbot
- hugging-face
- llm
- llm-local
- llm-prompting
- llm-security
- 14FlowiseAI/Flowise★ 55,468 · ⑂ 25,033
Build AI Agents, Visually
- artificial-intelligence
- chatgpt
- large-language-models
- low-code
- no-code
- javascript
- 15run-llama/llama_index★ 52,249 · ⑂ 8,180
LlamaIndex is the document processing platform for AI
- agents
- application
- data
- fine-tuning
- framework
- llamaindex
- 16bojieli/ai-agent-book★ 48,971 · ⑂ 5,498
《深入理解 AI Agent:设计原理与工程实践》(李博杰 著)开源主仓库:全书正文、编译版 PDF 与按章配套代码
- agent
- agent-memory
- ai-agent
- book
- coding-agent
- context-engineering
- Live search
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- 17jeecgboot/JeecgBoot★ 47,911 · ⑂ 16,195
【低代码v2.0,一句话即可生成整个系统】企业级AI低代码平台,一键生成前后端代码甚至整个系统。 AI Skills 一句话画流程、设计表单、生成报表、大屏。内置 AI应用平台涵盖:AI聊天、知识库、流程编排、MCP插件等,兼容主流大模型。引领AI低代码「Skills 生成 → 在线配置 → 代码生成 → 手工合并->AI修改」开发模式,解决 Java 项目 90% 重复工作,提高效率又不失灵活。
- antd
- activiti
- codegenerator
- springcloud
- springboot
- low-code
- 18milvus-io/milvus★ 46,176 · ⑂ 4,257
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
- anns
- nearest-neighbor-search
- faiss
- vector-search
- image-search
- hnsw
- 19langchain-ai/langgraph★ 42,025 · ⑂ 7,093
Build resilient agents.
- agents
- ai
- ai-agents
- chatgpt
- deepagents
- enterprise
Find engineers shipping RAG
The list above ranks the most-starred public repositories tagged with the RAG topic, drawn from the public GitHub graph. Across 1,530 repositories tagged this way, the maintainers and top contributors are a tight cluster of the people actually building RAG.
Looking for engineers who’ve worked on RAG for real, not just listed it on LinkedIn? The fastest path is the contributor list of these repos. Their commits, issues, and READMEs are public proof of depth.
Refolk turns this list into a search. Ask for “maintainers of top RAG repos who are hiring”, “RAG engineers in San Francisco”, or “founders shipping RAG” and Refolk returns a ranked shortlist with sources.
How this list is built
Last refreshed: Sun, 20 Sep 2026 20:23:41 GMT
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Common questions
How are these repositories ranked?
By stars, with forks and recent activity as tiebreakers, read from the public GitHub API. The methodology section above has the details.
How fresh is the data?
The ranking re-renders at least daily. Last refreshed: Sun, 20 Sep 2026 20:23:41 GMT.
Can I find the maintainers and contributors behind these repos?
Yes. Stars rank the projects; I can rank the engineers - maintainers, top contributors, and the people they work with. You start with 500 free credits, no card required.
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That's the point. I read hiring signals across GitHub, LinkedIn, and the open web, so a repo list turns into a shortlist of engineers worth talking to.
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Type one sentence. I plan the search, read GitHub, public LinkedIn and Crunchbase records, and the open web as it is right now, and hand back a ranked list with the reason next to every name.
01Describe them
One plain sentence. Role, city, stack, stage, whatever matters to you.
02I read the web live
GitHub, public LinkedIn and Crunchbase records, the open web. Not a database that went stale last quarter.
03You read the shortlist
Ranked, with the reasoning under every name. Open a profile, ask a follow-up, narrow it down.
- Staff backend engineers in NYC who shipped Rust in production
- Series A fintechs in SF under 50 people, growing headcount this year
- Maintainers of fast-growing Rust web frameworks on GitHub
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- Read at search time, so a profile updated yesterday counts today.
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