An open-source AI agent that brings the power of Gemini directly into your terminal.
Env Doctor
Diagnose and Fix CUDA / GPU environments compatibility issues locally, in Docker, and CI/CD. CLI + MCP server available.
Install
$
uvx env-doctor checkClaude Code
claude mcp add env-doctor -- uvx env-doctor checkCursor / Claude Desktop (mcp.json)
{
"mcpServers": {
"env-doctor": {
"command": "uvx",
"args": [
"env-doctor",
"check"
]
}
}
}Install command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →Env-Doctor
The missing link between your GPU and Python AI libraries
"Why does my PyTorch crash with CUDA errors when I just installed it?"
Because your driver supports CUDA 11.8, but
pip install torchgave you CUDA 12.4 wheels.
Env-Doctor diagnoses and fixes the #1 frustration in GPU computing: mismatched CUDA versions between your NVIDIA driver, system toolkit, cuDNN, and Python libraries.
It takes 5 seconds to find out if your environment is broken - and exactly how to fix it.
Doctor "Check" (Diagnosis)
Features
| Feature | What It Does |
|---|---|
| One-Command Diagnosis | Check compatibility: GPU Driver → CUDA Toolkit → cuDNN → PyTorch/TensorFlow/JAX |
| Compute Capability Check | Detect GPU architecture mismatches — catches why torch.cuda.is_available() returns False on new GPUs (e.g. Blackwell) even when driver and CUDA are healthy |
| Python Version Compatibility | Detect Python version conflicts with AI libraries and dependency cascade impacts |
| Jupyter / Notebook Output | from env_doctor import check; check() renders a rich HTML diagnosis inline in Jupyter, Colab, or VS Code notebooks — falls back to text in the terminal |
| CUDA Auto-Installer | Execute CUDA Toolkit installation directly with --run; CI-friendly with --yes; preview with --dry-run |
| Safe Install Commands | Get the exact pip install command that works with YOUR driver |
| Inference Engine Resolver | Resolve vLLM / SGLang → torch → CUDA build → kernel libs against your driver; get the install command that actually runs instead of a default CUDA 13 wheel on a CUDA 12 box |
| Extension Library Support | Install compilation packages (flash-attn, SageAttention, auto-gptq, apex, xformers) with CUDA version matching |
| AI Model Compatibility | Check if LLMs, Diffusion, or Audio models fit on your GPU before downloading |
| WSL2 GPU Support | Validate GPU forwarding, detect driver conflicts within WSL2 env for Windows users |
| Deep CUDA Analysis | Find multiple installations, PATH issues, environment misconfigurations |
| Container Validation | Catch GPU config errors in Dockerfiles before you build |
| MCP Server | Expose diagnostics to AI assistants (Claude Desktop, Zed) via Model Context Protocol |
| CI/CD Ready | JSON output, proper exit codes, and CI-aware env-var persistence (GitHub Actions, GitLab CI, CircleCI, Azure Pipelines, Jenkins) |
| Fleet Dashboard (optional) | Web UI for monitoring multiple GPU machines — aggregate status, drill-down diagnostics, history timeline. Install with pip install "env-doctor[dashboard]" |
Installation
Core CLI
The core CLI has no heavy dependencies — installs in seconds.
pip install env-doctor
# Or with uv (faster, isolated)
uv tool install env-doctor
uvx env-doctor check
With Fleet Dashboard
If you want to manage a distributed system of multiple GPU nodes, this dashboard can help you from a observability POV. It adds a web UI for monitoring multiple GPU machines and has no effect on the core CLI. You will be able to soon take action directly from the dashboard via distributed env-doctor cli instances on each VM!
pip install "env-doctor[dashboard]"
This adds: fastapi, uvicorn, sqlalchemy, aiosqlite
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