agentargus

v0.0.0 suspicious
4.0
Medium Risk

Production-grade evaluation, observability, and reliability for AI agents

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low direct risks such as network calls or shell executions, but its metadata raises concerns due to recent creation and lack of maintainer details.

  • Low activity and no maintainer information
  • Recently created package
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package is suspicious due to its recent creation, low activity, and lack of maintainer information.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • All 7 commits happened within 24 hours
⚠ 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 agentargus 
Create a fully-functional mini-application that monitors and evaluates the performance of AI agents using the 'agentargus' Python package. This application will serve as a dashboard for developers to observe the health, efficiency, and reliability of their AI models in real-time. Here’s a step-by-step guide on what your application should do and how it should utilize 'agentargus':

1. **Setup**: Begin by setting up a basic Flask web application. Ensure you have 'agentargus' installed.
2. **Integration**: Integrate 'agentargus' into your application to monitor the AI agents. Use its features to track key performance indicators such as response time, accuracy, and error rates.
3. **Dashboard Development**: Develop a simple yet effective dashboard within the Flask app where users can view the status of their AI agents. The dashboard should display graphs and charts representing the performance metrics over time.
4. **Real-Time Updates**: Implement real-time updates on the dashboard using WebSockets or similar technology so that users can see immediate changes in the performance of their AI agents.
5. **Alert System**: Incorporate an alert system that notifies users via email or SMS if any of the monitored AI agents exceed predefined thresholds in terms of performance.
6. **Customization Options**: Allow users to customize which metrics they want to monitor and set their own thresholds for alerts.
7. **Documentation & Deployment**: Provide comprehensive documentation on how to install, configure, and use the application. Also, include instructions on deploying the application to a cloud service like AWS or Heroku.

By following these steps, you will create a valuable tool for developers looking to ensure the optimal performance and reliability of their AI agents.