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eddy-antomnievo-experiment
主动沟通型的 AntOmniEvo 优化实验搭建助手的前置步骤集——帮用户把某个 eddy 业务 agent 接入 AntOmniEvo:对齐实验根目录(exp_root)与 candidate store 目录(两个概念分开)、创建 venv、装 AntOmniEvo、读 README 选 example;对齐数据格式并在 antomnievo/dataset 写 data_inst + data_loader;和用户对齐 agent module 在哪、启动入口、要优化哪些文件,照 example 可调产物写 AntOmniEvo 可调产物;在业务项目 scripts 写 generate(做 load 可调产物)和 evaluate;检查业务仓库代码 venv;冒烟测试并把产物路径给用户确认。当用户要"为某个 eddy 业务 agent 搭 AntOmniEvo 优化实验""接入 AntOmniEvo""写 generate/evaluate""先搞数据再写 gen/eval"、或想开始 AntOmniEvo 实验搭建时使用。
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// RATINGS
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// README
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 |
| 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
License
Licensed under the Apache License 2.0. Legal disclaimer: see LEGAL.md.
// HOW IT'S BUILT
KEY FILES