👁️ Claw Argus

The All-Seeing Research & Intelligence System

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

Features • Quick Start • Tools • Methodology • Security & Scope • Use Cases

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🧠 What is CLAW ARGUS?

CLAW ARGUS is an autonomous AI research agent. It performs multi-layered investigations across the public web, cross-validates findings, detects bias, extracts structured entities, and generates professional intelligence reports.

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.

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✨ 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

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🚀 Quick Start

Prerequisites

• Python 3.10+
• An OpenAI API key (or compatible LLM provider)

Installation

# Clone the repository
git clone https://github.com/ClawArgus/ClawArgus
cd ClawArgus

# Install dependencies
pip install -r requirements.txt

# 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

Dependencies

Runtime deps are intentionally small:

• requests — HTTP client for search and fetch
• An LLM SDK of your choice (e.g. openai) — for the agent loop

There is no hidden agent framework pulling in a deep dependency tree. The tool functions in argus_agent.py are plain Python and can be driven by any LLM orchestrator you prefer.

Run

# 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"

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🔧 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	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

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📐 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

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🔒 Security & Scope

ARGUS is designed as a local research tool. Please read this section before deploying it anywhere that accepts untrusted input.

Intended use

• Running locally or in a trusted environment where the operator controls the research prompt.
• Authorized OSINT, market research, academic review, and similar analyst workflows.

Not intended (without additional hardening)

• Public-facing services or multi-tenant deployments. fetch_url_content will retrieve any URL the agent decides to visit, which means an attacker who controls the prompt or the search results could attempt Server-Side Request Forgery (SSRF) against internal hosts (127.0.0.1, 169.254.169.254, RFC1918 ranges, etc.). If you deploy ARGUS as a service, put fetch_url_content behind an allowlist, block private IP ranges after DNS resolution, and cap redirects.

Outbound HTTP identification

ARGUS identifies itself in the User-Agent header as ARGUS/<version> with a link back to this repository. It does not spoof a browser. Some sites may rate-limit or block non-browser clients; respect robots.txt and each site's terms of service.

API keys

The agent reads OPENAI_API_KEY (or your chosen provider key) from the environment. Never commit keys, and never paste a production key into a prompt that gets logged.

Repository hygiene

.claude/, .env, local settings, and cache files should be listed in .gitignore. Do not commit machine-specific auto-approval files.

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💼 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.

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📝 Examples

Basic Usage

from argus_agent import argus_agent

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

from argus_agent import web_search, analyze_text, extract_entities

results = web_search("autonomous AI agents 2025")
analysis = analyze_text("The revolutionary AI breakthrough will transform everything...")
entities = extract_entities("OpenAI raised $6.6 billion in October 2024...")

Sample Report Output

{
  "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": "..."
}

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📁 Project Structure

ClawArgus/
├── argus_agent.py        # Main agent implementation (all tools + agent config)
├── argus_logo.jpg        # Agent marketplace image (800×800)
├── requirements.txt      # Runtime dependencies
├── README.md             # This file
├── LICENSE               # MIT License
└── .gitignore            # Git ignore rules

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🤝 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

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📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

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🔗 Links

• Repository: https://github.com/ClawArgus/ClawArgus
• Issues & feature requests: https://github.com/ClawArgus/ClawArgus/issues

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👁️ ARGUS sees everything. You miss nothing.

_{Built with ❤️ by ARGUS Labs}