R2R by SciPhi recently launched!

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^{"Enterprise-grade Deep Research API for Your Data plus the Web"}

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^{TL;DR: R2R is an open-source system that extends beyond conventional RAG by adding autonomous research capabilities across your documents and the web. After processing large document collections in production environments, they developed the Deep Research API to help applications better connect and synthesize information from multiple sources. R2R is available as both a cloud service and self-hostable solution.}

^{Cloud App Signup: app.sciphi.ai (generous free tier, no credit card required)
GitHub: R2R}

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Founded by Owen Colegrove

A team of AI veterans who contributed heavily to the open-source R2R project. Seeing how complicated “truly agentic” retrieval can be, they launched SciPhi Cloud—so you can enjoy advanced retrieval and multi-step reasoning at scale without the infrastructure burden.

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Demo

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The Problem

Traditional RAG systems have fundamental limitations:

• They match queries to documents but lack multi-step reasoning
• Complex questions require manual investigation across multiple sources
• Information synthesis remains a largely human task
• Citation tracking and verification are often missing
• The reasoning process remains opaque

These limitations create inefficiencies when dealing with complex information needs.

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Their Solution

The Deep Research API provides:

• Multi-step reasoning across documents and web sources
• Knowledge graph generation to establish connections between information
• Chained research workflows for exploring complex topics
• Citation-backed answers with source attribution
• Real-time visibility into the agent's thinking process
• RESTful endpoints for straightforward integration

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Key Features

Research Capabilities

• Research Agent: Advanced reasoning with computational tools
• Extended Thinking: Configurable reasoning depth with model support
• Citation Tracking: Source attribution for verification

Platform Components

• Multimodal Ingestion: Support for .txt, .pdf, .json, .png, .mp3, and more
• Hybrid Search: Semantic and keyword search with reciprocal rank fusion
• Knowledge Graphs: Entity and relationship extraction
• User/Document Management: Authentication and collection organization

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Why Now?

LLMs are evolving toward better reasoning, but simply uploading all data to models is costly and error-prone. Agentic RAG solves the "when/what to retrieve" challenge, ensuring LLMs access the right information, especially for enterprise applications with diverse data. Teams need less DevOps and faster market entry without debugging embeddings or managing infrastructure.

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Learn More

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^{🌐 Visit www.sciphi.ai to learn more.}

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^{⭐ Dive into repo on Github}

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^{🤝 Sign up at app.sciphi.ai (free tier included).}

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^{📅📞  Book an exploratory call here}

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^{‍‍👣 Follow R2R by SciPhi on LinkedIn.}