# Pi AutoResearch — Autonomous Experiment Loop for AI Agents

> An extension that enables AI agents to run autonomous research loops — formulating hypotheses, designing experiments, executing code, analyzing results, and iterating without human intervention.

## Install

Save as a script file and run:

# Pi AutoResearch — Autonomous Experiment Loop for AI Agents

## Quick Use
```bash
npm install -g pi-autoresearch

# Initialize in a research project
pi-autoresearch init --workspace ./my-experiment

# Start an autonomous research loop
pi-autoresearch run --hypothesis "batch size 32 outperforms 64 on this dataset" --max-iterations 10
```

## Introduction
Pi AutoResearch adds an autonomous experiment loop to AI coding agents. Given a research question or hypothesis, the agent designs experiments, writes and executes code, collects metrics, and iterates on its approach — all without requiring human approval at each step. It is designed for ML researchers and data scientists who want to accelerate exploratory work.

## What Pi AutoResearch Does
- Decomposes research questions into testable hypotheses
- Generates experiment code with proper controls and metrics
- Executes experiments in sandboxed environments and collects results
- Analyzes outcomes and decides whether to refine, pivot, or conclude
- Produces structured research reports with reproducible notebooks

## Architecture Overview
Pi AutoResearch operates as a TypeScript extension that wraps a coding agent in an experiment loop controller. The controller maintains a state machine with phases: hypothesis formulation, experiment design, execution, analysis, and decision. Each phase invokes the underlying agent with structured prompts. Execution happens in isolated containers to prevent side effects. Results are stored in a local SQLite database for cross-experiment comparison.

## Self-Hosting & Configuration
- Requires Node.js 18+ and Docker for sandboxed experiment execution
- Configure via `autoresearch.config.json` for model provider, iteration limits, and resource budgets
- Set compute constraints (max CPU time, memory, GPU) per experiment run
- Supports integration with MLflow or Weights and Biases for experiment tracking
- All data stays local unless external tracking services are configured

## Key Features
- Fully autonomous hypothesis-test-iterate loop
- Sandboxed execution prevents experiments from affecting the host system
- Structured decision framework for when to continue, pivot, or stop
- Built-in experiment comparison across iterations
- Exportable Jupyter notebooks for reproducibility

## Comparison with Similar Tools
- **AutoGen** — general multi-agent framework; Pi AutoResearch specializes in the experiment loop pattern
- **DSPy** — optimizes LLM programs; Pi AutoResearch runs open-ended experimental research
- **Kedro** — ML pipeline framework; Pi AutoResearch focuses on autonomous exploration, not production pipelines
- **Jupyter** — interactive notebooks; Pi AutoResearch automates the entire experiment cycle

## FAQ
**Q: What types of experiments can it run?**
A: Any experiment expressible as Python or TypeScript code — ML training runs, data analysis, benchmarking, API testing, and statistical simulations.

**Q: How does it decide when to stop?**
A: The controller uses configurable stopping criteria: maximum iterations, convergence thresholds, or budget limits on compute time and API cost.

**Q: Can I review experiments before they execute?**
A: Yes, a `--review` flag pauses before each execution for human approval, useful when running expensive GPU experiments.

**Q: Does it support GPU workloads?**
A: Yes, Docker containers can be configured with GPU passthrough for ML training experiments.

## Sources
- https://github.com/davebcn87/pi-autoresearch

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Source: https://tokrepo.com/en/workflows/asset-a0209cf4
Author: Script Depot