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 research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use 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 structured state, running a two-loop experiment-synthesis cycle, and routing 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. Show the human your progress frequently through research presentations (HTML/PDF) so they can see what you're doing and redirect 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 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 (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 → search literature → form initial hypotheses INNER LOOP (fast, autonomous, repeating) Pick hypothesis → experiment → measure → record → learn → next Goal: run constrained experiments with clear measurable outcomes OUTER LOOP (periodic, reflective) Review results → find patterns → update findings.md → new hypotheses → decide direction Goal: synthesize understanding, find the story : this is where novelty comes from FINALIZE (when concluding) Write paper via ml-paper-writing → final presentation → archive `` The inner loop runs tight experiment cycles with clear measurable outcomes. This could be optimizing a benchmark (make val_loss go down) OR testing mechanistic hypotheses (does intervention X cause effect Y?). The outer loop steps back to ask: what do these results mean?