kollabor 0.4.15

An advanced, highly customizable terminal-based chat application for interacting with LLMs

Kollabor

An advanced, highly customizable terminal-based chat application for interacting with Large Language Models (LLMs). Built with a powerful plugin system and comprehensive hook architecture for complete customization.

macOS: brew install kollaborai/tap/kollabor Other: curl -sS https://raw.githubusercontent.com/kollaborai/kollabor-cli/main/install.sh | bash Run: kollab

Features

• Event-Driven Architecture: Everything has hooks - every action triggers customizable hooks that plugins can attach to
• Advanced Plugin System: Dynamic plugin discovery and loading with comprehensive SDK
• Rich Terminal UI: Beautiful terminal rendering with status areas, visual effects, and modal overlays
• Conversation Management: Persistent conversation history with full logging support
• Model Context Protocol (MCP): Built-in support for MCP integration
• Tool Execution: Function calling and tool execution capabilities
• Pipe Mode: Non-interactive mode for scripting and automation
• Environment Variable Support: Complete configuration via environment variables (API settings, system prompts, etc.)
• Extensible Configuration: Flexible configuration system with plugin integration
• Async/Await Throughout: Modern Python async patterns for responsive performance

Installation

macOS (Recommended)

Standard Homebrew installation - what most macOS users expect:

brew install kollaborai/tap/kollabor

To upgrade:

brew upgrade kollabor

One-Line Install (Cross-Platform)

Auto-detects the best method (uvx > pipx > pip):

curl -sS https://raw.githubusercontent.com/kollaborai/kollabor-cli/main/install.sh | bash

Using uvx (Fastest, Isolated)

uvx runs the app in an isolated environment without installation:

uvx --from kollabor kollab

Or install to uv tool cache for instant startup:

uv tool install kollabor
kollab

Using pipx (Isolated, Clean)

Recommended for user-space installation without system conflicts:

pipx install kollabor

Using pip

Standard Python package installation:

pip install kollabor

From Source

git clone https://github.com/kollaborai/kollabor-cli.git
cd kollabor-cli
pip install -e .

Development Installation

pip install -e ".[dev]"

Quick Start

Interactive Mode

Simply run the CLI to start an interactive chat session:

kollab

Pipe Mode

Process a single query and exit:

# Direct query
kollab "What is the capital of France?"

# From stdin
echo "Explain quantum computing" | kollab -p

# From file
cat document.txt | kollab -p

# With custom timeout
kollab --timeout 5min "Complex analysis task"

Configuration

On first run, Kollabor creates a .kollabor-cli directory in your current working directory:

.kollabor-cli/
├── config.json           # User configuration
├── system_prompt/        # System prompt templates
├── logs/                 # Application logs
└── state.db              # Persistent state

Configuration Options

The configuration system uses dot notation:

• core.llm.* - LLM service settings
• terminal.* - Terminal rendering options
• application.* - Application metadata

Environment Variables

All configuration can be controlled via environment variables, which take precedence over config files:

API Configuration

KOLLABOR_API_ENDPOINT=https://api.example.com/v1/chat/completions
KOLLABOR_API_TOKEN=your-api-token-here        # or KOLLABOR_API_KEY
KOLLABOR_API_MODEL=gpt-4
KOLLABOR_API_MAX_TOKENS=4096
KOLLABOR_API_TEMPERATURE=0.7
KOLLABOR_API_TIMEOUT=30000

System Prompt Configuration

# Direct string (highest priority)
KOLLABOR_SYSTEM_PROMPT="You are a helpful coding assistant."

# Custom file path
KOLLABOR_SYSTEM_PROMPT_FILE="./my_custom_prompt.md"

Using .env Files

Create a .env file in your project root:

KOLLABOR_API_ENDPOINT=https://api.example.com/v1/chat/completions
KOLLABOR_API_TOKEN=your-token-here
KOLLABOR_API_MODEL=gpt-4
KOLLABOR_SYSTEM_PROMPT_FILE="./prompts/specialized.md"

Load and run:

export $(cat .env | xargs)
kollab

See ENV_VARS.md for complete documentation and examples.

Architecture

Kollabor follows a modular, event-driven architecture:

Core Components

• Application Core (core/application.py): Main orchestrator
• Event System (core/events/): Central event bus with hook system
• LLM Services (core/llm/): API communication, conversation management, tool execution
• I/O System (core/io/): Terminal rendering, input handling, visual effects
• Plugin System (core/plugins/): Dynamic plugin discovery and loading
• Configuration (core/config/): Flexible configuration management
• Storage (core/storage/): State management and persistence

Plugin Development

Create custom plugins by inheriting from base plugin classes:

from core.plugins import BasePlugin
from core.events import EventType

class MyPlugin(BasePlugin):
    def register_hooks(self):
        """Register plugin hooks."""
        self.event_bus.register_hook(
            EventType.PRE_USER_INPUT,
            self.on_user_input,
            priority=HookPriority.NORMAL
        )

    async def on_user_input(self, context):
        """Process user input before it's sent to the LLM."""
        # Your custom logic here
        return context

    def get_status_line(self):
        """Provide status information for the status bar."""
        return "MyPlugin: Active"

Hook System

The comprehensive hook system allows plugins to intercept and modify behavior at every stage:

• pre_user_input - Before processing user input
• pre_api_request - Before API calls to LLM
• post_api_response - After receiving LLM responses
• pre_message_display - Before displaying messages
• post_message_display - After displaying messages
• And many more...

Project Structure

kollabor/
├── core/              # Core application modules
│   ├── application.py # Main orchestrator
│   ├── config/        # Configuration management
│   ├── events/        # Event bus and hooks
│   ├── io/            # Terminal I/O
│   ├── llm/           # LLM services
│   ├── plugins/       # Plugin system
│   └── storage/       # State management
├── plugins/           # Plugin implementations
├── docs/              # Documentation
├── tests/             # Test suite
└── main.py            # Application entry point

Development

Running Tests

# All tests
python tests/run_tests.py

# Specific test file
python -m unittest tests.test_llm_plugin

# Individual test case
python -m unittest tests.test_llm_plugin.TestLLMPlugin.test_thinking_tags_removal

Code Quality

# Format code
python -m black core/ plugins/ tests/ main.py

# Type checking
python -m mypy core/ plugins/

# Linting
python -m flake8 core/ plugins/ tests/ main.py --max-line-length=88

# Clean up cache files and build artifacts
python scripts/clean.py

Requirements

• Python 3.12 or higher
• aiohttp 3.8.0 or higher

License

MIT License - see LICENSE file for details

Contributing

Contributions are welcome! Please see the documentation for development guidelines.

Links

• Documentation
• Bug Tracker
• Repository

Acknowledgments

Built with modern Python async/await patterns and designed for extensibility and customization.