🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network, shell, obfuscation, and credential areas, but its novelty and limited maintainer history raise some concerns about potential supply-chain risks.

New package with limited maintainer history
Potential supply-chain risk due to package novelty

Per-check LLM notes

Network: Network calls are likely for legitimate purposes like sending telemetry data or updating agent configurations.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is new and the maintainer has few packages, which may indicate potential risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

try: with httpx.Client(timeout=config.timeout_s) as client: resp =
als/v0" try: with httpx.Client(timeout=config.timeout_s) as client: resp = clie

✓ 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 4.0

2 maintainer concern(s) found

Package is very new: uploaded 2 day(s) ago
Author "Plivo Labs" 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 agent-observability-sdk

Create a comprehensive evaluation tool for AI voice agents using the 'agent-observability-sdk' Python package. This tool will serve as a critical component in assessing the performance of AI voice assistants across various metrics such as hallucination detection, response accuracy, intent recognition, and tool correctness. Your task is to develop a fully-functional mini-application that not only integrates these evaluation capabilities but also provides an intuitive user interface for input and output. Here’s a step-by-step guide on how to proceed:

1. **Setup**: Begin by installing the necessary packages including 'agent-observability-sdk'. Ensure you have Python and pip installed on your system.
2. **Project Structure**: Organize your project into modules for better management. For example, separate modules for data handling, evaluation logic, and UI.
3. **Evaluation Logic**: Implement functions to evaluate AI voice responses based on the provided criteria. Use the 'agent-observability-sdk' to perform evaluations like hallucination checks, accuracy assessments, and tool correctness verification.
4. **User Interface**: Develop a simple yet effective command-line interface (CLI) where users can input test queries and see the results of the evaluations.
5. **Testing**: Test your application thoroughly with different types of AI voice responses to ensure it works as expected across various scenarios.
6. **Documentation**: Write clear documentation explaining how to use your application, including setup instructions and examples of usage.

**Suggested Features**:
- Detailed reports for each evaluation metric.
- Support for multiple languages or dialects.
- Option to save evaluation results for future reference.
- Integration with external tools for more advanced analysis.

Utilize the 'agent-observability-sdk' package to its full potential by leveraging its built-in evaluators and any additional functionalities it offers. Remember, the goal is to create a versatile tool that can be easily adapted for different AI voice agents.