1517005260/graph-rag-agent
拼好RAG：手搓并融合了GraphRAG、LightRAG、Neo4j-llm-graph-builder进行知识图谱构建以及搜索；整合DeepSearch技术实现私域RAG的推理；自制针对GraphRAG的评估框架| Integrate GraphRAG, LightRAG, and Neo4j-llm-graph-builder for knowledge graph construction and search. Combine DeepSearch for private RAG reasoning. Create a custom evaluation framework for GraphRAG. observed · 2026-08-28
Health v2 · maintenance only
43/100
- Activity 50
- Release rhythm 35
- Longevity 40
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 567
- days_rel: n/a
- days_push: 301
- n_releases_24m: 0
Adoption not part of the score
2326 stars · 322 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A Python framework that integrates GraphRAG, LightRAG, and Neo4j-based graph building to construct knowledge graphs and perform explainable, reasoning-driven question answering. It combines multi-agent Plan-Execute-Report orchestration with DeepSearch techniques and includes a custom evaluation framework for GraphRAG pipelines.
Use cases
- build a knowledge graph from private documents and query it
- implement graph-based RAG question answering with reasoning traces
- run deep research agents over a private document corpus
- evaluate and compare GraphRAG retrieval quality
- visualize and explore a knowledge graph with a chat interface
- orchestrate multi-agent plan-execute-report research workflows
When to choose
- you need knowledge-graph-enhanced RAG over private domain data
- you want explainable, agentic retrieval with multi-hop graph exploration
- you need a built-in evaluation framework for GraphRAG pipelines
- you want a self-hosted full stack with API, frontend, and Neo4j backend
When to avoid
- you only need simple vector-based RAG without graph structure
- you want a lightweight plug-and-play library with minimal setup
- you cannot operate Neo4j and LLM API infrastructure
- you need production-hardened enterprise support
Facets
framework · maturity active
rag agent-framework search-engine llm-inference chatbot data-visualization benchmarking caching large-language-models databases developer-tools python self-hosted cross-platform graphrag lightrag knowledge-graph neo4j deepsearch multi-agent agentic-rag chain-of-exploration think-on-graph community-detection evaluation-framework fastapi retrieval-augmented-generation ai-agents natural-language-processing search docker web-server
2 sources
- readme: https://github.com/1517005260/graph-rag-agent · fetched 2026-08-28 · e76dec01c809
- homepage: https://deepwiki.com/1517005260/graph-rag-agent · fetched 2026-08-29 · 4c581a0c8b0e
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| 1517005260/graph-rag-agent | main | 43 |
For agents
markdown · JSON · MCP: product_card(name="1517005260/graph-rag-agent")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem