- Regenerated mcp_server/uv.lock after dependency changes
- Restored cd commands in railway.json for Railway compatibility
- Ensures lockfile is in sync with pyproject.toml changes
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Update railway.json to use absolute paths instead of cd commands
- Remove duplicate graphiti-core dependency from mcp_server/pyproject.toml
- Configure proper PYTHONPATH for Railway deployment context
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
PROBLEM:
Despite multiple attempts, MCP server still runs on port 8000.
uv run and environment variables aren't working as expected.
SOLUTION:
Simplest possible approach - direct Python execution:
- Remove 'uv run' complexity
- Remove environment variable setting
- Hardcode --port 8080 in command arguments
- Direct python execution
COMMAND:
cd mcp_server && python graphiti_mcp_server.py --transport sse --host 0.0.0.0 --port 8080
This MUST work - no complexity, just direct execution with explicit port.
🚀 Generated with Claude Code (https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
PROBLEM:
MCP server was using port 8000 despite PORT=8080 being set in Railway.
Environment variable wasn't being read correctly by the Python process.
SOLUTION:
- Set PORT=8080 in start command environment
- Add explicit --port 8080 command argument
- Double enforcement ensures port 8080 is used
EXPECTED RESULT:
- MCP server will start on port 8080 (not 8000)
- Railway load balancer can route traffic correctly
- SSE endpoint accessible for ChatGPT at /sse
- No more 502 errors
🚀 Generated with Claude Code (https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
PROBLEM FIXED:
Railway was showing hardcoded --port 8000 in start command, causing port mismatch.
Railway expects apps to listen on PORT environment variable (8080).
SOLUTION:
- Updated railway.json start command to include --host 0.0.0.0
- Explicitly removed any --port argument
- Let Railway set PORT via environment variable
RESULT:
MCP server will now listen on Railway's assigned port (8080) instead of
hardcoded 8000, fixing the 502 error and enabling ChatGPT SSE access.
🚀 Generated with Claude Code (https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
MAJOR CHANGES:
- Replace complex uv-based Dockerfile with simple pip-only approach
- Add requirements.txt for standard Python dependency management
- Remove all uv commands that might trigger cache mount behavior
- Add .dockerignore for clean Railway build context
- Add nixpacks.toml to force Dockerfile usage (disable auto-detection)
- Update railway.json with explicit Docker configuration
PROBLEM SOLVED:
Railway 'Cache mount ID is not prefixed with cache key' error should be resolved
by eliminating all potential sources of cache mount directives.
DEPLOYMENT STRATEGY:
- Single-stage Docker build using standard pip
- Install graphiti-core from source with 'pip install .'
- Install MCP dependencies with 'pip install -r requirements.txt'
- No complex build tools or caching mechanisms
- Explicit Railway Docker configuration
🚀 Generated with Claude Code (https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fix Docker cache mount issues that caused Railway build failures
- Add port argument support to MCP server for Railway compatibility
- Create Railway-optimized Dockerfile without cache mounts
- Add railway.json configuration for proper deployment
- Create comprehensive deployment and ChatGPT integration guides
- Add environment variable templates for Railway deployment
- Support Railway's PORT environment variable handling
- Ready for ChatGPT MCP SSE integration
🚀 Generated with Claude Code (https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
