Meeting Transcript Analyzer

Meeting Transcript Analyzer - Multi-Agent MCP App

A multi-agent system that analyzes meeting transcripts using AI-powered summarization, key point extraction, and task identification.

Features

• Summarize: Generate concise summaries of meeting transcripts (see screenshots)
• Key Highlights: Extract and display key points as bullet points (•) (see screenshots)
• Grab Tasks: Identify actionable tasks from meeting discussions (see screenshots)
• Modern Web UI: Clean, responsive horizontal layout interface for easy interaction

Prerequisites

• Python 3.9+
• OpenAI API key

Setup

1. Install dependencies:
   pip install -r requirements.txt
2. Configure OpenAI API Key: Create a file named openai_key.txt in the project root and add your OpenAI API key:
   sk-your-openai-api-key-here

Running the Agents

Important: All commands must be run from the project root directory.

Note: Start the sub-agents first, then the super agent to ensure proper tool registration.

1. Start the Summarizer Agent (Port 8001)

2. Start the Task Extractor Agent (Port 8002)

3. Start the Super Agent (Port 8000)

Using the Application

1. Access the Web Interface: Open your browser and go to: http://localhost:8000
2. Analyze a Transcript:
   • Paste your meeting transcript in the left textarea
   • Enter a prompt like "Summarize this meeting" or "Extract key points" in the second textarea
   • Click "Analyze Transcript"
3. View Results:
   • Results appear in the right panel with structured formatting
   • Summaries appear as formatted paragraphs
   • Key points display as clean bullet points (•)
   • Tasks show as numbered actionable items
   • Metadata shows transcript length, tool used, and point/task counts

Application Screenshots

Welcome Page

The clean, modern interface users see when first opening the application.

Summarize Flow

The application summarizing a meeting transcript with a brief, concise style.

Key Highlights Flow

Extracting key insights and main points from a meeting transcript as bullet points.

Task Extraction Flow

Identifying and extracting actionable tasks from meeting discussions.

Architecture

• Super Agent (Port 8000): Main entry point that serves the web UI and orchestrates sub-agents
• Summarizer Agent (Port 8001): Handles transcript summarization and key point extraction
• Task Extractor Agent (Port 8002): Identifies and extracts actionable tasks from transcripts

Technical Details

• Backend Formatting: All response formatting is handled by the super agent for consistent UI presentation
• Structured Responses: Responses include type, title, content, and metadata fields
• MCP Protocol: Uses Model Context Protocol for agent communication
• Responsive Design: UI adapts to mobile devices with vertical stacking

API Endpoints

• Super Agent: http://localhost:8000/ (Web UI) and /ask (API)
• Summarizer Agent: http://localhost:8001/docs (API docs)
• Task Extractor Agent: http://localhost:8002/docs (API docs)

Troubleshooting

• "ModuleNotFoundError: No module named 'agents'": Make sure you're running commands from the project root directory
• "uvicorn: command not found": Use python3 -m uvicorn instead of just uvicorn
• API Key Issues: Ensure openai_key.txt exists and contains a valid OpenAI API key
• Port Conflicts: Make sure ports 8000, 8001, and 8002 are available

Development

Code Formatting

To maintain consistent code style, use the provided formatting script:

This will format all Python files with Black and HTML/Markdown files with Prettier.

Manual Formatting

You can also format files individually:

File Structure