🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network, shell execution, obfuscation, and credential handling. However, the incomplete author information and the potential inactivity of the maintainer raise concerns about its trustworthiness.

Incomplete author information
Potential inactivity of the maintainer

Per-check LLM notes

Network: Network calls are expected if the package interacts with external services or APIs.
Shell: No shell execution patterns detected, indicating low risk.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author information is incomplete and the maintainer seems to be new or inactive, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

base_path response = requests.post(url=url, json=data, headers=headers, verify=self.ssl_verify,
: {data}") response = requests.post(url=url, json=data, headers=headers, verify=self.ssl_verify,

✓ 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

Email domain looks legitimate: bytedance.com>

✓ 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

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 agent-pilot-sdk

Create a Python-based application named 'LLMInspector' that leverages the 'agent-pilot-sdk' package to monitor and analyze interactions between users and a language model API (such as OpenAI's GPT). This application should provide insights into the performance and behavior of the language model, helping developers and researchers understand how their queries affect the output quality and processing time. The application will include several key features:

1. **User Interface**: Develop a simple web interface using Flask, allowing users to input their queries and view detailed analysis results.
2. **Query Submission**: Implement functionality within the application to submit user-generated queries to a chosen language model API.
3. **Tracing & Analysis**: Utilize the 'agent-pilot-sdk' to trace the execution path of each query through the language model. Analyze metrics such as response time, token usage, and error rates.
4. **Visualization**: Display analyzed data in an interactive format using libraries like Plotly or Matplotlib, enabling users to explore trends over time.
5. **Error Handling & Logging**: Ensure robust error handling and logging mechanisms are in place to capture issues during query processing and trace generation.
6. **Configuration Management**: Allow users to configure settings related to the language model API, including API keys and model parameters, directly from the web interface.

The 'agent-pilot-sdk' package plays a crucial role in this application by facilitating the tracing of requests made to the language model API. It enables real-time monitoring of request execution, providing valuable insights into performance bottlenecks and other critical metrics. Your task is to design and implement these features, ensuring the application is both functional and user-friendly.