🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network, shell, obfuscation, and credential handling, but the metadata risk due to the maintainer's information being incomplete or inactive suggests caution.

Metadata risk due to incomplete maintainer details
Package status is alpha, indicating it may not be fully developed

Per-check LLM notes

Network: No network calls suggest normal operation if the package does not require external services.
Shell: No shell executions indicate no immediate risk of command execution vulnerabilities.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer's author name is missing or very short, and they appear to be new or inactive, which raises some suspicion but does not definitively indicate malicious intent.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://github.com/microsoft/agent-governance-toolkit/tree/m
Detailed PyPI description (6126 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

32 type-annotated function signatures detected in source

✦ High Multiple Contributors 10.0

Active multi-contributor project

14 unique contributor(s) across 100 commits in microsoft/agent-governance-toolkit
Active community — 5 or more distinct contributors

🔬 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: microsoft.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository microsoft/agent-governance-toolkit appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

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 agentmesh-observability

Create a mini-application that monitors the performance of a simple web server using the 'agentmesh-observability' package. This application will serve as a basic observability tool for developers and system administrators. Here are the steps and features you should include:

1. Set up a basic Flask web server with a single endpoint '/healthcheck' that returns 'OK'.
2. Integrate the 'agentmesh-observability' package into your Flask application to enable OpenTelemetry tracing and Prometheus metrics collection.
3. Configure OpenTelemetry to trace requests made to the '/healthcheck' endpoint, including start time, end time, and duration.
4. Implement Prometheus metrics to track the number of requests made to the '/healthcheck' endpoint and their response times.
5. Create a Grafana dashboard that visualizes the collected metrics and traces, providing insights into the health and performance of the Flask server.
6. Ensure that the application can be easily deployed and monitored in a production environment, showcasing the real-world applicability of 'agentmesh-observability'.

By completing this project, you will gain hands-on experience with observability tools and understand how to monitor and improve the performance of web applications.