autoresearch

The two-loop architecture drives autonomous research by combining an inner loop for rapid experiment iterations with an outer loop for reflective synthesis. This mechanism automates experiments with clear optimization targets and continuously guides the research direction.

Running autonomous research experiment orchestration requires Python libraries like pypdf, pdfplumber, and pdf2image, alongside domain-specific skills for scholar evaluation, scientific writing, and ml-paper-writing. You also need Claude Code and OpenClaw heartbeat for continuous operation.

Yes, the autonomous agent supports scientific visualization and academic plotting by integrating dedicated skills for generating scientific schematics and images. These components synthesize experiment results into visual research presentations and papers.

The best way to orchestrate autonomous experiments with clear optimization targets is using the two-loop architecture. It automates inner and outer loops to run rapid iterations and perform reflective synthesis for research direction.

Yes, autonomous research supports continuous operation via Claude Code and OpenClaw heartbeat. This enables the agent to continuously manage complex research projects and orchestrate experiments without interruption.

Avoid using an autonomous agent for research synthesis when your project lacks clear optimization targets or requires domain-specific skills beyond its dependencies. It requires Python libraries and specific skills for scholar evaluation and scientific writing to function.