It’s time to talk about serious matters and introduce one of the most promising frameworks for creating agents. Mastra is an open-source framework in TypeScript, distinguished by its ability to provide the necessary primitives for building sophisticated AI applications and advanced features. In this article, we will explore in detail the characteristics and advantages of Mastra, and how it can transform your approach to AI agent development.
Mastra
Mastra is designed to offer maximum flexibility and power to developers. You can create AI agents with memory, capable of executing functions, and chaining calls to language models (LLMs) in deterministic workflows. The framework also allows you to chat with your agents in a local development environment (pnpm run dev), feed them with application-specific knowledge through RAG (Retrieval-Augmented Generation), and evaluate their outputs with Mastra’s built-in evaluation tools.
Features of Mastra
Model Routing
Mastra uses Vercel’s AI SDK for model routing, providing a unified interface to interact with any LLM provider, including OpenAI, Anthropic, and Google Gemini. This allows developers to easily switch between different models and providers without modifying their base code. This offers great flexibility and enables performance and cost optimization based on the specific needs of the application.
Agent Memory and Function Calling
You can equip your agents with functions they can call. Agent memory can be persisted and retrieved based on recency, semantic similarity, or conversation thread. This ability to maintain context and execute actions based on past interactions makes agents smarter and more responsive. For example, a customer support agent can remember previous interactions with a user and provide more relevant and personalized responses.
Workflow Graphs
When you want to execute LLM calls deterministically, Mastra offers a graph-based workflow engine. You can define discrete steps, record inputs and outputs at each step of each execution, and integrate them into an observability tool. Mastra workflows use simple syntax for flow control (step(), .then(), .after()) that allows for complex branching and chaining. This makes it easy to create robust and scalable workflows for complex tasks.
Agent Development Environment
During local development of an agent, you can chat with it and see its state and memory in Mastra’s agent development environment. This interactive environment allows developers to test and debug their agents in real-time, accelerating the development cycle and improving the quality of the final agents.
Retrieval-Augmented Generation (RAG)
Mastra provides APIs to process documents (text, HTML, Markdown, JSON) into chunks, create embeddings, and store them in a vector database. At query time, it retrieves relevant chunks to ground LLM responses in your data, with a unified API over multiple vector stores (Pinecone, pgvector, etc.) and embedding providers (OpenAI, Cohere, etc.). This feature is particularly useful for applications requiring precise and contextual responses, such as question-answering systems or virtual assistants.
Deployment
Mastra supports integrating your agents and workflows into an existing React, Next.js, or Node.js application, or into standalone endpoints. The Mastra deployment assistant makes it easy to bundle agents and workflows into a Node.js server using Hono, or deploy them on a serverless platform like Vercel, Cloudflare Workers, or Netlify. This simplifies the deployment process and enables rapid and efficient scaling of your AI applications.
Automated Evaluations
Mastra offers automated evaluation metrics that use model-based, rule-based, and statistical methods to evaluate LLM outputs. Built-in metrics for toxicity, bias, relevance, and factual accuracy are available, and you can also define your own evaluations. These tools are essential for ensuring that your AI agents produce high-quality responses that comply with ethical and safety standards.
Why Use Mastra?
Mastra is a powerful and versatile framework that provides AI developers with the tools needed to create intelligent and high-performing applications. With its advanced features of model routing, agent memory, deterministic workflows, RAG, simplified deployment, and automated evaluations, Mastra positions itself as a top choice for developers looking to push the boundaries of AI. Whether you are an experienced developer or just starting in the field of AI, Mastra can help you transform your ideas into real and impactful AI applications.
The project is led by one of the co-founders of Gatsby, a pioneer of the JAM stack, which gave rise to tools like NextJS and many others. The project is also backed by Y Combinator, which, while not a guarantee of success, is a strong indicator of the project’s seriousness and potential.