agentic-layer-testbench

v0.9.2 safe
4.0
Medium Risk

Testbench to evaluate agents using Ragas

🤖 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.

💬 Discussion Feed

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