Agent skill · product strategy · zechenzhangagi
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.
Why this skill is useful
Adds a complete framework for automating the evolution and optimization of AI agents, including executable scripts for iterative improvement cycles.
What it needs
Requires a-evolve, pyyaml installed locally. About 7k tokens when loaded. Last updated 2026-06-16. 11,472 stars on the source repository.
What this skill does
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 Three-Line Evolution This copies the built-in SWE seed workspace, runs 10 evolution cycles against SWE-bench Verified, and returns the optimized agent. Core Concepts The Agent Workspace All evolvable state lives as files in a workspace directory: The Evolution Loop Each cycle follows five phases: 1. Solve — Agent processes a batch of tasks from the benchmark 2. Observe — Benchmark evaluates trajectories, producing (task, trajectory, feedback) triples 3. Evolve — Evolution engine mutates workspace files based on observations 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills zechenzhangagi/a-evolve