Codebuff is an AI-powered terminal assistant designed to modify codebases using natural language instructions. It employs a sophisticated multi-agent system, coordinating specialized agents for tasks like file scanning, planning, implementation, and review. This approach aims to provide superior context understanding and edit accuracy compared to single-model solutions, targeting developers seeking efficient, intelligent code modification and automation.

How It Works

Codebuff utilizes a multi-agent architecture where distinct agents (e.g., File Explorer, Planner, Implementation, Review) collaborate. This modular approach allows for specialized task handling, leading to better project context comprehension, more precise code edits, and reduced error rates than monolithic AI coding assistants.

Quick Start & Requirements

• Install CLI: npm install -g codebuff
• Run CLI: Navigate to your project directory and execute codebuff.
• Prerequisites: Node.js and npm are required for installation and execution.
• Resources: Documentation is available at codebuff.com/docs. Local development details are mentioned in local-development.md.

Highlighted Details

• Multi-Agent System: Orchestrates specialized agents for enhanced code understanding and modification accuracy.
• Model Agnosticism: Supports any model available on OpenRouter, including GPT, Claude, Qwen, and DeepSeek, offering flexibility beyond proprietary model lock-ins.
• Deep Customizability: Enables creation of custom agents and workflows using TypeScript, allowing programmatic control, conditional logic, and sub-agent orchestration.
• SDK Integration: Provides a TypeScript SDK (@codebuff/sdk) for embedding Codebuff's capabilities into applications, CI/CD pipelines, or custom development environments.
• Performance Claims: Claims to outperform Claude Code in evaluations across 175+ coding tasks on real-world open-source repositories.

Maintenance & Community

• Community support is available via Discord.
• Direct support contact: support@codebuff.com.

Licensing & Compatibility

• The license type is not specified in the provided README.

Limitations & Caveats

• Performance claims are based on internal evaluations.
• The effectiveness may depend on the quality and configuration of the chosen underlying AI model.