TODO APP

Full-stack task management application with CLI, REST API, and AI-powered chatbot interface.

Projects

1. TODO CLI - In-memory Python console application with CRUD operations
2. TODO API - FastAPI REST backend with PostgreSQL database
3. Chatbot Backend - AI agent with conversational task management

Phase I: Basic CRUD - ✅ 100% COMPLETE

Status: Production-ready | Tasks: 88/88 (100%) | Constitution: v1.1.0

Completed Features

• ✅ Add todo item (sequential ID assignment)
• ✅ Delete todo item (ID reuse prevention)
• ✅ Update todo item (title and description)
• ✅ View all todo items (status symbols ✓/○)
• ✅ Mark todo item as complete (toggle operation)

Quality Verification

• ✅ 100% PEP 8 compliance (ruff check: 0 errors)
• ✅ 100% type hint coverage (mypy --strict: 0 errors)
• ✅ 100% docstring coverage (Google-style)
• ✅ All manual test scenarios passed (21/21)
• ✅ Performance benchmarks exceeded (5000x faster than requirements)

Agent Skills Extracted (Constitution v1.1.0 - Principle VI)

1. ID Architect - Sequential ID generation with immutable counter
2. UX Logic Anchor - Standardized visual feedback patterns
3. Error Handler - Centralized exception handling with graceful recovery

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Chatbot Backend - ✅ COMPLETE

Status: Production-ready | Phases: 5/6 (83%) | Branch: 006-chatbot-agent-backend

Features

✅ Phase 1-3: Foundation & Basic Chat

• OpenAI Agent SDK with Gemini 2.0 Flash model
• ChatKit protocol server implementation
• JWT authentication with Better Auth
• Streaming responses via Server-Sent Events (SSE)
• Request context with user isolation

✅ Phase 4: MCP Integration

• Model Context Protocol (MCP) client
• 5 task management tools: list_tasks, add_task, complete_task, update_task, delete_task
• Token forwarding for authenticated MCP requests
• Cross-user isolation enforcement

✅ Phase 5: Database Persistence

• NeonPostgresStore implementation (17 methods)
• PostgreSQL database with Neon hosting
• Chat threads and messages persistence
• Alembic migrations for schema management
• 20-message conversation history context

Architecture

Frontend (ChatKit React)
    ↓ HTTP POST /api/chatkit
Chatbot Backend (FastAPI)
    ↓ JWT Auth
ChatKitServer
    ↓ Load History (DB)
OpenAI Agent (Gemini 2.0)
    ↓ MCP Tools
TODO API (FastAPI)
    ↓ Database Query
PostgreSQL (Neon)

Key Implementation Details

Stateful Conversations:

• Agent instance: Stateless (created per request)
• Conversation: Stateful (persisted to database)
• History loading: Last 20 messages from chat_messages table
• User isolation: Query-level filtering by user_id

Database Schema:

chat_threads:
  - id (UUID, PK)
  - user_id (FK to user.id, CASCADE DELETE)
  - title (optional)
  - created_at, updated_at

chat_messages:
  - id (BigInt, auto-increment)
  - thread_id (FK to chat_threads.id, CASCADE DELETE)
  - role (user|assistant|system)
  - content (JSONB)
  - created_at

MCP Integration:

• Client: MCPServerStreamableHttp
• Endpoint: http://localhost:8000/mcp
• Authorization: Bearer token forwarding
• Tools: 5 task management operations

Technology Stack (Chatbot)

CLI Application:

• Language: Python 3.12+
• Package Manager: UV
• Type Checking: mypy (strict mode)
• Linting: ruff
• Formatting: ruff format

Chatbot Backend:

• Framework: FastAPI
• AI SDK: OpenAI Agent SDK (agents)
• Model: Google Gemini 2.0 Flash (via OpenAI-compatible API)
• Protocol: ChatKit (OpenAI)
• MCP Client: Model Context Protocol
• Database: PostgreSQL (Neon)
• ORM: AsyncPG + SQLAlchemy
• Migrations: Alembic
• Auth: Better Auth (JWT)

Installation & Usage

CLI Application

# Install dependencies
uv sync

# Run the application
uv run python -m src.todo_app

# Run quality checks
uv run ruff check src/
uv run ruff format src/
uv run mypy --strict --explicit-package-bases src/

Chatbot Backend

# Navigate to chatbot backend
cd chatbot-backend

# Install dependencies
uv sync

# Configure environment
cp .env.example .env
# Edit .env with your credentials:
# - DATABASE_URL (Neon PostgreSQL)
# - OPENAI_API_KEY (Gemini API key)
# - BETTER_AUTH_SECRET (from frontend)

# Run database migrations
uv run alembic upgrade head

# Start the server
uv run uvicorn app.main:app --port 8001 --reload

Prerequisites:

• TODO API running on port 9000
• MCP Server running on port 8000
• PostgreSQL database (Neon recommended)

Project Structure

.
├── .claude/skills/          # Reusable architectural patterns
│   ├── id_architect.md
│   ├── ux_logic_anchor.md
│   └── error_handler.md
├── chatbot-backend/         # AI Chatbot Backend
│   ├── app/
│   │   ├── auth/            # JWT authentication
│   │   ├── models/          # Request context models
│   │   ├── server/          # ChatKit server & Gemini config
│   │   ├── store/           # Database persistence (NeonPostgresStore)
│   │   ├── utils/           # Agent factory & helpers
│   │   ├── config.py        # Settings management
│   │   └── main.py          # FastAPI application
│   ├── alembic/             # Database migrations
│   │   └── versions/        # Migration scripts
│   └── .env                 # Environment variables
├── src/                     # CLI Application
│   ├── models/              # Data models (TodoItem)
│   ├── services/            # Business logic (TodoManager)
│   ├── ui/                  # User interface components
│   │   ├── display.py       # Formatting and rendering
│   │   ├── handlers.py      # Command handlers
│   │   ├── menu.py          # Menu and input
│   │   └── messages.py      # Standardized messages
│   └── todo_app.py          # Main entry point
├── specs/
│   ├── 001-basic-crud/      # CLI feature specification
│   └── 006-chatbot-agent-backend/  # Chatbot specification
│       ├── spec.md
│       ├── plan.md
│       └── tasks.md
├── history/
│   ├── adr/                 # Architecture Decision Records
│   └── prompts/             # Prompt History Records
└── .specify/
    ├── memory/              # Constitution and project memory
    └── templates/           # SDD-RI templates

Architecture Highlights

Data Model

• TodoItem dataclass with immutable ID
• Dictionary-based storage for O(1) lookups
• Type-safe with complete type hints

Service Layer

• TodoManager encapsulates business logic
• ID counter starts at 1, never decrements
• Deleted IDs never reused (validated in T082)

UI Layer

• Standardized message formats (SUCCESS:/ERROR:)
• Status symbols ([✓] completed, [○] pending)
• Centralized error handling with retry loops

Main Loop

• Command dispatch dictionary pattern
• Single TodoManager instance (ADR-002)
• Graceful error recovery

Development Methodology

This project follows Spec-Driven Development with Rigorous Implementation (SDD-RI):

1. Specification - User stories and acceptance criteria
2. Planning - Architecture and design decisions (3 ADRs)
3. Task Breakdown - 88 granular, testable tasks
4. Implementation - Sequential execution with validation
5. Skill Extraction - Patterns formalized for reuse

Governance

Constitution: v1.1.0 (Ratified 2026-01-07, Amended 2026-01-09)

Core Principles:

• I. SDD-RI Methodology
• II. Pythonic Excellence
• III. In-Memory State Management
• IV. Type Safety & Documentation
• V. Terminal-Based Verification
• VI. Reusable Intelligence (Agent Skills)

Performance

• Create: 0.000002s average (100 todos)
• View: 0.001696s (10,000 todos)
• Exceeds requirements: 5000x (create), 1000x (view)

Documentation

• Specification: specs/001-basic-crud/spec.md
• Implementation Plan: specs/001-basic-crud/plan.md
• Task Breakdown: specs/001-basic-crud/tasks.md
• ADRs: history/adr/ (3 architectural decisions)
• PHRs: history/prompts/ (15 prompt records)
• Skills: .claude/skills/ (3 reusable patterns)

License

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Author

Mohsin Raza