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