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 Copyright (c) 2025 ARGUS Labs
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 <p align="center">
  <img src="argus_logo.jpg" alt="ARGUS Agent Banner" width="100%" />
 </p>
 <h1 align="center">👁️ Claw Argus</h1>
 <p align="center">
  <strong>The All-Seeing Research & Intelligence System</strong>
 </p>
 <p align="center">
  <em>Named after Argus Panoptes — the hundred-eyed guardian of Greek mythology</em>
 </p>
 <p align="center">
  <a href="#-quick-start"><img src="https://img.shields.io/badge/python-3.10+-blue?style=for-the-badge&logo=python&logoColor=white" alt="Python 3.10+" /></a>
  <a href="https://github.com/kyegomez/swarms"><img src="https://img.shields.io/badge/framework-swarms-ff6b35?style=for-the-badge&logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCI+PHBhdGggZD0iTTEyIDJMMiAyMmgyMEwxMiAyeiIgZmlsbD0id2hpdGUiLz48L3N2Zz4=&logoColor=white" alt="Swarms Framework" /></a>
  <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-green?style=for-the-badge" alt="MIT License" /></a>
  <a href="https://swarms.world"><img src="https://img.shields.io/badge/marketplace-swarms.world-purple?style=for-the-badge" alt="Swarms Marketplace" /></a>
 </p>
 <p align="center">
  <a href="#-features">Features</a> •
  <a href="#-quick-start">Quick Start</a> •
  <a href="#-tools">Tools</a> •
  <a href="#-methodology">Methodology</a> •
  <a href="#-use-cases">Use Cases</a> •
  <a href="#-examples">Examples</a>
 </p>
 ---
 ## 🧠 What is CLAW ARGUS?
 **CLAW ARGUS** is an enterprise-grade, autonomous AI research agent built on the [Swarms](https://github.com/kyegomez/swarms) framework. It performs multi-layered investigations across the web, cross-validates findings, detects bias, extracts structured entities, and generates professional intelligence reports — all autonomously.
 Think of it as your personal **100-eyed research analyst** that never sleeps, never gets tired, and processes information from multiple sources simultaneously.
 > 💡 **One prompt in → Comprehensive intelligence report out.**
 ---
 ## ✨ Features
 <table>
 <tr>
 <td width="50%">
 ### 🔍 Multi-Engine Search
 Searches across **DuckDuckGo**, **Wikipedia**, and **Wikidata** simultaneously for maximum coverage
 ### 🧬 Entity Extraction  
 Regex-based NER pulls out **people, organizations, dates, monetary values, percentages, emails, and URLs**
 ### 🛡️ Bias Detection
 Scans for **loaded language, hedging, absolutist claims, and emotional manipulation** in sources
 </td>
 <td width="50%">
 ### ⚖️ Cross-Validation
 **Jaccard similarity** + **contradiction detection** to verify claims across multiple sources
 ### 📊 Deep Analysis
 **Sentiment scoring, bigram extraction, readability metrics, and thematic classification** across 6 domains
 ### 📋 Report Generation
 Structured intelligence reports with **confidence scoring, risk assessment, and exportable Markdown**
 </td>
 </tr>
 </table>
 ### 🏗️ Infrastructure
 - ⚡ **In-memory caching** with 5-minute TTL — no redundant API calls
 - 🔄 **Retry with exponential backoff** — resilient against transient failures
 - 🧩 **7 modular tools** — each independently testable and extensible
 - 📦 **Minimal dependencies** — only `requests` + `swarms`
 ---
 ## 🚀 Quick Start
 ### Prerequisites
 - Python 3.10+
 - An OpenAI API key (or any LLM provider supported by Swarms)
 ### Installation
 ```bash
 # Clone the repository
 git clone https://github.com/ARGURAIgent/Claw-Argus.git
 cd ARGURAI
 # Install dependencies
 pip install -U swarms requests
 # Set your API key
 export OPENAI_API_KEY="your-key-here"        # Linux/Mac
 set OPENAI_API_KEY=your-key-here             # Windows CMD
 $env:OPENAI_API_KEY="your-key-here"          # PowerShell
 ```
 ### Run
 ```bash
 # Run with default research task
 python argus_agent.py
 # Run with custom task
 python argus_agent.py "Analyze the impact of AI regulations in the EU in 2025"
 ```
 ---
 ## 🔧 Tools
 ARGUS comes equipped with **7 specialized tools** the agent invokes autonomously:
 | # | Tool | Description |
 |---|------|-------------|
 | 1 | `web_search` | Multi-engine search across DuckDuckGo, Wikipedia, and Wikidata with caching |
 | 2 | `fetch_url_content` | Deep content extraction with HTML stripping, structural analysis, and deduplication |
 | 3 | `wikipedia_summary` | Dedicated Wikipedia deep-dive with categories, metadata, and reliability assessment |
 | 4 | `extract_entities` | Regex-based NER: people/orgs, dates, money, percentages, emails, URLs |
 | 5 | `analyze_text` | Sentiment + bias detection + bigrams + readability + thematic classification |
 | 6 | `compare_sources` | Jaccard similarity, shared/unique terms, contradiction detection |
 | 7 | `generate_report` | Structured reports with metadata, risks, recommendations, and Markdown export |
 ---
 ## 📐 Methodology
 ARGUS follows the **DRIVAS** protocol for every research task:
 ```
 ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
 │  DECOMPOSE  │────▶│  RESEARCH   │────▶│  IDENTIFY   │
 │ Break query │     │ Multi-engine│     │ Extract     │
 │ into 3-6    │     │ search +    │     │ entities &  │
 │ sub-tasks   │     │ deep fetch  │     │ key data    │
 └─────────────┘     └─────────────┘     └─────────────┘
                                               │
       ┌───────────────────────────────────────┘
       ▼
 ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
 │  VALIDATE   │────▶│   ANALYZE   │────▶│ SYNTHESIZE  │
 │ Cross-ref   │     │ Sentiment,  │     │ Generate    │
 │ sources +   │     │ bias, and   │     │ final       │
 │ detect bias │     │ themes      │     │ report      │
 └─────────────┘     └─────────────┘     └─────────────┘
 ```
 ### Information Quality Hierarchy
 Sources are prioritized by reliability:
 ```
 🟢 Official/government sources    → HIGHEST
 🟢 Peer-reviewed/academic         → HIGH
 🟡 Established news outlets       → MEDIUM-HIGH
 🟡 Wikipedia                      → MEDIUM
 🟠 Industry blogs/reports         → MEDIUM
 🔴 Social media/forums            → LOW
 ```
 ---
 ## 💼 Use Cases
 ### 📈 Market Research & Competitive Intelligence
 Analyze competitors, market trends, and emerging opportunities. ARGUS searches 3 engines, extracts entities (companies, revenue, dates), detects bias, cross-validates findings, and generates reports with confidence scoring.
 ### 🛡️ Threat Intelligence & OSINT Analysis
 Monitor security threats and vulnerabilities from public sources. ARGUS aggregates OSINT data, detects contradictions between sources, assesses reliability, and produces structured threat reports with recommendations.
 ### 📚 Academic & Technical Research
 Conduct literature reviews and technical deep-dives. ARGUS decomposes research questions, gathers information from authoritative sources, validates findings, and synthesizes structured reports with full source attribution.
 ---
 ## 📝 Examples
 ### Basic Usage
 ```python
 from argus_agent import argus_agent
 # Run a research task
 result = argus_agent.run(
    "What are the latest developments in quantum computing? "
    "Who are the key players and what are the risks?"
 )
 print(result)
 ```
 ### Using Individual Tools
 ```python
 from deeprecon_agent import web_search, analyze_text, extract_entities
 # Search the web
 results = web_search("autonomous AI agents 2025")
 print(results)
 # Analyze text for sentiment and bias
 analysis = analyze_text("The revolutionary AI breakthrough will transform everything...")
 print(analysis)
 # Extract entities from text
 entities = extract_entities("OpenAI raised $6.6 billion in October 2024...")
 print(entities)
 ```
 ### Sample Report Output
 ```json
 {
  "report_metadata": {
    "report_id": "AR-4F8A2C1B3D9E",
    "title": "Autonomous AI Agents: 2025 Landscape",
    "confidence_level": "HIGH",
    "agent_version": "ARGUS v2.0.0",
    "methodology": "Multi-Source Open Intelligence (MOSINT)"
  },
  "executive_summary": "...",
  "detailed_findings": "...",
  "key_risks": ["..."],
  "recommendations": ["..."],
  "sources_consulted": ["..."],
  "markdown_export": "..."
 }
 ```
 ---
 ## 📁 Project Structure
 ```
 ARGURAI/
 ├── argus_agent.py        # Main agent implementation (all tools + agent config)
 ├── argus_logo.jpg    # Agent marketplace image (800×800)
 ├── README.md             # This file
 ├── LICENSE               # MIT License
 └── .gitignore            # Git ignore rules
 ```
 ---
 ## 🤝 Contributing
 Contributions are welcome! Feel free to:
 1. Fork the repository
 2. Create a feature branch (`git checkout -b feature/new-tool`)
 3. Commit your changes (`git commit -m 'Add new tool: xyz'`)
 4. Push to the branch (`git push origin feature/new-tool`)
 5. Open a Pull Request
 ---
 ## 📄 License
 This project is licensed under the **MIT License** — see the [LICENSE](LICENSE) file for details.
 ---
 ## 🔗 Links
 - **Swarms Marketplace:** [swarms.world](https://swarms.world)
 - **Swarms Framework:** [github.com/kyegomez/swarms](https://github.com/kyegomez/swarms)
 - **Documentation:** [docs.swarms.world](https://docs.swarms.world)
 ---
 <p align="center">
  <strong>👁️ ARGUS sees everything. You miss nothing.</strong>
 </p>
 <p align="center">
  <sub>Built with ❤️ by ARGUS Labs — Powered by the Swarms Framework</sub>
 </p>