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.

Core Concepts

The Agent Workspace

All evolvable state lives as files in a workspace directory:

my-agent/
├── manifest.yaml          # Metadata + entrypoint
├── prompts/
│   ├── system.md          # Main system prompt (evolved)
│   └── fragments/         # Modular prompt pieces
├── skills/
│   └── skill-name/
│       └── SKILL.md       # Reusable procedure with frontmatter
├── memory/
│   ├── episodic.jsonl     # Lessons from failures
│   └── semantic.jsonl     # General knowledge
├── tools/
│   ├── registry.yaml      # Tool manifest
│   └── tool_name.py       # Tool implementations
└── evolution/             # Managed by engine (metrics, history)

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. Gate — Validate mutations (git snapshot before/after for rollback)
5. Reload — Agent reinitializes from evolved filesystem state

Three Pluggable Interfaces

# 1. Agent — implements solve()
class MyAgent(ae.BaseAgent):
    def solve(self, task: ae.Task) -> ae.Trajectory:
        # Domain-specific solving logic
        return ae.Trajectory(task_id=task.id, output=result, steps=steps)

# 2. Benchmark — implements get_tasks() and evaluate()
class MyBenchmark(ae.BenchmarkAdapter):
    def get_tasks(self, split="train", limit=None) -> list[ae.Task]:
        return [ae.Task(id="1", input="...")]

    def evaluate(self, task: ae.Task, trajectory: ae.Trajectory) -> ae.Feedback:
        return ae.Feedback(success=True, score=0.95, detail="Passed")

# 3. Engine — implements step()
class MyEngine(ae.EvolutionEngine):
    def step(self, workspace, observations, history, trial):
        # Mutate workspace based on observations
        return ae.StepResult(mutated=True, summary="Updated prompts")

Workflow 1: Evolve an Existing Agent

Use when: You have a working agent and want to optimize it against a benchmark.

Critical Requirements:

• Agent implements BaseAgent.solve() returning Trajectory
• Benchmark implements BenchmarkAdapter with get_tasks() and evaluate()
• Seed workspace has manifest.yaml with entrypoint and evolvable layers
• System prompt exists at prompts/system.md
• Workspace is a git repo (run git init && git add -A && git commit -m "init")

Steps

import agent_evolve as ae

# Configure evolution parameters
config = ae.EvolveConfig(
    batch_size=10,           # Tasks per solve round
    max_cycles=20,           # Maximum evolution iterations
    evolve_prompts=True,     # Mutate system prompt
    evolve_skills=True,      # Discover and refine skills
    evolve_memory=True,      # Build episodic memory
    evolver_model="us.anthropic.claude-opus-4-6-v1",
)

# Point to your agent workspace and benchmark
evolver = ae.Evolver(
    agent="./my-agent-workspace",
    benchmark="swe-verified",     # Or custom BenchmarkAdapter instance
    config=config,
)

# Run evolution
results = evolver.run(cycles=10)

# Inspect results
print(f"Cycles completed: {results.cycles_completed}")
print(f"Final score: {results.final_score}")
print(f"Converged: {results.converged}")
for cycle_num, score in enumerate(results.score_history):
    print(f"  Cycle {cycle_num + 1}: {score:.3f}")

Post-Evolution

The workspace is now optimized. Inspect what changed:

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

Workflow 2: Add a Custom Benchmark

Use when: You want to evolve agents on your own domain-specific tasks.

Critical Requirements:

• Define task format (inputs, expected outputs)
• Implement scoring logic (0.0–1.0 scale)
• Prepare task dataset (train + holdout split)

Steps

import agent_evolve as ae

class CodeReviewBenchmark(ae.BenchmarkAdapter):
    """Evaluate agents on code review quality."""

    def get_tasks(self, split="train", limit=None):
        tasks = load_review_dataset(split)
        if limit:
            tasks = tasks[:limit]
        return [
            ae.Task(id=t["id"], input=t["diff"], metadata={"expected": t["comments"]})
            for t in tasks
        ]

    def evaluate(self, task, trajectory):
        expected = task.metadata["expected"]
        actual = trajectory.output
        precision, recall = compute_review_metrics(expected, actual)
        f1 = 2 * precision * recall / (precision + recall + 1e-9)
        return ae.Feedback(
            success=f1 > 0.7,
            score=f1,
            detail=f"P={precision:.2f} R={recall:.2f} F1={f1:.2f}",
        )

# Use with any agent
evolver = ae.Evolver(agent="./my-agent", benchmark=CodeReviewBenchmark())
results = evolver.run(cycles=5)

Workflow 3: Create a Custom Evolution Engine

Use when: The default LLM-driven mutation doesn't suit your domain.

Steps

import agent_evolve as ae

class RuleBasedEngine(ae.EvolutionEngine):
    def step(self, workspace, observations, history, trial):
        failures = [o for o in observations if not o.feedback.success]
        if not failures:
            return ae.StepResult(mutated=False, summary="No failures to address")

        # Analyze failure patterns
        error_types = categorize_errors(failures)
        prompt = workspace.read_prompt()

        # Append learned rules to prompt
        new_rules = generate_rules(error_types)
        workspace.write_prompt(prompt + "\n" + new_rules)

        return ae.StepResult(
            mutated=True,
            summary=f"Added {len(new_rules)} rules from {len(failures)} failures",
        )

evolver