Metadata-Version: 2.4
Name: a-evolve
Version: 0.1.0
Summary: Evolve any agent across any domain using any evolution algorithm — with zero human intervention
Project-URL: Homepage, https://github.com/A-EVO-Lab/a-evolve
Project-URL: Repository, https://github.com/A-EVO-Lab/a-evolve
Project-URL: Issues, https://github.com/A-EVO-Lab/a-evolve/issues
Author: A-EVO Lab
License-Expression: MIT
Keywords: agent-evolution,agentic-ai,benchmark,llm,prompt-optimization,self-improving-agents
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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Description-Content-Type: text/markdown

# A-Evolve 🧬: The Universal Infrastructure for Self-Improving Agents

[![GitHub stars](https://img.shields.io/github/stars/A-EVO-Lab/a-evolve?style=social)](https://github.com/A-EVO-Lab/a-evolve)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/)
[![arXiv](https://img.shields.io/badge/arXiv-2602.00359-b31b1b.svg)](https://arxiv.org/abs/2602.00359)

> **The PyTorch for Agentic AI.**
> A-Evolve is an open-source infrastructure that evolves *any* agent, across *any* domain, using *any* evolution algorithm — with zero human intervention.

[Quick Start](#quick-start) | [News](#news) | [Benchmark Highlights](#benchmark-highlights) | [Architecture & Design](#architecture--design) | [Contribution](#community--contributing)

![A-Evolve Teaser](figs/teaser.png)

---

## What Does A-Evolve Do?

You provide a Base Agent. A-Evolve returns a SOTA Agent. **3 lines of code. 0 hours of manual harness 
engineering.** One infra, any domain, any evolution algorithm.

```python
import agent_evolve as ae

evolver = ae.Evolver(agent="./my_agent", benchmark="swe-verified")
results = evolver.run(cycles=10)
```

### Benchmark Highlights

By applying our open-source **reference evolution algorithms** to a base Claude Opus-4.6 model with **zero manual harness engineering**, A-Evolve pushed agents into top-tier performance across four diverse benchmarks:

🟢 MCP-Atlas

🥇 #1
Baseline → 79.4% (+3.4pp)

🔵 SWE-bench Verified

~#5
Baseline → 76.8% (+2.6pp)

🟣 Terminal-Bench 2.0

~#7
Baseline → 76.5% (+13.0pp)

🟡 SkillsBench

#2
Baseline → 34.9% (+15.2pp)

![A-Evolve Benchmarks](figs/a_evolve_benchmarks.png)

> *All results achieved with a single Claude Opus-4.6 base model, evolved using A-Evolve's sample algorithms. 0 hours of human harness engineering. Data checked March 2026.*

### News
- **03/30** **Integration**, A-Evolve is officially integrated into [AutoResearchClaw](https://github.com/aiming-lab/AutoResearchClaw) 
- **03/25** 🚀 **Open-source A-Evolve**, the universal infrastructure for developing and testing evolving algorithms.
- **03/25** 📊 **Open-source 4 evolving algorithms** developed with A-Evolve, achieving SOTA **(#1, ~#5, ~#7, #2)** on MCP-Atlas, SWE-bench Verified, Terminal-Bench 2.0, and SkillsBench.
- **02/17** 📄 Release the official implementation of [*Position: Agentic Evolution is the Path to Evolving LLMs*](https://arxiv.org/abs/2602.00359) (arXiv 2602.00359).

We are evolving fast! Support our research by leaving a ⭐.

### What Does an Evolved Agent Look Like?

A-Evolve mutates real files in the workspace. Here's a before/after from our MCP-Atlas evolution:

 Before (Seed Workspace)
 After (Evolved — 79.4% on MCP-Atlas)

```
mcp_agent/
├── manifest.yaml
├── prompts/system.md      ← 20 lines, generic
├── skills/                ← empty
└── memory/                ← empty
```


```
mcp_agent/
├── manifest.yaml
├── prompts/system.md      ← 20 lines, unchanged
├── skills/
│   ├── entity-verification/SKILL.md   ← NEW
│   ├── search-iteration/SKILL.md      ← NEW
│   ├── multi-requirement/SKILL.md     ← NEW
│   ├── code-execution/SKILL.md        ← NEW
│   └── conditional-handler/SKILL.md   ← NEW
└── memory/
    └── episodic.jsonl     ← 6 entries
```


5 targeted skills outperformed 10 generic ones. Every mutation is git-tagged (`evo-1`, `evo-2`, …) for full reproducibility.

---

## Quick Start

### 1. Install

```bash
# PyPI (recommended)
pip install a-evolve              # core
pip install a-evolve[anthropic]   # Claude support
pip install a-evolve[mcp]         # MCP-Atlas benchmark
pip install a-evolve[swe]         # SWE-bench benchmark
pip install a-evolve[all]         # everything

# From source (for development)
git clone https://github.com/A-EVO-Lab/a-evolve.git && cd a-evolve
pip install -e ".[all,dev]"
```

### 2. Evolve — 3 Lines of Code

```python
import agent_evolve as ae

evolver = ae.Evolver(
    agent="swe-verified",           # built-in seed workspace (or path to yours)
    benchmark="swe-verified",       # built-in benchmark adapter
)
results = evolver.run(cycles=10)

print(f"Final score: {results.final_score:.3f}")
print(f"Converged:   {results.converged}")
```

A-Evolve ships with built-in seed workspaces (`swe`, `mcp`, `terminal`, `skillbench`) and benchmark adapters (`swe-verified`, `mcp-atlas`, `terminal-bench 2.0`, `skill-bench`). Point `agent=` at any of them — or at your own workspace directory.

### 3. Bring Your Own Agent (BYOA)

To make any agent evolvable, implement one method — `solve()`:

```python
from agent_evolve.protocol.base_agent import BaseAgent
from agent_evolve.types import Task, Trajectory

class MyAgent(BaseAgent):
    def solve(self, task: Task) -> Trajectory:
        return Trajectory(task_id=task.id, output="result")
```

Then evolve it:

```python
evolver = ae.Evolver(agent=MyAgent("./my_workspace"), benchmark="mcp-atlas")
results = evolver.run(cycles=10)
```

Your agent's evolvable state (prompts, skills, memory) lives as a standard directory — the [Agent Workspace](#the-agent-workspace-a-file-system-contract). A-Evolve mutates these files; your agent reloads. See [Architecture & Design](#architecture--design) for the full picture.

For benchmark-specific walkthroughs, see [SWE-bench Demo Guide](docs/swe-bench-demo.md), [MCP-Atlas Demo Guide](docs/mcp-atlas-demo.md), and [SkillBench Setup Guide](docs/skillbench-setup.md).

---

## Architecture & Design

![A-Evolve Framework](figs/A-EVOLVE-FRAMEWORK.png)

### The Agent Workspace: A File System Contract

A-Evolve's core insight: **all evolvable agent state lives on the file system as a standard directory structure.** This lets the evolution engine mutate any agent via LLM-driven file operations — without knowing the agent's internals.

```
my_agent/
├── manifest.yaml          # identity, entrypoint, evolvable layers
├── prompts/system.md      # system prompt
├── skills/                # SKILL.md files (dynamic skill library)
├── tools/                 # tool configurations
└── memory/                # episodic + semantic memory (JSONL)
```

The evolution engine reads these files, analyzes performance logs, and writes mutations back. The agent reloads. That's the entire contract.

### The Evolution Loop

Every cycle follows five phases:

```
┌─────────┐    ┌─────────┐    ┌─────────┐    ┌──────┐    ┌────────┐
│  Solve  │───▶│ Observe │───▶│ Evolve  │───▶│ Gate │───▶│ Reload │
└─────────┘    └─────────┘    └─────────┘    └──────┘    └────────┘
```

1. **Solve** — Agent processes a batch of tasks (black-box execution).
2. **Observe** — Collect trajectories + benchmark feedback into structured logs.
3. **Evolve** — Evolution engine analyzes observations and mutates workspace files (prompts, skills, memory).
4. **Gate** — Validate mutations on holdout tasks. Regressed mutations are rolled back via git.
5. **Reload** — Agent reloads from the (possibly rolled-back) workspace.

The loop converges when EGL (Evolutionary Generality Loss) stabilizes or `max_cycles` is reached. Every accepted mutation is git-tagged (`evo-1`, `evo-2`, …), providing a full audit trail.

### Built-in Adapters

A-Evolve ships with ready-to-use benchmark adapters and seed workspaces:

| Adapter | Domain | Seed Workspace | Best Result |
| :--- | :--- | :--- | :--- |
| [`swe-verified`](docs/swe-bench-demo.md) | Real-world GitHub issues (Python repos) | `seed_workspaces/swe/` | **76.8%** (~#5) |
| [`mcp-atlas`](docs/mcp-atlas-demo.md) | Tool-calling via MCP (16+ servers) | `seed_workspaces/mcp/` | **79.4%** (🥇 #1) |
| [`terminal-bench`](docs/terminal-bench-demo.md) | Terminal/CLI ops in Docker | `seed_workspaces/terminal/` | **76.5%** (~#7) |
| [`skill-bench`](docs/skillbench-setup.md) | Agentic skill discovery | `seed_workspaces/skillbench/` | **34.9%** (~#2)|

### Pluggability: Bring Your Own Everything

A-Evolve is a **framework**, not a standalone agent. Every axis is pluggable:

| Axis | Interface | You Provide | Built-in Examples |
| :--- | :--- | :--- | :--- |
| **Agent (BYOA)** | `BaseAgent.solve()` | Any agent architecture — ReAct, Plan-and-Solve, custom | `SweAgent`, `McpAgent` |
| **Benchmark (BYOE)** | `BenchmarkAdapter.get_tasks()` / `.evaluate()` | Any domain with task + evaluation signal | SWE-bench, MCP-Atlas, Terminal-Bench 2.0, SkillsBench |
| **Algorithm (BYO-Algo)** | `EvolutionEngine.step()` | Any evolution strategy | `AEvolveEngine` (LLM-driven mutation) |
| **LLM Provider** | `LLMProvider.complete()` | Any model API | Anthropic, OpenAI, AWS Bedrock |

### Built-in Evolution Algorithms

A-Evolve ships with 4 reference evolution algorithms, each targeting different domains and strategies:

| Algorithm | Strategy | Best For | Docs |
| :--- | :--- | :--- | :--- |
| [`adaptive_evolve`](docs/algorithms/adaptive-evolve.md) | Per-claim feedback analysis + meta-learning | MCP-Atlas (🥇 #1, 79.4%) | [Guide](docs/algorithms/adaptive-evolve.md) |
| [`adaptive_skill`](docs/algorithms/adaptive-skill.md) | LLM-driven workspace mutation with bash tool access | Terminal-Bench 2.0 (~#7, 76.5%)  | [Guide](docs/algorithms/adaptive-skill.md) |
| [`skillforge`](docs/algorithms/skillforge.md) | LLM-driven workspace mutation with EGL gating | SkillsBench (#2, 34.9%) | [Guide](docs/algorithms/skillforge.md) |
| [`guided_synth`](docs/algorithms/guided-synth.md) | Memory-first evolution + LLM-guided intervention synthesis |  General-purpose, SWE-bench (~#5, 76.8%) | [Guide](docs/algorithms/guided-synth.md) |

#### Plugging in a custom evolution algorithm

Each algorithm lives in its own directory under `algorithms/`. Implement a single method:

```python
from agent_evolve.engine.base import EvolutionEngine
from agent_evolve.types import StepResult

class MyEvolutionEngine(EvolutionEngine):
    def step(self, workspace, observations, history, trial) -> StepResult:
        # Analyze observations, mutate workspace files, optionally run trial tasks
        ...
        return StepResult(accepted=True, score=new_score)
```

Then pass it to the Evolver:

```python
evolver = ae.Evolver(
    agent="swe-verified",
    benchmark="swe-verified",
    engine=MyEvolutionEngine(config),
)
```

The engine has full access to shared primitives — `TrialRunner` (on-demand validation), `EvolutionHistory` (observation + version queries), and `VersionControl` (git-based rollback) — but is never forced to use them. Minimal contract, maximum freedom.

---

## Community & Contributing

A-Evolve is built for the research community. We welcome contributions across every axis of the framework.

### For Algorithm Researchers

If you work in LLM self-optimization, reinforcement learning, or agent architectures — implement the `EvolutionEngine` interface and your algorithm instantly gains access to:

- Diverse environments (SWE-bench, MCP-Atlas, Terminal-Bench 2.0, SkillsBench, and more).
- Standardized agent workspace representations.
- Rigorous evaluation, gating, and logging infrastructure.

Drop your algorithm into `agent_evolve/algorithms/your_algo/` and open a PR.

### For Benchmark Authors

Implement `BenchmarkAdapter` to plug any new evaluation domain into A-Evolve. The interface is two methods: `get_tasks()` and `evaluate()`.

### Get Involved

- ⭐ **Star this repo** to support our research — we are evolving fast.
- 🐛 **[Open an issue](https://github.com/A-EVO-Lab/a-evolve/issues)** to report bugs or request features.
- 🔀 **[Submit a PR](https://github.com/A-EVO-Lab/a-evolve/pulls)** — new evolution algorithms, benchmark adapters, agent implementations, and documentation improvements are all welcome.
- 💬 **[Join our Discord]()** to discuss research directions, share results, and collaborate.

---

## Citation

If you use A-Evolve in your research, please cite our position paper:

```bibtex
@article{lin2026position,
  title={Position: Agentic Evolution is the Path to Evolving LLMs},
  author={Lin, Minhua and Lu, Hanqing and Shi, Zhan and He, Bing and Mao, Rui and Zhang, Zhiwei and Wu, Zongyu and Tang, Xianfeng and Liu, Hui and Dai, Zhenwei and others},
  journal={arXiv preprint arXiv:2602.00359},
  year={2026}
}
```

---

## License

[MIT](https://opensource.org/licenses/MIT)
