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Working PR: Leverage @AI-SDK directly (#1776)
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- Embedding moves
- Updating docs
- Leverage @AI-SDK directly

---------

Co-authored-by: NikAiyer <nick.aiyer@gmail.com>
Co-authored-by: Shane Thomas <shane@codekarate.com>
Co-authored-by: Your Name <you@example.com>
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@abhiaiyer91 @NikAiyer @smthomas
4 people authored Feb 9, 2025
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27 changes: 11 additions & 16 deletions README.md
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# Mastra <img align="cener" alt="Project Status: Alpha" src="https://img.shields.io/badge/Status-Alpha-red"> <img align="cener" alt="Project Status: Alpha" src="https://img.shields.io/badge/Y%20Combinator-W25-orange?style=flat-square">



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 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](https://mastra.ai/docs/llm-models/00-overview) | 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](https://mastra.ai/docs/agents/00-overview) | 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](https://mastra.ai/docs/agents/02-adding-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](https://mastra.ai/docs/workflows/00-overview) | 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](https://mastra.ai/docs/rag/overview) | 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](https://mastra.ai/docs/local-dev/integrations) | In Mastra, 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](https://mastra.ai/docs/08-running-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. |
| Features | Description |
| ------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| LLM Models | Mastra uses the [Vercel AI SDK](https://sdk.vercel.ai/docs/introduction) for model routing, providing a unified interface to interact with any LLM provider including OpenAI, Anthropic, and Google Gemini. You can choose the specific model and provider, and decide whether to stream the response. |
| [Agents](https://mastra.ai/docs/agents/00-overview) | 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](https://mastra.ai/docs/agents/02-adding-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](https://mastra.ai/docs/workflows/00-overview) | 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](https://mastra.ai/docs/rag/overview) | 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](https://mastra.ai/docs/local-dev/integrations) | In Mastra, 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](https://mastra.ai/docs/08-running-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. |

## Quick Start

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Then, add this code to `src/index.ts`:

```typescript
import { openai } from '@ai-sdk/openai';
import { Agent } from '@mastra/core/agent';

async function main() {
const agent = new Agent({
name: 'story-writer',
model: {
provider: 'OPEN_AI',
name: 'gpt-4o-mini',
toolChoice: 'auto',
},
model: openai('gpt-4o-mini'),
instructions: `You are a helpful assistant who writes creative stories.`,
tools: {},
});
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