mochi-quest
Enables AI agents to act as personal growth coaches, allowing users to set goals, receive personalized daily tasks, and track progress with dynamic replanning and reward systems.
README
Mochi Quest 🍡
An open-source, AI-powered personal growth coaching system.
Mochi Quest lets you describe your goals — lose weight, learn English, become a Googler — and an AI coach builds a personalized plan, assigns daily tasks, tracks your progress, and dynamically adjusts when things get too hard or too easy.
Agent-agnostic: works with Claude, GPT, Gemini, or any MCP-capable AI agent.
Features
- Goal clarification — AI interviews you to understand your situation, constraints, and current level before building a plan
- Two-layer task system — AI pre-generates a task pool; daily allocation runs instantly from the DB (no LLM latency)
- Dynamic replan — triggers automatically when skip rate is high, you're breezing through optional tasks, or your task pool runs low
- Multi-goal balance — set a weight per goal; daily tasks are allocated proportionally within your daily limit
- Coin + reward system — earn coins from tasks, redeem for self-defined rewards; AI adjusts pricing if a reward conflicts with your goals
- Streak tracking — per-goal streaks + global streak (all goals done = global +1); milestone bonuses at 7/30/100/365 days
- Web dashboard — local UI for checking off tasks, viewing plan roadmap, wallet, and streaks
- Real-time updates — SSE pushes replan completion to the UI instantly
- Background daemon —
node-crondaily check + cross-platform notifications (macOS / Windows / Linux) even when you haven't opened the app
Architecture
┌──────────────────────────────────────────┐
│ AI Agent Layer │
│ Claude / GPT / Gemini / any MCP agent │
│ ┌──────────────────────────────────┐ │
│ │ SKILL.md │ │
│ │ coaching behavior & decisions │ │
│ └──────────────────────────────────┘ │
└──────────────────┬───────────────────────┘
│ MCP (stdio)
┌──────────────────▼───────────────────────┐
│ MCP Server (Node.js) │
│ Goals · Plans · Tasks · Wallet · Streaks│
│ ┌─────────────────────────────────────┐ │
│ │ SQLite (~/.mochi-quest/data.db) │ │
│ └─────────────────────────────────────┘ │
│ REST API :3030 ←──── Web UI (React) │
│ node-cron + node-notifier (daemon) │
└──────────────────────────────────────────┘
One command (mochi-quest start) runs the MCP server, REST API, and scheduler together.
Quick Start
Prerequisites
- Node.js 20+
- pnpm 9+
- An MCP-capable AI agent (Claude Code, Cursor, etc.)
Install
git clone https://github.com/YOUR_USERNAME/mochi-quest.git
cd mochi-quest
pnpm install
Build
# Build server
cd packages/server && pnpm build
# Build web UI
cd packages/web && pnpm build
Run
# Start everything (MCP + REST API + scheduler + built Web UI)
node packages/server/dist/index.js start
# Or as a background daemon
node packages/server/dist/index.js start --daemon
The web dashboard is available at http://localhost:3030.
Docker
docker compose up -d --build
The Docker server stores SQLite data in the mochi_quest_data volume and serves the built Web UI, REST API, scheduler, and MCP entrypoint from one container.
Full deployment notes: docs/deployment.md.
Connect to your AI agent
Add the MCP server to your agent's config:
Claude Code (~/.claude/settings.json or project .mcp.json):
{
"mcpServers": {
"mochi-quest": {
"command": "node",
"args": ["/path/to/mochi-quest/packages/server/dist/index.js", "mcp"]
}
}
}
Then add packages/skill/SKILL.md as a skill (or paste it into your system prompt).
MCP Tools
| Tool | Description |
|---|---|
mq_get_dashboard |
Full overview: goals, today's tasks, wallet, streaks, replan status |
mq_list_goals / mq_create_goal / mq_update_goal |
Goal management |
mq_get_plan / mq_generate_plan / mq_adjust_plan |
Plan management |
mq_get_today_tasks / mq_get_optional_tasks |
Fetch tasks |
mq_complete_task / mq_skip_task |
Report task status |
mq_get_wallet / mq_list_rewards / mq_redeem_reward |
Coin & reward system |
mq_add_assessment / mq_get_user_state |
Track progress assessments |
mq_get_streak / mq_get_streak_milestones |
Streak info |
mq_get_replan_status |
Check if AI action is needed |
mq_get_settings / mq_update_settings |
Global settings |
Full tool reference: packages/skill/SKILL.md
Project Structure
mochi-quest/
├── packages/
│ ├── server/ # MCP Server + REST API (Node.js + TypeScript)
│ │ └── src/
│ │ ├── db/ # SQLite schema & queries
│ │ ├── mcp/ # MCP tool implementations
│ │ ├── api/ # REST API routes (Hono)
│ │ └── scheduler.ts # node-cron daily check + notifications
│ ├── web/ # Web dashboard (React + Vite + Tailwind)
│ │ └── src/
│ │ ├── pages/ # Dashboard, Goals, Tasks, Wallet, Settings
│ │ ├── components/
│ │ ├── hooks/ # useSSE for real-time updates
│ │ └── lib/ # API client + types
│ └── skill/
│ └── SKILL.md # AI coaching behavior definition
└── docs/
└── spec.md # Full system specification
How It Works
Planning vs Execution
The AI generates a task template pool (7–14 days of tasks) during planning sessions. The server allocates daily tasks from this pool using a rule engine — no LLM call needed, so the UI loads instantly.
Event-driven Replan
The server monitors four signals and marks replan_pending = true when triggered:
| Signal | Threshold |
|---|---|
| Skip rate | >50% over last 3 days |
| Optional completion rate | >80% over last 3 days (too easy) |
| Task pool size | <3 days remaining |
| Assessment change | New assessment recorded |
The AI checks mq_get_replan_status() at the start of each conversation and regenerates the plan if needed.
Multi-goal Task Allocation
Each goal has a daily_task_weight (1–5). Tasks are allocated proportionally:
weights = [3, 2, 1] → budget = 6 → tasks = [3, 2, 1]
Adjust weights any time: "Focus more on English this week."
Data Storage
All data is stored locally in ~/.mochi-quest/data.db (SQLite). No cloud sync, no accounts.
Notifications (Daemon Mode)
node packages/server/dist/index.js start --daemon
The built-in scheduler runs a daily check at the configured notification time (default: 08:00) and sends a native OS notification when there are pending tasks.
- macOS: Notification Center
- Windows: Toast Notification
- Linux: libnotify (
notify-send)
Roadmap
- [ ] Integration adapters (Fitbit, Garmin, Duolingo, LeetCode)
- [ ] Habitica sync (push tasks to Habitica, webhook completion back)
- [ ] Server-driven replan (server calls LLM directly in daemon mode)
- [ ] Apple Health companion app
- [ ] Auto-start installer (
mochi-quest setup)
Contributing
Pull requests welcome. Please open an issue first to discuss larger changes.
License
MIT
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