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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>