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What you can do with Ant Omni Evo

AntOmniEvo is a Python framework that evolves a system's tunable files (skills, prompts, workflow configs, or single-file algorithms) through a loop where AI coding agents propose edits scored by your own evaluator.

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ant-research/AntOmniEvo on GitHub

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What do you want to do?

4 use cases

  • Evolve an agent skill directory against your eval set

    Let agents rewrite SKILL.md, references and scripts until an evaluator scores the agent higher.

    View prompt
    Using the AntOmniEvo repo (ant-research/AntOmniEvo), set up an evolution run for the skill directory at <skill-dir> with an Evaluator that scores outputs from 0 to 1. Expected output: the best candidate's tunable artifacts as a directory plus its change lineage. Verify that the CandidateStore holds every candidate, run, analysis, and changelog.

  • Optimize a retrieval workflow's pipeline.json and node code

    Have agents edit pipeline config and node scripts until a workflow scores better on your data.

    View prompt
    Using the AntOmniEvo repo (ant-research/AntOmniEvo), evolve the workflow at <pipeline-dir> where pipeline.json and nodes/*.py are the tunable artifacts, scored by an Evaluator returning 0 to 1. Expected output: the best candidate's pipeline directory and changelog. Verify that each changed file in the best candidate traces back to a recorded failure.

  • Optimize a single-file algorithm and its description

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

    View prompt
    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.

  • Set up an experiment with the bundled agent skill template

    Use the skills/ template so a coding agent interviews you and drives the experiment setup.

    View prompt
    Using the AntOmniEvo repo (ant-research/AntOmniEvo), hand its skills/eddy-antomnievo-experiment skill to the coding agent and set up an experiment for <target-repo> with dataset <dataset> and metric <metric>. Expected output: a written entry script and a launched run. Verify that the agent asked for the repo, dataset and metric before writing any files.

How to use Ant Omni Evo

  1. Check you have what it needs: 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.
  2. Open ant-research/AntOmniEvo on GitHub and read the README and license.
  3. Write a System that runs your agent on one eval instance and returns its output.
  4. Write an Evaluator that scores each output from 0 to 1; its scoring criteria become the optimization objective.
  5. Prepare train and validation instances that define what good means for your agent.
  6. Describe the skill directory as a TunableArtifactSchema: the file tree and what each file is for.

These steps come from our top use case, Evolve an agent skill directory against your eval set, which also has a copy-paste agent prompt.

Ant Omni Evo alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • 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)
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Questions about Ant Omni Evo

What is Ant Omni Evo?

AntOmniEvo is a Python framework that evolves a system's tunable files (skills, prompts, workflow configs, or single-file algorithms) through a loop where AI coding agents propose edits scored by your own evaluator. It is an open-source project on GitHub: https://github.com/ant-research/AntOmniEvo.

Who is Ant Omni Evo for?

Built for engineers who run AI agent systems or algorithms that can be scored repeatedly and want an automated loop to improve them.

What can I do with Ant Omni Evo?

4 use cases so far, for example: Evolve an agent skill directory against your eval set; Optimize a retrieval workflow's pipeline.json and node code; Optimize a single-file algorithm and its description. Each has steps and a copy-paste agent prompt.

What do I need to run Ant Omni Evo?

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.

What are alternatives to Ant Omni Evo?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is Ant Omni Evo 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.