Mastra: Build Production-Ready AI Agents in TypeScript with 22.3k+ GitHub Stars

Tosin Akinosho

— 4 min read

What is Mastra?

Mastra is an open-source TypeScript framework for building production-ready AI agents, created by the team behind Gatsby. Launched in January 2026 after graduating from Y Combinator's W25 batch with $13M in funding, Mastra has rapidly gained traction with 22.3k+ GitHub stars and over 300k weekly npm downloads. Unlike Python-dominated agent frameworks, Mastra is purpose-built for TypeScript developers and the JavaScript ecosystem, making AI agent development accessible to the millions of web developers who work with TypeScript daily.

The framework addresses a critical gap in the AI agent landscape. While frameworks like LangChain and AutoGen started in Python and added JavaScript ports later, Mastra was designed from the ground up for TypeScript. This isn't just a language choice—it reflects a fundamental shift in how developers approach AI. As Mastra co-creator Sam Bhagwat explains, "Developers don't have to know Python to build agents because they don't require the same heavy computational work that work on models does." Building agents is fundamentally different from training models; it's about orchestrating tools, managing context, and making decisions—tasks that are much closer to web application development.

Core Features and Architecture

Mastra organizes AI agent development around three core primitives that work together seamlessly:

1. Agents: Autonomous Tool-Calling Loops

Agents in Mastra are autonomous systems that use LLMs to decide which tools to call and in what sequence. You define an agent with a system prompt, attach tools with clear descriptions, and let the LLM figure out the execution path. The framework handles the reasoning loop, tool invocation, and response generation. This is perfect for exploratory, open-ended tasks where the exact sequence of steps isn't known in advance.

2. Tools: Typed Functions with Zod Schemas

Tools are the bridge between agents and external systems. In Mastra, you define tools using `createTool` with Zod schemas for input and output validation. Each tool gets an ID, a description that the LLM reads at runtime, and an execute function. The description is critical—it tells the model when and how to use the tool. Well-written tool descriptions are the difference between an agent that works and one that doesn't.

3. Workflows: Deterministic Pipelines

While agents are flexible, workflows are predictable. Workflows lock down the execution path at build time, ensuring every run follows the same steps in the same order. This is essential for batch jobs, scheduled tasks, and any scenario where consistency matters. Workflows use the same `createStep` pattern as tools but compose them into fixed DAGs (directed acyclic graphs) using methods like `.foreach()`, `.then()`, and `.parallel()`.

Additional Capabilities

Mastra Studio: A local web-based IDE for testing agents and workflows in real-time. You can visualize tool calls, see LLM reasoning, and debug before deploying to production.

Memory Systems: Both short-term and long-term memory that allow agents to maintain context across threads and sessions. Integrates with storage backends like libSQL and Postgres.

Model Context Protocol (MCP) Support: Connect agents to pre-built tools for Google Sheets, GitHub, databases, and more without writing custom integrations.

Structured Output: Pass Zod schemas to LLM calls and get back typed JSON instead of free-form text. The response is already parsed and type-checked.

Observability and Tracing: Built-in integration with logging and tracing tools to track agent decisions, tool latency, and token usage.

Evals and Scorers: Tools to measure agent performance using model-graded or rule-based metrics, helping you refine prompts before production.

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Getting Started with Mastra

Setting up a Mastra project is straightforward. The CLI scaffolds the project structure and generates starter code:

npx create-mastra@latest --llm openai

This creates a `src/mastra/` directory with subdirectories for agents, tools, and workflows. You'll need Node.js 18+, an OpenAI API key (or another supported LLM provider), and basic TypeScript knowledge.

Here's a minimal agent example:

import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const myAgent = new Agent({
  id: "my-agent",
  name: "My Agent",
  instructions: "You are a helpful assistant.",
  model: openai("gpt-4.1"),
  tools: { /* your tools here */ },
});

Tools follow the same pattern with `createTool`, Zod schemas for validation, and an execute function. Workflows compose steps using a fluent API that reads like a pipeline definition.

Real-World Use Cases

Changelog Tracking: Build an agent that searches for library changelogs, scrapes their content, and summarizes recent changes. The agent decides whether to search or scrape based on the input, and a workflow can batch-process multiple URLs in parallel.

Customer Support Automation: Create agents that handle common support tickets by searching your knowledge base, extracting relevant information, and drafting responses. Workflows can process incoming tickets in batches and route complex cases to humans.

Data Extraction and Enrichment: Use agents to extract structured data from unstructured sources like PDFs, web pages, or emails. Workflows ensure consistent output shapes for database insertion or downstream processing.

Internal Documentation Assistants: Deploy agents that understand your internal docs, codebase, and processes. They can answer questions, suggest solutions, and even generate code snippets based on your patterns.

How Mastra Compares to Alternatives

vs. LangChain: LangChain started in Python and added a JavaScript port later. Mastra is TypeScript-native from the ground up, with better integration for web frameworks like Next.js, Nuxt, and Astro. Mastra also includes Mastra Studio, a built-in IDE for testing agents, whereas LangChain requires external tools.

vs. AutoGen (Microsoft): AutoGen excels at multi-agent conversations and role-based simulations. Mastra is more focused on single-agent tool calling and deterministic workflows. If you need agents debating solutions, AutoGen is the better fit. If you need reliable, predictable automation, Mastra wins.

vs. CrewAI: CrewAI emphasizes role-playing agents that collaborate on tasks. Mastra is more developer-focused with stronger TypeScript support and better integration with web frameworks. CrewAI is great for creative multi-agent scenarios; Mastra is better for production applications.

What's Next for Mastra

The roadmap includes expanded memory backends, more pre-built integrations via MCP, improved streaming support for real-time agent responses, and enhanced observability features. The community is actively contributing skills and integrations, and the framework is evolving rapidly based on production usage.

Mastra represents a maturation of the AI agent ecosystem. It's no longer just researchers and Python experts building agents—web developers can now build production-grade AI systems using the tools and languages they already know. With 22.3k+ stars and growing adoption, Mastra is positioning itself as the go-to framework for TypeScript-first AI agent development.

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