🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe despite low maintainer activity and metadata quality, as there are no indications of malicious activities such as obfuscation, shell execution, or credential harvesting.

Low risk for network, shell, obfuscation, and credential risks.
Metadata quality and maintainer activity suggest caution but not necessarily malicious intent.

Per-check LLM notes

Network: The package makes network calls which could be legitimate if it requires external resources or updates.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or credential theft.
Metadata: The package shows signs of low maintainer activity and metadata quality, which may indicate low effort or potential malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

nt from %s...", url) with urllib.request.urlopen(url) as response: # noqa: S310 # nosec B310
) async with httpx.AsyncClient(timeout=httpx.Timeout(300)) as client: self._htt

✓ 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 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ 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 agentic-layer-testbench

Create a mini-application called 'AgentEvaluator' that leverages the 'agentic-layer-testbench' package to evaluate the performance of different AI agents in generating responses to user queries. This application should serve as a testbed where developers can input their own agents and compare their performance against each other using various evaluation metrics provided by Ragas.

Step-by-Step Instructions:
1. Setup the environment: Ensure you have Python installed and create a virtual environment for your project. Install the necessary packages including 'agentic-layer-testbench' and any dependencies it requires.
2. Design the UI: Develop a simple but intuitive user interface where users can input their queries and select which agents they wish to evaluate.
3. Implement Agent Integration: Allow users to integrate their own agents into the system. Provide documentation on how to structure agent inputs and outputs according to the requirements of 'agentic-layer-testbench'.
4. Evaluation Metrics: Utilize the evaluation capabilities of 'agentic-layer-testbench' to assess the responses from the agents. Consider implementing common metrics such as accuracy, relevance, coherence, and fluency.
5. Reporting: After evaluating the agents, provide a comprehensive report detailing the performance of each agent based on the chosen metrics.
6. User Feedback Loop: Incorporate a feature where users can provide feedback on the agent's performance, which could be used to refine the evaluation process.
7. Continuous Improvement: Plan for updates and improvements to the application based on user feedback and advancements in the 'agentic-layer-testbench' package.

Suggested Features:
- Support for multiple types of agents (text-based, image generation, etc.)
- Ability to save and load evaluations for future reference
- Graphical representation of evaluation results
- Customizable evaluation criteria based on user preferences
- Integration with popular chat platforms for real-time testing

How to Utilize 'agentic-layer-testbench':
- Use the package to define the structure of the test cases and expected outcomes.
- Apply the provided evaluation functions to assess the quality of the agents' responses.
- Leverage the reporting tools within the package to generate detailed analysis reports.