A Pi extension that lets a smaller/local model ask a stronger advisor model when an autonomous optimization loop gets stuck.

Inspired by Tobi Lütke's observation that local models can run pi-autoresearch effectively when they occasionally ask a stronger model for ideas.

pi-lifeline adds:

• Tool: phone_a_friend — ask a configured stronger model for strategy, critique, debugging help, or next experiment ideas.
• Command: /lifeline — inspect status, thresholds, and advisor config.
• Autoresearch trigger detection: watches log_experiment results and detects repeated failures or plateaus.
• Rate limiting: avoids calling the expensive model every iteration.

The larger model is a strategy reset mechanism, not the inner loop.

small/local model: edit → run_experiment → log_experiment → repeat
                                  │
                                  │ only when stuck/plateaued
                                  ▼
                     phone_a_friend → stronger advisor model

Calling the larger model every run defeats the point. The default policy only triggers after evidence that the local model is stuck:

{
  "auto": true,
  "action": "nudge",
  "minRunsBetweenCalls": 5,
  "triggerAfterConsecutiveFailures": 3,
  "triggerAfterPlateauRuns": 6,
  "maxCallsPerSession": 10
}

Default behavior is nudge, not hidden spending: when stuck, the extension sends a steer message telling the agent to call phone_a_friend. If you want fully automatic advisor calls, set "action": "ask".

Install from npm with Pi:

pi install npm:pi-lifeline

Then reload Pi:

/reload

Verify it loaded:

/lifeline

Create a global starter config:

/lifeline init

Then edit ~/.pi/agent/pi-lifeline.json to adjust your advisor provider/model or thresholds.

From this repo:

pi -e ./extensions/pi-lifeline/index.ts

As a Pi package, package.json exposes:

{
  "pi": {
    "extensions": ["./extensions/pi-lifeline"]
  }
}

Create pi-lifeline.json in your Pi agent config directory, usually ~/.pi/agent/pi-lifeline.json:

{
  "auto": true,
  "action": "nudge",
  "minRunsBetweenCalls": 5,
  "triggerAfterConsecutiveFailures": 3,
  "triggerAfterPlateauRuns": 6,
  "maxCallsPerSession": 10,
  "advisor": {
    "provider": "openai",
    "model": "gpt-5.5",
    "thinking": "high",
    "maxTokens": 4096,
    "temperature": 0.7
  },
  "includeAutoresearchContext": true
}

You can also set:

export PI_LIFELINE_ADVISOR_PROVIDER=openai
export PI_LIFELINE_ADVISOR_MODEL=gpt-5.5
export PI_LIFELINE_THINKING=high
export PI_LIFELINE_MAX_TOKENS=4096
export PI_LIFELINE_TEMPERATURE=0.7

For tests/smoke runs without spending tokens:

export PI_LIFELINE_FAKE_RESPONSE="Try profiling phase timings and attack the largest non-noisy bucket."

Inputs:

• question — specific question for the advisor.
• context — optional logs, metrics, code summary, failed ideas.
• mode — one of:
  • ideas
  • critique
  • debug
  • next_experiment
• max_ideas — default 5.
• provider / model — optional per-call override.

Example use:

{
  "question": "We have three discarded parser optimization attempts. What should we try next?",
  "mode": "next_experiment",
  "context": "Recent runs: #2 discard inline cache, #3 crash arena reuse, #4 discard branchless scan",
  "max_ideas": 4
}

The advisor prompt explicitly asks for strategic, testable advice — not full patches — and warns against benchmark cheating.

/lifeline

Shows:

• active config source
• advisor provider/model
• thresholds
• calls this session
• autoresearch run count
• current trigger decision

/lifeline init

Opens a small setup wizard, asks for the advisor model, thinking/reasoning level, and auto-action policy, then creates global ~/.pi/agent/pi-lifeline.json without overwriting an existing file.

/lifeline sample-config

Prints the starter config without writing a file.

When pi-autoresearch is active, pi-lifeline reads autoresearch.jsonl and watches log_experiment results.

It triggers when either:

1. trailing failures reach triggerAfterConsecutiveFailures
   • statuses: discard, crash, checks_failed
2. no kept improvement has happened for triggerAfterPlateauRuns

It respects:

• minRunsBetweenCalls
• maxCallsPerSession
• auto: false

With includeAutoresearchContext: true, the tool includes recent autoresearch.jsonl runs in the advisor prompt.

npm run check

This verifies the extension and policy modules parse under Node's TypeScript stripping.

npm test

Tests cover:

• default config normalization
• consecutive failure detection
• keep resetting failure streak
• plateau detection
• higher and lower metric directions
• minRunsBetweenCalls
• maxCallsPerSession
• auto: false

export PI_LIFELINE_FAKE_RESPONSE="Try measuring phase timings before further code changes."
pi -e ./extensions/pi-lifeline/index.ts

Then ask the agent to call phone_a_friend. Expected: the tool returns the fake response and records a session call without requiring real model auth.

Create autoresearch.jsonl:

{"type":"config","name":"test","metricName":"score","metricUnit":"","bestDirection":"lower"}
{"run":1,"metric":100,"status":"keep","description":"baseline","timestamp":1}
{"run":2,"metric":101,"status":"discard","description":"bad 1","timestamp":2}
{"run":3,"metric":102,"status":"discard","description":"bad 2","timestamp":3}
{"run":4,"metric":103,"status":"discard","description":"bad 3","timestamp":4}

Start Pi with the extension and run /lifeline. Expected: current decision says trigger due to 3 consecutive failures.

Configure a cheap available model first:

{
  "auto": true,
  "action": "ask",
  "minRunsBetweenCalls": 0,
  "triggerAfterConsecutiveFailures": 1,
  "triggerAfterPlateauRuns": 99,
  "maxCallsPerSession": 1,
  "advisor": {
    "provider": "google",
    "model": "gemini-2.5-flash",
    "maxTokens": 1024,
    "temperature": 0.3
  }
}

Expected:

• advisor auth resolves via Pi model registry
• main model is not changed
• advisor returns a concise strategy message
• no code is modified by the advisor directly

• The small model remains responsible for edits and experiments.
• The strong model is used only for strategic advice.
• Defaults are intentionally conservative to avoid token waste.
• action: "nudge" makes cost explicit; action: "ask" is available for trusted unattended runs.