OpenCode Lifeline Plugin

An OpenCode plugin that lets a smaller/local model phone-a-friend (consult a stronger advisor model) when it gets stuck in an optimization or debugging loop. Hybrid stuck detection combines explicit experiment tracking via log_experiment with implicit heuristics (consecutive tool errors, no-progress plateau). Supports nudge mode (suggest advisor call) or ask mode (auto-call advisor).

Features

• Hybrid stuck detection: Explicit tracking via log_experiment tool + implicit heuristics
• Two action modes:
  • nudge (default): Injects a message suggesting the agent call phone_a_friend
  • ask: Automatically calls the advisor and injects the response
• Flexible advisor configuration:
  • OpenCode-native: use any provider/model from your OpenCode registry
  • External API: use any OpenAI-compatible API directly
• Session-scoped state: Tracking resets per session; no persistence across sessions
• Rate limiting: Configurable minimum runs between calls and max calls per session
• Compaction-aware context: Preserves bounded Lifeline state during OpenCode session compaction

Installation

OpenCode plugins are loaded from .opencode/plugins/<name>/ directories (project-level or global).

1. Install OpenCode

# macOS / Linux
brew install opencode-ai

# Or via npm/pnpm/bun
npm install -g opencode-ai

# Verify
opencode --version

2. Create plugin directory

mkdir -p .opencode/plugins/opencode-lifeline

3. Copy plugin files

# Project-level (recommended for per-project config)
cp -r src/* .opencode/plugins/opencode-lifeline/

# Or globally
cp -r src/* ~/.config/opencode/plugins/opencode-lifeline/

For development, use a symlink so changes reload on restart:

ln -s $(pwd)/src ~/.config/opencode/plugins/opencode-lifeline

4. Configure the plugin

Create lifeline.json in one of the following locations (first found wins):

1. .opencode/lifeline.json — project-level
2. ~/.config/opencode/lifeline.json — global (XDG)
3. ~/.opencode/lifeline.json — global (legacy)

{
  "auto": true,
  "action": "nudge",
  "minRunsBetweenCalls": 5,
  "triggerAfterConsecutiveFailures": 3,
  "triggerAfterPlateauRuns": 6,
  "maxCallsPerSession": 10,
  "advisor": {
    "provider": "anthropic",
    "model": "claude-sonnet-4-20250514",
    "maxTokens": 4096,
    "temperature": 0.7
  },
  "implicitDetection": true,
  "includeContext": true
}

See lifeline.json.example for the full schema.

5. Restart OpenCode

OpenCode loads plugins at startup. Restart to pick up the new plugin.

Configuration

Option	Type	Default	Description
auto	boolean	true	Enable automatic stuck detection
action	"nudge" | "ask"	"nudge"	nudge = suggest phone_a_friend; ask = auto-call advisor
minRunsBetweenCalls	number	5	Minimum turns between advisor calls
triggerAfterConsecutiveFailures	number	3	Trigger after N consecutive failures
triggerAfterPlateauRuns	number	6	Trigger after N runs without improvement
maxCallsPerSession	number	10	Max advisor calls per session
advisor.provider	string	—	OpenCode provider ID (e.g., anthropic, openai)
advisor.model	string	—	Model ID (e.g., claude-sonnet-4-20250514)
advisor.maxTokens	number	4096	Max tokens for advisor response
advisor.temperature	number	0.7	Temperature for advisor generation
advisor.apiKey	string	—	API key for external advisor calls
advisor.baseUrl	string	—	Base URL for external advisor (OpenAI-compatible)
implicitDetection	boolean	true	Enable implicit stuck detection
includeContext	boolean	true	Include session context in advisor prompts

Environment variables

Advisor options can be overridden via environment variables:

export LIFELINE_ADVISOR_PROVIDER=openai
export LIFELINE_ADVISOR_MODEL=gpt-5
export LIFELINE_ADVISOR_API_KEY=sk-...
export LIFELINE_ADVISOR_BASE_URL=https://api.openai.com/v1
export LIFELINE_ADVISOR_MAX_TOKENS=4096
export LIFELINE_ADVISOR_TEMPERATURE=0.7

Testing:

export LIFELINE_FAKE_RESPONSE="Test advisor response without spending tokens"

Custom Tools

phone_a_friend

Manually ask the advisor for help:

phone_a_friend
question: "We've tried three parser optimizations and all failed. What should we try next?"
mode: "next_experiment"
context: "Attempts: inline cache (segfault), arena reuse (no improvement), branchless scan (slower)"
max_ideas: 4
provider: "openai"
model: "gpt-5"

Modes:

• ideas — brainstorming
• critique — critique current approach
• debug — debugging help
• next_experiment — what to try next (default)

Overrides:

• provider — override the configured advisor provider for this call
• model — override the configured advisor model for this call

log_experiment

Log an experiment result for explicit tracking:

log_experiment
run: 5
metric: 142.3
status: "discard"
description: "Tried memoizing parser tokens — no improvement"

Status values:

• keep — improvement kept
• discard — no improvement or regression
• crash — caused a crash
• checks_failed — tests/checks failed

Logs are appended to autoresearch.jsonl in the current directory.

How It Works

Explicit Detection

When the agent uses log_experiment to track optimization attempts, the plugin reads autoresearch.jsonl and triggers when:

1. Consecutive failures: N discard/crash/checks_failed in a row
2. Plateau: N runs without a keep

Implicit Detection

Even without log_experiment, the plugin detects stuck patterns by monitoring:

1. Consecutive tool errors: Repeated failures of the same tool
2. No-progress plateau: Many turns without successful file edits/writes
3. Run counter: Incremented on each session.idle event

Implicit detection only activates after a coding-relevant tool (bash, edit, write, grep, glob, read) has been used, preventing false triggers on purely conversational sessions.

Compaction Context

OpenCode can compact long sessions. Lifeline adds a bounded summary to the experimental.session.compacting hook so continuation summaries retain useful stuck-detection context.

The summary includes run counts, advisor call count, the last trigger reason, recent advisor advice, recent autoresearch.jsonl runs, and recent failed tool names. It is truncated before injection to avoid bloating the compaction prompt.

Trigger Action

• nudge mode (default, recommended): Injects a context message suggesting the agent call phone_a_friend. Costs nothing until the agent decides to act.

• ask mode: Automatically calls the advisor model and injects the response. Useful for unattended runs but consumes tokens immediately.

Development

# Type-check
npm run check

# Test with fake response (no tokens spent)
export LIFELINE_FAKE_RESPONSE="Try measuring phase timings before further code changes."

License

MIT