AntOmniEvo

AntOmniEvo

An auto-evolution framework that optimizes anything — your 7×24 team of algorithm engineers.

English · 中文

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AntOmniEvo is an auto-evolution framework with a strict division of labor: you define your system's tunable artifacts and what "good" means — the framework controls the loop, AI agents do the work — and it delivers optimized tunable artifacts.

Your system's tunable parts are abstracted as tunable artifacts — a real directory of files: an agent's SKILL.md + references + scripts, a workflow's pipeline.json + node scripts, a single-file algorithm + its description. Anything so representable, and repeatably evaluatable, AntOmniEvo can optimize — optimization becomes plain file editing. The system-under-optimization need not contain an LLM; the proposer must be agents.

🚀 What it is

AntOmniEvo is an auto-evolution framework for AI agent systems. It treats your system's tunable artifacts (skills, prompts, workflow configs, pipeline code) as the genome, and runs a concurrent evolution loop where a coding-agent Proposer reads failure trajectories and rewrites those artifacts — the way a human would edit code.

It works for any system that can be expressed as a directory of tunable files and has a repeatable, reasonably-cheap evaluation:

• AI agents — skill / harness / memory / extension directories (NL2SQL skills, coding-agent skills+harness, agentic-API skills, system prompts + strategy docs, etc).
• Workflows / pipelines — config + node code (a retrieval DAG's pipeline.json + nodes/*.py).
• Single-file algorithms — a .py / .ts + its description.

🧩 How it works

Division of labor: you define, framework controls, AI works.

You define — five things, once:

You provide	Role
System	how to run your system on one eval instance
Evaluator	how to score its output (0–1) — its scoring criteria is the optimization objective
data	the train/val instances that define "good"
TunableArtifactSchema	maps your system's tunable artifacts onto a directory: the file tree + what each file is for
initial tunable artifacts	the starting point

The framework controls — it runs the evolution loop, and all the control and engineering work inside it: scheduling, budgets, selection / elimination, persistence — deterministic machinery you don't write, keeping the strongest candidates in the population. Every candidate, run, analysis, and changelog is persisted to a CandidateStore — interruptible and resumable.

The AI works — the changing itself is done by a coding-agent Proposer: it reads failure trajectories, locates which file to edit, and lands a structured change as a new candidate's tunable artifacts — the way a human would edit code.

It delivers — the best candidate's tunable artifacts: a real directory of files you can diff, review, and deploy, with a change lineage attributing every edit to the failure evidence that motivated it.

🤖 Skills

skills/ ships per-scenario Agent Skills: hand one to your coding agent and it drives the experiment setup for you — interviews you for the few facts only you know (repo, dataset, metric), then picks components, writes the entry script, launches the run, and triages failures against its built-in pitfall log. It currently contains eddy-antomnievo-experiment (for the eddy high-code agent runtime; content in Chinese) — follow it as a template to distill your own scenario's best practices into a skill of your own.

📚 Documentation

Topic	English	中文
Install & quick start	docs/quickstart.md	docs/quickstart.zh-CN.md
Features	docs/features.md	docs/features.zh-CN.md
Extensibility	docs/extensibility.md	docs/extensibility.zh-CN.md
When to use it	docs/when-to-use.md	docs/when-to-use.zh-CN.md
System design	docs/system-design.md	docs/system-design.zh-CN.md
Workspace artifacts & attribution	docs/workspace-artifacts.md	docs/workspace-artifacts.zh-CN.md
Checkpoint resume & crash recovery	docs/checkpoint-resume.md	docs/checkpoint-resume.zh-CN.md
Visualizer	docs/visualizer.md	docs/visualizer.zh-CN.md

🧪 Paper Resources

The paper's experiment scripts, artifacts before and after evolution, and datasets are open-sourced at ant-research/AntOmniEvoDatasetsAndScripts.

📄 Papers

If you find this work useful, please cite the relevant paper:

• Mara Chain: Rethinking Failure as a Stepping Stone for AI System Auto-Evolution

  @misc{lyu2026marachain,
        title={Mara Chain: Rethinking Failure as a Stepping Stone for AI System Auto-Evolution},
        author={Yubin Lyu and Fu Li and Jiawei Fei and Yang Zhao and Weixing Mei and Yinan Wu},
        year={2026},
        eprint={2609.35855},
        archivePrefix={arXiv},
        primaryClass={cs.LG},
        url={https://arxiv.org/abs/2609.35855},
  }
  

License

Licensed under the Apache License 2.0. Legal disclaimer: see LEGAL.md.