R2R is an agentic retrieval-augmented generation platform that uses reasoning agents to perform multi-step data fetching for context-aware answering. It functions as a multimodal vector database manager and knowledge graph engine designed to ground artificial intelligence responses in verified factual knowledge.
The platform distinguishes itself by combining reasoning agents for complex research automation with a knowledge graph that maps entity relationships. This allows the system to perform structured data traversal alongside unstructured vector search to resolve complex questions from internal knowledge bases and the web.
The system covers multimodal content ingestion for various file types, hybrid semantic-keyword search, and collection-based data isolation for multi-tenant access control. These capabilities are exposed through a programmable REST API gateway.
