Flexible GraphRAG: Python, LlamaIndex, Docker Compose: 8 Graph dbs, 10 Vector dbs, OpenSearch, Elasticsearch, Alfresco. 13 data sources (9 auto-sync), KG auto-building, schemas, LLMs, Docling or LlamaParse doc processing, GraphRAG, RAG only, Hybrid search, AI chat. React, Vue, Angular frontends, FastAPI backend, REST API, MCP Server. Please π Star
Config is the same across clients β only the file and path differ.
{
"mcpServers": {
"flexible-graphrag": {
"args": [
"arcadedb"
],
"command": "uvx"
}
}
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New Flexible GraphRAG now supports RDF-based ontologies for both property graph databases and RDF triple store databases (Graphwise Ontotext GraphDB, Fuseki, and Oxigraph). Document ingestion with KG extraction, auto incremental data source change detection, and UI search (hybrid search, AI query, and AI chat) are all supported with both database types.
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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. (Data source reading is LlamaIndex only; RDF stores use framework-independent adapters with LangChain Text-to-SPARQL retrieval.)
New: Flexible GraphRAG now supports RDF-based ontologies for both property graph databases and RDF triple store databases (Graphwise Ontotext GraphDB, Fuseki, and Oxigraph). Document ingestion with KG extraction, auto incremental data source change detection, and UI search (hybrid search, AI query, and AI chat) are all supported with both database types.
New: Flexible GraphRAG supports automatic incremental updates (Optional) from most data sources, keeping your Vector, Search and Graph databases synchronized in real-time or near real-time.
New: KG Spaces Integration of Flexible GraphRAG in Alfresco ACA Client
Flexible GraphRAG is an open source AI context platform supporting a document processing pipeline (Docling or LlamaParse), knowledge graph auto-building, ontologies, schemas, many LLM providers, GraphRAG and RAG, hybrid semantic search (fulltext, vector, property graph, RDF/SPARQL), AI query, and AI chat. The backend is Python with LlamaIndex and LangChain as peer frameworks. LlamaIndex is the default for each pipeline stage; LangChain can be selected per stage in environment configuration. The API is a REST FastAPI service. Angular, React, and Vue TypeScript frontends and an MCP server are included. The stack supports 13 data sources (9 with incremental auto-sync), 15 property graph databases, 4 RDF triple stores (Apache Jena Fuseki, Ontotext GraphDB, Oxigraph, Amazon Neptune RDF), 10 vector databases, OpenSearch / Elasticsearch / BM25 search, and Alfresco. Services and dashboards can be enabled with the provided Docker Compose layout.
Flexible GraphRAG data sources, processing tab, auto-sync document states in Postgres, Neo4j
Version 0.6.0 broadens framework and database choice: LangChain is