name: kragen-knowledge-graph description: Graph-RAG Solver keywords:

• knowledge-graph
• RAG
• reasoning
• graph-of-thoughts
• biomedical-qa measurable_outcome: Return a reasoning path and an answer supported by ≥3 knowledge graph nodes for complex biomedical questions with <5s latency. license: MIT metadata: author: Bioinformatics Oxford version: "1.0.0" compatibility:
• system: Python 3.9+ allowed-tools:
• run_shell_command
• web_fetch

KRAGEN (Knowledge Graph Enhanced RAG)

A knowledge graph-enhanced Retrieval-Augmented Generation system for biomedical problem solving, using Graph-of-Thoughts (GoT) reasoning.

When to Use

• Complex Reasoning: Questions requiring multi-hop deduction (e.g., "How does gene A influence disease B via protein C?").
• Hypothesis Verification: Checking if a proposed mechanism is supported by existing knowledge graphs.
• Literature Synthesis: Combining facts from structured DBs and unstructured text.

Core Capabilities

1. Graph Retrieval: Query biomedical knowledge graphs (e.g., PrimeKG, SPOKE).
2. Graph-of-Thoughts: structured reasoning over retrieved nodes.
3. Vector DB Integration: Combines graph data with vector embeddings for hybrid search.

Workflow

1. Input: Natural language question.
2. Retrieval: Fetch relevant sub-graph and similar text chunks.
3. Reasoning: LLM traverses the graph to find connecting paths.
4. Answer: Generate response with citation of graph nodes.

Example Usage

User: "Explain the mechanism connecting BRCA1 mutations to ovarian cancer."

Agent Action:

python -m kragen.solve --question "BRCA1 mutations to ovarian cancer mechanism"