Tool overview
R2R is listed under AI Infrastructure & MLOps AI tools.
What is R2R?
R2R is an MIT-licensed retrieval system for agentic RAG. It provides multimodal ingestion, hybrid search, knowledge graphs, document management, user access controls, Deep Research, REST APIs, and Python and JavaScript clients for self-hosted applications.
Best for
Developers building self-hosted agentic retrieval and research systems
Who is it for?
Decision note
Rebuilt from the original export under the complete V411 factual-source-verification workflow. Preview only. Apply remains blocked until Failures = 0, Warnings = 0, Unmapped = 0, Missing = 0, all explicit clears are reviewed, image import is disabled or V380 accepts the asset, one representative WordPress edit screen is compared with the export and proposed row, and a post-Apply zero-change Preview is completed.
Key features
RESTful API with Python and JavaScript clients
Multimodal ingestion and hybrid search
Knowledge graphs and agentic retrieval
Docker, Kubernetes, and self-hosting support
Use cases
Build private RAG applications
Create deep-research agents
Manage searchable document collections
Add hybrid retrieval to products
Pros
- MIT-licensed and self-hostable
- Current release and API are documented
- Supports multimodal and graph retrieval
Cons
- Operating infrastructure remains the user’s responsibility
- Model and parser dependencies add complexity
Limitations
Retrieval, citations, graph extraction, and agent actions can be incomplete or incorrect and require testing.
Teams must secure credentials, uploaded data, collection permissions, internet tools, and model-provider settings.
Pricing details
Billing options
Pricing note
R2R is free under MIT. Users pay their own compute, storage, databases, model-provider, parsing, and operational costs when self-hosting the system.
Supported languages
- English
Integrations
PostgreSQL
S3-compatible storage
OpenAI-compatible models
Ollama
Firecrawl
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