todo-agent 0.3.5

A natural language interface for todo.sh task management

Todo Agent

A natural language interface for todo.sh task management powered by LLM function calling.

What it does

Transform natural language into todo.sh commands with intelligent task management:

# Use interactively
todo-agent
# Instead of: todo.sh add "Buy groceries +shopping"
todo-agent "add buy groceries to shopping list"
# Instead of: todo.sh list +work
todo-agent "show my work tasks"

Why Todo Agent?

Speak naturally instead of memorizing commands. Todo Agent understands "add dentist appointment next Monday" and automatically sets the right date, project, and context.

Get intelligent insights beyond basic task lists. It organizes tasks strategically, suggests priorities, and recommends optimal timing based on your patterns.

Work smarter with automatic duplicate detection and calendar-aware scheduling.

Choose your privacy - use cloud AI (OpenRouter) or run locally (Ollama).

Quick Start

1. Install

Install todo.sh (required)

macOS: brew install todo-txt
Linux: sudo apt-get install todo-txt-cli (Ubuntu/Debian) or sudo pacman -S todo-txt-cli (Arch)
Windows: choco install todo-txt-cli (Chocolatey) or scoop install todo-txt-cli (Scoop)

Set up todo.sh

# Create and initialize your todo directory
mkdir ~/todo && cd ~/todo
todo.sh init

# Add to your shell profile (.bashrc, .zshrc, etc.)
export TODO_DIR="$HOME/todo"

Install todo-agent

git clone https://github.com/codeprimate/todo-agent.git
cd todo_agent
make install

2. Set up your LLM provider

Option A: OpenRouter (recommended)

export OPENROUTER_API_KEY="your-api-key-here"

Option B: Ollama (local)

# Install and start Ollama
ollama pull mistral-small3.1

# Configure environment
export LLM_PROVIDER=ollama
export OLLAMA_MODEL=mistral-small3.1

3. Use it

# Interactive mode
todo-agent

# Single command
todo-agent "add urgent meeting with team +work @office"

Examples

Task Management

todo-agent "add buy groceries to shopping list"
todo-agent "list my work tasks"
todo-agent "complete the shopping task"
todo-agent "delete task 5"

Task Modification

todo-agent "change task 2 to buy organic milk"
todo-agent "add urgent to task 1"
todo-agent "set task 3 as high priority"

Discovery

todo-agent "what projects do I have?"
todo-agent "show completed tasks"
todo-agent "list my contexts"

Strategic Planning

todo-agent "what should I do next?"
todo-agent "organize my tasks by priority"
todo-agent "show me everything due this week"
todo-agent "what tasks are blocking other work?"

Natural Language Intelligence

todo-agent "add dentist appointment next Monday"

todo-agent "move all completed tasks to archive"
todo-agent "show me tasks I can do from home"

Configuration

Configuration Variables

Variable	Description	Default	Required
LLM_PROVIDER	LLM provider: openrouter or ollama	openrouter	No (defaults to openrouter)
TODO_DIR	Path to your todo.txt repository	—	Yes
OPENROUTER_API_KEY	Your OpenRouter API key	—	Yes (if using OpenRouter)
OLLAMA_MODEL	Model name for Ollama	mistral-small3.1	No
LOG_LEVEL	Logging verbosity (INFO, DEBUG, etc.)	INFO	No

Note:

• TODO_DIR is required for all configurations.
• OPENROUTER_API_KEY is only required if you use the OpenRouter provider.
• The TODO_FILE, DONE_FILE, and REPORT_FILE are automatically inferred from TODO_DIR.

The TODO_FILE, DONE_FILE, and REPORT_FILE are automatically inferred from TODO_DIR.

Development

# Clone and install
git clone https://github.com/codeprimate/todo-agent.git
cd todo_agent

# Install options:
# - Built package (like production install)
make install
# - Development mode with dev dependencies (recommended for development)
make install-dev
# - Basic development mode
pip install -e .

# Available Makefile tasks:
make test      # Run tests with coverage
make format    # Format and lint code
make lint      # Run linting only
make build     # Build package distribution
make clean     # Clean build artifacts
make install   # Build and install package locally
make install-dev # Install in development mode with dev dependencies

Code Quality and Linting

This project uses comprehensive linting to maintain code quality:

Linting Tools

• Ruff: Fast Python linter and formatter (replaces Black, isort, and Flake8)
• MyPy: Static type checking
• Bandit: Security vulnerability scanning

Note: Ruff is configured to be compatible with Black's formatting style and provides 10-100x faster performance than traditional tools.

Pre-commit Hooks

Install pre-commit hooks for automatic linting on commits:

pre-commit install

Linting in Test Suite

Linting checks are integrated into the test suite via tests/test_linting.py. The make test command runs all tests including linting checks. You can also run linting tests separately:

# Run linting tests only
pytest -m lint

Configuration Files

• pyproject.toml: Ruff, MyPy, and pytest configuration
• .pre-commit-config.yaml: Pre-commit hooks configuration

## Architecture

The todo-agent follows a clean, layered architecture with clear separation of concerns:

### **Interface Layer** (`todo_agent/interface/`)
- **CLI**: User interaction, input/output handling, and application loop
- **Tools**: Function schemas and execution logic for LLM function calling
- **Formatters**: Output formatting and presentation

### **Core Layer** (`todo_agent/core/`)
- **TodoManager**: Business logic orchestrator that translates high-level operations into todo.sh commands
- **ConversationManager**: Manages conversation state, memory, and context for multi-turn interactions
- **TaskParser**: Parses and validates task-related operations
- **Exceptions**: Custom exception classes for error handling

### **Infrastructure Layer** (`todo_agent/infrastructure/`)
- **Inference Engine**: Orchestrates LLM interactions, tool calling, and conversation flow
- **LLM Clients**: Provider-specific implementations (OpenRouter, Ollama) with factory pattern
- **TodoShell**: Subprocess wrapper for executing todo.sh commands
- **Configuration**: Environment and settings management
- **Logging**: Structured logging throughout the application
- **Token Counter**: Manages conversation token limits and costs

### **How It Works**

1. **User Input** → Natural language request (e.g., "add buy groceries to shopping list")
2. **CLI** → Captures input and passes to inference engine
3. **Inference Engine** → Sends request to LLM with available tools
4. **LLM** → Analyzes request and decides which tools to call
5. **Tool Execution** → TodoManager → TodoShell → todo.sh
6. **Response** → Results returned through conversation manager to user

### **Key Features**
- **Function Calling**: LLM intelligently selects and executes appropriate tools
- **Conversation Memory**: Maintains context across interactions
- **Multi-Provider Support**: Works with cloud (OpenRouter) and local (Ollama) LLMs
- **Error Handling**: Robust error management with detailed logging
- **Performance Monitoring**: Tracks thinking time and conversation metrics

## License

GNU General Public License v3.0

This project is licensed under the GNU General Public License v3.0 - see the [LICENSE](LICENSE) file for details.