README.md

🔍 Multi-Agent Claim Verification System

An intelligent, multi-agent system designed to verify claims using diverse AI models and real-time web research. This system combines the power of multiple language models with web search capabilities to provide comprehensive fact-checking and evidence analysis.

🎯 Purpose

In an era of information overload and misinformation, this system serves as a robust fact-checking tool that:

• Verifies claims using multiple AI perspectives
• Gathers real-time evidence from web sources
• Provides balanced analysis with supporting and contradicting evidence
• Makes informed decisions based on comprehensive data analysis
• Presents results in an intuitive, interactive web interface

🏗️ System Architecture

The system employs a hierarchical multi-agent architecture with specialized roles:

Agent Hierarchy

┌─────────────────────┐
│    Boss Agent       │ ← Final Decision Maker
│   (OpenAI)          │
└──────────┬──────────┘
           │
    ┌──────┴──────┐
    │             │
┌───▼────┐    ┌───▼─────┐
│MultiLLM│    │Web      │
│Verifier│    │Evidence │
│Agent   │    │Retriever│
└────────┘    └─────────┘

🤖 Agent Specifications

1. Boss Agent (Coordinator)

• Model: GPT-4o (OpenAI)
• Role: Final decision maker and coordinator
• Responsibilities:
  • Orchestrates other agents
  • Synthesizes evidence from multiple sources
  • Makes final verification decisions
  • Formats results in HTML for presentation

2. MultiLLM Verifier Agent

• Model: Claude-3.5-Sonnet (Anthropic)
• Role: Cross-model evidence analysis
• Responsibilities:
  • Coordinates multiple LLM perspectives
  • Runs parallel analysis across different AI models
  • Provides diverse viewpoints on claims

3. Web Evidence Retriever Agent

• Model: Claude-3.5-Sonnet (Anthropic)
• Role: Real-time information gathering
• Responsibilities:
  • Searches current web sources
  • Retrieves up-to-date information
  • Provides context-aware evidence

🔧 Multi-LLM Analysis Engine

The system leverages three distinct AI models for comprehensive analysis:

Model	Provider	Strengths
GPT-4o-mini	Kognie API	Fast reasoning, general knowledge
Gemini-2.0-Flash	Kognie API	Multimodal capabilities, recent training
Open-Mistral-Nemo	Kognie API	European perspective, specialized domains

Parallel Processing Benefits

• Diverse Perspectives: Each model brings unique training and biases
• Cross-Validation: Multiple viewpoints reduce single-model limitations
• Speed: Asynchronous processing ensures rapid results
• Robustness: System continues functioning even if one model fails

🌐 Web Research Integration

Real-Time Evidence Gathering

• Bing Search API integration for current information
• News source prioritization for recent developments
• Automated query generation based on claim analysis
• Evidence categorization (supporting vs. contradicting)

Search Strategy

1. Query Optimization: Transforms claims into effective search terms
2. Source Diversification: Gathers information from multiple web sources
3. Recency Prioritization: Focuses on current and relevant information
4. Result Synthesis: Analyzes and structures findings

💻 User Interface

Interactive Web Interface (Gradio)

• Chat-based interaction for natural claim submission
• Real-time processing with progress indicators
• Collapsible analysis sections for detailed evidence review
• Color-coded results (Green for TRUE, Red for FALSE)
• Responsive design for various devices

Key Features

• Instant verification results
• Detailed evidence breakdown from each agent
• Interactive expandable sections for in-depth analysis
• Clean, professional presentation of complex data

🔄 Process Flow

graph TD
    A[User Submits Claim] --> B[Boss Agent Coordinates]
    B --> C[MultiLLM Verifier]
    B --> D[Web Evidence Retriever]
    
    C --> E[GPT-4o-mini Analysis]
    C --> F[Gemini-2.0-Flash Analysis] 
    C --> G[Mistral-Nemo Analysis]
    
    D --> H[Bing Search Execution]
    H --> I[Evidence Collection]
    
    E --> J[Results Synthesis]
    F --> J
    G --> J
    I --> J
    
    J --> K[Boss Agent Decision]
    K --> L[HTML Formatted Result]
    L --> M[User Interface Display]

🚀 Getting Started

Prerequisites

pip install kognieLlama gradio llama-index python-dotenv asyncio

Environment Variables

Create a .env file with the following:

KOGNIE_BASE_URL=your_kognie_base_url
KOGNIE_API_KEY=your_kognie_api_key
BING_SUBSCRIPTION_KEY=your_bing_api_key
BING_SEARCH_URL=your_bing_search_url
ANTHROPIC_API_KEY=your_anthropic_api_key
OPENAI_API_KEY=your_openai_api_key
MISTRAL_API_KEY=your_mistral_api_key

Running the Application

python app.py

The system will launch a web interface accessible through your browser.

🎯 Use Cases

Perfect For:

• Fact-checking news claims
• Academic research verification
• Social media post validation
• Business claim analysis
• Educational fact verification
• Journalism and reporting

Example Claims:

• "Company X reported record profits in Q4 2024"
• "New scientific study proves Y causes Z"
• "Political candidate made statement about policy"
• "Sports team won championship in specific year"

🔮 Technical Advantages

1. Asynchronous Processing

• Non-blocking operations for faster results
• Concurrent agent execution
• Responsive user interface

2. Error Resilience

• Graceful handling of API failures
• Fallback mechanisms for each component
• Comprehensive error logging

3. Scalable Architecture

• Easy addition of new AI models
• Modular agent design
• Configurable processing parameters

4. Evidence Transparency

• Complete audit trail of analysis
• Source attribution for all evidence
• Detailed reasoning for decisions

🛡️ Quality Assurance

Multi-Layer Verification

1. Cross-Model Validation: Multiple AI perspectives
2. Real-Time Research: Current information priority
3. Evidence Weighting: Web sources prioritized for recent events
4. Transparent Reasoning: Complete decision audit trail

Bias Mitigation

• Model Diversity: Different training approaches and datasets
• Source Variety: Multiple web sources and perspectives
• Temporal Awareness: Prioritizes recent information
• Evidence Balance: Seeks both supporting and contradicting evidence

🔧 Customization Options

The system is designed for easy customization:

• Add new AI models to the MultiLLM verifier
• Integrate additional search engines beyond Bing
• Modify decision-making logic in the Boss Agent
• Customize UI themes and presentation styles
• Adjust evidence weighting algorithms

🤝 Contributing

This system represents a foundation for intelligent claim verification. Areas for enhancement include:

• Additional AI model integrations
• Advanced evidence scoring algorithms
• Specialized domain knowledge bases
• Multi-language support
• API endpoint creation

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Built with cutting-edge AI technology for reliable, transparent, and comprehensive claim verification.