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How to profile a Python benchmark for runtime hotspots with Flameox

Capture a benchmark run and get bounded hotspot evidence without a project setup.

Short answer: Open a terminal in the project folder and note its absolute path with pwd; then run uvx flameox capture --provider direct --cwd "$PWD" -- python benchmark.py with your own benchmark script in place of benchmark.py; read the returned evidence and any next_page value; replay a next_page continuation using its named analysis tool and arguments unchanged. All steps.

Who it is forA developer who has a slow Python script or benchmark and wants to see where time goes before changing code.

The problemProfiling output is hard to read and easy to over-interpret, and a manual profiling setup takes time.

The outcomeA bounded evidence result for the run that points to hotspots, with its limitations stated, ready for a hypothesis to be tested.

What you need first

  • Node.js (for npx) or uv (for uvx) installed
  • Python 3.12 or newer for the local development path
  • An absolute path to an existing artifact file, or an explicit command to run from an absolute working directory

Steps

  1. Open a terminal in the project folder and note its absolute path with pwd.
  2. Run uvx flameox capture --provider direct --cwd "$PWD" -- python benchmark.py with your own benchmark script in place of benchmark.py.
  3. Read the returned evidence and any next_page value; replay a next_page continuation using its named analysis tool and arguments unchanged.
  4. Treat the hotspots as exploration leads only; plan a separate experiment before claiming any speedup.

Prompt for your agent

Using the flameox repo, capture a profile of <benchmark command> run from the absolute working directory <project path> with `uvx flameox capture --provider direct --cwd <project path> -- <benchmark command>`. Return the bounded hotspot evidence and its stated limitations. Verify that the result states what was observed versus inferred and does not claim causality.

Caveat. Flameox records what the profile shows and does not establish causality or performance improvement, so any fix still needs its own measured experiment.

Use this in your AI agent

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Using the Deduped MCP, get the setup steps for morluto/flameox and "Profile a Python benchmark for runtime hotspots"

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