Agentic Study Planner

A memory-driven, multi-agent study planning system that converts long-term goals into daily tasks, syncs with Google Calendar, and autonomously adapts based on your behavior.

Features

• 🎯 Goal Structuring: Convert study goals into actionable plans
• 📅 Daily Planning: Automatic generation of phase-appropriate tasks
• 📆 Calendar Sync: Auto-add tasks to Google Calendar with reminders
• 📊 Progress Tracking: Log completions and collect feedback
• 🔄 Adaptive Replanning: Autonomously adjust plans based on performance

Architecture

3-Layer design following AGENT.md:

• Layer 1 (Directives): SOPs in directives/ define agent workflows
• Layer 2 (Orchestration): main.py routes to appropriate agents
• Layer 3 (Execution): Python scripts in execution/ handle deterministic operations

Setup

1. Install dependencies:

   pip install -r execution/requirements.txt

2. Configure Google Calendar API:

   • Go to Google Cloud Console
   • Enable Google Calendar API
   • Download OAuth credentials as credentials.json
   • Place in project root

3. Configure environment:

   • Update .env with your timezone (default: Asia/Kolkata)

Usage

Run the main orchestrator:

python main.py

Workflow

1. Create Goal - Define study domain, target, duration
2. Generate Plans - System creates daily tasks and syncs to calendar
3. Log Progress - Track what you completed and why you didn't complete tasks
4. Auto-Adapt - System adjusts future plans based on your patterns

Memory Structure

All data stored in .tmp/memory/:

• goals/ - Structured goal definitions
• plans/ - Daily task plans with calendar event IDs
• progress/ - Completion logs and feedback
• replanning/ - Adaptation decisions

Multi-Agent System

1. Goal Agent

• Directive: directives/goal_agent.md
• Script: execution/goal_structurer.py
• Purpose: Structure raw goals into JSON format

2. Planning Agent

• Directive: directives/planning_agent.md
• Script: execution/task_planner.py
• Purpose: Generate daily tasks and sync to calendar

3. Progress Agent

• Directive: directives/progress_agent.md
• Script: execution/progress_tracker.py
• Purpose: Track completion and identify patterns

4. Replanning Agent

• Directive: directives/replanning_agent.md
• Script: execution/replanner.py
• Purpose: Adapt future plans based on behavior

Example

🎯 Goal: Learn Python in 30 days, 2 hrs/day
📅 Days 1-10: Foundation phase (concepts + exercises)
📅 Days 11-21: Building phase (projects + advanced topics)
📅 Days 22-30: Mastery phase (final projects + documentation)

📊 After 5 days: 40% completion detected
🔄 System adapts: Reduces task complexity for next 7 days