evolving-ai-agents

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

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

Three-Line Evolution

import agent_evolve as ae

evolver = ae.Evolver(agent="swe", benchmark="swe-verified")
results = evolver.run(cycles=10)
print(f"Final score: {results.final_score}")

This copies the built-in SWE seed workspace, runs 10 evolution cycles against SWE-bench Verified, and returns the optimized agent.