Quick Start

Install

uv tool install 'graphsignal[cu12]'   # CUDA 12.x
# or
uv tool install 'graphsignal[cu13]'   # CUDA 13.x

Profile

Wrap your launch command with graphsignal-run:

export GRAPHSIGNAL_API_KEY=<my-api-key>
graphsignal-run vllm serve <model> --port 8001

Environment variables read by the profiler:

Variable	Purpose
GRAPHSIGNAL_API_KEY (required)	Your account API key.
GRAPHSIGNAL_TAG_<KEY>=<value>	Arbitrary tag attached to all signals (e.g. GRAPHSIGNAL_TAG_DEPLOYMENT=us-prod).

Sign up for a free account at graphsignal.com; you’ll find the API key in Settings / API Keys.

See the Profiler CLI reference for the full set of options.

Applications that bootstrap themselves can call graphsignal.watch() from Python instead — see the Profiler API reference.

See integration documentation for libraries and inference engines:

• PyTorch
• vLLM
• SGLang

Optimize

Log in to Graphsignal to monitor and analyze your application.

Optimize with AI

Install the Graphsignal skill to let your AI coding agent (Claude Code, Codex, or Gemini) fetch and analyze signal context directly from your agent. See AI Optimization for setup instructions.

Overhead

The profiler has minimal impact on production performance. CUPTI activity is collected with low-overhead APIs in a sidecar process, and the in-process injection only writes raw activity records — analysis and upload happen in the sidecar.

Security and Privacy

The profiler runs as a sidecar process and does not require root or elevated privileges. It only establishes outbound connections to api.graphsignal.com to send data; inbound connections or commands are not possible.

Content and sensitive information, such as prompts and completions, are not recorded.

See Security and Privacy for details.

Troubleshooting

If something doesn’t look right, report it to our support team via your account.

In case of connection issues, please make sure outgoing connections to https://api.graphsignal.com are allowed.