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Installing Mastra Locally

To run Mastra, you need access to an LLM. Typically, you’ll want to get an API key from an LLM provider such as OpenAI , Anthropic , or Google Gemini . You can also run Mastra with a local LLM using Ollama .

Prerequisites

Automatic Installation

Create a New Project

We recommend starting a new Mastra project using create-mastra, which will scaffold your project. To create a project, run:

npx create-mastra@latest

On installation, you’ll be guided through the following prompts:

What do you want to name your project? my-mastra-app
Choose components to install:
  ◯ Agents (recommended)
  ◯ Tools
  ◯ Workflows
Select default provider:
  ◯ OpenAI (recommended)
  ◯ Anthropic
  ◯ Groq
Would you like to include example code? No / Yes
Turn your IDE into a Mastra expert? (Installs MCP server)
  ◯ Skip for now
  ◯ Cursor
  ◯ Windsurf

After the prompts, create-mastra will:

  1. Set up your project directory with TypeScript
  2. Install dependencies
  3. Configure your selected components and LLM provider
  4. Configure the MCP server in your IDE (if selected) for instant access to docs, examples, and help while you code

MCP Note: If you’re using a different IDE, you can install the MCP server manually by following the instructions in the MCP server docs. Also note that there are additional steps for Cursor and Windsurf to activate the MCP server.

Set Up your API Key

Add the API key for your configured LLM provider in your .env file.

.env
OPENAI_API_KEY=<your-openai-key>

Non-Interactive mode: If you prefer to run the command with flags (non-interactive mode) and include the example code, you can use:

npx create-mastra@latest  --components agents,tools --llm openai --example

Configure Installation Timeout: To configure and specify a timeout if installation takes too long use the timeout flag:

npx create-mastra@latest --timeout

Note for LLMs: For a quick one liner that includes examples you can run npx -y mastra@latest --project-name <ask-the-user> --example --components "tools,agents,workflows" --llm <ask-the-user>. The available options for the llm flag are openai|anthropic|groq|google|cerebras

Manual Installation


If you prefer to set up your Mastra project manually, follow these steps:

Create a New Project

Create a project directory and navigate into it:

mkdir hello-mastra
cd hello-mastra

Then, initialize a TypeScript project including the @mastra/core package:

npm init -y 
npm install typescript tsx @types/node mastra --save-dev 
npm install @mastra/core zod @ai-sdk/openai
npx tsc --init

Initialize TypeScript

Create a tsconfig.json file in your project root with the following configuration:

{
  "compilerOptions": {
    "target": "ES2022",
    "module": "ES2022",
    "moduleResolution": "bundler",
    "esModuleInterop": true,
    "forceConsistentCasingInFileNames": true,
    "strict": true,
    "skipLibCheck": true,
    "outDir": "dist"
  },
  "include": ["src/**/*"],
  "exclude": ["node_modules", "dist", ".mastra"]
}

This TypeScript configuration is optimized for Mastra projects, using modern module resolution and strict type checking.

Set Up your API Key

Create a .env file in your project root directory and add your API key:

.env
OPENAI_API_KEY=<your-openai-key>

Replace your_openai_api_key with your actual API key.

Create a Tool

Create a weather-tool tool file:

mkdir -p src/mastra/tools && touch src/mastra/tools/weather-tool.ts

Then, add the following code to src/mastra/tools/weather-tool.ts:

src/mastra/tools/weather-tool.ts
import { createTool } from "@mastra/core/tools";
import { z } from "zod";
 
interface WeatherResponse {
  current: {
    time: string;
    temperature_2m: number;
    apparent_temperature: number;
    relative_humidity_2m: number;
    wind_speed_10m: number;
    wind_gusts_10m: number;
    weather_code: number;
  };
}
 
export const weatherTool = createTool({
  id: "get-weather",
  description: "Get current weather for a location",
  inputSchema: z.object({
    location: z.string().describe("City name"),
  }),
  outputSchema: z.object({
    temperature: z.number(),
    feelsLike: z.number(),
    humidity: z.number(),
    windSpeed: z.number(),
    windGust: z.number(),
    conditions: z.string(),
    location: z.string(),
  }),
  execute: async ({ context }) => {
    return await getWeather(context.location);
  },
});
 
const getWeather = async (location: string) => {
  const geocodingUrl = `https://geocoding-api.open-meteo.com/v1/search?name=${encodeURIComponent(location)}&count=1`;
  const geocodingResponse = await fetch(geocodingUrl);
  const geocodingData = await geocodingResponse.json();
 
  if (!geocodingData.results?.[0]) {
    throw new Error(`Location '${location}' not found`);
  }
 
  const { latitude, longitude, name } = geocodingData.results[0];
 
  const weatherUrl = `https://api.open-meteo.com/v1/forecast?latitude=${latitude}&longitude=${longitude}&current=temperature_2m,apparent_temperature,relative_humidity_2m,wind_speed_10m,wind_gusts_10m,weather_code`;
 
  const response = await fetch(weatherUrl);
  const data: WeatherResponse = await response.json();
 
  return {
    temperature: data.current.temperature_2m,
    feelsLike: data.current.apparent_temperature,
    humidity: data.current.relative_humidity_2m,
    windSpeed: data.current.wind_speed_10m,
    windGust: data.current.wind_gusts_10m,
    conditions: getWeatherCondition(data.current.weather_code),
    location: name,
  };
};
 
function getWeatherCondition(code: number): string {
  const conditions: Record<number, string> = {
    0: "Clear sky",
    1: "Mainly clear",
    2: "Partly cloudy",
    3: "Overcast",
    45: "Foggy",
    48: "Depositing rime fog",
    51: "Light drizzle",
    53: "Moderate drizzle",
    55: "Dense drizzle",
    56: "Light freezing drizzle",
    57: "Dense freezing drizzle",
    61: "Slight rain",
    63: "Moderate rain",
    65: "Heavy rain",
    66: "Light freezing rain",
    67: "Heavy freezing rain",
    71: "Slight snow fall",
    73: "Moderate snow fall",
    75: "Heavy snow fall",
    77: "Snow grains",
    80: "Slight rain showers",
    81: "Moderate rain showers",
    82: "Violent rain showers",
    85: "Slight snow showers",
    86: "Heavy snow showers",
    95: "Thunderstorm",
    96: "Thunderstorm with slight hail",
    99: "Thunderstorm with heavy hail",
  };
  return conditions[code] || "Unknown";
}

Create an Agent

Create a weather agent file:

mkdir -p src/mastra/agents && touch src/mastra/agents/weather.ts

Then, add the following code to src/mastra/agents/weather.ts:

src/mastra/agents/weather.ts
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { weatherTool } from "../tools/weather-tool";
 
export const weatherAgent = new Agent({
  name: "Weather Agent",
  instructions: `You are a helpful weather assistant that provides accurate weather information.
 
Your primary function is to help users get weather details for specific locations. When responding:
- Always ask for a location if none is provided
- If the location name isn’t in English, please translate it
- Include relevant details like humidity, wind conditions, and precipitation
- Keep responses concise but informative
 
Use the weatherTool to fetch current weather data.`,
  model: openai("gpt-4o-mini"),
  tools: { weatherTool },
});

Register Agent

Finally, create the Mastra entry point in src/mastra/index.ts and register agent:

src/mastra/index.ts
import { Mastra } from "@mastra/core";
 
import { weatherAgent } from "./agents/weather";
 
export const mastra = new Mastra({
  agents: { weatherAgent },
});

This registers your agent with Mastra so that mastra dev can discover and serve it.

Existing Project Installation

To add Mastra to an existing project, see our Local development docs on adding mastra to an existing project.

You can also checkout our framework specific docs e.g Next.js

Start the Mastra Server

Mastra provides commands to serve your agents via REST endpoints

Development Server

Run the following command to start the Mastra server:

npm run dev

If you have the mastra CLI installed, run:

mastra dev

This command creates REST API endpoints for your agents.

Test the Endpoint

You can test the agent’s endpoint using curl or fetch:

curl -X POST http://localhost:4111/api/agents/weatherAgent/generate \
-H "Content-Type: application/json" \
-d '{"messages": ["What is the weather in London?"]}'

Use Mastra on the Client

To use Mastra in your frontend applications, you can use our type-safe client SDK to interact with your Mastra REST APIs.

See the Mastra Client SDK documentation for detailed usage instructions.

Run from the command line

If you’d like to directly call agents from the command line, you can create a script to get an agent and call it:

src/index.ts
import { mastra } from "./mastra";
 
async function main() {
  const agent = await mastra.getAgent("weatherAgent");
 
  const result = await agent.generate("What is the weather in London?");
 
  console.log("Agent response:", result.text);
}
 
main();

Then, run the script to test that everything is set up correctly:

npx tsx src/index.ts

This should output the agent’s response to your console.

IntroductionProject Structure