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.

This package is the MCP server — it connects to the Flexible GraphRAG FastAPI backend (status, ingest, hybrid search, AI Q&A) for Claude Desktop and other MCP clients.

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

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Flexible GraphRAG MCP Server

Model Context Protocol (MCP) server for Flexible GraphRAG: connects to the FastAPI backend (status, ingest, hybrid search, AI Q&A). Same project scope as the main stack — LlamaIndex / LangChain peer frameworks, 14 data sources, 15 property graph databases, 4 RDF stores, vector databases, and search engines.

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

Quick Start

1. Choose Your Platform & Method

Platform	Recommended	Alternative	Why
Windows	pipx	uvx	Clean system install vs. no install needed
macOS	pipx	uvx	Clean system install vs. no install needed

2. Install

pipx (Recommended)

cd flexible-graphrag-mcp
pipx install .

uvx (No installation)

# Auto-installs when first used
uvx flexible-graphrag-mcp

3. Configure Claude Desktop

Copy the appropriate config file to your Claude Desktop configuration:

Windows

• Config location: %APPDATA%\Claude\claude_desktop_config.json
• pipx: Use claude-desktop-configs/windows/pipx-config.json
• uvx: Use claude-desktop-configs/windows/uvx-config.json

macOS

• Config location: ~/Library/Application Support/Claude/claude_desktop_config.json
• pipx: Use claude-desktop-configs/macos/pipx-config.json
• uvx: Use claude-desktop-configs/macos/uvx-config.json

4. Test Installation

Restart Claude Desktop and test:

@flexible-graphrag Check system status

Configuration Files

Claude Desktop Configs

claude-desktop-configs/
├── windows/
│   ├── pipx-config.json    # Windows + pipx
│   └── uvx-config.json     # Windows + uvx
└── macos/
    ├── pipx-config.json    # macOS + pipx
    └── uvx-config.json     # macOS + uvx

mcp-inspector/
├── pipx-stdio-config.json  # MCP Inspector + pipx (stdio - try first)
├── pipx-http-config.json   # MCP Inspector + pipx (HTTP - fallback)
├── uvx-stdio-config.json   # MCP Inspector + uvx (stdio - try first)
└── uvx-http-config.json    # MCP Inspector + uvx (HTTP - fallback)

Key Differences

Windows Configs

• Include Unicode environment variables (PYTHONIOENCODING, PYTHONLEGACYWINDOWSSTDIO)
• Prevent Unicode encoding errors with emojis and special characters

macOS Configs

• Clean and simple - no special environment variables needed
• Standard MCP protocol over stdio

MCP Inspector Configs

• stdio configs: Standard MCP protocol - try these first
• http configs: HTTP transport fallback if stdio has issues (like proxy problems)
• HTTP mode runs on port 3001 by default (configurable with --port argument)
• Platform-independent - works on Windows, macOS, and Linux

Installation Methods

pipx (Recommended)

Advantages:

• ✅ Clean system-level installation
• ✅ Isolated dependencies
• ✅ Simple flexible-graphrag-mcp command
• ✅ Automatic PATH management

Installation:

cd flexible-graphrag-mcp
pipx install .

Update:

pipx reinstall flexible-graphrag-mcp

uvx (Alternative)

Advantages:

• ✅ No installation required
• ✅ Automatic dependency management
• ✅ Always runs latest version
• ✅ Great for testing

Usage:

uvx flexible-graphrag-mcp

Prerequisites

Backend Server Required

The MCP server communicates with the FastAPI backend, so you must have it running:

cd flexible-graphrag
uv run uvicorn main:app --host 0.0.0.0 --port 8000

Environment Configuration

Ensure your .env file is properly configured in the main project directory:

# Neo4j Configuration
NEO4J_URI=bolt://localhost:7687
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD=your-password
NEO4J_DATABASE=neo4j

# LLM Configuration
OPENAI_API_KEY=your-key
LLM_PROVIDER=openai
EMBEDDING_PROVIDER=openai

HTTP Mode for MCP Inspector

For debugging with MCP Inspector, the server supports HTTP transport:

# Using pipx
flexible-graphrag-mcp --http --port 3001

# Using uvx  
uvx flexible-graphrag-mcp --http --port 3001

# Custom port
flexible-graphrag-mcp --http --port 8080

The HTTP mode is automatically configured in the mcp-inspector/ config files and works better than stdio for debugging complex MCP interactions.

Securing the MCP transport (OAuth2 bearer)

You can require callers of this MCP server to present an OAuth2 bearer token, validated against an OIDC IdP's JWKS (e.g. Keycloak). This uses FastMCP's JWT verifier and applies to the HTTP transport only (stdio ignores it). It's separate from the data-source credentials passed through in alfresco_config / nuxeo_config.

# Require a valid bearer token on the HTTP endpoint
MCP_TRANSPORT_AUTH=true \
MCP_AUTH_JWKS_URI=http://host.docker.internal:8091/realms/alfresco/protocol/openid-connect/certs \
flexible-graphrag-mcp --http --port 3001

RS256 tokens are validated against the JWKS, so only genuine IdP-signed tokens are accepted:

• no token → 401
• Authorization: Bearer <valid-token> → 200

MCP Inspector: set the server URL to http://localhost:3001/mcp and add an Authorization: Bearer <token> header (obtain the token out-of-band from your IdP; the server validates but does not issue tokens).

Env vars: MCP_TRANSPORT_AUTH (default false), MCP_AUTH_JWKS_URI, MCP_AUTH_ISSUER (optional — the MCP SDK requires an HTTPS issuer, so leave unset for a local http Keycloak; the JWKS signature check still gates access), MCP_AUTH_AUDIENCE (optional).

Available Tools

• get_system_status() - System status and configuration
• ingest_documents() - Ingest documents from 14 data sources (all support skip_graph; filesystem/Alfresco/CMIS use paths; Alfresco also supports nodeDetails list)
• ingest_text(content, source_name) - Ingest custom text content
• search_documents(query, top_k) - Hybrid search for document retrieval
• query_documents(query, top_k) - AI-generated answers from documents
• test_with_sample() - Quick test with sample text
• check_processing_status(processing_id) - Check async operation status
• get_python_info() - Python environment information
• health_check() - Backend connectivity check

Tool Details

ingest_documents

Ingest documents from various sources into the knowledge graph.

Parameters:

• data_source (string, default: "filesystem"): Type of data source
  • Options: filesystem, cmis, alfresco, web, wikipedia, youtube, s3, gcs, azure_blob, onedrive, sharepoint, box, google_drive
• paths (string, optional): File path(s) to process (for filesystem, Alfresco, and CMIS sources)
  • Single path: "/path/to/file.pdf"
  • Multiple paths (JSON array): ["file1.pdf", "file2.docx"]
• skip_graph (boolean, default: false): Skip knowledge graph extraction on a per-ingest basis for faster performance (vector + search only)
• cmis_config (string, optional): CMIS configuration as JSON string
• alfresco_config (string, optional): Alfresco configuration as JSON string (also supports nodeDetails list for multi-select)
• web_config (string, optional): Web page configuration as JSON string
• wikipedia_config (string, optional): Wikipedia configuration as JSON string
• youtube_config (string, optional): YouTube configuration as JSON string
• s3_config (string, optional): Amazon S3 configuration as JSON string
• gcs_config (string, optional): Google Cloud Storage configuration as JSON string
• azure_blob_config (string, optional): Azure Blob Storage configuration as JSON string
• onedrive_config (string, optional): Microsoft OneDrive configuration as JSON string
• sharepoint_config (string, optional): Microsoft SharePoint configuration as JSON string
• box_config (string, optional): Box configuration as JSON string
• google_drive_config (string, optional): Google Drive configuration as JSON string

Example - Basic filesystem with skip_graph:

{
  "data_source": "filesystem",
  "paths": "[\"./sample-docs/cmispress.txt\", \"./sample-docs/space-station.txt\"]",
  "skip_graph": true
}

Example - CMIS with single path:

{
  "data_source": "cmis",
  "paths": "[\"/Shared/GraphRAG/cmispress.txt\"]",
  "cmis_config": "{\"url\": \"https://cmis.example.com\", \"username\": \"admin\", \"password\": \"password\", \"folder_path\": \"/Shared/GraphRAG\"}"
}

Example - Alfresco with single path:

{
  "data_source": "alfresco",
  "paths": "[\"/Shared/GraphRAG/space-station.txt\"]",
  "alfresco_config": "{\"url\": \"https://alfresco.example.com\", \"username\": \"admin\", \"password\": \"password\", \"path\": \"/Shared/GraphRAG\"}"
}

Example - Alfresco with nodeDetails (multi-select from ACA):

{
  "data_source": "alfresco",
  "alfresco_config": "{\"url\": \"https://alfresco.example.com\", \"username\": \"admin\", \"password\": \"password\", \"nodeDetails\": [{\"id\": \"abc123\", \"name\": \"doc1.pdf\", \"path\": \"/Shared/GraphRAG/doc1.pdf\", \"isFile\": true, \"isFolder\": false}], \"recursive\": false}"
}

Example - Amazon S3:

{
  "data_source": "s3",
  "s3_config": "{\"bucket_name\": \"my-bucket\", \"prefix\": \"documents/\", \"access_key\": \"AKIAIOSFODNN7EXAMPLE\", \"secret_key\": \"wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY\", \"region_name\": \"us-east-1\"}"
}

Example Usage

Basic Document Ingestion

@flexible-graphrag Please ingest documents from C:/Documents/research

Fast Ingestion (Skip Graph)

@flexible-graphrag Ingest from ./sample-docs/ with skip_graph=true for faster processing (works with all data sources)

Alfresco Multi-Select

@flexible-graphrag Ingest from Alfresco with this config: {"url": "https://alfresco.example.com", "username": "admin", "password": "password", "nodeDetails": [{"id": "abc123", "name": "report.pdf", "path": "/Shared/Reports/report.pdf", "isFile": true, "isFolder": false}]}

Custom Text Processing

@flexible-graphrag Ingest this text: "Claude is an AI assistant created by Anthropic."

Search and Q&A

@flexible-graphrag Search for "machine learning algorithms" in the documents

@flexible-graphrag What are the main conclusions from the research papers?

Async Processing

@flexible-graphrag Check processing status for ID abc123

Troubleshooting

Common Issues

pipx Command Not Found

# Install pipx
python -m pip install --user pipx
pipx ensurepath

uvx Command Not Found

# Install uvx via uv
uv tool install uvx

Unicode Errors on Windows

• Windows configs include required environment variables automatically
• If issues persist, check that you're using the correct Windows config file

Backend Connection Error

• Ensure FastAPI backend is running on localhost:8000
• Check that .env file is properly configured
• Test backend directly: curl http://localhost:8000/api/health

Claude Desktop Not Recognizing Server

• Restart Claude Desktop after config changes
• Check config file path and JSON syntax
• Verify command exists: run flexible-graphrag-mcp or uvx flexible-graphrag-mcp in terminal

Development

Test Scripts

# Windows
.\test-installation.ps1

# macOS/Linux
./test-installation.sh

These scripts test both installation methods and help verify everything works correctly.

Adding New Tools

1. Add tool function to main.py with @mcp.tool() decorator
2. Update tool list in README
3. Test with MCP Inspector for debugging

MCP Inspector Integration

Use the configs in mcp-inspector/ directory for debugging with the MCP Inspector tool. These work with both pipx and uvx installations and are platform-independent.