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How to optimize a single-file algorithm and its description with Ant Omni Evo

Let agents rewrite one .py or .ts file and its description until scores improve.

Who it is forA developer with one algorithm file and a clear way to measure its output who wants it improved without manual trial and error.

The problemManual rounds of editing one algorithm file and rerunning tests are slow, and gains are hard to attribute.

The outcomeA better version of the algorithm file and its description, with a record of each edit and the evidence for it.

What you need first

  • A Python environment (the repo language is Python)
  • A system that can run on one eval instance and produce output
  • An Evaluator that scores each output from 0 to 1
  • Train and validation data that define what good looks like
  • Starting tunable artifacts as a directory of files
  • A coding-agent Proposer to make the edits (the README says the proposer must be agents)

Steps

  1. Write a System that runs the algorithm on one eval instance and returns its result.
  2. Write an Evaluator that scores each result from 0 to 1.
  3. Prepare train and validation instances that reflect the target task.
  4. Map the algorithm file and its description into a TunableArtifactSchema as a small two-file directory.
  5. Start the evolution loop from the current file and review the best candidate when the run ends.

Prompt for your agent

Using the AntOmniEvo repo (ant-research/AntOmniEvo), evolve the single-file algorithm at <file-path> and its description, scored by an Evaluator returning 0 to 1. Expected output: the best candidate's file and description plus its change lineage. Verify that the best candidate's score on the validation instances beats the starting file.

Caveat. The README says the system under optimization need not contain an LLM, but the Proposer must be agents, so plan for agent access even for a simple algorithm.

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