ant-research/AntOmniEvo

选用指南

AI 根据 README 整理 · 2026/10/14 14:16:08

适用场景

适用于可表示为可调文件目录且具有可重复评估的系统，如AI代理技能、工作流配置或单文件算法。

查看 README 依据

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 |

使用方式

用户需定义系统运行方式、评估器、评估数据、可调工件模式和初始工件，框架自动运行进化循环。

查看 README 依据

- **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 |

部署要求

需要提供可重复且成本合理的评估，以及可调工件目录。

查看 README 依据

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: