# evolving-ai-agents

A skill for A-Evolve: it runs solve-observe-evolve cycles that rewrite an agent's prompts, skills and memory against benchmark scores.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 833 installs · ★ 13956
- Author: A-EVO Lab
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/14-agents/a-evolve/SKILL.md
- Updated: 2026-10-11
- HTML: https://affpapa.org/en/nevibecode/skills/evolving-ai-agents

## What it does

The skill covers A-Evolve, infrastructure that optimizes an agent you already have instead of building one. Every evolvable part of the agent lives as a file in a workspace: `prompts/system.md`, `skills/`, `memory/episodic.jsonl`, `tools/registry.yaml`. The engine mutates those files with an LLM and reloads the agent after each cycle.

A cycle has five phases. The agent solves a batch of benchmark tasks, the benchmark scores the trajectories, the engine edits the workspace, a gate validates the mutations against a git snapshot, and the agent reinitializes from the new filesystem state. Rollback comes free with that snapshot, so every change is versioned and you can read the history with `git diff evo-1 evo-10`.

Three pieces have to exist first: an agent with `solve()`, a benchmark with `get_tasks()` and `evaluate()`, and a seed workspace with `manifest.yaml`. The workspace must be a git repo — run `git init && git add -A && git commit -m "init"` before the first cycle, otherwise the engine has nothing to roll back to. Dependencies are separate: `a-evolve>=0.1.0` and `pyyaml>=6.0`. Building multi-agent orchestration from scratch or running a one-shot task with no iteration is a different job.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill evolving-ai-agents -a claude-code
```
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill a-evolve -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/a-evolve/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/a-evolve/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill a-evolve -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/a-evolve/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill a-evolve -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/a-evolve/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Orchestra-Research/AI-Research-SKILLs a-evolve
```

2. **Manually: put the files in place**
- File: `.github/skills/a-evolve/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill a-evolve -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Orchestra-Research/AI-Research-SKILLs --path 14-agents/a-evolve
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/a-evolve/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill a-evolve -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/14-agents/a-evolve/SKILL.md

## When to use it
- raise an agent's score on your own benchmark through evolution cycles
- keep a git history of prompt edits with rollback
- write a custom BenchmarkAdapter for domain tasks
- build episodic memory out of the agent's failures
- discover and refine agent skills automatically

## Example prompts
- Set up evolution for my agent in `./my-agent-workspace` against `swe-verified`, 10 cycles
- Help me write a `BenchmarkAdapter` that scores code review quality from 0.0 to 1.0
- Show what changed in `prompts/system.md` between `evo-1` and `evo-10`

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 838f3a099c1b76c1.

BEGIN THIRD-PARTY FILE 838f3a099c1b76c1
````text
---
name: evolving-ai-agents
description: Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
version: 1.0.0
author: A-EVO Lab
license: MIT
tags: [Agent Evolution, Self-Improving Agents, Prompt Optimization, LLM, Benchmark Evaluation, Skill Discovery, Agentic AI]
dependencies: [a-evolve>=0.1.0, pyyaml>=6.0]
---

# Evolving AI Agents with A-Evolve

## Overview

A-Evolve is universal infrastructure for evolving any AI agent across any domain using any evolution algorithm with zero manual engineering. It represents all evolvable agent state as files (prompts, skills, memory, tools), runs iterative solve-observe-evolve cycles against benchmarks, and uses LLM-driven mutation to improve agent performance automatically.

**Benchmark results** (Claude Opus 4.6):
- MCP-Atlas: 79.4% (#1)
- SWE-bench Verified: 76.8% (~#5)
- Terminal-Bench 2.0: 76.5% (~#7)
- SkillsBench: 34.9% (#2)

## When to Use A-Evolve

**Use A-Evolve when:**
- Optimizing agent prompts, skills, or memory against a measurable benchmark
- Building self-improving agents with automated gating and rollback
- Evolving domain-specific tool usage and procedures through LLM-driven mutation
- Running iterative solve-observe-evolve loops to maximize agent performance
- Needing reproducible, git-versioned evolution history for every change

**Key differentiator**: Other frameworks _build_ agents; A-Evolve _optimizes_ them. It sits on top of any agent framework and makes it better through automated evolution.

**Do NOT use A-Evolve for:**
- Building multi-agent orchestration from scratch (use CrewAI, LangGraph)
- One-shot agent tasks with no iteration needed (use LangChain, LlamaIndex)
- RAG pipeline optimization (use LlamaIndex, Chroma)
- Prompt-only optimization without skill/memory evolution (use DSPy)

## Quick Start

### Installation

```bash
pip install a-evolve                    # Core
pip install a-evolve[anthropic]         # With Claude support
pip install a-evolve[all]               # All providers
```

### Three-Line Evolution

```python
import agent_evolve as ae
````
END THIRD-PARTY FILE 838f3a099c1b76c1

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