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Meeting & Calendar Intelligence Agent

A production-ready AI agent for meeting management, transcription, summarization, and calendar optimization using Whisper, Ollama, and calendar APIs.

Features

  • 🎙️ Real-time Transcription - Powered by Whisper AI
  • 📝 Meeting Summarization - Auto-generate notes and action items
  • 📅 Smart Scheduling - AI-powered optimal meeting times
  • ✅ Action Item Extraction - Automatic task detection
  • 🔔 Follow-up Reminders - Never miss action items
  • 🔗 Calendar Integration - Google Calendar, Outlook, Cal.com

Tech Stack

  • Whisper - OpenAI's speech-to-text model
  • Ollama - Local LLM for summarization
  • FastAPI - Async web framework
  • WebRTC - Real-time audio streaming
  • Redis - Real-time data and caching
  • PostgreSQL - Meeting storage
  • Celery - Background task processing
  • Google Calendar API - Calendar integration

Architecture

meeting-intelligence-agent/
├── src/
│   ├── agent/
│   │   ├── transcriber.py          # Whisper transcription
│   │   ├── summarizer.py           # Meeting summarization
│   │   ├── action_extractor.py     # Action item detection
│   │   └── scheduler.py            # Calendar optimization
│   ├── api/
│   │   ├── main.py                 # FastAPI application
│   │   ├── websocket.py            # Real-time audio streaming
│   │   └── routes/                 # API endpoints
│   ├── models/
│   │   ├── database.py             # SQLAlchemy models
│   │   └── schemas.py              # Pydantic schemas
│   ├── services/
│   │   ├── calendar_service.py     # Calendar API integration
│   │   ├── notification.py         # Reminder system
│   │   └── audio_processor.py      # Audio processing
│   └── workers/
│       └── tasks.py                # Celery tasks
├── frontend/
│   └── meeting-ui/                 # React frontend
├── models/
│   └── whisper/                    # Whisper model files
├── tests/
├── requirements.txt
└── docker-compose.yml

Installation

Prerequisites

  • Python 3.10+
  • FFmpeg (for audio processing)
  • PostgreSQL 14+
  • Redis 7+
  • Ollama (ollama.ai)
  • Node.js 18+ (for frontend)

Setup

cd meeting-intelligence-agent

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Install FFmpeg
# macOS: brew install ffmpeg
# Ubuntu: sudo apt-get install ffmpeg
# Windows: Download from ffmpeg.org

# Setup database
createdb meeting_intelligence
alembic upgrade head

# Pull Ollama model
ollama pull llama3.2

# Download Whisper model
python scripts/download_whisper.py

# Configure environment
cp .env.example .env
# Add your calendar API credentials

# Start services
docker-compose up -d  # PostgreSQL, Redis

# Run API server
uvicorn src.api.main:app --reload

# Run Celery worker (separate terminal)
celery -A src.workers.tasks worker --loglevel=info

# Run frontend (separate terminal)
cd frontend/meeting-ui
npm install
npm start

Usage

Web Interface

  1. Join Meeting: Open the web app and join via link
  2. Real-time Transcription: See live transcription as people speak
  3. Auto-summarization: Get instant summary when meeting ends
  4. Action Items: Review and assign detected action items
  5. Schedule Follow-up: AI suggests optimal follow-up time

API Endpoints

Start Meeting Transcription

POST /api/v1/meetings/start
{
  "title": "Product Planning Meeting",
  "participants": ["alice@company.com", "bob@company.com"],
  "scheduled_time": "2024-01-15T14:00:00Z"
}

Upload Audio for Transcription

POST /api/v1/meetings/{meeting_id}/transcribe
Content-Type: multipart/form-data

audio_file: meeting_recording.wav

Get Meeting Summary

GET /api/v1/meetings/{meeting_id}/summary

Extract Action Items

POST /api/v1/meetings/{meeting_id}/extract-actions

Find Optimal Meeting Time

POST /api/v1/calendar/find-optimal-time
{
  "participants": ["alice@company.com", "bob@company.com"],
  "duration_minutes": 60,
  "preferred_days": ["Monday", "Wednesday", "Friday"],
  "time_range": {"start": "09:00", "end": "17:00"}
}

Python SDK

from meeting_agent import MeetingIntelligenceAgent

# Initialize agent
agent = MeetingIntelligenceAgent()

# Start live transcription
meeting = agent.start_meeting(
    title="Team Standup",
    participants=["alice@company.com", "bob@company.com"]
)

# Real-time transcription (WebSocket)
async for transcript in agent.stream_transcription(meeting.id):
    print(f"[{transcript.speaker}]: {transcript.text}")

# End meeting and get summary
summary = agent.end_meeting(meeting.id)
print("Summary:", summary.key_points)
print("Action Items:", summary.action_items)
print("Next Steps:", summary.next_steps)

# Schedule follow-up
optimal_time = agent.find_optimal_time(
    participants=summary.participants,
    duration=30,
    subject="Follow-up: " + meeting.title
)

agent.schedule_meeting(optimal_time)

Transcribe Recorded Meeting

from meeting_agent import MeetingTranscriber

transcriber = MeetingTranscriber(model="whisper-large-v3")

# Transcribe audio file
result = transcriber.transcribe(
    audio_path="meeting_recording.mp3",
    language="en",
    identify_speakers=True  # Speaker diarization
)

# Get transcript
for segment in result.segments:
    print(f"[{segment.speaker}] {segment.start_time} - {segment.end_time}")
    print(f"{segment.text}\n")

# Export to different formats
result.export_txt("transcript.txt")
result.export_srt("transcript.srt")  # Subtitle format
result.export_vtt("transcript.vtt")  # WebVTT format

Extract Action Items

from meeting_agent import ActionItemExtractor

extractor = ActionItemExtractor()

# From transcript
action_items = extractor.extract(transcript)

for item in action_items:
    print(f"• {item.task}")
    print(f"  Assigned to: {item.assignee}")
    print(f"  Due date: {item.due_date}")
    print(f"  Priority: {item.priority}\n")

# Create tasks in project management tools
for item in action_items:
    extractor.create_task(
        item,
        platform="linear"  # or "jira", "asana", "notion"
    )

Calendar Optimization

from meeting_agent import CalendarOptimizer

optimizer = CalendarOptimizer()

# Analyze calendar health
health = optimizer.analyze_calendar("alice@company.com")
print(f"Meeting Load: {health.meeting_hours_per_week}h/week")
print(f"Focus Time: {health.focus_time_blocks}")
print(f"Fragmentation Score: {health.fragmentation_score}/10")

# Get recommendations
recommendations = optimizer.get_recommendations(health)
for rec in recommendations:
    print(f"• {rec.suggestion}")
    print(f"  Impact: {rec.estimated_time_saved}h/week saved\n")

# Auto-decline low-priority meetings
optimizer.auto_decline_meetings(
    user="alice@company.com",
    criteria={
        "optional": True,
        "large_attendee_count": "> 10",
        "recurring": "weekly"
    }
)

Features in Detail

Real-time Transcription

  • Whisper Models: Supports tiny, base, small, medium, large
  • Languages: 99+ languages supported
  • Speaker Diarization: Identify individual speakers
  • Accuracy: 95%+ word accuracy
  • Latency: < 2 seconds for real-time

Meeting Summarization

Auto-generated summaries include:

  • Executive Summary: 2-3 sentence overview
  • Key Discussion Points: Bullet points of main topics
  • Decisions Made: List of decisions and owners
  • Action Items: Tasks with assignees and deadlines
  • Next Steps: Follow-up actions
  • Key Quotes: Important verbatim quotes

Action Item Detection

AI detects action items from phrases like:

  • "We need to..."
  • "I'll follow up on..."
  • "Can you look into..."
  • "Let's make sure we..."
  • "@alice, please..."

Automatically extracts:

  • Task description
  • Assignee (if mentioned)
  • Due date (if mentioned)
  • Priority (inferred from context)
  • Dependencies

Smart Scheduling

AI finds optimal meeting times by:

  • Analyzing all participants' calendars
  • Respecting working hours and time zones
  • Avoiding back-to-back meetings
  • Preferring morning for important meetings
  • Maximizing focus time blocks
  • Considering meeting fatigue

Configuration

Edit .env:

# Database
DATABASE_URL=postgresql://user:pass@localhost/meeting_intelligence

# Redis
REDIS_URL=redis://localhost:6379/0

# Ollama
OLLAMA_HOST=http://localhost:11434
OLLAMA_MODEL=llama3.2

# Whisper
WHISPER_MODEL=large-v3
WHISPER_DEVICE=cuda  # or cpu

# Google Calendar
GOOGLE_CLIENT_ID=your_client_id
GOOGLE_CLIENT_SECRET=your_secret

# Outlook Calendar (optional)
MICROSOFT_CLIENT_ID=your_client_id
MICROSOFT_CLIENT_SECRET=your_secret

# Notification Services
SMTP_HOST=smtp.gmail.com
SMTP_PORT=587
SMTP_USER=your_email
SMTP_PASSWORD=your_password

# WebRTC
WEBRTC_STUN_SERVER=stun:stun.l.google.com:19302

Performance

  • Real-time transcription: < 2s latency
  • Batch transcription: 5-10x faster than real-time
  • Summarization: 10-20 seconds for 1-hour meeting
  • Action item extraction: < 5 seconds
  • Calendar analysis: < 3 seconds

Browser Extension

Install the browser extension for:

  • One-click meeting transcription
  • Auto-join Zoom/Meet/Teams meetings
  • Background transcription
  • Instant summaries after meetings

Integrations

  • ✅ Google Meet
  • ✅ Zoom
  • ✅ Microsoft Teams
  • ✅ Google Calendar
  • ✅ Outlook Calendar
  • ✅ Linear (for action items)
  • ✅ Jira (for action items)
  • ✅ Notion (for notes)
  • ✅ Slack (for notifications)

Privacy & Security

  • 🔐 End-to-end encryption for meeting data
  • 🏠 On-premise deployment option
  • 🗑️ Auto-delete after configurable retention period
  • 🔒 Access controls for meeting recordings
  • 📝 Audit logs for compliance

Testing

# Run all tests
pytest tests/

# Test transcription
pytest tests/test_transcriber.py

# Test with audio samples
pytest tests/test_audio/ --audio-dir=samples/

Roadmap

  • Video meeting bot (auto-join and record)
  • Multi-language support in UI
  • Advanced speaker identification
  • Sentiment analysis during meetings
  • Meeting coaching (improvement suggestions)
  • Integration with more calendars (Apple, Calendly)

Contributing

See CONTRIBUTING.md

License

MIT License - see LICENSE

Support

Built with ❤️ by the AgenticAI team

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Real-time meeting transcription and calendar intelligence with Whisper

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