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217 stars · Python

How to use Flameox: what you can do with it

What is Flameox? Flameox is a local tool that turns profiler, benchmark, and trace output into bounded runtime evidence that coding agents can use.

How do I use it? 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. Full steps below.

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What do you want to do?

3 use cases

  • Profile a Python benchmark for runtime hotspots

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

    View prompt
    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.

  • Analyze an existing profiler artifact without rerunning it

    Turn a saved profiler file into bounded evidence without running the workload again.

    View prompt
    Using the flameox repo, run `uvx flameox analyze preview_artifact <absolute artifact path>` on the file at <absolute artifact path>. Report the typed evidence and coverage, separating observed from derived claims. Verify that the artifact path you passed is the exact file and that no parent directory was searched.

  • Connect a coding agent to Flameox runtime evidence

    Register the Flameox MCP server so an agent can request profiles and evidence itself.

    View prompt
    Using the flameox repo's MCP server, capture runtime evidence for the hypothesis <hypothesis> on the target <target command> with its working directory <absolute path>. Return the bounded evidence, coverage, and limitations for each claim. Verify that each claim is labeled observed, derived, or inferred and that no causal or speedup claim is made without a separate experiment.

Quick start with Flameox

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

These steps come from our top use case, Profile a Python benchmark for runtime hotspots, which also has a copy-paste agent prompt.

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About this repo

  • Who it is forDevelopers and coding-agent users who need to find and check performance hotspots in local code without setting up a workspace.
  • Why it is hot217 stars
  • LanguagePython
  • Sourcegithub.com/morluto/flameox · Back to Hot repos

Requirements

  • 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

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Questions about Flameox

What is Flameox?

Flameox is a local tool that turns profiler, benchmark, and trace output into bounded runtime evidence that coding agents can use. It is an open-source project on GitHub: https://github.com/morluto/flameox.

How do I use Flameox?

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. The use cases on this page give the full steps and a copy-paste agent prompt for each job.

Who is Flameox for?

Developers and coding-agent users who need to find and check performance hotspots in local code without setting up a workspace.

What can I do with Flameox?

3 use cases so far, for example: Profile a Python benchmark for runtime hotspots; Analyze an existing profiler artifact without rerunning it; Connect a coding agent to Flameox runtime evidence. Each has steps and a copy-paste agent prompt.

What do I need to run Flameox?

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.

What are alternatives to Flameox?

Other hot AI repos in Software and DevOps: ECC, Thinkrail, Superpowers, Deep Infer, Instructor.

Is Flameox safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.