🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network calls, shell execution, obfuscation, and credential harvesting. However, the metadata risk score is elevated due to low maintainer activity and lack of package description, raising concerns about its quality and intent.

Metadata risk due to low maintainer activity
Lack of package description

Per-check LLM notes

Network: The network call appears to be an internal health check and does not indicate malicious activity.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: Low maintainer activity and lack of details suggest potential low-quality or suspicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

request try: with urllib.request.urlopen(f"http://localhost:{port}/_health", timeout=1) as r:

✓ 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aeyeagent

Create a mini-application that leverages the 'aeyeagent' package to visualize and debug PydanticAI agent pipelines in real-time. Your application should serve as a user-friendly interface for developers to monitor the execution of their AI agents, allowing them to spot issues quickly and make adjustments on the fly. Here’s a step-by-step guide on how to build this app:

1. **Setup Environment**: Begin by setting up your development environment with Python and the necessary packages including 'aeyeagent'. Ensure you have PydanticAI installed as well.
2. **Define Agent Pipelines**: Develop a few sample PydanticAI agent pipelines that perform basic tasks such as data processing or simple decision-making processes. These will act as test cases for your debugging tool.
3. **Integrate aeyeagent**: Use 'aeyeagent' to integrate visual debugging capabilities into these pipelines. This includes setting up logging mechanisms and visualization tools within 'aeyeagent' to capture pipeline states and transitions.
4. **Build UI**: Create a simple web-based UI using a framework like Flask or Django to display the pipeline statuses and logs captured by 'aeyeagent'. This UI should allow users to interact with the running pipelines, such as pausing, resuming, or resetting them.
5. **Enhance Functionality**: Implement additional features such as real-time alerts for errors or performance bottlenecks, and historical log analysis tools to help diagnose past issues.
6. **Test Thoroughly**: Conduct thorough testing with various scenarios to ensure the application works as expected under different conditions.
7. **Document**: Write comprehensive documentation detailing how to use the application, including setup instructions and best practices for integrating it with existing PydanticAI projects.

This project not only showcases the power of 'aeyeagent' but also provides a practical tool for developers working with PydanticAI agents.