flexible-graphrag

Name: flexible-graphrag
Author: stevereiner

Verified

Python, LlamaIndex, LangChain, 15 Property Graph, 4 RDF , 10 Vector, OpenSearch, Elasticsearch, Alfresco, Nuxeo DBs. 14 data sources (10 auto-sync), KG auto-building, Ontologies, LLMs, Docling, LlamaParse, LiteParse, GraphRAG, RAG, Hybrid Search, AI Chat. TypeScript React, Vue, Angular frontends, REST, MCP Server. Options: Langflow, CocoIndex

180stars

34forks

Python

Installation

# Add to your Claude Code skills
git clone https://github.com/stevereiner/flexible-graphrag

Getting Started

Guides for using mcp servers skills like flexible-graphrag.

Security ReportVerified

Last scanned: 5/30/2026

{
  "issues": [],
  "status": "PASSED",
  "scannedAt": "2026-05-30T16:20:52.448Z",
  "npmAuditRan": true,
  "pipAuditRan": true
}

README.md

Frequently Asked Questions

What is flexible-graphrag?

flexible-graphrag is an open-source mcp servers skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by stevereiner. Python, LlamaIndex, LangChain, 15 Property Graph, 4 RDF , 10 Vector, OpenSearch, Elasticsearch, Alfresco, Nuxeo DBs. 14 data sources (10 auto-sync), KG auto-building, Ontologies, LLMs, Docling, LlamaParse, LiteParse, GraphRAG, RAG, Hybrid Search, AI Chat. TypeScript React, Vue, Angular frontends, REST, MCP Server. Options: Langflow, CocoIndex. It has 180 GitHub stars.

Is flexible-graphrag safe to use?

Yes. flexible-graphrag passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install flexible-graphrag?

Clone the repository with "git clone https://github.com/stevereiner/flexible-graphrag" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is flexible-graphrag written in?

flexible-graphrag is primarily written in Python. It is open-source under stevereiner on GitHub, so you can review or fork the full source.

Are there alternatives to flexible-graphrag?

Yes. SkillsLLM lists many other MCP Servers skills you can browse and compare side by side. Open the MCP Servers category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh flexible-graphrag against similar tools.

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New 8/18/26 — v0.8.0 release: Optional CocoIndex integration — Rust-backed CocoIndex pipeline mixed with Flexible GraphRAG sources (incl. detectors), functions, and targets (more PG/vector/RDF/search); same UI/REST/MCP. Standalone app.py also supported. Now with custom KG extractors (bring your own, or fall back to the built-in one per document) and entity resolution. New meeting-notes example (examples/cocoindex/meeting_notes_graph_any/) — a CocoIndex example ported to run against any configured graph store and source. See CocoIndex Integration.

New 8/8/26 — v0.7.2 release: Nuxeo added as a data source — all 3 UIs (React/Vue/Angular) plus REST/MCP, with basic / token / OAuth2 auth and real-time incremental sync via the Nuxeo audit event stream (Kafka). Alfresco OAuth2 and ticket authentication added across the source, all 3 UIs, initial ingest, and real-time sync. Alfresco Community 26.1 Docker upgrade. The MCP server gained optional OAuth2 on its transport (bearer token via your IdP) and moved to FastMCP 3. Requires python-alfresco-api >= 1.2.1.

New 7/20/26 — v0.7.1 release: Document processing now supports LiteParse in addition to the previous Docling and LlamaParse support. Langflow integration ships with fixes and an optional Langflow Docker image bundling the 12 "Flexible" components. MS Graph delta query support was added for more efficient incremental updating with SharePoint and OneDrive data sources.

New 7/5/26: Optional Langflow visual flows — the app can run its ingest pipeline, hybrid search, and AI query through customizable Langflow flows (12 custom Flexible GraphRAG components), using your existing .env config. See Langflow Integration.

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

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 and automatic delete reconciliation.

v0.6.0 in brief

Version 0.6.0 broadens framework and database choice: LangChain is a full peer to LlamaIndex (per-stage env pickers for chunking, vector, search, property graph, KG extraction, fusion). 15 property graph backends: 8 on both frameworks, Google Cloud Spanner (LlamaIndex-only), 6 LangChain-only (ArangoDB, Apache AGE, Azure Cosmos DB for Gremlin, HugeGraph, SurrealDB, TigerGraph). RDF includes Apache Jena Fuseki, Ontotext GraphDB, Oxigraph, and Amazon Neptune RDF. Incremental delete, LangChain adapters, and cleanup paths were extended across stores (see CHANGELOG.md).

Features

Hybrid Search: Configurable hybrid search combining vector search, full-text search, property-graph GraphRAG, and SPARQL against RDF stores.
Knowledge Graph GraphRAG: Extracts entities and relationships from documents to build graphs in property graph databases and RDF stores. Optional schemas and ontologies guide extraction or act as a starting point for the LLM to extend.
RDF/Ontology Support: Load OWL/RDFS ontologies to guide KG extraction into any property graph or RDF store; SPARQL 1.1 queries; RDF 1.2 triple annotations; full UI pipeline (ingest, hybrid search, AI query/chat, incremental auto-sync). See Ontology and RDF Support below.
15 Property Graph Databases: 8 on both LI+LC (Neo4j, ArcadeDB, FalkorDB, Ladybug, Memgraph, NebulaGraph, Amazon Neptune, Neptune Analytics), 1 LI-only (Google Cloud Spanner), 6 LC-only (ArangoDB, Apache AGE, Cosmos Gremlin, HugeGraph, SurrealDB, TigerGraph) — with KG extraction, hybrid search, and AI query/chat
4 RDF Triple Stores: Apache Jena Fuseki, Ontotext GraphDB, Oxigraph, Amazon Neptune RDF.
10 Vector Databases: Qdrant, Elasticsearch, OpenSearch, Neo4j, Chroma, Milvus, Weaviate, Pinecone, PostgreSQL pgvector, LanceDB — for semantic similarity search
3 Search Databases: Elasticsearch, OpenSearch, BM25 (built-in) — for full-text search and hybrid ranking
LLM providers (KG extraction & chat): Ollama, OpenAI, Azure OpenAI, Google Gemini, Anthropic Claude, Google Vertex AI, Amazon Bedrock, Groq, Fireworks AI, OpenAI-compatible endpoints (openai_like), OpenRouter, LiteLLM proxy, and vLLM — configurable via LLM_PROVIDER; see Supported LLM Providers
Embedding providers: OpenAI, Ollama, Azure OpenAI, Google GenAI, Vertex AI, Bedrock, Fireworks, OpenAI-like (EMBEDDING_KIND=openai_like), and LiteLLM — see LLM Configuration
Dual-framework pipeline: LlamaIndex and LangChain are first-class choices for chunking, vector and search adapters, property graphs, KG extraction, RDF text-to-SPARQL retrieval, and hybrid fusion—each stage can be set independently (LlamaIndex defaults). See Framework Configuration.
Multi-Source Ingestion: Processes documents from 14 data sources (10 with incremental auto sync): (file upload, cloud storage, enterprise repositories, web sources) with Docling (default), LlamaParse (cloud API), or LiteParse (local, lightweight) document parsing.
Observability: Built-in OpenTelemetry instrumentation with automatic LlamaIndex tracing, Prometheus metrics, Jaeger traces, and Grafana dashboards for production monitoring
FastAPI Server with REST API: Python based FastAPI server with REST APIs for document ingesting, hybrid search, AI query, and AI chat.
MCP Server: MCP server providing Claude Desktop and other MCP clients with tools for document/text ingesting (all 14 data sources with 10 supporting incremental auto sync), hybrid search,