Agentic Workflows

import { Mastra } from "@mastra/core";
import { Agent } from "@mastra/core/agent";
import { Step, Workflow } from "@mastra/core/workflows";
import { z } from "zod";
import { openai } from "@ai-sdk/openai";
 
const agent = new Agent({
  name: 'Weather Agent',
  instructions: `
        You are a local activities and travel expert who excels at weather-based planning. Analyze the weather data and provide practical activity recommendations.
        For each day in the forecast, structure your response exactly as follows:
        📅 [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.
      `,
  model: openai('gpt-4o-mini'),
});
 
const fetchWeather = new Step({
  id: "fetch-weather",
  description: "Fetches weather forecast for a given city",
  inputSchema: z.object({
    city: z.string().describe("The city to get the weather for"),
  }),
  execute: async ({ context }) => {
    const triggerData = context?.getStepResult<{
      city: string;
    }>("trigger");
 
    if (!triggerData) {
      throw new Error("Trigger data not found");
    }
 
    const geocodingUrl = `https://geocoding-api.open-meteo.com/v1/search?name=${encodeURIComponent(triggerData.city)}&count=1`;
    const geocodingResponse = await fetch(geocodingUrl);
    const geocodingData = await geocodingResponse.json();
 
    if (!geocodingData.results?.[0]) {
      throw new Error(`Location '${triggerData.city}' not found`);
    }
 
    const { latitude, longitude, name } = geocodingData.results[0];
 
    const weatherUrl = `https://api.open-meteo.com/v1/forecast?latitude=${latitude}&longitude=${longitude}&daily=temperature_2m_max,temperature_2m_min,precipitation_probability_mean,weathercode&timezone=auto`;
    const response = await fetch(weatherUrl);
    const data = await response.json();
 
    const forecast = data.daily.time.map((date: string, index: number) => ({
      date,
      maxTemp: data.daily.temperature_2m_max[index],
      minTemp: data.daily.temperature_2m_min[index],
      precipitationChance: data.daily.precipitation_probability_mean[index],
      condition: getWeatherCondition(data.daily.weathercode[index]),
      location: name,
    }));
 
    return forecast;
  },
});
 
const forecastSchema = z.array(
  z.object({
    date: z.string(),
    maxTemp: z.number(),
    minTemp: z.number(),
    precipitationChance: z.number(),
    condition: z.string(),
    location: z.string(),
  }),
);
 
const planActivities = new Step({
  id: "plan-activities",
  description: "Suggests activities based on weather conditions",
  inputSchema: forecastSchema,
  execute: async ({ context, mastra }) => {
    const forecast =
      context?.getStepResult<z.infer<typeof forecastSchema>>(
        "fetch-weather",
      );
 
    if (!forecast) {
      throw new Error("Forecast data not found");
    }
 
    const prompt = `Based on the following weather forecast for ${forecast[0].location}, suggest appropriate activities:
      ${JSON.stringify(forecast, null, 2)}
      `;
 
    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,
    };
  },
});
 
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 weatherWorkflow = new Workflow({
  name: "weather-workflow",
  triggerSchema: z.object({
    city: z.string().describe("The city to get the weather for"),
  }),
})
  .step(fetchWeather)
  .then(planActivities);
 
weatherWorkflow.commit();
 
const mastra = new Mastra({
  workflows: {
    weatherWorkflow,
  },
});
 
async function main() {
  const { start } = mastra.getWorkflow("weatherWorkflow").createRun();
 
  const result = await start({
    triggerData: {
      city: "London",
    },
  });
 
  console.log("\n \n");
  console.log(result);
}
 
main();