claude-code-mlx-proxy

Run Claude Code with local MLX-powered models. Operations teams benefit from on-premises AI coding assistance. Connects to local MLX models via Python server, replacing cloud-based Anthropic services.

[{"step":"Start the MLX model server","action":"Run `python -m mlx.serve --model mlx-community/code-llama-7b-instruct-4bit --port 8000` in your terminal to start the local model server.","tip":"Use a GPU-enabled machine for best performance. Monitor GPU memory usage with `nvidia-smi` if available."},{"step":"Configure claude-code-mlx-proxy","action":"Set the proxy endpoint in your environment: `export CLAUDE_CODE_MLX_PROXY=http://localhost:8000/v1` or configure in your IDE settings.","tip":"Test the connection with `curl http://localhost:8000/v1/models` to verify the server is responsive."},{"step":"Execute coding tasks","action":"Use standard claude-code commands (e.g., 'Write a function to...') but the proxy will route requests to your local MLX model.","tip":"For large codebases, pre-load relevant files into the context window to improve response quality."},{"step":"Review and save outputs","action":"Inspect the generated code, verify functionality, and save to your project directory. Use `git diff` to track changes.","tip":"Compare outputs with previous versions using `git diff HEAD~1 -- path/to/file.py` to ensure no regressions."},{"step":"Optimize performance","action":"Adjust model parameters (temperature, max_tokens) in your proxy configuration for faster inference or higher quality outputs.","tip":"For debugging, add `--verbose` flag to the proxy server to log model interactions."}]