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5 Best Open Source Graph RAG Tools (2026)

Ryan Musser
Ryan Musser
Founder

Vanilla vector RAG works fine until your users start asking questions whose answers live across multiple documents. Graph RAG - augmenting retrieval with an extracted knowledge graph of entities and relations - closes that gap. It also helps with entity disambiguation, temporal reasoning, and explainability.

The open source Graph RAG ecosystem moved fast in 2025. Here are the five tools worth your time, what they're good at, and where they fall short. We've tried to be honest, including about TypeGraph, which is our own project.

1. Microsoft GraphRAG

Stack: Python. License: MIT.

The reference implementation that put "Graph RAG" on the map. Microsoft Research's pipeline does community detection (Leiden) over the extracted graph and uses hierarchical summaries to answer global questions ("what are the major themes?") that vector RAG can't.

  • Pros: Strong baselines on academic benchmarks; the original paper that defined the category; great for global/sensemaking queries over large corpora.
  • Cons: Heavy. The indexing pipeline is expensive (LLM calls per chunk for community summaries) and slow on real-world corpora. Python-only.
  • GraphRAG-Bench: 50.93% novel, 45.16% medical.
  • Link: github.com/microsoft/graphrag

2. Graphiti (Zep)

Stack: Python on Neo4j. License: Apache 2.0.

Graphiti's differentiator is temporal knowledge graphs - every edge has a validity interval, so the graph correctly handles facts that change over time ("Alice was CTO at X until 2025, now CTO at Y"). Built by the Zep team for agent memory.

  • Pros: Best-in-class temporal modeling. Incremental ingestion is fast (no full re-index per write). Great fit for agent memory and long-running conversations.
  • Cons: Purely memory oriented; doesn't really support document/knowledge type stuff. Requires Neo4j. Python-only client. The temporal model adds conceptual overhead if you don't actually need it.
  • Link: github.com/getzep/graphiti

3. LightRAG

Stack: Python. License: MIT.

An academic project (HKU) that argues you don't need community detection to do Graph RAG well. Uses a dual-level retrieval (low-level entities + high-level concepts) and is dramatically cheaper to index than Microsoft GraphRAG.

  • Pros: Cheap to index. Solid retrieval quality for the cost. Active GitHub community (10k+ stars).
  • Cons: Less mature than the alternatives. Production hardening is on you. Python-only.
  • GraphRAG-Bench: 45.09% novel, 62.59% medical.
  • Link: github.com/HKUDS/LightRAG

4. Cognee

Stack: Python (TS client in progress). License: Apache 2.0.

Cognee positions itself as a modular memory engine - pipelines, tasks, and DAGs you compose to ingest, normalize, and query. It supports multiple graph stores (Kuzu, Neo4j, FalkorDB) and vector stores.

  • Pros: Flexible architecture. Multi-backend. Good for teams that want to swap components. Strong team and community support.
  • Cons: Purely memory oriented; doesn't really support document/knowledge type stuff. Flexibility is a learning curve. Defaults aren't always production-ready. TS support is early.
  • Link: github.com/topoteretes/cognee

5. TypeGraph

Stack: TypeScript on Postgres + pgvector. License: MIT.

TypeGraph is the TypeScript-native option. It runs on the Postgres you already have, ships an MCP server for Claude Code / Cursor, and integrates directly with the Vercel AI SDK. The graph (entities + relations) lives alongside vector embeddings in the same Postgres instance - no separate graph DB.

  • Pros: TS-native. No new infrastructure (Postgres only). MCP server included. Built-in entity resolution and contradiction detection. Strong fit for Next.js / Vercel / Mastra stacks.
  • Cons: Early stage. Small community, low GitHub adoption today, not yet battle-tested at enterprise scale. If you need years of production references, pick Graphiti or Microsoft GraphRAG and revisit TypeGraph in 6–12 months.
  • GraphRAG-Bench: 62.65% novel, 67.68% medical.
  • Link: typegraph.ai

How to choose

  • Building a Python research pipeline? Microsoft GraphRAG or LightRAG.
  • Building agent memory with temporal facts? Graphiti.
  • Want maximum architectural flexibility? Cognee.
  • Building on Next.js / Vercel AI SDK / TypeScript? TypeGraph.

Whichever tool you pick, the methodology of evaluation matters more than the tool. See our guide to RAG benchmarks before you commit.

Want to try it on your own stack? We have hands-on guides for vanilla RAG on Vercel + Neon and Graph RAG on Vercel + Neon.

5 Best Open Source Graph RAG Tools (2026) | TypeGraph