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
- init creates a git worktree per agent in a sibling directory, each with a different research strategy
- Each agent works independently in its worktree directory using autojudge + autosteer
- leaderboard ranks agents by best val_bpb with keep rate tracking
- pollinate writes the leader's best experiments to
evolve-hints.mdin each agent's worktree - Agents incorporate hints and continue competing
- 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)
| File | Size | Uploaded | |
|---|---|---|---|
| autoevolve-1.1.1.tar.gz | 12.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| 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 |
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No |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
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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 |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
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