claudecodex

agentx

Get your agent's real runs into AgentX, then score them, then turn what they measure into a code fix. /instrument sets up a Python agent and proves it with traces it reads back; /run-eval evaluates it against a dataset it can also create - templates, files, or cases curated from live traces; /eval-fix triages the evaluation against the source and re-runs it on the same dataset.

3 on GitHubby AgentXv2.9.0updated 2 months agodevelopment
/

Plugin agentx from marketplace agentx in AgentX-ai/AgentX-Eval-Skill. Commands read from the repo's manifest — 1 month ago.

What it installs

Skills (4)

  • auto-improve

    Turn an AgentX improvement report - production failures a human CONFIRMED during signal review, clustered into issues with recommendations - into triaged fixes applied to the agent's source code. Use whenever someone has an improvement report id from a self-host engine (AgentX-trace-eval, normally h

  • eval-fix

    Turn an AgentX self-host evaluation into a triaged code fix and a re-run against the same dataset, so a before-and-after comparison means something. Use whenever someone has an evaluation on a local AgentX self-host engine (AgentX-trace-eval, normally http://localhost:4700) and wants to know what to

  • instrument

    Set an existing Python agent up on AgentX, end to end: read the project API key off the engine, write .env.agentx, install agentx-python from PyPI, initialise the SDK once, instrument the calls that are worth tracing, and then prove it by running the agent and reading its own traces back. Use whenev

  • run-eval

    Run an AgentX self-host evaluation of the local agent: pick or create the dataset and grading config (templates, an existing id, a CSV/file of Q&A, or cases curated from the agent's own live traces), write the harness that calls the agent per case with a linked trace, execute the run, and hand back

View source on GitHub

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