Agentic Graph RAG for Medical Diagnosis is an open-source system that answers complex clinical questions by combining semantic retrieval and knowledge graph traversal in parallel. Built on LangGraph, it decomposes questions into sub-queries, runs two iterative retrieval channels concurrently, and synthesizes a final evidence-backed answer — all driven by a stateful agentic loop.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/avnlp/agentic-med-diag/llms.txt
Use this file to discover all available pages before exploring further.
Introduction
Understand what the system does, why it was built, and how the pieces fit together.
Quickstart
Set up the environment, configure backends, and run your first medical query.
Architecture
Explore the LangGraph state machine, parent/child subgraphs, and dual retrieval channels.
Backends
Compare LightRAG, MiniRAG, PathRAG, and HyperGraphRAG — switchable at runtime.
How It Works
The system is implemented as a hierarchical LangGraph state machine. A parent graph fans out to two parallel subgraphs — the semantic channel and the relational channel — then synthesizes their outputs into a final answer.Install dependencies
Install uv and run
uv sync to install all Python dependencies, including LangGraph, Neo4j, Milvus, and your chosen Graph RAG backend.Configure storage
Start Neo4j for knowledge graph storage and Milvus for dense vector embeddings. Set connection credentials in your environment.
Choose a backend
Select from LightRAG, MiniRAG, PathRAG, or HyperGraphRAG. Each backend offers a different graph construction and retrieval strategy, all switchable at runtime.
Key Features
Agentic Multi-Hop Reasoning
Iterative sub-query decomposition with back-references (
#N) across hops enables complex multi-step clinical reasoning.Dual Retrieval Channels
Parallel semantic (text chunks) and relational (SPO triples) channels are run simultaneously and merged before synthesis.
Four Graph RAG Backends
LightRAG, MiniRAG, PathRAG, and HyperGraphRAG — each with distinct graph construction, indexing, and retrieval strategies.
Medical Benchmarks
Evaluated on HealthBench, MedCaseReasoning, MetaMedQA, and PubMedQA using DeepEval metrics.