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Calling an Agent From a Workflow

This example demonstrates how to create a workflow that calls an AI agent to suggest activities for the provided weather conditions, and execute it within a workflow step.

Setup

npm install @ai-sdk/openai @mastra/core

Define Planning Agent

Define a planning agent which leverages an LLM call to plan activities given a location and corresponding weather conditions.

agents/planning-agent.ts
import { Agent } from "@mastra/core/agent"; import { openai } from "@ai-sdk/openai"; const llm = openai("gpt-4o"); // Create a new agent for activity planning const planningAgent = new Agent({ name: "planningAgent", model: llm, instructions: ` You are a local activities and travel expert who excels at weather-based planning. Analyze the weather data and provide practical activity recommendations. 📅 [Day, Month Date, Year] ═══════════════════════════ 🌡️ WEATHER SUMMARY • Conditions: [brief description] • Temperature: [X°C/Y°F to A°C/B°F] • Precipitation: [X% chance] 🌅 MORNING ACTIVITIES Outdoor: • [Activity Name] - [Brief description including specific location/route] Best timing: [specific time range] Note: [relevant weather consideration] 🌞 AFTERNOON ACTIVITIES Outdoor: • [Activity Name] - [Brief description including specific location/route] Best timing: [specific time range] Note: [relevant weather consideration] 🏠 INDOOR ALTERNATIVES • [Activity Name] - [Brief description including specific venue] Ideal for: [weather condition that would trigger this alternative] ⚠️ SPECIAL CONSIDERATIONS • [Any relevant weather warnings, UV index, wind conditions, etc.] Guidelines: - Suggest 2-3 time-specific outdoor activities per day - Include 1-2 indoor backup options - For precipitation >50%, lead with indoor activities - All activities must be specific to the location - Include specific venues, trails, or locations - Consider activity intensity based on temperature - Keep descriptions concise but informative Maintain this exact formatting for consistency, using the emoji and section headers as shown. `, }); export { planningAgent };

Define Activity Planning Workflow

Define the activity planning workflow with 2 steps: one to fetch the weather via a network call, and another to plan activities using the planning agent.

workflows/agent-workflow.ts
import { createWorkflow, createStep } from '@mastra/core/workflows/vNext' import { z } from 'zod' // Helper function to convert numeric weather codes to human-readable descriptions 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", 61: "Slight rain", 63: "Moderate rain", 65: "Heavy rain", 71: "Slight snow fall", 73: "Moderate snow fall", 75: "Heavy snow fall", 95: "Thunderstorm", }; return conditions[code] || "Unknown"; } const forecastSchema = z.object({ date: z.string(), maxTemp: z.number(), minTemp: z.number(), precipitationChance: z.number(), condition: z.string(), location: z.string(), }) // Step 1: Create a step to fetch weather data for a given city const fetchWeather = createStep({ id: "fetch-weather", description: "Fetches weather forecast for a given city", inputSchema: z.object({ city: z.string(), }), outputSchema: forecastSchema, execute: async ({ inputData }) => { if (!inputData) { throw new Error("Trigger data not found"); } // First API call: Convert city name to latitude and longitude const geocodingUrl = `https://geocoding-api.open-meteo.com/v1/search?name=${encodeURIComponent(inputData.city)}&count=1` const geocodingResponse = await fetch(geocodingUrl) const geocodingData = (await geocodingResponse.json()) as { results: { latitude: number; longitude: number; name: string }[] } if (!geocodingData.results?.[0]) { throw new Error(`Location '${inputData.city}' not found`); } const { latitude, longitude, name } = geocodingData.results[0] // Second API call: Get weather data using coordinates const weatherUrl = `https://api.open-meteo.com/v1/forecast?latitude=${latitude}&longitude=${longitude}&current=precipitation,weathercode&timezone=auto,&hourly=precipitation_probability,temperature_2m` const response = await fetch(weatherUrl) const data = (await response.json()) as { current: { time: string; precipitation: number; weathercode: number; }; hourly: { precipitation_probability: number[] temperature_2m: number[] } } const forecast = { date: new Date().toISOString(), maxTemp: Math.max(...data.hourly.temperature_2m), minTemp: Math.min(...data.hourly.temperature_2m), condition: getWeatherCondition(data.current.weathercode), location: name, precipitationChance: data.hourly.precipitation_probability.reduce( (acc, curr) => Math.max(acc, curr), 0, ), } return forecast }, }) // Step 2: Create a step to generate activity recommendations using the agent const planActivities = createStep({ id: "plan-activities", description: "Suggests activities based on weather conditions", inputSchema: forecastSchema, outputSchema: z.object({ activities: z.string(), }), execute: async ({ inputData, mastra }) => { const forecast = inputData if (!forecast) { throw new Error("Forecast data not found"); } const prompt = `Based on the following weather forecast for ${forecast.location}, suggest appropriate activities: ${JSON.stringify(forecast, null, 2)} ` const agent = mastra?.getAgent('planningAgent') if (!agent) { throw new Error("Planning agent not found"); } const response = await agent.stream([ { role: "user", content: prompt, }, ]) let activitiesText = '' for await (const chunk of response.textStream) { process.stdout.write(chunk); activitiesText += chunk; } return { activities: activitiesText, }; }, }) const activityPlanningWorkflow = createWorkflow({ steps: [fetchWeather, planActivities], id: 'activity-planning-step1-single-day', inputSchema: z.object({ city: z.string().describe("The city to get the weather for"), }), outputSchema: z.object({ activities: z.string(), }), }) .then(fetchWeather) .then(planActivities) activityPlanningWorkflow.commit() export { activityPlanningWorkflow }

Register Agent and Workflow instances with Mastra class

Register the planning agent and activity planning workflow with the mastra instance. This is critical for enabling access to the planning agent within the activity planning workflow.

index.ts
import { Mastra } from '@mastra/core' import { PinoLogger } from '@mastra/loggers' import { activityPlanningWorkflow } from './workflows/agent-workflow' import { planningAgent } from './agents/planning-agent' // Create a new Mastra instance and register components const mastra = new Mastra({ vnext_workflows: { activityPlanningWorkflow, }, agents: { planningAgent, }, logger: PinoLogger({ name: "Mastra", level: "info", }), }) export { mastra }

Execute the activity planning workflow

Here, we’ll get the activity planning workflow from the mastra instance, then create a run and execute the created run with the required inputData.

exec.ts
import { mastra } from "./"; const workflow = mastra.vnext_getWorkflow('activityPlanningWorkflow') const run = workflow.createRun() // Start the workflow with New York as the city input const result = await run.start({ inputData: { city: 'New York' } }) console.dir(result, { depth: null })