🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity or unusual behavior, with low risk scores across all assessed categories.

No network calls detected.
No shell execution patterns found.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access to function.
Shell: No shell execution patterns detected, indicating no direct command execution risk.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating low risk of unauthorized access.

🔬 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

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

Only one version has ever been released — brand new package
Author "AgentInsight Team" 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 agentinsight

Your task is to create a simple yet functional mini-application that leverages the 'agentinsight' Python package to monitor and analyze the performance of a simulated agentic system. This agentic system will consist of a set of agents that perform tasks asynchronously. Your goal is to implement observability features using 'agentinsight' to track the health, performance, and behavior of these agents in real-time.

Step 1: Define the Simulated Agentic System
- Design a basic structure where multiple agents can perform different types of tasks (e.g., fetching data from an API, processing information, etc.). Each agent should have unique identifiers and should be able to operate independently but concurrently.

Step 2: Implement Basic Functionality
- Write Python code to initialize and start your agents. Ensure that each agent has a task it performs periodically.

Step 3: Integrate 'agentinsight'
- Use 'agentinsight' to instrument your agents. Track metrics such as execution time, error rates, and success rates for each task performed by the agents.
- Set up logging to capture events like task start, task end, and any errors encountered during task execution.

Step 4: Real-Time Monitoring
- Develop a simple interface (could be a command-line tool or a web-based dashboard) to display real-time performance statistics and logs collected by 'agentinsight'.
- Allow users to filter and sort through the data based on various criteria (e.g., by agent ID, by task type).

Suggested Features:
- Visualize key performance indicators (KPIs) in real-time graphs.
- Provide historical data analysis capabilities.
- Enable alerts when certain thresholds are exceeded (e.g., high error rate).
- Offer customizable dashboards for different user roles.

How 'agentinsight' is Utilized:
- 'agentinsight' will be used to collect and aggregate data from all running agents. It will provide insights into the operational status and efficiency of the system, helping you to identify bottlenecks and areas for optimization.