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Mastra AI 框架

Mastra是一个基于TypeScript的AI应用开发框架,提供LLM模型集成、智能代理、工具系统、可视化工作流、RAG知识库增强、第三方集成及自动化评估等核心功能。支持本地或云端部署,帮助开发者快速构建可扩展的AI应用,兼容OpenAI、Anthropic等主流模型,并提供工具链和社区支持。

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README

Mastra

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Mastra is a framework for building AI-powered applications and agents with a modern TypeScript stack.

It includes everything you need to go from early prototypes to production-ready applications. Mastra integrates with frontend and backend frameworks like React, Next.js, and Node, or you can deploy it anywhere as a standalone server. It's the easiest way to build, tune, and scale reliable AI products.

Why Mastra?

Purpose-built for TypeScript and designed around established AI patterns, Mastra gives you everything you need to build great AI applications out-of-the-box.

Some highlights include:

  • Model routing - Connect to 40+ providers through one standard interface. Use models from OpenAI, Anthropic, Gemini, and more.

  • Agents - Build autonomous agents that use LLMs and tools to solve open-ended tasks. Agents reason about goals, decide which tools to use, and iterate internally until the model emits a final answer or an optional stopping condition is met.

  • Workflows - When you need explicit control over execution, use Mastra's graph-based workflow engine to orchestrate complex multi-step processes. Mastra workflows use an intuitive syntax for control flow (.then(), .branch(), .parallel()).

  • Human-in-the-loop - Suspend an agent or workflow and await user input or approval before resuming. Mastra uses storage to remember execution state, so you can pause indefinitely and resume where you left off.

  • Context management - Give your agents the right context at the right time. Provide conversation history, retrieve data from your sources (APIs, databases, files), and add human-like working and semantic memory so your agents behave coherently.

  • Integrations - Bundle agents and workflows into existing React, Next.js, or Node.js apps, or ship them as standalone endpoints. When building UIs, integrate with agentic libraries like Vercel's AI SDK UI and CopilotKit to bring your AI assistant to life on the web.

  • MCP servers - Author Model Context Protocol servers, exposing agents, tools, and other structured resources via the MCP interface. These can then be accessed by any system or agent that supports the protocol.

  • Production essentials - Shipping reliable agents takes ongoing insight, evaluation, and iteration. With built-in evals and observability, Mastra gives you the tools to observe, measure, and refine continuously.

Get started

The recommended way to get started with Mastra is by running the command below:

npm create mastra@latest

Follow the Installation guide for step-by-step setup with the CLI or a manual install.

If you're new to AI agents, check out our templates, course, and YouTube videos to start building with Mastra today.

Documentation

Visit our official documentation.

MCP Servers

Learn how to make your IDE a Mastra expert by following the @mastra/mcp-docs-server guide.

Contributing

Looking to contribute? All types of help are appreciated, from coding to testing and feature specification.

If you are a developer and would like to contribute with code, please open an issue to discuss before opening a Pull Request.

Information about the project setup can be found in the development documentation

Support

We have an open community Discord. Come and say hello and let us know if you have any questions or need any help getting things running.

It's also super helpful if you leave the project a star here at the top of the page

Security

We are committed to maintaining the security of this repo and of Mastra as a whole. If you discover a security finding we ask you to please responsibly disclose this to us at security@mastra.ai and we will get back to you.

help

运行方式说明

cloud

托管运行

托管运行通常表示这个 MCP Server 由服务方环境承载,用户一般按页面提供的连接方式或授权流程接入,不需要在本地长期启动一个 MCP 进程

  1. 打开服务方连接页
  2. 完成授权或复制端点
  3. 在 MCP 客户端中连接
terminal

本地运行 / 其它方式

本地运行通常需要用户在自己的电脑或服务器上安装依赖,把 server_config 复制到 MCP 客户端,并按 env_schema 补齐环境变量、密钥或其它配置

  1. 复制 server_config
  2. 安装所需依赖
  3. 补齐环境变量后重启客户端