Updrage infrastucture

2026-04-16 12:36:51 +07:00
parent ff07cb7b5f
commit cb69168ba2
3 changed files with 157 additions and 38 deletions
@@ -2,7 +2,8 @@
  "permissions": {
    "allow": [
      "Bash(git -C \"C:/Users/ADMIN/Downloads/new projects/project7.1\" status)",
-      "Bash(git -C \"C:/Users/ADMIN/Downloads/new projects/project7.1\" log --oneline -5)"
+      "Bash(git -C \"C:/Users/ADMIN/Downloads/new projects/project7.1\" log --oneline -5)",
+      "Bash(python *)"
    ]
  }
 }
@@ -22,15 +22,15 @@
  <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>
+  <a href="#-security--scope">Security & Scope</a> •
+  <a href="#-use-cases">Use Cases</a>
 </p>

 ---

 ## 🧠 What is CLAW ARGUS?

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

@@ -74,7 +74,6 @@ Structured intelligence reports with **confidence scoring, risk assessment, and
 - ⚡ **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` 

 ---

@@ -83,14 +82,17 @@ Structured intelligence reports with **confidence scoring, risk assessment, and
 ### Prerequisites

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

 ### Installation

 ```bash
 # Clone the repository
-git clone https://github.com/ARGURAIgent/Claw-Argus.git
-cd ARGURAI
+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
@@ -98,6 +100,15 @@ 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

 ```bash
@@ -117,7 +128,7 @@ 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 |
+| 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 |
@@ -163,6 +174,28 @@ Sources are prioritized by reliability:

 ---

+## 🔒 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.
+
+---
+
 ## 💼 Use Cases

 ### 📈 Market Research & Competitive Intelligence
@@ -183,7 +216,6 @@ Conduct literature reviews and technical deep-dives. ARGUS decomposes research q
 ```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?"
@@ -195,19 +227,11 @@ print(result)
 ### Using Individual Tools

 ```python
-from deeprecon_agent import web_search, analyze_text, extract_entities
+from argus_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
@@ -235,9 +259,10 @@ print(entities)
 ## 📁 Project Structure

 ```
-ARGURAI/
+ClawArgus/
 ├── argus_agent.py        # Main agent implementation (all tools + agent config)
-├── argus_logo.jpg    # Agent marketplace image (800×800)
+├── argus_logo.jpg        # Agent marketplace image (800×800)
+├── requirements.txt      # Runtime dependencies
 ├── README.md             # This file
 ├── LICENSE               # MIT License
 └── .gitignore            # Git ignore rules
@@ -265,6 +290,9 @@ This project is licensed under the **MIT License** — see the [LICENSE](LICENSE

 ## 🔗 Links

+- **Repository:** https://github.com/ClawArgus/ClawArgus
+- **Issues & feature requests:** https://github.com/ClawArgus/ClawArgus/issues
+
 ---

 <p align="center">
@@ -7,7 +7,6 @@ detects bias, and generates comprehensive intelligence reports with
 confidence scoring, source attribution, and exportable markdown output.

 Named after Argus Panoptes, the all-seeing giant of Greek mythology.
-Built on the Swarms framework for production-ready agent orchestration.

 Author: ARGUS Labs
 Version: 2.0.0
@@ -18,13 +17,15 @@ import os
 import re
 import json
 import hashlib
+import inspect
 import requests
 import time
 import functools
 from collections import Counter
 from typing import Dict, List, Optional, Any, Callable
 from datetime import datetime, timezone
-from swarms import Agent
+
+from openai import OpenAI


 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
@@ -75,11 +76,7 @@ def _retry(max_attempts: int = 3, delay: float = 1.0):


 _HEADERS = {
-    "User-Agent": (
-        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
-        "AppleWebKit/537.36 (KHTML, like Gecko) "
-        "Chrome/124.0.0.0 Safari/537.36"
-    )
+    "User-Agent": "ARGUS/2.0 (+https://github.com/ClawArgus/ClawArgus)"
 }

 _STOP_WORDS = frozenset({
@@ -974,7 +971,108 @@ Prioritize sources in this order:
 # AGENT INITIALIZATION
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

-argus_agent = Agent(
+_JSON_TYPE_MAP = {
+    str: "string",
+    int: "integer",
+    float: "number",
+    bool: "boolean",
+}
+
+
+def _build_tool_schema(func: Callable) -> Dict[str, Any]:
+    sig = inspect.signature(func)
+    properties: Dict[str, Any] = {}
+    required: List[str] = []
+    for name, param in sig.parameters.items():
+        json_type = _JSON_TYPE_MAP.get(param.annotation, "string")
+        properties[name] = {"type": json_type}
+        if param.default is inspect.Parameter.empty:
+            required.append(name)
+    doc = (func.__doc__ or "").strip().split("\n\n", 1)[0]
+    return {
+        "type": "function",
+        "function": {
+            "name": func.__name__,
+            "description": doc[:1024],
+            "parameters": {
+                "type": "object",
+                "properties": properties,
+                "required": required,
+            },
+        },
+    }
+
+
+class ArgusAgent:
+    """
+    Minimal tool-calling loop around the OpenAI Chat Completions API.
+
+    Holds the system prompt, the tool registry, and a .run(task) entry point
+    that drives the model-tool-model cycle until the model returns a final
+    answer or max_loops is exceeded.
+    """
+
+    def __init__(
+        self,
+        agent_name: str,
+        agent_description: str,
+        system_prompt: str,
+        model_name: str,
+        tools: List[Callable],
+        max_loops: int = 5,
+        verbose: bool = True,
+    ):
+        self.agent_name = agent_name
+        self.agent_description = agent_description
+        self.system_prompt = system_prompt
+        self.model_name = model_name
+        self.max_loops = max_loops
+        self.verbose = verbose
+        self._tools_by_name = {t.__name__: t for t in tools}
+        self._tool_schemas = [_build_tool_schema(t) for t in tools]
+        self._client = OpenAI()
+
+    def run(self, task: str) -> str:
+        messages: List[Dict[str, Any]] = [
+            {"role": "system", "content": self.system_prompt},
+            {"role": "user", "content": task},
+        ]
+
+        for loop in range(self.max_loops):
+            response = self._client.chat.completions.create(
+                model=self.model_name,
+                messages=messages,
+                tools=self._tool_schemas,
+                tool_choice="auto",
+            )
+            msg = response.choices[0].message
+            messages.append(msg.model_dump(exclude_none=True))
+
+            if not msg.tool_calls:
+                return msg.content or ""
+
+            for call in msg.tool_calls:
+                fn = self._tools_by_name.get(call.function.name)
+                if self.verbose:
+                    print(f"  🔧 [loop {loop + 1}] {call.function.name}({call.function.arguments})")
+                if fn is None:
+                    result = f"Unknown tool: {call.function.name}"
+                else:
+                    try:
+                        args = json.loads(call.function.arguments or "{}")
+                        result = fn(**args)
+                    except Exception as exc:
+                        result = f"Error running {call.function.name}: {exc}"
+                messages.append({
+                    "role": "tool",
+                    "tool_call_id": call.id,
+                    "content": str(result),
+                })
+
+        return "Max tool loops reached without a final answer."
+
+
+argus_agent = ArgusAgent(
    agent_name="ARGUS",
    agent_description=(
        "ARGUS — The All-Seeing Research & Intelligence Agent v2.0. "
@@ -987,13 +1085,6 @@ argus_agent = Agent(
    system_prompt=ARGUS_SYSTEM_PROMPT,
    model_name="gpt-4o-mini",
    max_loops=5,
-    autosave=True,
-    verbose=True,
-    dynamic_temperature_enabled=True,
-    saved_state_path="argus_state.json",
-    retry_attempts=3,
-    context_length=128000,
-    output_type="string",
    tools=[
        web_search,
        fetch_url_content,
@@ -1003,7 +1094,6 @@ argus_agent = Agent(
        compare_sources,
        generate_report,
    ],
-    tool_choice="auto",
 )