flexible-graphrag - MCP Servers | SkillsLLM

flexible-graphrag

Name: flexible-graphrag
Author: stevereiner

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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 🌟 Star

117stars

27forks

Python

Added 2/16/2026

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MCP Serversaiai-chatalfrescoarcadedbdoc-processing

Installation

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

README.md

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

MCP Fundamentals for Building AI Agents

Model Context Protocol - how autonomous AI agents communicate and scale

sponsoredEducative6 hoursIntermediate

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Knowledge Graph GraphRAG: Extracts entities and relationships from documents to auto create graphs in property graph databases for GraphRAG. Configuration for schemas to use or use as a starting point for LLM to expand on is supported.
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.
8 Property Graph Databases: Neo4j, ArcadeDB, FalkorDB, Ladybug, MemGraph, NebulaGraph, Amazon Neptune, and Amazon Neptune Analytics — with KG extraction, hybrid search, and AI query/chat
3 RDF Triple Stores: Apache Jena Fuseki, Ontotext GraphDB, Oxigraph
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
Configurable Architecture: LlamaIndex provides abstractions for allowing multiple vector databases, property graph databases, RDF triple stores, search engines, and LLM providers to be supported
Multi-Source Ingestion: Processes documents from 13 data sources (file upload, cloud storage, enterprise repositories, web sources) with Docling (default) or LlamaParse (cloud API) document parsing.
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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 13 data sources), hybrid search, and AI query. Uses FastAPI backend REST APIs.
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Docker Deployment Flexibility: Supports both standalone and Docker deployment modes. Docker infrastructure provides modular database selection via docker-compose includes - vector, graph, search engines, and Alfresco can be included or excluded with a single comment. Choose between hybrid deployment (databases in Docker, backend and UIs standalone) or full containerization.