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

### 3. Publish First Version

```bash
# 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)
```bash
uvx graphiti-mcp-varming
```

### With FalkorDB Support
```bash
uvx --with graphiti-mcp-varming[falkordb] graphiti-mcp-varming
```

### With All LLM Providers
```bash
uvx --with graphiti-mcp-varming[providers] graphiti-mcp-varming
```

### With Everything
```bash
uvx --with graphiti-mcp-varming[all] graphiti-mcp-varming
```

### In LibreChat (stdio mode)
```yaml
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.

---

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