argosvix

v0.4.0a1 suspicious
6.0
Medium Risk

Argosvix Python SDK = AI agent observability (cost / latency / tokens / errors) for OpenAI / Anthropic / Gemini / Mistral

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell, obfuscation, and credential manipulation, but significant concerns arise from its metadata, including a non-existent git repository and sparse author information.

  • Non-existent git repository
  • Sparse author information
Per-check LLM notes
  • Network: The observed network call is likely intended for legitimate API interaction based on the provided code snippet.
  • Shell: No shell execution patterns were detected, indicating low risk for direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The package shows several red flags including a non-existent git repository, a single version release, and an author with minimal information.

📦 Package Quality Overall: Low (4.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/argosvix/Argosvix/tree/main/packages/sdk-
  • Detailed PyPI description (5399 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 33 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • try: req = urllib.request.Request( self.config.endpoint,
  • ) with urllib.request.urlopen(req, timeout=10) as resp: if 200
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: argosvix.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with argosvix 
Create a Python-based command-line utility named 'AIInspector' that leverages the 'argosvix' package to monitor and analyze the performance of various AI agents from different providers such as OpenAI, Anthropic, Gemini, and Mistral. The utility should allow users to input API requests and receive detailed feedback on the cost, latency, token usage, and error rates of these interactions. Here are the steps and features to implement:

1. **Setup**: Install necessary packages including 'argosvix', configure environment variables for API keys.
2. **User Interface**: Develop a simple CLI interface where users can select which AI provider they want to interact with and provide the required inputs.
3. **Request Handling**: Use 'argosvix' to send API requests to the chosen AI service and gather real-time data on cost, latency, tokens used, and any errors encountered during the request processing.
4. **Reporting**: Display the gathered information in a user-friendly format, including visual aids like graphs or tables if possible.
5. **Logging & Saving**: Implement logging capabilities so that all interactions and their outcomes can be saved for later analysis.
6. **Error Handling**: Ensure robust error handling is in place to manage unexpected issues gracefully and provide useful feedback to the user.
7. **Customization Options**: Allow users to customize the level of detail in reports, frequency of data collection, and more.
8. **Security Measures**: Ensure sensitive data like API keys are handled securely.

The goal is to create a tool that not only serves as a practical utility but also demonstrates the power and versatility of the 'argosvix' package in managing and optimizing interactions with AI services.

💬 Discussion Feed

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