Skip to main content

autoevolve

Multi-agent research competition orchestrator for autoresearch. Run parallel AI agents with different strategies and cross-pollinate winning ideas.

Install

pip install autoevolve

Usage

# Initialize a 3-agent competition
autoevolve init --agents 3 --tag mar15

# Check who's winning
autoevolve status
autoevolve leaderboard --detailed

# Spread winning ideas to all agents
autoevolve pollinate

# Export results
autoevolve export --format json -o evolve-results.json

How It Works

  1. init creates a git worktree per agent in a sibling directory, each with a different research strategy
  2. Each agent works independently in its worktree directory using autojudge + autosteer
  3. leaderboard ranks agents by best val_bpb with keep rate tracking
  4. pollinate writes the leader's best experiments to evolve-hints.md in each agent's worktree
  5. Agents incorporate hints and continue competing
  6. cleanup removes worktrees, branches, and config when done

Built-in Strategies

Strategy Approach
Architecture First Explore model structure before tuning
Hyperparams First Sweep learning rates and schedules first
Optimizer First Tune Muon/Adam parameters first
Regularization First Explore weight decay, dropout, z-loss
Efficiency First Maximize compute efficiency to run more experiments
Radical Bold, unconventional changes

Strategies are assigned round-robin. With 3 agents, you get 3 different strategies competing.

Commands

Command Description
autoevolve init --agents N --tag TAG Create N agent worktrees
autoevolve init ... --worktree-dir DIR Place worktrees in custom directory
autoevolve status Quick overview with current leader
autoevolve leaderboard Ranked table with keep rates
autoevolve leaderboard --detailed Full trajectories + strategy effectiveness
autoevolve pollinate Cross-pollinate winning ideas
autoevolve export --format json|tsv Export results for analysis
autoevolve cleanup Remove worktrees, branches, and config

Requirements

  • Python >= 3.10
  • A git repository with autoresearch set up
  • Multiple compute environments (one per agent)

License

MIT

Release files for autoevolve 1.1.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for autoevolve 1.1.1
File Size Uploaded
autoevolve-1.1.1.tar.gz 12.2 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for autoevolve 1.1.1
File Interpreter ABI Platform
autoevolve-1.1.1-py3-none-any.whl Python 3 none any Details

Total release size:25.0 kB

Release files / autoevolve-1.1.1.tar.gz

Download URL autoevolve-1.1.1.tar.gz
Size 12.2 kB
Tags Source
SHA-256 checksum
How to use checksums
2185f899ab2324e40d79bf9c79639bb20c02ff6d1e677a262ec2b0823f3529d6
BLAKE2b-256 checksum
How to use checksums
9bc174e38f4745452725827e423dda597ce39ce06bb338426848edca807fa5ff
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.13.7

Release files / autoevolve-1.1.1-py3-none-any.whl

Download URL autoevolve-1.1.1-py3-none-any.whl
Size 12.8 kB
Tags Python 3
SHA-256 checksum
How to use checksums
5378a9d3af66c87e87b350954b1a9614b22b33c50841ed62b5a0ea06264e78a8
BLAKE2b-256 checksum
How to use checksums
d21a09d7c54fee9058f5638f0e2d4262fc740912465360e3a81a4baa1524d15b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.13.7

Release history Release notifications | RSS feed

This release

1.1.1 This release

2 release files

1.1.0

2 release files

1.0.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page