Mastra框架深度解析：使用TypeScript构建生产级AI应用与智能体的完整指南

Mastra is an AI application and agent-building framework designed for the modern TypeScript technology stack. It connects to over 40 model providers through a unified interface, offering core functionalities like agents, workflows, and human-in-the-loop collaboration. With built-in evaluation and observability tools, Mastra aims to help developers seamlessly transition from prototyping to production deployment.

Mastra 是一个专为现代 TypeScript 技术栈设计的 AI 应用与智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。构建框架。它通过统一的接口连接 40 多个模型供应商，提供智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。、工作流基于图的系统，连接多个智能体和函数以执行复杂的多步骤任务，支持类型路由、嵌套、检查点和人机交互模式。及人机协作Mastra内置的功能，允许暂停和恢复正在运行的智能体或工作流，通过存储机制记住执行状态，支持无限期暂停后无缝恢复。等核心功能，并内置评估与观测工具，旨在帮助开发者从原型阶段无缝过渡到生产环境部署。

Introduction: Why Choose TypeScript for Building AI Applications?

In today's technological landscape, integrating Artificial Intelligence (AI) has become a core requirement for application development. However, developers face numerous challenges when moving from early-stage prototyping to reliable production deployment, including model integration, state management, workflow orchestration, and system stability.

在当今的技术领域，人工智能（AI）的集成已成为应用程序开发的核心需求。然而，从早期的原型验证到生产环境的可靠部署，开发者面临着模型集成、状态管理、工作流基于图的系统，连接多个智能体和函数以执行复杂的多步骤任务，支持类型路由、嵌套、检查点和人机交互模式。编排以及系统稳定性等诸多挑战。

The Mastra framework emerges as a solution. It is more than just a utility library; it is a full-stack solution purpose-built for TypeScript. Whether you are a React, Next.js, or Node.js developer, you can leverage Mastra to build, tune, and scale reliable AI products. It allows developers to deploy AI capabilities as a standalone server or deeply integrate them into existing frontend and backend frameworks.

Mastra框架应运而生，它不仅仅是一个工具库，更是一个专为TypeScript打造的全栈解决方案。无论是React、Next.js还是Node.js开发者，都可以利用Mastra构建、调优和扩展可靠的AI产品。它允许开发者将AI能力作为独立服务器部署，也可以将其深度集成到现有的前端和后端框架中。

Deep Dive into Mastra's Core Features

Mastra's ability to simplify AI development stems from a comprehensive set of core features designed around established AI patterns. Below, we will dissect these key components and explore how they work together to deliver a powerful development experience.

Mastra之所以能够简化AI开发流程，归功于其围绕成熟AI模式设计的一整套核心功能。以下我们将详细拆解这些关键组件，探讨它们如何协同工作以提供强大的开发体验。

1. Model Routing: A Unified Interface to 40+ Providers

When building AI applications, selecting the appropriate Large Language Model (LLM) is crucial. Different scenarios may require different models—some excel at code generation, while others are better at natural language understanding. Traditionally, integrating APIs from different vendors requires handling their unique interface specifications, which significantly increases development costs.

在构建AI应用时，选择合适的大语言模型（LLM）至关重要。不同的场景可能需要不同的模型，例如有的擅长代码生成，有的擅长自然语言理解。传统方式下，接入不同供应商的API需要处理各自独特的接口规范，这极大地增加了开发成本。

Mastra addresses this issue with its Model Routing feature. It provides a standardized interface that allows developers to seamlessly connect to over 40 model providers. This means that whether you want to use OpenAI, Anthropic, Gemini, or other mainstream models, you only need to deal with one unified calling pattern at the code level. This abstraction layer not only simplifies the development process but also makes future model switching effortless, eliminating the need to rewrite core business logic.

Mastra通过其**模型路由通过统一接口连接多个AI模型提供商的技术，允许开发者在不同模型间灵活切换。（Model Routing）**功能解决了这一问题。它提供了一个标准化的接口，允许开发者无缝连接超过40个模型提供商。这意味着，无论您想使用OpenAI、Anthropic、Gemini还是其他主流模型，在代码层面只需要面对一种统一的调用方式。这种抽象层不仅简化了开发流程，还让未来的模型切换变得轻而易举，无需重写核心业务逻辑。

2. Agents: Building Systems That Solve Problems Autonomously

Agents are a core concept of the Mastra framework. Unlike simple API calls, Mastra's agents possess the capability to autonomously solve open-ended tasks.

智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。是Mastra框架的核心概念之一。不同于简单的API调用，Mastra的智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。具备自主解决开放式任务的能力。

These agents utilize Large Language Models (LLMs) and various tools for reasoning. Their workflow can be understood as:

这些智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。利用大语言模型（LLM）和各种工具进行推理。它们的工作流基于图的系统，连接多个智能体和函数以执行复杂的多步骤任务，支持类型路由、嵌套、检查点和人机交互模式。程可以被理解为：

Goal Setting: Receiving a specific task objective. (目标设定：接收一个具体的任务目标。)
Tool Decision: The agent decides which tools to use to gather information or perform actions based on the current situation. (工具决策：智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。根据当前情况，决定使用哪些工具来获取信息或执行操作。)
Internal Iteration: The agent performs multiple rounds of internal thinking and iteration, gradually refining the result until it reaches a final answer or meets a predefined stopping condition. (内部迭代：在达到最终答案或满足预设的停止条件之前，智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。会在内部进行多次思考和迭代，逐步优化结果。)

This mechanism enables agents not just to generate text, but to execute complex, multi-step operational flows, truly possessing the ability to "act."

这种机制使得智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。不仅仅是生成文本，而是能够执行复杂的、多步骤的操作流程，真正具备“行动”的能力。

3. Workflows: Explicit Control Over Complex Multi-Step Processes

While agents excel at autonomous reasoning, in certain business scenarios, developers need precise, explicit control over the execution process. This is where workflows come into play.

虽然智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。擅长自主推理，但在某些业务场景下，开发者需要对执行过程进行精确、显式的控制。这就是工作流基于图的系统，连接多个智能体和函数以执行复杂的多步骤任务，支持类型路由、嵌套、检查点和人机交互模式。发挥作用的地方。

Mastra provides a graph-based workflow engine for orchestrating complex, multi-step processes. Unlike traditional linear code, the workflow engine allows developers to define very intuitive control flow syntax. For example:

Mastra提供了一个基于图的工作流基于图的系统，连接多个智能体和函数以执行复杂的多步骤任务，支持类型路由、嵌套、检查点和人机交互模式。引擎，用于编排复杂的多步骤过程。与传统的线性代码不同，工作流基于图的系统，连接多个智能体和函数以执行复杂的多步骤任务，支持类型路由、嵌套、检查点和人机交互模式。引擎允许开发者定义非常直观的控制流语法。例如：

.then(): Execute the next step sequentially. (.then()：顺序执行下一步。)
.branch(): Perform conditional branching. (.branch()：根据条件进行分支判断。)
.parallel(): Execute multiple tasks in parallel. (.parallel()：并行执行多个任务。)

This graphical orchestration approach makes complex business logic clear, visible, and easy to maintain and debug, making it ideal for handling business processes with strict requirements on execution order and conditions.

这种图形化的编排方式，让复杂的业务逻辑变得清晰可见，极易维护和调试，非常适合处理那些对执行顺序和条件有严格要求的业务流程。

4. Human-in-the-Loop: Pause and Resume Mechanism

In many practical applications, fully automated decision-making can pose risks or require human confirmation. Mastra's built-in human-in-the-loop functionality allows developers to pause a running agent or workflow.

在许多实际应用中，完全自动化的决策可能存在风险，或者需要人类的确认。Mastra内置的人机协作Mastra内置的功能，允许暂停和恢复正在运行的智能体或工作流，通过存储机制记住执行状态，支持无限期暂停后无缝恢复。功能允许开发者暂停一个正在运行的智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。或工作流基于图的系统，连接多个智能体和函数以执行复杂的多步骤任务，支持类型路由、嵌套、检查点和人机交互模式。。

When the system requires user input, approval, or manual intervention, it can suspend its execution state. Most importantly, Mastra leverages its Storage mechanism to remember the execution state. This means the pause can be indefinite—no matter how much time passes, as long as the user is ready to continue, the system can seamlessly resume from where it left off without losing any contextual information.

当系统需要用户输入、审批或人工干预时，它可以挂起执行状态。最重要的是，Mastra利用其**存储（Storage）**机制来记住执行状态。这意味着，暂停的时间可以是无限期的——无论过去多久，只要用户准备好继续，系统都可以从上次中断的地方无缝恢复，不会丢失任何上下文信息。

5. Context Management: Endowing Agents with Memory and Perception

A smart AI assistant must know "to have the right context at the right time." Mastra provides multi-dimensional support for context management:

一个聪明的AI助手必须知道“在正确的时间拥有正确的上下文”。Mastra在上下文管理为智能体提供多维度的记忆和感知支持，包括对话历史、数据检索、工作记忆和语义回忆等功能。方面提供了多维度的支持：

Conversation History: Enables the agent to remember previous exchanges, maintaining conversational coherence. (对话历史：让智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。能够记住之前的交流内容，保持对话的连贯性。)
Data Retrieval: Retrieves relevant data from APIs, databases, or files, providing the agent with real-time business information support. (数据检索：从APIs、数据库或文件中检索相关数据，为智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。提供实时的业务信息支持。)
Working Memory: Simulates human short-term memory, helping the agent handle temporary information within the current task. (工作记忆：模拟人类的短期记忆，帮助智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。处理当前任务中的临时信息。)
Semantic Recall: Allows the agent to recall relevant past information or knowledge through semantic search mechanisms. (语义回忆：通过语义搜索机制，让智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。能够回忆起相关的过往信息或知识。)

Through these mechanisms, Mastra ensures that agent behavior is coherent and contextually appropriate, avoiding the degradation of interaction experience caused by "forgetting."

通过这些机制，Mastra确保了智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。的行为是连贯且符合语境的，避免了“遗忘”带来的交互体验下降。

6. System Integration & Frontend Support

Mastra is designed from the ground up to be highly integrable. Developers can package agents and workflows and embed them directly into existing React, Next.js, or Node.js applications. For teams wishing to deploy AI capabilities independently, Mastra also supports deployment as standalone endpoints.

Mastra的设计初衷是高度集成化。开发者可以将智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。和工作流基于图的系统，连接多个智能体和函数以执行复杂的多步骤任务，支持类型路由、嵌套、检查点和人机交互模式。打包，直接嵌入到现有的React、Next.js或Node.js应用程序中。对于那些希望独立部署AI能力的团队，Mastra也支持将其作为独立端点进行部署。

When building user interfaces (UI), Mastra can integrate seamlessly with agent libraries like Vercel AI SDK UI and CopilotKit. This makes bringing AI assistants to life on the web exceptionally simple, allowing developers to focus on the interaction experience without worrying about underlying communication protocols.

在构建用户界面（UI）时，Mastra可以与Vercel AI SDK UI和CopilotKit等智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。库无缝集成。这使得在Web端赋予AI助手生命变得异常简单，开发者可以专注于交互体验，而无需担心底层的通信协议。

7. MCP Server: Model Context Protocol Support

The Model Context Protocol (MCP) is a standardized interface for exposing structured resources. Mastra allows developers to write MCP servers to expose agents, tools, and other resources through this interface.

**模型上下文协议（MCP）**是一种标准化的接口，用于暴露结构化资源。Mastra允许开发者编写MCP服务器模型上下文协议服务器，允许开发者通过标准化接口暴露智能体、工具和其他资源，增强生态兼容性。，通过这个接口暴露智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。、工具和其他资源。

This means that any system or agent supporting the MCP protocol can easily access the capabilities built with Mastra. This openness significantly expands Mastra's ecosystem compatibility, allowing it to integrate into a broader AI toolchain.

这意味着，任何支持MCP协议的系统或智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。，都可以轻松访问Mastra构建的能力。这种开放性极大地扩展了Mastra的生态兼容性，使其能够融入更广泛的AI工具链中。

8. Production-Ready Essentials: Evaluation & Observability

Deploying a model to production is just the beginning; continuous monitoring and optimization are key to long-term stable operation. Mastra understands this well and therefore includes built-in evaluation and observability tools.

将模型部署到生产环境只是开始，持续的监控和优化才是长期稳定运行的关键。Mastra深知这一点，因此内置了评估和观测工具。

Developers can observe the system's operational status in real-time, measure key performance metrics, and continuously iterate and optimize model performance based on evaluation results. This production-grade infrastructure provides a solid foundation for building reliable AI products.

开发者可以实时观察系统的运行状态，测量关键性能指标，并通过评估结果不断迭代和优化模型表现。这套生产级的基础设施，为构建可靠的AI产品提供了坚实的保障。

Getting Started: How to Begin with Mastra

Having understood Mastra's powerful features, how do you start building? Mastra offers an extremely convenient entry point, allowing developers to set up a basic environment within minutes.

了解了Mastra的强大功能后，如何开始动手构建呢？Mastra提供了极其便捷的入门方式，让开发者可以在几分钟内搭建起基础环境。

Recommended Installation Method

The fastest and most recommended way to get Mastra is by using its official command-line tool. Simply run the following command in your terminal:

获取Mastra最快、最推荐的方法是使用其官方命令行工具。只需在终端中运行以下命令：

npm create mastra@latest

This command will automatically set up your project structure and guide you through the initial configuration. For beginners, this is the most accessible starting point.

这条命令会自动为您设置好项目结构，引导您完成初始化配置。对于新手来说，这是最无障碍的起点。

Detailed Learning Path

If you are new to AI development or wish to learn Mastra's usage techniques more deeply, the official documentation provides abundant resources:

如果您对AI开发尚不熟悉，或者希望更深入地学习Mastra的使用技巧，官方提供了丰富的资源：

Installation Guide: Provides step-by-step instructions for using the CLI or manual installation, suitable for developers needing custom configurations. (安装指南：提供了使用CLI或手动安装的分步说明，适合需要自定义配置的开发者。)
Template Library: Offers a large number of ready-made templates. You can start your project directly based on these templates, avoiding starting from scratch. (模板库：提供了大量的现成模板，您可以直接基于这些模板开始项目，避免从零开始。)
Official Course: Systematic learning materials to help you master concepts from basic to advanced. (官方课程：系统性的学习材料，帮助您掌握从基础到高级的概念。)
Video Tutorials: Learn practical skills intuitively through video content provided on the YouTube channel. (视频教程：通过YouTube频道提供的视频内容，直观地学习实战技巧。)

Advanced Development & Resource Support

Beyond the core framework, Mastra has built a comprehensive ecosystem to support developers in efficient collaboration within IDEs and the community.

除了核心框架，Mastra还构建了完善的生态系统，支持开发者在IDE和社区中进行高效协作。

Make Your IDE a Mastra Expert

To enhance development efficiency, Mastra provides @mastra/mcp-docs-server. By following the relevant guide, you can connect your Integrated Development Environment (IDE) directly to Mastra's documentation and knowledge base. This means that while writing code, your IDE can provide real-time intelligent suggestions and documentation support, significantly improving the coding experience.

为了提升开发效率，Mastra提供了@mastra/mcp-docs-server。通过遵循相关指南，您可以让您的集成开发环境（IDE）直接接入Mastra的文档和知识库。这意味着，在编写代码时，IDE就能为您提供实时的智能提示和文档支持，极大地提升编码体验。

Community Contribution & Collaboration

Mastra is an open project that encourages community participation. Whether you are a code contributor, tester, or someone suggesting feature specifications, your involvement is welcome.

Mastra是一个开放的项目，鼓励社区参与。无论您是代码贡献者、测试者还是功能规范的建议者，您的参与都受到欢迎。

Contribution Process: If you wish to contribute code, it is recommended to first open an Issue on GitHub for discussion before submitting a Pull Request. (贡献流程：如果您希望贡献代码，建议先在GitHub上开启Issue进行讨论，然后再提交Pull Request。)
Development Documentation: Project setup information and development standards can be found in the project's DEVELOPMENT.md file, which is essential reading before participating in core development. (开发文档：项目的设置信息和开发规范可以在项目的DEVELOPMENT.md文件中找到，这是参与核心开发前必读的文档。)

Getting Help & Support

Encountering problems during development is inevitable. Mastra has an active Discord community where you can join at any time to communicate with other developers and maintainers, ask questions, or share experiences. Additionally, starring the GitHub repository is a great way to support the project's growth.

在开发过程中遇到问题是难免的。Mastra拥有一个活跃的Discord社区，您可以随时加入，与其他开发者和维护者交流，提问或分享经验。此外，给GitHub仓库点星也是支持项目发展的好方法。

Security Commitment

Mastra places a high priority on code and system security. The project team is committed to maintaining the security of the repository and the entire Mastra framework. If you discover any security vulnerabilities, please disclose them responsibly via email to security@mastra.ai, and the team will respond and address them promptly.

Mastra高度重视代码和系统的安全性。项目团队致力于维护仓库和整个Mastra框架的安全。如果您发现了任何安全漏洞，请通过邮件security@mastra.ai进行负责任的披露，团队会迅速响应并处理。

Conclusion

Mastra is more than just a library; it is a complete, well-considered methodology for building AI applications. From the underlying unified interface connecting 40+ models, to the mid-level orchestration of agents and workflows, and up to the top-level evaluation, observability, and human-in-the-loop collaboration, it covers every aspect of the AI application lifecycle.

Mastra不仅仅是一个库，它是一套完整的、经过深思熟虑的AI应用构建方法论。从底层连接40+模型的统一接口，到中层智能体Mastra框架的核心组件，具备自主解决开放式任务的能力，利用大语言模型和各种工具进行推理，能够执行复杂的多步骤操作流程。与工作流基于图的系统，连接多个智能体和函数以执行复杂的多步骤任务，支持类型路由、嵌套、检查点和人机交互模式。的编排，再到顶层的评估观测与人机协作Mastra内置的功能，允许暂停和恢复正在运行的智能体或工作流，通过存储机制记住执行状态，支持无限期暂停后无缝恢复。，它覆盖了AI应用生命周期的每一个环节。

For developers looking to build production-grade AI applications within the TypeScript ecosystem, Mastra provides a high-starting-point, high-efficiency, and high-reliability solution. With its rich documentation, templates, and community support, developers can focus on innovation while leaving the complex underlying AI engineering challenges to Mastra.

对于希望在TypeScript生态系统中构建生产级AI应用的开发者来说，Mastra提供了一个高起点、高效率且高可靠性的解决方案。借助其丰富的文档、模板和社区支持，开发者可以专注于创新，而将底层复杂的AI工程化问题交给Mastra处理。

Frequently Asked Questions (FAQ)

What technology stacks is Mastra primarily suitable for? (Mastra主要适用于哪些技术栈？)
Mastra is designed for TypeScript, but it integrates well with frontend and backend frameworks like React, Next.js, and Node.js. It also supports deployment as a standalone server and can run almost anywhere.

Mastra是专为TypeScript设计的，但它可以很好地集成到React、Next.js以及Node.js等前端和后端框架中。同时，它也支持作为独立服务器部署，几乎可以在任何地方运行。
Can I mix different AI model providers in a single project? (我可以在一个项目中混合使用不同的AI模型供应商吗？)
Yes, Mastra's Model Routing feature allows you to connect to over 40 model providers, including OpenAI, Anthropic, and Gemini, through a single standard interface. This means you can easily switch between or mix different models within the same project based on your needs.

是的，Mastra的