条件分岐を持つワークフロー

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

import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";
 
const llm = openai("gpt-4o");
 
// Define the planning agent that generates activity recommendations
// based on weather conditions and location
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 };

import { z } from 'zod'
import { createWorkflow, createStep } from "@mastra/core/workflows/vNext";
 
// Helper function to convert weather codes to human-readable conditions
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 to fetch weather data for a given city
// Makes API calls to get current weather conditions and forecast
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");
    }
 
    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];
 
    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 to plan activities based on weather conditions
// Uses the planning agent to generate activity recommendations
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,
    };
  },
});
 
// Step to plan indoor activities only
// Used when precipitation chance is high
const planIndoorActivities = createStep({
  id: "plan-indoor-activities",
  description: "天候状況に基づいて屋内活動を提案します",
  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 = `雨が降った場合、${forecast.location}の${forecast.date}における屋内活動を計画してください`;
 
    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,
    };
  },
});
 
// Main workflow that:
// 1. Fetches weather data
// 2. Branches based on precipitation chance:
//    - If >50%: Plans indoor activities
//    - If <=50%: Plans outdoor activities
const activityPlanningWorkflow = createWorkflow({
  id: 'activity-planning-workflow-step2-if-else',
  inputSchema: z.object({
    city: z.string().describe("天気を取得する都市"),
  }),
  outputSchema: z.object({
    activities: z.string(),
  }),
})
  .then(fetchWeather)
  .branch([
    // Branch for high precipitation (indoor activities)
    [
      async ({ inputData }) => {
        return inputData?.precipitationChance > 50;
      },
      planIndoorActivities,
    ],
    // Branch for low precipitation (outdoor activities)
    [
      async ({ inputData }) => {
        return inputData?.precipitationChance <= 50;
      },
      planActivities,
    ],
  ]);
 
activityPlanningWorkflow.commit()
 
export { activityPlanningWorkflow }

import { Mastra } from '@mastra/core/mastra'
import { createLogger } from '@mastra/core/logger'
import { activityPlanningWorkflow } from './workflows/conditional-workflow'
import { planningAgent } from './agents/planning-agent'
 
// Initialize Mastra with the activity planning workflow
// This enables the workflow to be executed and access the planning agent
const mastra = new Mastra({
  vnext_workflows: {
    activityPlanningWorkflow,
  },
  agents: {
    planningAgent,
  },
  logger: createLogger({
    name: "Mastra",
    level: "info",
  }),
})
 
export { mastra }

import { mastra } from "./";
 
const workflow = mastra.vnext_getWorkflow('activityPlanningWorkflow')
const run = workflow.createRun()
 
// Start the workflow with a city
// This will fetch weather and plan activities based on conditions
const result = await run.start({ inputData: { city: 'New York' } })
console.dir(result, { depth: null })