Mastra Code is a terminal-based AI coding assistant that integrates directly into your development workflow. It connects to 70+ AI models, provides tools for reading, editing, searching, and executing code, and offers different modes for various coding tasks. It helps developers understand codebases, implement changes, run tests, and manage projects efficiently.
Free
How to use Mastra Code?
Install Mastra Code globally via npm, navigate to your project directory, and run 'mastracode'. Set your API key for preferred AI providers, then start interacting through your terminal. Use slash commands to switch modes, manage threads, or change models. Ask questions about your code, request edits, run tests, or explore architecture - the agent responds with streaming text and can directly manipulate your project files.
Mastra Code 's Core Features
Terminal-based interface that integrates seamlessly with existing development workflows without leaving the command line
Connects to 70+ AI models from different providers, allowing flexibility in choosing the most suitable model for each task
Three distinct modes: Build for comprehensive coding, Plan for architecture analysis, and Fast for quick lookups with minimal latency
Built-in tools for file operations including viewing, editing, searching, executing shell commands, and web searches
Project-scoped threads with persistent memory, allowing context to be maintained across multiple sessions
Customizable through MCP servers, hooks, custom commands, skills, and database settings for personalized workflows
Programmable extension system supporting custom modes, tools, subagents, and storage implementations
Mastra Code 's Use Cases
Daily coding assistance where developers need quick answers about their codebase, make edits, run tests, or manage Git operations without context switching
Code exploration and architecture analysis using Plan mode to understand complex systems and create implementation plans before writing actual code
Quick debugging sessions where developers need immediate answers to specific questions or small code edits with minimal setup overhead
Multi-model comparison workflows where developers want to test different AI providers' responses to the same coding problem
Learning new codebases by asking questions about unfamiliar projects and getting explanations directly in the terminal environment
