Pi Harness Runtime

⚠️ Beta Notice — Not Production Ready ⚠️ This project is in active development. Core features work but E2E testing is not yet implemented.

Autonomous AI coding harness for pi: local token tracking + provider mirror + task orchestration.

Two Flavors

1. /usage — Token Tracking (stable ✅)

Codex-style usage tracking with local tokens + provider mirror.

2. /harness — Autonomous Coding Loop (beta 🔨)

Give the runtime a requirement. It keeps working, survives interruption, and returns with code ready for review.

Install

pi install npm:pi-harness-runtime

Requires Pi v0.37.3+.

Usage Commands

/usage              # show full status (local tracking + provider mirror)
/usage refresh     # force refresh quota from MiniMax console
/usage today       # today's usage + 5h window
/usage week        # this week's usage + lifetime totals
/usage reset       # clear provider mirror

Harness Commands

/harness start <requirement>   # Start a new harness job (beta)
/harness status               # Show current job status
/harness tasks                # List all tasks
/harness pause                # Pause when quota low
/harness resume               # Resume after quota reset
/harness cancel               # Cancel job

Example Session

/harness start Build a REST API with JWT authentication

# Runtime creates task graph:
# task-001: Analyze requirements → ready
# task-002: Implement API → depends on task-001
# task-003: Write tests → depends on task-002
# task-004: Code review → depends on task-003

/harness status
# Job: job-123, Status: running
# Tasks: 1/4 done, 1 running

# When quota runs low:
/harness pause
# Job paused. Resume when quota resets.

# When quota resets:
/harness resume
# Job resumed. Continue working.

# When all tasks complete:
/harness status
# Job: job-123, Status: ready_for_client
# Tasks: 4/4 done ✓

Architecture

pi-harness-runtime/
├── index.ts                    # Extension entry point
├── harness/
│   ├── job-state-machine.ts   # 14-state lifecycle machine
│   ├── task-graph.ts           # DAG-based task management
│   ├── master-planner.ts       # Requirement → task graph
│   ├── loop-runtime.ts         # Core execution loop
│   ├── repair-engine.ts        # Auto-fix failures
│   ├── blackboard.ts           # Agent coordination
│   ├── context-window-manager.ts # Context tracking
│   ├── agent-handoff.ts        # Clean agent transitions
│   ├── e2e/
│   │   ├── test-engine.ts     # E2E test runner (coming soon)
│   │   └── playwright-runner.ts # Browser automation
│   └── project-detector/
│       └── detector.ts         # Auto-detect project type
├── packages/
│   ├── providers/adapters.ts   # MiniMax, OpenAI adapters
│   ├── quota-manager/          # Quota signal collection
│   └── worktree/              # Git worktree per task
└── skills/
    └── harness-runtime/
        └── SKILL.md           # Skill documentation

Job State Machine

created → planning → queued → running → testing → reviewing
    ↓        ↓         ↓        ↓         ↓
cancelled  blocked   waiting_human  repairing  ready_for_client
                                        ↓            ↓
                                  paused_quota    archived

Key Features

• Resumable: Every state change is checkpointed to disk
• Quota-aware: Detects quota exhaustion, pauses, resumes after reset
• Provider-agnostic: MiniMax, OpenAI, Claude adapters
• Task DAG: Dependencies tracked, topological execution
• Auto-repair: Failure classification + retry with exponential backoff
• E2E testing: Scenario-based Playwright integration (coming soon)
• Project detection: Auto-detects Frappe, Next.js, React, Django, Laravel

Data Directory

All data stored locally in ~/.pi/:

~/.pi/
├── usage-status/         # /usage data
│   ├── usage.jsonl
│   └── mirror.json
├── harness/            # /harness data
│   └── jobs/
│       └── <job-id>/
│           ├── checkpoint.json
│           ├── events.jsonl
│           ├── task-graph.json
│           ├── blackboard/
│           └── repair-tasks.jsonl
└── okf/               # (optional) Your custom OKF knowledge

Custom OKF Knowledge (Optional)

Create ~/.pi/okf/ to add your custom knowledge that the runtime reads during context compilation:

~/.pi/okf/
├── <skill-name>.md      # Your custom skill/procedure
├── <domain>.md          # Domain-specific knowledge
└── <project>.md        # Project-specific rules

The runtime automatically detects and reads these files. If the folder doesn't exist, compilation proceeds without them.

Example:

# my-custom-skill

## When to use

This skill applies when...

## Procedure

1. Step one
2. Step two

See packages/context-compiler/ for how OKF concepts are loaded.

Safety Properties

• ✅ Local-first: All data stays on disk
• ✅ No credentials stored: No passwords, tokens, or cookies
• ✅ Human-on-the-loop: Clear intervention points
• ✅ Checkpointed: Resume from any state
• ✅ Idempotent: Safe to run multiple times

Testing

bun test   # 131+ tests passing

Automatic Quota Fetching

Quota data is automatically fetched from MiniMax console every 5 minutes:

1. Setup cookies (one-time):

   # Install EditThisCookie Chrome extension
   # Go to platform.minimax.io
   # Export cookies → Netscape format
   # Save to ~/.config/minimax-cookies.txt
   

2. Environment (optional):

   export QUOTA_COOKIE_FILE=~/.config/minimax-cookies.txt
   

3. Done! Quota auto-refreshes every 5 minutes

Roadmap

• Core harness infrastructure (job state machine, task graph)
• Master planner
• Repair engine
• Shared blackboard
• MiniMax Web automatic quota detection
• E2E test engine integration
• Claude adapter
• Production stress testing

License

MIT © 2026 MooCoding

Related

• pi-coding-agent — Underlying pi agent
• context-mode — Context window tracking
• pi-web-access — Web search for pi

Day 6 adds runtime recovery and remote-observability architecture.

RFCs

• RFC-0019 Auto Compact and Continue
• RFC-0020 Output Token Limit Handler
• RFC-0021 Partial Response Recovery
• RFC-0022 Notification Center
• RFC-0023 Local Browser Agent

Why this matters

These RFCs address real runtime failures:

• MiniMax auto-compacts but does not automatically resume.
• A model may stop because max output token limit is reached.
• Partial responses must be preserved and continued.
• The human needs mobile/tablet notifications.
• Headless servers cannot open interactive login windows for MiniMax usage console.