Try an idea, measure it, keep what works, discard what doesn't, repeat forever.
An extension for pi — an AI coding agent that runs in your terminal. pi-autoresearch gives pi the tools and workflow to run autonomous optimization loops: try an idea, benchmark it, keep improvements, revert regressions, repeat.
Inspired by karpathy/autoresearch. Works for any optimization target: test speed, bundle size, LLM training, build times, Lighthouse scores.
Quick start
pi install npm:pi-autoresearch
pi
Or load the package for one session only with pi -e npm:pi-autoresearch.
Then start the loop inside pi:
/autoresearch optimize unit test runtime, monitor correctness
What's included
|
|
| Extension |
Tools + live widget + /autoresearch dashboard |
| Skill |
Gathers what to optimize, writes session files, starts the loop |
| Tool |
Description |
init_experiment |
One-time session config — name, metric, unit, direction |
run_experiment |
Runs any command, times wall-clock duration, captures output |
log_experiment |
Records result, auto-commits, updates widget and dashboard |
/autoresearch command
| Subcommand |
Description |
/autoresearch |
Show help without activating autoresearch mode. |
/autoresearch <text> |
Enter autoresearch mode. If .auto/prompt.md exists, resumes the loop with <text> as context. Otherwise, sets up a new session. |
/autoresearch off |
Leave autoresearch mode. Stops auto-resume and clears runtime state but keeps .auto/log.jsonl intact. |
/autoresearch clear |
Delete .auto/log.jsonl, reset all state, and turn autoresearch mode off. Use this for a clean start. |
/autoresearch export |
Open a live dashboard in your browser. Auto-updates as experiments run. |
/autoresearch dashboard |
Open the fullscreen scrollable dashboard overlay in the terminal. Navigate with ↑/↓/j/k, PageUp/PageDown/u/d, g/G for top/bottom, Escape or q to close. |
Examples:
/autoresearch optimize unit test runtime, monitor correctness
/autoresearch model training, run 5 minutes of train.py and note the loss ratio as optimization target
/autoresearch export
/autoresearch dashboard
/autoresearch off
/autoresearch clear
Keyboard shortcuts (opt-in)
No shortcuts are bound by default — pi's built-in keymap grows with every release,
so any default chord eventually collides with it (this happened with ctrl+shift+f
vs. pi's transcript search). Every action is available as a /autoresearch
subcommand instead.
To opt in, add chords to <agent-dir>/extensions/pi-autoresearch.json
(<agent-dir> is usually ~/.pi/agent, or PI_CODING_AGENT_DIR when set).
Omitted or null keys stay unbound:
{
"shortcuts": {
"fullscreenDashboard": "ctrl+shift+y",
"export": "alt+shift+e",
"off": null
}
}
Each key binds its /autoresearch subcommand: fullscreenDashboard → dashboard,
export → export, off → off.
Pick a chord that's free in pi's built-in keybindings
and your <agent-dir>/keybindings.json — extension shortcuts win conflicts, so a
clashing chord hijacks the built-in action.
Picking a free chord (guidance for agents)
Don't guess a chord — check it against the installed pi's built-in keymap first:
PI_ROOT=$(ls -d ~/.pi/pkg/pi-* 2>/dev/null | sort -V | tail -1)
[ -n "$PI_ROOT" ] || PI_ROOT="$(npm root -g)/@earendil-works/pi-coding-agent"
node --input-type=module -e '
const { KEYBINDINGS } = await import(process.argv[1] + "/dist/core/keybindings.js");
const taken = new Set(Object.values(KEYBINDINGS).flatMap(({ defaultKeys }) => defaultKeys).map((key) => key.toLowerCase()));
console.log(taken.has(process.argv[2].toLowerCase()) ? "TAKEN" : "FREE");
' "$PI_ROOT" ctrl+shift+y
Chords in <agent-dir>/keybindings.json values are also taken. Write a
verified-free chord into <agent-dir>/extensions/pi-autoresearch.json, then
/reload pi and confirm no Extension shortcut conflict warning appears.
ctrl+shift+y, ctrl+shift+u, alt+shift+f, and ctrl+alt+d are free as of
pi 0.84.x. Always re-verify — pi's keymap grows over time, which is why this
extension binds nothing by default.
UI
- Dashboard widget — always visible above the editor: a full results table with columns for commit, metric, status, and description.
- Confidence score — after 3+ runs, shows how the best improvement compares to the session noise floor. ≥2.0× (green) = likely real, 1.0–2.0× (yellow) = above noise but marginal, <1.0× (red) = within noise.
- Fullscreen overlay —
/autoresearch dashboard opens a scrollable full-terminal dashboard. Shows a live spinner with elapsed time for running experiments.
Skills
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:
autoresearch-finalize turns a noisy autoresearch branch into clean, independent branches — one per logical change, each starting from the merge-base. Groups must not share files, so each branch can be reviewed and merged independently.
autoresearch-hooks (optional) helps author .auto/hooks/before.sh and .auto/hooks/after.sh for a session. It ships with ten reference scripts in skills/autoresearch-hooks/examples/ (external search, learnings journal, native notifications, anti-thrash, idea rotation, and more) — the skill handles the contract, you pick the inspiration. The core autoresearch loop has no hook awareness.
All session files live in a single .auto/ subfolder at the working-directory root — one folder to preserve across reverts, gitignore, and clean up. (Legacy flat autoresearch.* files are still read for in-flight sessions.)
| File |
Purpose |
.auto/prompt.md |
Session document — objective, metrics, files in scope, what's been tried. A fresh agent can resume from this alone. |
.auto/measure.sh |
Benchmark script — pre-checks, runs the workload, outputs METRIC name=number lines. |
.auto/log.jsonl |
Append-only log of every run (written by the tools). |
.auto/checks.sh |
(optional) Backpressure checks — tests, types, lint. Runs after each passing benchmark. Failures block keep. |
.auto/hooks/ |
(optional) Executable scripts (before.sh, after.sh) that fire around iterations. Stdout is delivered to the agent as a steer message. |
Install
pi install npm:pi-autoresearch
Manual install
cp -r extensions/pi-autoresearch ~/.pi/agent/extensions/
cp -r skills/autoresearch-create ~/.pi/agent/skills/
Then /reload in pi.
Usage
1. Start autoresearch
/autoresearch optimize unit test runtime, monitor correctness
This activates autoresearch mode and makes the experiment tools available. If .auto/prompt.md does not exist, it also loads the autoresearch-create skill. Calling /skill:autoresearch-create directly does not activate the mode, so the tools remain unavailable in a fresh session.
The agent asks about your goal, command, metric, and files in scope — or infers them from context. It then creates a branch, writes .auto/prompt.md and .auto/measure.sh, runs the baseline, and starts looping immediately.
2. The loop
The agent runs autonomously: edit → commit → run_experiment → log_experiment → keep or revert → repeat. It never stops unless interrupted.
Every result is appended to .auto/log.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 —
.auto/prompt.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
3. Finalize into reviewable branches
/skill:autoresearch-finalize
The agent reads .auto/log.jsonl, groups kept experiments into logical changesets, proposes the grouping for your approval, then creates independent branches from the merge-base. Each commit includes metric improvements in the message. Groups must not share files, so branches can be reviewed and merged independently.
4. Monitor progress
- Widget — full results table, always visible above the editor
/autoresearch dashboard — fullscreen scrollable dashboard overlay (optional chord: shortcuts.fullscreenDashboard)/autoresearch export — open a live browser dashboard with chart and share cardEscape — interrupt anytime and ask for a summary
Example domains
| Domain |
Metric |
Command |
| Test speed |
seconds ↓ |
pnpm test |
| Bundle size |
KB ↓ |
pnpm build && du -sb dist |
| LLM training |
val_bpb ↓ |
uv run train.py |
| Build speed |
seconds ↓ |
pnpm build |
| Lighthouse |
perf score ↑ |
lighthouse http://localhost:3000 --output=json |
How it works
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:
.auto/log.jsonl — append-only log of every run (metric, status, commit, description)
.auto/prompt.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.
Configuration (optional)
Create .auto/config.json in your pi session directory to customize behavior:
{
"workingDir": "/path/to/project",
"maxIterations": 50
}
| Field |
Type |
Description |
workingDir |
string |
Override the directory for all autoresearch operations — file I/O, command execution, and git. Supports absolute or relative paths (resolved against the pi session cwd). The config file itself always stays under the session cwd. Fails if the directory doesn't exist. |
maxIterations |
number |
Maximum experiments before auto-stopping. The agent is told to stop and won't run more experiments until a new segment is initialized. |
Long-running loops and context
The loop is designed to run unattended across context limits. When pi's auto-compaction summarizes the older portion of the conversation, autoresearch detects the resulting idle and re-prompts the agent to re-read .auto/prompt.md, the tail of .auto/log.jsonl, .auto/ideas.md, and git log before continuing. All progress is persisted in those files, so the post-summary turn rehydrates from the source of truth instead of relying on whatever survived compaction. No tuning required — if pi's auto-compaction is enabled (the default), this just works.
Confidence scoring
After 3+ experiments in a session, pi-autoresearch computes a confidence score — how the best improvement compares to the session's noise floor. This helps distinguish real gains from benchmark jitter, especially on noisy signals like ML training, Lighthouse scores, or flaky benchmarks.
How it works:
- Uses Median Absolute Deviation (MAD) of all metric values in the current segment as a robust noise estimator.
- Confidence =
|best_improvement| / MAD. A score of 2.0× means the best improvement is twice the noise floor.
- Shown in the widget, fullscreen dashboard, and
log_experiment output.
- Persisted to
.auto/log.jsonl on each result for post-hoc analysis.
- Advisory only — never auto-discards. The agent is guided to re-run experiments when confidence is low, but the final keep/discard decision stays with the agent.
| Confidence |
Color |
Meaning |
| ≥ 2.0× |
🟢 green |
Improvement is likely real |
| 1.0–2.0× |
🟡 yellow |
Above noise but marginal |
| < 1.0× |
🔴 red |
Within noise — consider re-running to confirm |
Backpressure checks (optional)
Create .auto/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).
Hooks (optional)
Drop executable scripts in .auto/hooks/ to run code at iteration boundaries. Hooks are transparent to the agent — the agent calls tools and sees results; hooks run alongside without any agent-facing surface.
.auto/hooks/before.sh — fires before every iteration (at /autoresearch activation and at the end of every log_experiment, after after.sh). Use for prospective work: fetch research, prime context for the next attempt..auto/hooks/after.sh — fires at the end of every log_experiment. Use for retrospective work: annotate learnings, send notifications.
Contract:
- Must be executable (
chmod +x). Preserved on revert — the entire .auto/ folder survives (as do legacy autoresearch.* artefacts).
- Stdin — a JSON object on a single line. Shape depends on the stage (see below). Extract fields with
jq.
- Stdout is delivered to the agent as a steer message (capped at 8 KB). Empty stdout = silent.
- Non-zero exit or >30s timeout surfaces an error steer to the agent.
- Each fire appends a
{"type":"hook",…} entry to .auto/log.jsonl for observability.
before.sh stdin (on fresh activation last_run is null):
{
"event": "before",
"cwd": "/path/to/workdir",
"next_run": 6,
"last_run": {
"run": 5, "status": "discard", "metric": 42.1,
"description": "…",
"asi": { "hypothesis": "…", "next_focus": "…" }
},
"session": {
"metric_name": "total_ms", "metric_unit": "ms", "direction": "lower",
"baseline_metric": 40.7, "best_metric": 33.5,
"run_count": 5, "goal": "optimize sort speed"
}
}
after.sh stdin:
{
"event": "after",
"cwd": "/path/to/workdir",
"run_entry": {
"run": 6, "status": "discard", "metric": 38.9,
"description": "…",
"asi": { "hypothesis": "…", "learned": "…" }
},
"session": { "metric_name": "total_ms", "direction": "lower", "baseline_metric": 40.7, "best_metric": 33.5, "run_count": 6, "goal": "…" }
}
Agent signal. The agent writes description and asi.* fields in its log_experiment calls for its own future-self reasoning. The hook opportunistically mines whichever fields the agent naturally uses — asi.hypothesis, asi.next_focus, description, etc. There is no dedicated "hook input" field; the agent is unaware the hook exists.
Examples. Reference scripts for both stages live at skills/autoresearch-hooks/examples/ — external search, qmd document search, persistent learnings, native notifications, git tagging, anti-thrash, idea rotator, hypothesis reflection, context rotation. Copy one to your session's .auto/hooks/ directory, adapt, chmod +x.
Prerequisites
- Install pi — follow the instructions at pi.dev
- An API key for your preferred LLM provider (configured in pi)
Security and trust
Pi packages run with your full user permissions. Review this repository before installing it, and run autoresearch in a dedicated branch or worktree with a clean working tree.
Autoresearch intentionally edits files, creates and reverts commits, and executes the commands in .auto/measure.sh, .auto/checks.sh, and .auto/hooks/. Treat those files as executable code: review them before each session, keep credentials and sensitive files out of scope, and use a sandbox or restricted environment for untrusted projects.
For reproducible installs, pin a version you have reviewed, for example pi install npm:[email protected]. Published npm releases include provenance attestations.
Controlling costs
Autoresearch loops run autonomously and can burn through tokens. Two ways to cap spend:
- API key limits — most providers let you set per-key or monthly budgets. Check your provider's dashboard.
maxIterations — cap experiments per session in .auto/config.json:{
"maxIterations": 30
}
License
MIT
pi-autoresearch is an extension for pi, a terminal-based AI coding agent, that runs autonomous optimization loops: it tries a code change, benchmarks it, keeps the change if it's an improvement, reverts it if it isn't, and repeats indefinitely.