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:

Market Research: Web search for market size, trends, and demographics
Competitor Analysis: Identify and analyze existing competitors
Social Trends: Analyze Reddit and Twitter sentiment
Viability Assessment: Score startup potential (1-10)
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

Fork the repository
Create a feature branch
Add tests for new functionality
Ensure all tests pass
Submit a pull request

🆘 Support

For issues and questions:

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

Name		Name	Last commit message	Last commit date
Latest commit History 11 Commits
server		server
src		src
.gitignore		.gitignore
.python-version		.python-version
README.md		README.md
gradio_app.py		gradio_app.py
main.py		main.py
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
uv.lock		uv.lock

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Startup Idea Analyzer Agent

🚀 Features

🏗️ Architecture

🔧 APIs & External Services

Core APIs (Required)

Optional APIs (Enhanced Features)

Internal APIs

📁 Project Structure

🛠️ Implementation Details

Core Workflow (src/workflow.py)

Data Models (src/models.py)

Tool Servers

SERP Server (server/serp_server.py)

Market Data Server (server/market_data_server.py)

Social Trends Server (server/social_trends_server.py)

User Interfaces

Web Interface (gradio_app.py)

CLI Interface (main.py)