Mastra is an opinionated Typescript framework that helps you build AI applications and features quickly. It gives you the set of primitives you need: workflows, agents, RAG, integrations, syncs and evals. You can run Mastra on your local machine, or deploy to a serverless cloud.
The main Mastra features are:
| Features | Description |
|---|---|
| LLM Models | Mastra supports a variety of LLM providers, including OpenAI, Anthropic, Google Gemini. You can choose the specific model and provider, choose system and user prompts, and decide whether to stream the response. |
| Agents | Agents are systems where the language model chooses a sequence of actions. In Mastra, agents provide LLM models with tools, workflows, and synced data. Agents can call your own functions or APIs of third-party integrations and access knowledge bases you build. |
| Tools | Tools are typed functions that can be executed by agents or workflows, with built-in integration access and parameter validation. Each tool has a schema that defines its inputs, an executor function that implements its logic, and access to configured integrations. |
| Workflows | Workflows are durable graph-based state machines. They have loops, branching, wait for human input, embed other workflows, do error handling, retries, parsing and so on. They can be built in code or with a visual editor. Each step in a workflow has built-in OpenTelemetry tracing. |
| RAG | Retrieval-augemented generation (RAG) lets you construct a knowledge base for agents. RAG is an ETL pipeline with specific querying techniques, including chunking, embedding, and vector search. |
| Integrations & Syncs | In Mastra, syncs are async functions that can be deployed as background tasks across different execution environments. Integrations are auto-generated, type-safe API clients for third-party services that can be used as tools for agents or steps in workflows. |
| Evals | Evals are automated tests that evaluate LLM outputs using model-graded, rule-based, and statistical methods. Each eval returns a normalized score between 0-1 that can be logged and compared. Evals can be customized with your own prompts and scoring functions. |
- Node.js (v20.0+)
If you don't have an API key for an LLM provider, you can get one from the following services:
If you don't have an account with these providers, you can sign up and get an API key. OpenAI and Anthropic require a credit card to get an API key. Gemini does not and has a generous free tier for its API.
As a first step, create a project directory and navigate into it:
mkdir hello-mastra
cd hello-mastraNext, initialize a TypeScript project using npm:
npm init -y
npm install typescript tsx @types/node @mastra/core@alpha --save-devmkdir src
touch src/index.tsThen, add this code to src/index.ts:
import { Agent } from '@mastra/core';
async function main() {
const agent = new Agent({
name: 'story-writer',
maxSteps: 3,
model: {
provider: 'OPEN_AI',
name: 'gpt-4o',
toolChoice: 'auto',
},
instructions: `You are a helpful assistant who writes creative stories.`,
tools: {},
});
const result = await agent.text({
messages: ['Write a short story about a robot learning to paint.'],
});
console.log('Agent response:', result.text);
}
main();Finally, run the script:
OPENAI_API_KEY=<your-openai-api-key> npx tsx src/index.tsIf you're using Anthropic, set the ANTHROPIC_API_KEY. If you're using Gemini, set the GOOGLE_GENERATIVE_AI_API_KEY.
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