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mastra-ai/mastra

📈 Trending★ 24,011TypeScriptAudience · developerComplexity · 4/5ActiveSetup · moderate

TLDR

TypeScript framework for building AI agents and applications with model routing, memory, workflows, and human-in-the-loop controls, everything needed to go from prototype to production.

Mindmap

mindmap
  root((Mastra))
    What it does
      AI agent builder
      Workflow orchestration
      Model routing
      Memory management
    Key features
      40+ AI providers
      Human-in-the-loop
      Tool integration
      Evaluation tools
    Tech stack
      TypeScript
      Next.js support
      Model Context Protocol
    Use cases
      Chatbots
      Research agents
      Workflow automation
      Production AI apps

Things people build with this

USE CASE 1

Build a chatbot that remembers conversation history and routes requests to the best AI model for each task.

USE CASE 2

Create an autonomous research agent that uses tools to gather information, reason through findings, and produce reports.

USE CASE 3

Automate multi-step workflows where an AI makes decisions but pauses for human approval on critical steps.

USE CASE 4

Deploy production AI applications with built-in evaluation to measure and improve agent performance over time.

Tech stack

TypeScriptNext.jsOpenAIAnthropicGeminiModel Context Protocol

Getting it running

Difficulty · moderate Time to first run · 30min

Requires API keys for at least one LLM provider (OpenAI, Anthropic, or Gemini).

License could not be detected automatically. Check the repository's LICENSE file before use.

In plain English

Mastra is a TypeScript framework for building AI-powered applications and agents. It is designed to take developers from early prototypes to production-ready AI products, with all the infrastructure needed bundled into one framework. An "agent" in this context is a program that uses a large language model (LLM) to reason through a goal, decide which tools to use, and iterate until it produces a final result, without you having to script every step. Mastra makes building these agents easier by providing model routing (connect to over 40 different AI providers including OpenAI, Anthropic, and Gemini through one consistent interface), memory (so agents remember past conversations and can recall relevant information), tool integration, and workflow orchestration for multi-step processes. The workflow system lets you chain multiple steps, add branching logic, and even pause execution to wait for a human to approve something before continuing. This last feature, human-in-the-loop, is important for production use cases where some decisions need a person's oversight. Mastra integrates with web frameworks like Next.js and supports the Model Context Protocol (MCP), a standard for exposing tools and resources to AI models. It also includes built-in evaluation tools for measuring and improving agent quality over time. You would use Mastra when building a TypeScript-based application that needs AI capabilities, chatbots, autonomous research agents, workflow automation with AI decision-making, or any system where you want structured control over how an LLM behaves in production.

Copy-paste prompts

Prompt 1
Show me how to set up a Mastra agent that can route requests between OpenAI and Anthropic models based on task complexity.
Prompt 2
How do I add memory to a Mastra agent so it can recall previous conversations and context?
Prompt 3
Walk me through creating a multi-step workflow in Mastra with human-in-the-loop approval for sensitive decisions.
Prompt 4
How do I integrate custom tools into a Mastra agent and expose them via the Model Context Protocol?
Prompt 5
Show me how to evaluate and measure the quality of my Mastra agent's responses over time.
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.