autoresearch

Autonomous research automation converts natural language goals into a self-driven loop of hypothesis generation, experiment execution, and evaluation. By reading a research.md file, the system iterates without human intervention until the target metric is achieved.

An autonomous research agent handles continuous experimentation by automatically suggesting hypotheses based on past data, running experiments, and evaluating results. It maintains a continuous iteration loop, improving based on feedback until the target metric is met.

To start an autonomous research loop, provide a research.md file containing your research goal. Execute the autoresearch command without subcommands, and the agent will begin processing the provided file to automate the experimental research stages.

Yes, autonomous research loop execution supports integration with various tools including Claude Code, Codex CLI, OpenCode, and Gemini CLI. These dependencies facilitate the automated experimentation and hypothesis generation processes across different domains.

Yes, autonomous experiment automation is capable of handling tasks across various domains, including machine learning. It automates the research stages from understanding the goal to evaluating results and maintaining improvements without requiring further human input.

Autonomous research agents rely on predefined target metrics to conclude the iteration loop. If the research goal in the research.md file is ambiguous or the target metric cannot be measured, the automated evaluation and continuous improvement process may not function correctly.