Zouroboros Autoloop

Autonomous optimization loop inspired by Andrej Karpathy's autoresearch: edit, experiment, measure, keep or revert. Best for any task with a numeric metric.

Autonomous single-metric optimization loop. Reads a program.md spec, creates a git branch, and loops: propose change → commit → run experiment → measure metric → keep improvements, revert regressions. Inspired by Andrej Karpathy's autoresearch concept.

Quick Start

bash

npm install -g zouroboros-autoloop

# Create a program.md (see template below), then:
autoloop --program ./program.md --executor "openclaw ask"

Usage

bash

autoloop --program <path/to/program.md> [--executor <command>] [--resume] [--dry-run]

• --program — Path to your program.md specification (required)
• --executor — Shell command that reads a prompt from stdin and outputs a response
  • OpenClaw: "openclaw ask"
  • Claude Code: "claude --print"
  • Any LLM CLI that reads stdin
• --resume — Resume from an existing autoloop branch
• --dry-run — Validate program.md without running

MCP Server

Autoloop includes an MCP server for tool-based integration:

bash

autoloop-mcp --results-dir /path/to/projects

Tools exposed: autoloop_start, autoloop_status, autoloop_results, autoloop_stop, autoloop_list

program.md Template

markdown

# Program: my-optimization

## Objective
Optimize the trading strategy parameters for maximum Sharpe ratio.

## Metric
- **name**: sharpe_ratio
- **direction**: higher_is_better
- **extract**: `tail -1 results.csv | cut -d, -f3`

## Target File
`params.json`

## Run Command
```bash
node backtest.js --config params.json

Constraints

• Time budget per run: 60
• Max experiments: 50
• Max duration: 4
• Max cost: 5

Stagnation

• Threshold: 8
• Double threshold: 15
• Triple threshold: 25

Read-Only Files

• backtest.js
• historical-data.csv

Setup

bash

npm install

Notes

Focus on risk-adjusted returns. Avoid overfitting to recent data.

text

## Use Cases

- **Trading backtests** — Optimize strategy parameters against historical data
- **Prompt optimization** — Tune prompts to maximize a quality metric
- **Site performance** — Reduce load time, optimize bundle size
- **Model fine-tuning** — Iterate on hyperparameters with measurable output

## Part of the Zouroboros Ecosystem

Zouroboros is a self-improving AI orchestration framework. These standalone packages give you a taste of what's possible. For the full experience — persistent memory, swarm orchestration, scheduled agents, persona routing, and self-healing infrastructure — get a [Zo Computer](https://zo-computer.cello.so/IgX9SnGpKnR).

Built by [@Xmarlandoj](https://x.com/Xmarlandoj)