不安定なエージェント構築はやめよう：ワークフローとメモリを使え

import { createWorkflow, createStep } from '@mastra/core/workflows';
import { Agent } from '@mastra/core/agent';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

// Step 1: Fetch weather data (Deterministic)
const fetchWeather = createStep({
  id: 'fetch-weather',
  description: 'Fetches weather forecast for a given city',
  inputSchema: z.object({
    city: z.string(),
  }),
  outputSchema: z.object({
    location: z.string(),
    temperature: z.number(),
    conditions: z.string(),
    precipitationChance: z.number(),
  }),
  execute: async ({ inputData }) => {
    // ... (fetch logic) ...
    const weather = await fetch(`https://api.open-meteo.com/v1/forecast?latitude=52.52&longitude=13.41&current=temperature_2m,weather_code&daily=precipitation_probability_mean`).then(r => r.json());

    return {
      location: inputData.city,
      temperature: weather.current.temperature_2m,
      conditions: getWeatherCondition(weather.current.weather_code),
      precipitationChance: weather.daily.precipitation_probability_mean[0],
    };
  },
});

// Step 2: Agent suggests activities (Creative)
const activityPlanner = new Agent({
  id: 'activity-planner-agent',
  name: 'Activity Planner',
  instructions: `You are a local activities expert. Based on weather conditions, suggest 3-5 appropriate activities.
    - For rain (>50% precipitation), prioritize indoor activities
    - For extreme temperatures, consider climate-appropriate options
    - Always include one adventurous and one relaxing option`,
  model: openai('gpt-5'),
});

const planActivities = createStep({
  id: 'plan-activities',
  description: 'Uses AI to suggest activities based on weather',
  inputSchema: z.object({
    location: z.string(),
    temperature: z.number(),
    conditions: z.string(),
    precipitationChance: z.number(),
  }),
  outputSchema: z.object({
    activities: z.string(),
  }),
  execute: async ({ inputData }) => {
    const prompt = `Weather in ${inputData.location}: ${inputData.temperature}°C...`;
    const response = await activityPlanner.generate(prompt);
    return { activities: response.text };
  },
});

// The Pipeline
export const activityPlannerWorkflow = createWorkflow({
  id: 'activity-planner',
  inputSchema: z.object({ city: z.string() }),
  outputSchema: z.object({ activities: z.string() }),
})
  .then(fetchWeather)
  .then(planActivities);

activityPlannerWorkflow.commit();

import { Agent } from '@mastra/core/agent';
import { Memory } from '@mastra/memory';
import { LibSQLStore } from '@mastra/libsql';

export const memoryAgent = new Agent({
  id: 'memory-agent',
  name: 'Memory Agent',
  instructions: 'You are a helpful assistant with perfect recall of our conversations.',
  model: openai('gpt-5'),
  memory: new Memory({
    storage: new LibSQLStore({
      id: 'memory-agent-store',
      url: 'file:../mastra.db',
    }),
    options: {
      lastMessages: 20,  // Keep last 20 messages in context
      semanticRecall: {
        enabled: true,  // Use embeddings to find old stuff
        topK: 5,
        threshold: 0.7,
      },
    },
  }),
});