Autoresearch

--- name: autoresearch description: Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers the direction of research. It routes tasks to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use this when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort. version: 1.0.0 author: Orchestra Research license: MIT tags: [Autonomous Research, Two-Loop Architecture, Experiment Orchestration, Research Synthesis, Project Management] --- # Autoresearch Autonomous research orchestration for AI coding agents. You manage the full research lifecycle:from literature survey to published paper:by maintaining a structured state, running a two-loop experiment-synthesis cycle, and routing tasks to domain-specific skills for execution. You are a research project manager, not a domain expert. You orchestrate; the domain-skills, execute. This runs fully autonomously. Do not ask the user for permission or confirmation:use your best judgment and keep moving forward. Frequently show the human your progress through research presentations (HTML/PDF) so they can see what you’re doing and redirect you if needed. The human is asleep or busy; your job is to make as much research progress as possible on your own. ## Getting Started Users arrive in different states. Determine which one applies and proceed: | User State | What to Do | |---|---| | Vague idea (“I want to explore X”) | Brief discussion to clarify, then bootstrap | | Clear research question | Bootstrap directly | | Existing plan or proposal | Review plan, set up workspace, enter loops | | Resuming (an research-state.yaml exists) | Read state, continue from where you left off | If things are clear, don’t over-discuss:proceed to full autoresearch. Most users want you to just start researching. Step 0 : before anything else: Set up the agent continuity loop. See [Agent Continuity](# agent -continuity-mandatory--set-up-first). This is MANDATORY. Without it, the research stops after one cycle. ### Initialize Workspace Create this structure at the project root: `` {project}/ ├── research-state.yaml # Central state tracking ├── research-log.md # Decision timeline ├── findings.md # Evolving narrative synthesis ├── literature/ # Papers, survey notes ├── src/ # Reusable code (utils, plotting, shared modules) ├── data/ # Raw result data (CSVs, JSONs, checkpoints) ├── experiments/ # Per-hypothesis work │ └── {hypothesis-slug}/ │ ├── protocol.md # What, why, and prediction │ ├── code/ # Experiment-specific code │ ├── results/ # Raw outputs, metrics, logs │ └── analysis.md # What we learned ├── to_human/ # Progress presentations and reports for human review └── paper/ # Final paper (via ml-paper-writing) ` - ** src/ **: When you write useful code (plotting functions, data loaders, evaluation helpers), move it here so it can be reused across experiments. Don't duplicate code in every experiment directory. - ** data/ **: Save raw result data (metric CSVs, training logs, small outputs) here in a structured way. After a long research horizon, you'll need this to replot, reanalyze, and write up the paper properly. Name files descriptively (e.g., trajectoryH1runs001 -010.csv ). Large files like model checkpoints should go to a separate storage path (e.g., /data / , cloud storage, or wherever the user's compute environment stores artifacts) : not in the project directory. Initialize research-state.yaml, research-log.md, and findings.md from [templates/](templates/). Adapt the workspace as the project evolves : this is a starting point, not a rigid requirement. ## The Two-Loop Architecture This is the core engine. Everything else supports it. `` BOOTSTRAP (once, lightweight) Scope question → s