flexible-graphrag 0.6.1

Open source AI context platform: document processing, knowledge graph auto-building, ontologies, GraphRAG/RAG, hybrid search (vector, property graph, RDF/SPARQL, fulltext), 13 LLM providers, 15 property graph DBs, 4 RDF triple stores, 10 vector DBs, LlamaIndex + LangChain, FastAPI, MCP server

Flexible GraphRAG

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.

Quick install (PyPI):

uv pip install flexible-graphrag

Then copy env-sample.txt to .env, set your LLM API key (e.g. OPENAI_API_KEY=...) and any other provider config, and run flexible-graphrag to start the API server. If you use ontology schemas, also copy the rdf/schemas/ directory from the repository to your working directory. This gives you a LlamaIndex-only setup; for LangChain or mixed LlamaIndex/LangChain per-stage configuration see the Prerequisites, Setup, and Framework Config sections of the full README, or the Framework Configuration docs page.

Optional dependency groups (langchain, RDF extras, observability, and more) are available. For Docker services, frontend installs, source checkout setup, and optional extras (which involve extras-overrides.txt), refer to the Prerequisites and Setup sections of the full README and documentation linked below.

• Repository: github.com/stevereiner/flexible-graphrag
• Full README: README.md on GitHub
• Documentation: stevereiner.github.io/flexible-graphrag