agentmetrics

v0.1.2 suspicious
4.0
Medium Risk

Monitor your AI agents. Track cost, latency, and errors in two lines of code.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows high metadata risk due to suspicious git repository activity and maintainer history. However, other risk factors such as network, shell, obfuscation, and credential risks are low.

  • High metadata risk
  • No evidence of malicious activity in code
Per-check LLM notes
  • Network: The observed network calls are likely for reporting usage metrics to a server, which is common for analytics and logging purposes.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: High risk due to suspicious git repository activity and maintainer history.

📦 Package Quality Overall: Low (3.8/10)

○ 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://agentmetrics.dev/docs
  • Detailed PyPI description (3709 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 22 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 3 commits in andalabx/agentmetrics-sdk
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • try: resp = requests.post(url, json={"events": payloads}, headers=headers, timeout=10)
  • try: resp = requests.post(url, json=payload, headers=headers, timeout=5)
✓ 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 score 7.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Single contributor with only 3 commit(s) — possibly throwaway account
  • All 3 commits happened within 24 hours
⚠ 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 agentmetrics 
Create a Python-based mini-application that monitors and analyzes the performance of various AI agents deployed in a simulated environment. This application will utilize the 'agentmetrics' package to track critical metrics such as cost, latency, and error rates for each agent. The goal is to provide real-time insights into the efficiency and reliability of these AI agents, enabling users to make informed decisions about their deployment and optimization.

Steps to follow:
1. Set up a virtual environment and install the required packages, including 'agentmetrics'.
2. Design a simple simulation environment where different types of AI agents (e.g., text generation models, image recognition systems) can operate independently.
3. Integrate 'agentmetrics' into your simulation to start tracking key performance indicators for each agent.
4. Develop a user-friendly dashboard that visualizes the collected data, allowing users to compare the performance of different agents at a glance.
5. Implement features that allow users to filter and analyze specific aspects of the performance data, such as identifying which agents are most cost-effective under certain conditions.
6. Add functionality to log and review historical performance data, helping users understand trends over time.
7. Ensure the application can handle multiple concurrent simulations and gracefully manage any exceptions or errors that may occur during monitoring.

Suggested Features:
- Real-time performance visualization for active agents.
- Historical data analysis tools for trend identification.
- Comparative analysis between different types of agents.
- Customizable alerts for significant deviations from expected performance metrics.
- Detailed logs for troubleshooting and auditing purposes.

How 'agentmetrics' is utilized:
- Initialize 'agentmetrics' with appropriate configurations to monitor cost, latency, and errors for each AI agent.
- Use 'agentmetrics' API to periodically update performance data as the agents operate within the simulation.
- Leverage 'agentmetrics' reporting capabilities to generate summaries and visualizations for the user interface.