AntOmniEvo
An auto-evolution framework that optimizes anything — your 7×24 team of algorithm engineers.
English · 中文
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
🧪 Paper Resources
The paper's experiment scripts, artifacts before and after evolution, and datasets are being prepared for open source and will be released in a separate repository — stay tuned.
📄 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.
