by stevereiner · MCP Server · ★ 162
New 5/6/26: 15 property graph databases total: 8 supported on both LlamaIndex and LangChain, 1 LI-only (Google Cloud Spanner Graph), 6 LC-only (ArangoDB, Apache AGE, Azure Cosmos DB for Gremlin, Apache HugeGraph, SurrealDB, TigerGraph). AWS Neptune RDF/SPARQL added. All 10 vector databases, all 3 search engines, and all LLM/embedding providers work with both LlamaIndex and LangChain. Every pipeline stage (chunking, KG extraction, graph write, vector write, search write, and retrieval fusion) can be configured independently.
| Stars | 162 |
| Forks | 32 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 56.9721542977398/100 |
| Open Issues | 3 |
| Last Updated | 2026-07-06 |
| Created | 2025-08-05 |
| Platforms | cli, docker, mcp, python |
| Est. Tokens | ~25k |
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flexible-graphrag is Python, LlamaIndex, LangChain, Docker Compose: 15 Property Graph, 4 RDF , 10 Vector, OpenSearch, Elasticsearch, Alfresco DBs. 13 data sources (9 auto-sync), KG auto-building, Ontologies, LLMs, Docling. It is categorized as a MCP Server with 162 GitHub stars.
flexible-graphrag is primarily written in Python. It covers topics such as ai-agent-memory, ai-chat, ai-context-extraction.
You can find installation instructions and usage details in the flexible-graphrag GitHub repository at github.com/stevereiner/flexible-graphrag. The project has 162 stars and 32 forks, indicating an active community.
flexible-graphrag is released under the Apache-2.0 license, making it free to use and modify according to the license terms.