The most advanced AI retrieval system.

Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.

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About

R2R (Reason to Retrieve) is an advanced AI retrieval system supporting Retrieval-Augmented Generation (RAG) with production-ready features. Built around a RESTful API, R2R offers multimodal content ingestion, hybrid search, knowledge graphs, and comprehensive document management.

R2R also includes a Deep Research API, a multi-step reasoning system that fetches relevant data from your knowledgebase and/or the internet to deliver richer, context-aware answers for complex queries.

Getting Started

Cloud Option: SciPhi Cloud

Access R2R through SciPhi's managed deployment with a generous free tier. No credit card required.

Self-Hosting Option

# Quick install and run in light mode
pip install r2r
export OPENAI_API_KEY=sk-...
python -m r2r.serve

# Or run in full mode with Docker
# git clone git@github.com:SciPhi-AI/R2R.git && cd R2R
# export R2R_CONFIG_NAME=full OPENAI_API_KEY=sk-...
# docker compose -f compose.full.yaml --profile postgres up -d

For detailed self-hosting instructions, see the self-hosting docs.

Demo

https://github.com/user-attachments/assets/173f7a1f-7c0b-4055-b667-e2cdcf70128b

Using the API

1. Install SDK & Setup

# Install SDK
pip install r2r  # Python
# or
npm i r2r-js    # JavaScript

# Setup API key
export R2R_API_KEY=pk_..sk_...  # Get from SciPhi Cloud dashboard

2. Client Initialization

from r2r import R2RClient
client = R2RClient()  # Use base_url=... for self-hosted

const { r2rClient } = require('r2r-js');
const client = new r2rClient();  // Use baseURL=... for self-hosted

3. Document Operations

# Ingest sample or your own document
client.documents.create_sample(hi_res=True)
# client.documents.create(file_path="/path/to/file")

# List documents
client.documents.list()

4. Search & RAG

# Basic search
results = client.retrieval.search(query="What is DeepSeek R1?")

# RAG with citations
response = client.retrieval.rag(query="What is DeepSeek R1?")

# Agentic reasoning with RAG
response = client.retrieval.agent(
  message={"role":"user", "content": "What does deepseek r1 imply? Think about market, societal implications, and more."},
  rag_generation_config={
    "model"="anthropic/claude-3-7-sonnet-20250219",
    "extended_thinking": True,
    "thinking_budget": 4096,
    "temperature": 1,
    "top_p": None,
    "max_tokens_to_sample": 16000,
  },
  mode="research" # for Deep Research style output
)

Key Features

• 📁 Multimodal Ingestion: Parse .txt, .pdf, .json, .png, .mp3, and more
• 🔍 Hybrid Search: Semantic + keyword search with reciprocal rank fusion
• 🔗 Knowledge Graphs: Automatic entity & relationship extraction
• 🤖 Agentic RAG: Reasoning agent integrated with retrieval
• 🔐 User & Access Management: Complete authentication & collection system

Community & Contributing

• Join our Discord for support and discussion
• Submit feature requests or bug reports
• Open PRs for new features, improvements, or documentation
• Book a demo call with the SciPhi founders

Our Contributors