Install · Usage · How it works

Try an idea, measure it, keep what works, discard what doesn't, repeat forever.

Inspired by karpathy/autoresearch. Works for any optimization target: test speed, bundle size, LLM training, build times, Lighthouse scores.

• Status widget — always visible above the editor: 🔬 autoresearch 12 runs 8 kept │ best: 42.3s
• /autoresearch — full results dashboard (Ctrl+X to toggle, Escape to close)

autoresearch-create asks a few questions (or infers from context) about your goal, command, metric, and files in scope — then writes two files and starts the loop immediately:

pi install https://github.com/davebcn87/pi-autoresearch

cp -r extensions/pi-autoresearch ~/.pi/agent/extensions/
cp -r skills/autoresearch-create ~/.pi/agent/skills/

/skill:autoresearch-create

The agent asks about your goal, command, metric, and files in scope — or infers them from context. It then creates a branch, writes autoresearch.md and autoresearch.sh, runs the baseline, and starts looping immediately.

The agent runs autonomously: edit → commit → run_experiment → log_experiment → keep or revert → repeat. It never stops unless interrupted.

Every result is appended to autoresearch.jsonl in your project — one line per run. This means:

• Survives restarts — the agent can resume a session by reading the file
• Survives context resets — autoresearch.md captures what's been tried so a fresh agent has full context
• Human readable — open it anytime to see the full history
• Branch-aware — each branch has its own session

• Widget — always visible above the editor
• /autoresearch — full dashboard with results table and best run
• Escape — interrupt anytime and ask for a summary

The extension is domain-agnostic infrastructure. The skill encodes domain knowledge. This separation means one extension serves unlimited domains.

┌──────────────────────┐     ┌──────────────────────────┐
│  Extension (global)  │     │  Skill (per-domain)       │
│                      │     │                           │
│  run_experiment      │◄────│  command: pnpm test       │
│  log_experiment      │     │  metric: seconds (lower)  │
│  widget + dashboard  │     │  scope: vitest configs    │
│                      │     │  ideas: pool, parallel…   │
└──────────────────────┘     └──────────────────────────┘

Two files keep the session alive across restarts and context resets:

autoresearch.jsonl   — append-only log of every run (metric, status, commit, description)
autoresearch.md      — living document: objective, what's been tried, dead ends, key wins

A fresh agent with no memory can read these two files and continue exactly where the previous session left off.

Create autoresearch.checks.sh to run correctness checks (tests, types, lint) after every passing benchmark. This ensures optimizations don't break things.

#!/bin/bash
set -euo pipefail
pnpm test --run
pnpm typecheck

How it works:

• If the file doesn't exist, everything behaves exactly as before — no changes to the loop.
• If it exists, it runs automatically after every benchmark that exits 0.
• Checks execution time does not affect the primary metric.
• If checks fail, the experiment is logged as checks_failed (same behavior as a crash — no commit, revert changes).
• The checks_failed status is shown separately in the dashboard so you can distinguish correctness failures from benchmark crashes.
• Checks have a separate timeout (default 300s, configurable via checks_timeout_seconds in run_experiment).

MIT