Mastra is a TypeScript framework designed for building AI-powered applications and agents, offering a comprehensive toolkit that bridges the gap between prototype development and production-ready systems. The framework integrates seamlessly with popular frontend and backend technologies including React, Next.js, and Node.js, while also supporting deployment as a standalone server across various environments.
The core functionality centers on several interconnected features. Model routing provides access to over 40 different AI providers through a unified interface, enabling developers to work with models from OpenAI, Anthropic, Gemini, and others without provider-specific implementation details. The agents system allows developers to build autonomous agents that leverage large language models and tools to tackle open-ended tasks, with agents capable of reasoning about goals, selecting appropriate tools, and iterating internally until reaching conclusions. For scenarios requiring more explicit control, the workflow engine uses a graph-based architecture with intuitive syntax for control flow operations including sequential execution via .then(), branching logic via .branch(), and parallel processing via .parallel().
Human-in-the-loop capabilities enable workflows and agents to suspend execution and await user input or approval before resuming, with state persistence through storage systems allowing indefinite pauses and resumption from the exact point of interruption. Context management features provide agents with appropriate information at critical moments through conversation history, data retrieval from APIs and databases, and observational memory that enables coherent agent behavior. The framework supports integration with agentic libraries like Vercel's AI SDK UI and CopilotKit for web-based implementations, and includes Model Context Protocol server authoring capabilities for exposing agents and tools via the MCP interface.
Production readiness is addressed through built-in evaluation systems and observability tools that enable continuous measurement and refinement of agent behavior.
The codebase employs a dual-licensing model combining Apache License 2.0 for the core framework with a source-available Mastra Enterprise License for enterprise features located in ee/ directories. This structure allows free development and testing while requiring valid enterprise licenses for production use of premium features. The project maintains active community engagement through Discord, comprehensive documentation, educational templates, and video resources to support developers at various experience levels with AI agent development.
