Flexible GraphRAG

Flexible GraphRAG is an open source AI context platform supporting a document processing pipeline (Docling, LlamaParse, or LiteParse), 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 14 data sources (10 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, Alfresco, and Nuxeo. Databases and dashboards can be enabled with the provided Docker Compose layout. Optionally, the ingest pipeline, hybrid search, and AI query can run through customizable Langflow visual flows (12 custom Langflow components). As a further option, ingest can run on a CocoIndex (Rust engine) pipeline (PIPELINE_BACKEND=cocoindex) that reuses the same sources, targets, parsers and KG extractors, adding step-level memoization, automatic delete reconciliation, custom KG extractors, and entity resolution. A meeting-notes example is included.

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, the schemas/ directory lives at the repository root (one level above flexible-graphrag/), so .env paths use ../schemas/... — matching env-sample.txt. For a PyPI install, copy schemas/ to the parent of your working directory so the same paths apply. See the RDF Ontology Examples and Configuration docs page for path options and examples. 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