gianfrancopiana/openclaw-autoresearch

Open Source

Autonomous Agents Updated Apr 5, 2026

🔄 Updated Apr 2026 🖥️ Self-hostable

Overview

OpenClaw-autoresearch is an autonomous experiment loop for any optimization target with statistical confidence scoring. It is a port of pi-autoresearch and utilizes the OpenClaw framework. This tool enables automated experimentation and optimization with statistically significant results.

Problem It Solves

Automating experiments for optimization targets with statistical confidence

Target Audience: Researchers and developers working on optimization and automation projects

Inputs

• Optimization target
• Experiment parameters
• Statistical confidence threshold
• Data sources

Outputs

• Optimization results
• Statistical confidence scores
• Experiment logs
• Visualization of results

Example Workflow

1 Defining optimization target
2 Configuring experiment parameters
3 Running autonomous experiment loop
4 Analyzing results with statistical confidence scoring
5 Visualizing and logging results
6 Refining and iterating on the optimization process

Sample System Prompt


              Run an autonomous experiment to optimize a machine learning model's hyperparameters with a statistical confidence threshold of 0.95

Tools & Technologies

OpenClaw pi-autoresearch Python libraries for optimization and statistics

Alternatives

• Google AutoML
• Microsoft NNI
• Optuna

FAQs

Is this agent open-source?: Yes
Can this agent be self-hosted?: Yes
What skill level is required?: Advanced

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