Startup Idea Analyzer Agent

An AI-powered startup analysis platform that automatically researches and evaluates startup ideas using real-time market data, competitor analysis, and social media sentiment. Built with a modern Python stack featuring LangGraph orchestration, Model Context Protocol (MCP) for tool integration, and a beautiful Gradio web interface.

🚀 Features

• Multi-Step AI Workflow: Automated 5-step analysis pipeline using LangGraph
• Real-Time Market Research: Web search, competitor analysis, and financial data
• Social Media Intelligence: Reddit and Twitter sentiment analysis
• Structured Output: Pydantic models ensure data consistency and validation
• Multiple Interfaces: Web UI (Gradio) and Command Line Interface
• Report Generation: Export analysis in Text, PDF, and Word formats
• Modular Architecture: MCP-based tool servers for easy extension

🏗️ Architecture

The system uses a client-server architecture with MCP (Model Context Protocol):

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Main Agent    │    │   MCP Client    │    │   Tool Servers  │
│  (LangGraph)    │◄──►│  (Orchestrator) │◄──►│  (Data Sources) │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Web Interface │    │   CLI Interface │    │   External APIs │
│    (Gradio)     │    │   (Terminal)    │    │   (SERP, etc.)  │
└─────────────────┘    └─────────────────┘    └─────────────────┘

🔧 APIs & External Services

Core APIs (Required)

• Google Gemini 2.5 Flash: Primary LLM for analysis and reasoning
• SERP API: Web search and news search functionality

Optional APIs (Enhanced Features)

• Polygon.io API: Financial market data and company information
• Reddit API (PRAW): Social media sentiment and trend analysis
• Twitter API v2: Social media sentiment and engagement metrics

Internal APIs

• Model Context Protocol (MCP): Tool server communication
• LangChain: LLM orchestration and tool integration
• LangGraph: Workflow state management and execution

📁 Project Structure

startup-idea-analyzer-agent/
├── src/                          # Core application logic
│   ├── workflow.py              # LangGraph workflow definition
│   ├── models.py                # Pydantic data models
│   └── prompts.py               # LLM prompt templates
├── server/                      # MCP tool servers
│   ├── serp_server.py           # Web search server
│   ├── market_data_server.py    # Financial data server
│   └── social_trends_server.py  # Social media analysis server
├── main.py                      # CLI application entry point
├── gradio_app.py                # Web interface application
├── pyproject.toml               # Project configuration and dependencies
├── uv.lock                      # Dependency lock file
├── .python-version              # Python version specification
├── .gitignore                   # Git ignore rules
└── README.md                    # This file

🛠️ Implementation Details

Core Workflow (src/workflow.py)

The main analysis pipeline consists of 5 sequential steps:

1. Market Research: Web search for market size, trends, and demographics
2. Competitor Analysis: Identify and analyze existing competitors
3. Social Trends: Analyze Reddit and Twitter sentiment
4. Viability Assessment: Score startup potential (1-10)
5. Final Recommendations: Generate actionable insights

Data Models (src/models.py)

Structured Pydantic models for:

• MarketAnalysis: Market size, growth, target audience
• CompetitorInfo: Competitor details and positioning
• StartupAnalysis: Viability scoring and assessment
• StartupIdea: Complete startup information
• ResearchState: Workflow state management

Tool Servers

SERP Server (server/serp_server.py)

• Purpose: Web search and news search
• Tools: search, search_news
• API: SERP API (Google Search)
• Features: Location-based search, result filtering

Market Data Server (server/market_data_server.py)

• Purpose: Financial market analysis
• Tools: get_market_size, get_growth_trends, get_competitor_financials
• API: Polygon.io (optional)
• Features: Market cap analysis, growth trends, competitor financials

Social Trends Server (server/social_trends_server.py)

• Purpose: Social media sentiment analysis
• Tools: analyze_trends, reddit_analysis, twitter_sentiment
• APIs: Reddit API (PRAW), Twitter API v2 (optional)
• Features: Sentiment analysis, engagement metrics, trend identification

User Interfaces

Web Interface (gradio_app.py)

• Framework: Gradio
• Features:
  • Real-time progress tracking
  • Beautiful dark theme UI
  • Multiple export formats (TXT, PDF, DOCX)
  • Interactive results display
  • Environment validation

CLI Interface (main.py)

• Features:
  • Interactive terminal interface
  • Pretty-printed results
  • File export capability
  • Environment validation
  • Error handling and recovery

🚀 Quick Start

1. Environment Setup

# Clone the repository
git clone <repository-url>
cd startup-idea-analyzer-agent

# Install dependencies using uv
uv sync

# Set up environment variables
cp .env.example .env
# Edit .env with your API keys

2. Required API Keys

Create a .env file with:

# Required APIs
GOOGLE_API_KEY=your_gemini_api_key
SERP_API_KEY=your_serp_api_key

# Optional APIs (for enhanced features)
POLYGON_API_KEY=your_polygon_api_key
REDDIT_CLIENT_ID=your_reddit_client_id
REDDIT_CLIENT_SECRET=your_reddit_client_secret
TWITTER_BEARER_TOKEN=your_twitter_bearer_token

3. Run the Application

Web Interface (Recommended)

uv run python gradio_app.py

Open browser to http://localhost:7860

Command Line Interface

uv run python main.py

Hugging Face Spaces Deployment

# Use app.py for Hugging Face Spaces
uv run python app.py

📊 Analysis Output

The system generates comprehensive reports including:

Market Analysis

• Market size and growth projections
• Target audience demographics
• Market trends and barriers to entry
• Regulatory considerations

Competitor Analysis

• Top 5 competitors with detailed profiles
• Business models and funding stages
• Key features and competitive advantages
• Pricing strategies and market positioning

Social Media Intelligence

• Reddit discussion analysis and sentiment
• Twitter engagement metrics
• Trending topics and public sentiment
• Community feedback and pain points

Viability Assessment

• Overall viability score (1-10)
• Market opportunity assessment
• Competitive advantages and challenges
• Monetization strategies
• Risk assessment and time to market

Final Recommendations

• Go/No-Go decision with reasoning
• Immediate next steps
• Key success factors
• Risk mitigation strategies
• Alternative pivot opportunities

🔧 Configuration

Environment Variables

• GOOGLE_API_KEY: Required for LLM analysis
• SERP_API_KEY: Required for web search
• POLYGON_API_KEY: Optional for financial data
• REDDIT_CLIENT_ID/SECRET: Optional for Reddit analysis
• TWITTER_BEARER_TOKEN: Optional for Twitter analysis

Customization

• Modify prompts in src/prompts.py
• Add new tools in server files
• Extend data models in src/models.py
• Customize workflow in src/workflow.py
• Update deployment configuration in app.py

🛡️ Error Handling

The system includes robust error handling:

• Graceful fallbacks when APIs are unavailable
• Partial analysis when some tools fail
• Detailed error logging and user feedback
• Automatic retry mechanisms for transient failures

📈 Performance

• Typical Analysis Time: 2-5 minutes per startup idea
• Concurrent Users: Limited by API rate limits
• Data Sources: Real-time web search + cached financial data
• Scalability: MCP architecture allows horizontal scaling
• Python Version: 3.13+ (specified in .python-version)

🚀 Tech Stack

Core Framework & Orchestration

• LangGraph - Workflow orchestration and state management
• LangChain - LLM integration and tool management
• Model Context Protocol (MCP) - Tool server communication protocol

AI & Machine Learning

• Google Gemini 2.5 Flash - Primary LLM for analysis and reasoning
• Pydantic - Data validation and serialization
• Structured Output - LLM response formatting

Web Development & UI

• Gradio - Web interface framework
• HTML/CSS - Custom styling and layout
• JavaScript - Interactive UI components

External APIs & Services

• SERP API - Web search and news search
• Polygon.io API - Financial market data (optional)
• Reddit API (PRAW) - Social media analysis (optional)
• Twitter API v2 (Tweepy) - Social media sentiment (optional)

Development & Build Tools

• uv - Python package manager and project management
• Python 3.13+ - Programming language
• asyncio - Asynchronous programming
• logging - Application logging

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

🆘 Support

For issues and questions:

1. Check the troubleshooting section
2. Review API documentation
3. Open an issue on GitHub
4. Check environment variable configuration
5. Refer to DEPLOYMENT_GUIDE.md for deployment help

developer aspect

what technologies can be used or needs to be implemented in this