pi-goal

Autonomous goal pursuit with sub-agent workers for pi. Break work into tasks, spawn isolated workers, track progress via files on disk.

Install

pi install npm:@ogulcancelik/pi-goal

Or add manually to ~/.pi/agent/settings.json:

{
  "packages": ["npm:@ogulcancelik/pi-goal"]
}

What it does

Turns multi-step work into structured goals with task breakdowns. You discuss a goal with the agent, it creates task documents with specific file paths and acceptance criteria, then spawns fresh worker agents to implement each task independently. Progress lives on disk — survives compaction, restarts, and context loss.

How it works

1. You and the agent discuss a goal
2. Agent creates structured tasks with goal add_task — rich markdown docs with file paths, constraints, and acceptance criteria
3. Agent runs workers with goal run — each gets a fresh pi subprocess, reads only its task doc, implements immediately
4. Results and learnings accumulate on disk in the goal directory
5. Agent stays grounded via system prompt injection — active goal state is re-injected every turn, even after compaction

File structure

.pi/goals/
├── ACTIVE                          # slug of active goal
└── <goal-slug>/
    ├── GOAL.md                     # goal description
    ├── STATE.json                  # machine-readable state (tasks, status)
    ├── LEARNINGS.md                # cross-task knowledge (auto-appended by workers)
    ├── tasks/
    │   ├── 01-<task-name>.md       # task spec (what workers read)
    │   └── 02-<task-name>.md
    ├── results/
    │   ├── 01.md                   # worker output
    │   └── 02.md
    └── sessions/                   # worker session logs (for observability)

Actions

Action	Description
create	Create a new goal with name, description, optional workerModel
add_task	Add a task with a name and full markdown spec
run	Execute all pending tasks sequentially with isolated worker agents
status	Check current goal progress, task states, and learnings

Worker behavior

• Fresh pi subprocess per task — no shared context between workers
• Tools available: read, edit, write, grep, find, ls (no bash)
• Workers read their task doc first, then implement immediately
• Learnings from each worker are auto-extracted and appended to LEARNINGS.md
• Worker sessions are saved in the goal directory for observability

Configuration

Pass workerModel when creating a goal to control which model runs workers. Defaults to the facilitator's current model.

goal create —  name: "my goal", workerModel: "anthropic/claude-sonnet-4-20250514"

This is v0.1 — more configuration options coming.

Requirements

• pi v0.40+

License

MIT