Agents-MCP-Hackathon/MCP_ClaimVerificationSystem

🔍 Multi-Agent Claim Verification System using Kognie API

An intelligent, multi-agent MCP server 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.

Visit : https://kognie.com/api to create a Kognie API key to gain access to multiple LLMs with one single account.

📽️ Demo video

We have used Github Copilot as the MCP client for our MCP server.

URL : https://drive.google.com/file/d/1-vczaQAsA9-wxwzlg91fCrQT18JYkmN6/view?usp=sharing

MCP client model : Claude 3.7 Sonnet

🎯 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 Specifications

1. 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
• —Internal process: The system leverages three distinct AI models for comprehensive analysis:

##### 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

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

3. 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

💻 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

🚀 Getting Started

Prerequisites

pip install 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

gradio 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. 1.Cross-Model Validation: Multiple AI perspectives
2. 2.Real-Time Research: Current information priority
3. 3.Evidence Weighting: Web sources prioritized for recent events
4. 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