evolving-ai-agents
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.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill a-evolve --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Overview
- When to Use A-Evolve
- Quick Start
- Installation
- Three-Line Evolution
- Core Concepts
- The Agent Workspace
- The Evolution Loop
- Three Pluggable Interfaces
- Workflow 1: Evolve an Existing Agent
- Steps
- Post-Evolution
- Workflow 2: Add a Custom Benchmark
- Workflow 3: Create a Custom Evolution Engine
pip install a-evolve # Core pip install a-evolve[anthropic] # With Claude support pip install a-evolve[all] # All providers cd my-agent-workspace git log --oneline # See evo-1, evo-2, ... tags git diff evo-1 evo-10 # Compare first and last evolution cat prompts/system.md # Read evolved prompt ls skills/ # See discovered skills
What does the evolving-ai-agents skill do?
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.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill a-evolve --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.