graphiti/mcp_server/PYPI_SETUP_COMPLETE.md

PyPI Package Setup Complete! 🚀

Summary

Your Graphiti MCP Server is now ready to be published to PyPI as graphiti-mcp-varming!

What Was Done

1. ✅ Package Configuration (mcp_server/pyproject.toml)

• Name: graphiti-mcp-varming (clearly distinguishes your enhanced fork)
• Entry point: graphiti-mcp-varming command added
• Dependencies: Standalone package with:
  • graphiti-core>=0.16.0 (includes Neo4j support by default)
  • All MCP and OpenAI dependencies
  • Optional extras for FalkorDB, other LLM providers (Anthropic, Groq, Gemini, Voyage)
• Metadata: Proper description, keywords, classifiers for PyPI

2. ✅ GitHub Actions Workflow (.github/workflows/publish-mcp-pypi.yml)

• Automatic publishing to PyPI when you push tags like mcp-v1.0.0
• Builds package with uv build
• Publishes with uv publish
• Creates GitHub releases with dist files

3. ✅ Documentation Updates

• DOCS/LibreChat-Unraid-Stdio-Setup.md: Updated with uvx commands
• mcp_server/PYPI_PUBLISHING.md: Complete publishing guide
• mcp_server/README.md: Added PyPI package notice

Next Steps (What You Need To Do)

1. Add PyPI Token to GitHub

1. Go to: https://github.com/Varming73/graphiti/settings/secrets/actions
2. Click "New repository secret"
3. Name: PYPI_API_TOKEN
4. Value: Your PyPI token (starts with pypi-)
5. Click "Add secret"

2. Update Your Email in pyproject.toml (Optional)

Edit mcp_server/pyproject.toml line with your real email:

authors = [
    {name = "Varming", email = "your-real-email@example.com"}
]

3. Publish First Version

# Make sure all changes are committed
git add .
git commit -m "Prepare graphiti-mcp-varming for PyPI publishing"
git push

# Create and push the first release tag
git tag mcp-v1.0.0
git push origin mcp-v1.0.0

4. Monitor Publishing

• Go to: https://github.com/Varming73/graphiti/actions
• Watch the "Publish MCP Server to PyPI" workflow
• Should complete in ~2-3 minutes

5. Verify Publication

After workflow completes:

• Check PyPI: https://pypi.org/project/graphiti-mcp-varming/
• Test installation: uvx graphiti-mcp-varming --help

Usage After Publishing

Users can now install your enhanced MCP server easily:

Basic Installation (Neo4j support included)

uvx graphiti-mcp-varming

With FalkorDB Support

uvx --with graphiti-mcp-varming[falkordb] graphiti-mcp-varming

With All LLM Providers

uvx --with graphiti-mcp-varming[providers] graphiti-mcp-varming

With Everything

uvx --with graphiti-mcp-varming[all] graphiti-mcp-varming

In LibreChat (stdio mode)

mcpServers:
  graphiti:
    type: stdio
    command: uvx
    args:
      - graphiti-mcp-varming
    env:
      GRAPHITI_GROUP_ID: "{{LIBRECHAT_USER_ID}}"
      NEO4J_URI: "bolt://neo4j:7687"
      NEO4J_USER: "neo4j"
      NEO4J_PASSWORD: "your_password"
      OPENAI_API_KEY: "${OPENAI_API_KEY}"

Future Releases

To publish updates:

1. Update version in mcp_server/pyproject.toml
2. Commit and push changes
3. Create and push new tag: git tag mcp-v1.0.1 && git push origin mcp-v1.0.1
4. GitHub Actions handles the rest!

Package Features

Your graphiti-mcp-varming package includes:

✅ Standalone: All dependencies bundled (graphiti-core, neo4j driver, etc.) ✅ Multi-database: Neo4j (default) + optional FalkorDB support ✅ Multi-LLM: OpenAI (default) + optional Anthropic, Groq, Gemini, Azure ✅ Enhanced Tools: Your custom get_entities_by_type and compare_facts_over_time ✅ Per-user Isolation: Full support for LibreChat multi-user via stdio mode ✅ Easy Install: One command with uvx

Troubleshooting

See mcp_server/PYPI_PUBLISHING.md for detailed troubleshooting guide.

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Questions? The publishing guide has all the details: mcp_server/PYPI_PUBLISHING.md