AntOmniEvo README

The project's README from github.com/ant-research/AntOmniEvo. As one markdown file: README.md.

AntOmniEvo

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

License arXiv

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:

🧩 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
eval 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.

📚 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

Coming soon. We will link the paper here once it is released.

⭐ Star History

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License

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

AntOmniEvo (ant-research/AntOmniEvo)

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AntOmniEvo
这是什么？AntOmniEvo 是一个自动进化框架：你把系统中的可调工件（技能、提示词、工作流配置或代码）连同一种打分方式交给它，它就会运行并发的进化循环，由一个编码智能体「Proposer」重写这些文件，产出表现更好的候选版本。
这是接入我自己系统的框架，还是独立应用？它是一个框架，不是独立应用，也不是你直接调用的普通库。你提供一个 System 运行器、一个 Evaluator、评估数据和一个 TunableArtifactSchema，框架就会替你驱动优化循环和编码智能体 Proposer。
怎么让它在我自己的系统上跑起来？安装和快速上手步骤见 docs/quickstart.md：你需要接好一个 System（如何在单个评估实例上运行你的系统）、一个 Evaluator（如何给输出打 0-1 分）、评估数据，以及描述可调文件树的 TunableArtifactSchema，然后启动进化循环。
开始一次进化运行前我需要提供什么？你需要自己的 System、Evaluator 和评估数据，外加一个充当 Proposer 来编辑工件的编码智能体——框架本身不带 LLM 或评估器。
在依赖它之前有什么需要注意的吗？被优化的系统不必包含 LLM，但负责编辑文件的 Proposer 必须是智能体，而且项目随附的论文尚未发表。
它是给谁用的？它面向这样的团队：他们拥有已经能完全用文件表达、并且能低成本反复打分的智能体系统、工作流或算法——相当于给他们配了一支全天候的算法工程师团队来自动完成调优。
到底什么才算「可调工件」？任何可以表达为一个文件目录且有可重复评估的东西：智能体的 SKILL.md 加上参考资料和脚本、工作流的 pipeline.json 加上节点代码，或者单个算法文件及其描述。
Proposer 是怎么决定改什么的？它会从评估运行中读取失败轨迹，定位是哪个文件造成了失败，然后像人类工程师改代码那样，以结构化修改的方式提交一个新的候选版本。
长时间的进化运行能中断再继续吗？可以——每个候选、运行、分析和变更记录都会持久化到 CandidateStore，因此运行可中断、可恢复，包括崩溃后的恢复。
最后我实际能拿到什么？最佳候选的可调工件，是一个真实的文件目录，你可以对它做 diff、审查和部署，还附带变更谱系，把每一次修改追溯回引发它的失败证据。
What are similar projects?PocketFlow, Raven, rrsi, harness-sdk, spring-ai-alibaba, evolver, qwen-code, foreman
Which topics is it in?self-improvement, loop-engineering, llm-agent, self-evolving, agentic, optimization, agent-framework, workflow-automation, agent-orchestration, multi-agent, agents, coding-agent
Creator
ant-research
Size
44.6 MB
Stars
1.2K
Updated
CommitOct 15
Age
1mo
Commits · 90 Days