Knowledge-Graph Agentic RAG Runtime Model Card

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Knowledge-Graph Agentic RAG Runtime Model Card

1. Role in Self Evolve

GraphMind is an agentic RAG runtime that combines knowledge graphs, dual orchestration engines, and self-evaluating retrieval pipelines for autonomous knowledge work. It matters because self-evolving agents need explicit feedback loops, observable retention mechanisms, and auditable evaluation pressure before improvement claims become useful.

2. Working Principle

receive query -> choose LangGraph or CrewAI engine -> retrieve over hybrid graph layer -> self-evaluate the answer -> retry when score stays below threshold

3. Evidence Path

web-observed GitHub page showed 1 star, 0 forks, 15 commits, MIT license, dual LangGraph and CrewAI orchestration, MCP and FastAPI entry points, and a self-evaluating retrieval loop over a shared knowledge-graph substrate. This iteration keeps freshness honest: the snapshot comes from the current public GitHub page, while shell GitHub API access remained blocked in this workspace.

4. Teaching Use

Use this card to explain Knowledge-Graph Agentic RAG Runtime: it shows how coding-agent, self-rewarding, optimization, or graph-runtime layers convert agent behavior into a reproducible engineering story.

5. Limits

The repository was not cloned in this iteration; no benchmark run, workflow execution, or training experiment was executed. Counts and claims are visible public-page signals unless independently revalidated later.