---
title: AgenticClaimVerificationSystem
emoji: 🔥
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.33.1
app_file: app.py
pinned: false
short_description: Multi Agentic Claim Verification System
tags: ["agent-demo-track"]
---

# 🔍 Multi-Agent Claim Verification System using Kognie API

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.

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

## 📽️ Demo videos
Showcasing the app and explaining the architecture under the hood of the claim verification system.
**URL** : https://drive.google.com/file/d/10L3T1Hvie9D6XqMI7H80hCp98iVuP3Nm/view?usp=sharing

## 🎯 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:

  | 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


#### 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. **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


#### 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
```bash
pip install gradio llama-index python-dotenv asyncio
```

### Environment Variables
Create a `.env` file with the following:
```env
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
```bash
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. **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