MetaClaw framework trains AI agents while you're in meetings by checking your Google Calendar

Introduction

In this tutorial, we'll build a framework similar to MetaClaw that trains AI agents using Google Calendar data. The framework will automatically detect when users are in meetings and schedule AI training sessions during those times. This approach optimizes training efficiency by leveraging existing calendar data rather than requiring manual scheduling.

Prerequisites

• Python 3.8+
• Google Cloud account with enabled Calendar API
• Basic understanding of AI/ML concepts and Python programming
• Installed libraries: google-api-python-client, google-auth, pandas, schedule

Step-by-Step Instructions

Step 1: Set Up Google Cloud Project and API Access

First, we need to create a Google Cloud project and enable the Calendar API. This will allow us to programmatically access calendar data.

1.1 Create Google Cloud Project

Visit the Google Cloud Console and create a new project named "MetaClawFramework".

1.2 Enable Calendar API

Navigate to the API Library section and search for "Google Calendar API". Click on it and enable the API for your project.

1.3 Create Credentials

Go to the "Credentials" section and create a new "OAuth 2.0 Client ID". Download the JSON file and save it as credentials.json in your project directory.

Step 2: Install Required Python Libraries

We'll need several Python libraries to interact with Google Calendar and manage AI training sessions.

pip install google-api-python-client google-auth pandas schedule

Step 3: Create Google Calendar Authentication

Next, we'll implement the authentication system to access the user's calendar data.

import pickle
import os
from google.auth.transport.requests import Request
from google_auth_oauthlib.flow import InstalledAppFlow
from googleapiclient.discovery import build

# If modifying these scopes, delete the file token.pickle.
SCOPES = ['https://www.googleapis.com/auth/calendar.readonly']


def authenticate_calendar():
    """Authenticate and return the Google Calendar service object"""
    creds = None
    # The file token.pickle stores the user's access and refresh tokens.
    if os.path.exists('token.pickle'):
        with open('token.pickle', 'rb') as token:
            creds = pickle.load(token)

    # If there are no (valid) credentials available, let the user log in.
    if not creds or not creds.valid:
        if creds and creds.expired and creds.refresh_token:
            creds.refresh(Request())
        else:
            flow = InstalledAppFlow.from_client_secrets_file(
                'credentials.json', SCOPES)
            creds = flow.run_local_server(port=0)
        # Save the credentials for the next run
        with open('token.pickle', 'wb') as token:
            pickle.dump(creds, token)

    return build('calendar', 'v3', credentials=creds)

Step 4: Fetch Calendar Events

Now we'll create a function to fetch upcoming calendar events and identify meeting times.

from datetime import datetime, timedelta
import pandas as pd


def get_upcoming_meetings(service, days_ahead=7):
    """Fetch upcoming meetings from Google Calendar"""
    # Call the Calendar API
    now = datetime.utcnow().isoformat() + 'Z'  # 'Z' indicates UTC time
    end = (datetime.utcnow() + timedelta(days=days_ahead)).isoformat() + 'Z'

    events_result = service.events().list(
        calendarId='primary',
        timeMin=now,
        timeMax=end,
        singleEvents=True,
        orderBy='startTime'
    ).execute()

    events = events_result.get('items', [])

    meetings = []
    for event in events:
        start = event['start'].get('dateTime', event['start'].get('date'))
        end_time = event['end'].get('dateTime', event['end'].get('date'))
        
        # Filter for meetings (events with a summary)
        if 'summary' in event and event['summary']:
            meetings.append({
                'summary': event['summary'],
                'start': start,
                'end': end_time,
                'id': event['id']
            })

    return meetings

Step 5: Implement AI Training Scheduler

We'll now create the core logic that determines when to schedule AI training based on calendar meetings.

import schedule
import time
from datetime import datetime


class AITrainingScheduler:
    def __init__(self, calendar_service):
        self.calendar_service = calendar_service
        self.training_sessions = []

    def schedule_training_during_meetings(self):
        """Schedule AI training during upcoming meetings"""
        meetings = get_upcoming_meetings(self.calendar_service)
        
        for meeting in meetings:
            # Convert meeting times to datetime objects
            start_time = datetime.fromisoformat(meeting['start'].replace('Z', '+00:00'))
            end_time = datetime.fromisoformat(meeting['end'].replace('Z', '+00:00'))
            
            # Schedule training 30 minutes before meeting starts
            training_time = start_time - timedelta(minutes=30)
            
            # Only schedule if training time is in the future
            if training_time > datetime.now():
                self.training_sessions.append({
                    'meeting_id': meeting['id'],
                    'training_time': training_time,
                    'meeting_summary': meeting['summary']
                })
                
                # Schedule the actual training
                schedule.every().day.at(training_time.strftime('%H:%M')).do(
                    self.run_ai_training,
                    meeting=meeting['summary']
                )
                
                print(f"Scheduled training for {meeting['summary']} at {training_time}")

    def run_ai_training(self, meeting):
        """Placeholder function for actual AI training logic"""
        print(f"Running AI training session for meeting: {meeting}")
        # Here you would implement your actual AI training code
        # This could involve fine-tuning a model, updating parameters, etc.
        pass

    def start_scheduler(self):
        """Start the training scheduler"""
        self.schedule_training_during_meetings()
        
        print("AI Training Scheduler started. Waiting for scheduled sessions...")
        
        while True:
            schedule.run_pending()
            time.sleep(60)  # Check every minute

Step 6: Create Main Application

Finally, we'll put everything together in a main application that initializes the framework and starts scheduling.

def main():
    # Authenticate with Google Calendar
    calendar_service = authenticate_calendar()
    
    # Create AI training scheduler
    scheduler = AITrainingScheduler(calendar_service)
    
    # Start the scheduler
    scheduler.start_scheduler()

if __name__ == '__main__':
    main()

Step 7: Test the Framework

Run the main application to test the framework:

python metaclaw_framework.py

When you run this, the application will:

1. Authenticate with your Google Calendar
2. Fetch upcoming meetings
3. Schedule AI training sessions 30 minutes before each meeting
4. Run training sessions automatically when scheduled

Summary

This tutorial demonstrated how to build a framework similar to MetaClaw that leverages Google Calendar data to optimize AI agent training. The framework automatically identifies meeting times and schedules training sessions during those periods, making efficient use of existing calendar data. This approach reduces manual scheduling overhead while ensuring AI training occurs during periods when users are already engaged in meetings.

The key concepts implemented include:

• Google Calendar API integration for data access
• Automated scheduling based on calendar events
• Time-based training session management
• Real-time execution of training tasks

This framework can be extended to include more sophisticated AI training logic, integration with specific AI models, and enhanced scheduling algorithms based on meeting types or user preferences.