AI Analysis
Final verdict: SUSPICIOUS
The package shows some potential risks, particularly due to its network activity and the fact that the maintainer has only one other package, which might indicate a less established or potentially suspicious origin.
- Network risk due to external service calls
- Low number of packages from maintainer
Per-check LLM notes
- Network: The use of network calls is common for packages that require external data or services, but unusual naming might warrant further investigation.
- Shell: No shell execution patterns detected, indicating low risk.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.
Package Quality Overall: Low (2.8/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (1363 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
3 type-annotated function signatures (partial)
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
teway async with httpx.AsyncClient() as client: headers = {async with httpx.AsyncClient() as client: try: header
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 2.0
1 maintainer concern(s) found
Author "AgentPulse Team" 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 autonomous-agentpulse
Create a self-healing monitoring system for AI agents using the 'autonomous-agentpulse' Python package. This mini-application will serve as a telemetry proxy that collects and analyzes data from various AI agents in real-time. It should have the capability to detect anomalies and automatically initiate corrective actions to ensure the smooth operation of these AI agents. Here’s a detailed plan on how to approach this project: 1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with the 'autonomous-agentpulse' package. 2. **Define Agent Interface**: Design a simple interface for AI agents that allows them to send their operational status and metrics to the telemetry proxy. 3. **Implement Telemetry Proxy**: Use 'autonomous-agentpulse' to implement the telemetry proxy. This involves configuring it to listen for incoming data from AI agents, process this data, and store it for analysis. 4. **Anomaly Detection Module**: Develop an anomaly detection module that periodically checks the collected data against predefined thresholds or patterns. If anomalies are detected, the system should log them and trigger alerts. 5. **Self-Healing Mechanisms**: Implement self-healing mechanisms based on the severity of detected anomalies. For instance, if CPU usage exceeds a certain threshold, the system could automatically scale resources or restart the affected agent. 6. **User Interface (Optional)**: Consider adding a basic web-based UI where users can view live metrics, historical data, and manage alerts. 7. **Testing & Validation**: Rigorously test the application under different scenarios to ensure reliability and effectiveness. Validate its performance by simulating various anomalies and verifying the system's response. 8. **Documentation**: Finally, document your setup process, configuration options, and how each feature works within the application. This will make it easier for others to understand and extend your work.
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