AntOmniEvo

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

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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.

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.

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

You define — five things, once:

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.

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},
  }

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