Mastra with AI Gateway

Mastra is a framework for building and deploying AI-powered features using a modern JavaScript stack powered by the Edgenivo AI SDK. Integrating with AI Gateway provides unified model management and routing capabilities.

For project setup, deployment, and storage options, see Mastra on Edgenivo.

1. Create a new Mastra project

   First, create a new Mastra project using the CLI:

   terminal

   pnpm dlx create-mastra@latest

   During the setup, the system prompts you to name your project, choose a default provider, and more. and more. Feel free to use the default settings.

2. Install dependencies

   To use the AI Gateway provider, install the @ai-sdk/gateway package along with Mastra:

   pnpmyarnnpmbun

   Terminal

   pnpm i @ai-sdk/gateway mastra @mastra/core @mastra/memory

   Terminal

   yarn add @ai-sdk/gateway mastra @mastra/core @mastra/memory

   Terminal

   npm i @ai-sdk/gateway mastra @mastra/core @mastra/memory

   Terminal

   bun add @ai-sdk/gateway mastra @mastra/core @mastra/memory

3. Configure environment variables

   Create or update your .env file with your Edgenivo AI Gateway API key:

   .env

   AI_GATEWAY_API_KEY=your-api-key-here

4. Configure your agent to use AI Gateway

   Now, swap out the @ai-sdk/openai package (or your existing model provider) for the @ai-sdk/gateway package.

   Update your agent configuration file, typically src/mastra/agents/weather-agent.ts to the following code:

   src/mastra/agents/weather-agent.ts

   import 'dotenv/config';
   import { gateway } from '@ai-sdk/gateway';
   import { Agent } from '@mastra/core/agent';
   import { Memory } from '@mastra/memory';
   import { LibSQLStore } from '@mastra/libsql';
   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 and can help planning activities based on the weather.
    
         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
         - If giving a location with multiple parts (e.g. "New York, NY"), use the most relevant part (e.g. "New York")
         - Include relevant details like humidity, wind conditions, and precipitation
         - Keep responses concise but informative
         - If the user asks for activities and provides the weather forecast, suggest activities based on the weather forecast.
         - If the user asks for activities, respond in the format they request.
    
         Use the weatherTool to fetch current weather data.
   `,
     model: gateway('google/gemini-3.1-pro-preview'),
     tools: { weatherTool },
     memory: new Memory({
       storage: new LibSQLStore({
         url: 'file:../mastra.db', // path is relative to the .mastra/output directory
       }),
     }),
   });
    
   (async () => {
     try {
       const response = await weatherAgent.generate(
         "What's the weather in San Francisco today?",
       );
       console.log('Weather Agent Response:', response.text);
     } catch (error) {
       console.error('Error invoking weather agent:', error);
     }
   })();

5. Running the application

   Since your agent is now configured to use AI Gateway, run the Mastra development server:

   pnpmyarnnpmbun

   Terminal

   pnpm dev 

   Terminal

   yarn dev 

   Terminal

   npm run dev 

   Terminal

   bun run dev 

   Open the Mastra API to test your agents, workflows, and tools.

To give your Mastra agent isolated command execution, add a workspace with the VercelSandbox adapter. Follow Mastra with Edgenivo Sandbox to configure authentication, run an agent, and clean up its sandbox.

Connect the Mastra agent you deploy on Edgenivo to Slack, Discord, or Microsoft Teams with Chat SDK. Users can send prompts in chat while AI Gateway routes the agent's model requests. Inspect those requests in AI Gateway observability.

Mastra's channels integration accepts Chat SDK adapters through channels.adapters and registers their webhook routes. Point the platform's webhook URL to your deployed Mastra server. See Mastra's channels guide for adapter credentials, webhook configuration, and persistent storage.