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👁️ Claw Argus

The All-Seeing Research & Intelligence System

Named after Argus Panoptes — the hundred-eyed guardian of Greek mythology

Features • Quick Start • Tools • Methodology • Use Cases • Examples

--- ## 🧠 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

### 🔍 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

### ⚖️ 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**

### 🏗️ 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) ---

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