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R2R

Stale
GitHub Python MIT

Description

A production-ready Agentic RAG system with RESTful API, featuring multimodal document ingestion, hybrid search, knowledge graph construction, and agent-driven retrieval-augmented generation workflows.

Key Features

  • Production-ready Agentic RAG system with RESTful API for retrieval-augmented generation workflows
  • Multimodal document ingestion supporting .txt, .pdf, .json, .png, .mp3 and more formats
  • Hybrid search combining semantic and keyword search with reciprocal rank fusion
  • Automatic knowledge graph construction with entity and relationship extraction
  • Deep Research API for multi-step reasoning across knowledgebase and internet sources
  • Complete user and access management with authentication and collection-based organization

Use Cases

💡 Building enterprise knowledge management systems with intelligent document retrieval
💡 Creating AI-powered Q&A systems over internal company documents and data
💡 Implementing deep research workflows that combine private data with web sources
💡 Deploying production RAG pipelines with full authentication and access control

Strengths & Limitations

✅ Strengths

  • • High community interest (8.0k stars)
  • • Permissive open-source license (MIT)
  • • Established track record (2 years in production)

⚠️ Limitations

  • • No updates in over 10 months

Tags

rag retrieval knowledge-graph multimodal rest-api agent

Categories

📚 RAG Tools

Quick Start

Install: pip install r2r. Set OPENAI_API_KEY. Run: python -m r2r.serve. Use client = R2RClient(base_url='http://localhost:7272') for API access.
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