evolving-ai-agentslisted

# 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 ### Installatio