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smartrag
by aymenfurter
Graph-backed multi-agent research workflows for deep retrieval and evidence chaining
JavaScript
Updated Aug 21, 2025
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Summary
Orchestrates multi-agent research workflows that combine GraphRAG (graph-based RAG) with specialist agents for deep domain exploration. Agents query and augment a shared knowledge graph, pass context and evidence between peers, and produce consolidated findings. Distinctive features include graph-backed retrieval across agent outputs and multimodal/voice modes for richer interaction graph-backed retrieval and multimodal/voice modes for richer interaction multimodal/voice modes.
Key Benefits
As agents tackle longer, evidence-heavy tasks, understanding how knowledge flows between them becomes critical for trust and repeatability. Smartrag makes those flows explicit by using a shared graph as the substrate for agent communication and evidence linking, which helps surface provenance and failure modes. For teams studying agent behaviour or building evaluation layers, this repo provides a research-oriented pattern for tracing agent reasoning and delegated work. provenance and failure modes
Ideal For
Researchers and engineers prototyping multi-agent information retrieval systems or studying agent delegation and provenance using graph-based RAG. agent delegation
Real-World Examples
- Experimenting with agent delegation patterns and tracing evidence across agents using a shared knowledge graph
- Prototyping multimodal research agents that combine text, voice, and retrieved graph context
- Investigating agent failure modes and provenance by linking outputs in a GraphRAG retrieval layer
Works With
autogenopenaiazuregpt-4o
Topics
autogenazuredeep-researchgpt-4ographragllmmulti-agent-systemsmultimodalopenaivoice-mode
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autogenlangchain
Keywords
multi-agent trustgraphragagent-delegationagent-to-agent evaluation