🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, or obfuscations detected. The metadata risk is slightly elevated due to low activity and missing classifiers, but there are no clear signs of malicious intent.

No network calls detected.
Low metadata activity and missing classifiers.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: Low activity and lack of classifiers suggest low effort, but no immediate signs of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: gmail.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 "Markus Ecker" 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 ag-ui-protocol

Create a fully-functional mini-application using the Python package 'ag-ui-protocol' that simulates a simple chatbot interface for handling user queries and providing responses based on predefined rules. The application should include the following core functionalities:

1. User Input Handling: Allow users to input their queries through a text-based interface.
2. Query Processing: Use the 'ag-ui-protocol' package to encode and decode user queries into a structured format that can be easily processed by the server.
3. Response Generation: Based on the structured query received from the user, generate appropriate responses. These responses could be pre-defined for certain keywords or phrases.
4. Feedback Loop: Implement a feedback mechanism where the application can request additional information from the user if the initial query is not clear enough.
5. Logging: Log all interactions between the user and the bot for future analysis and improvement of the bot's response generation capabilities.

In addition to these core functionalities, consider adding the following advanced features to enhance the application:

- Integrate a basic Natural Language Processing (NLP) module to understand the context of the user's queries better.
- Implement a learning feature where the bot can improve its responses over time based on user feedback.
- Add support for multiple languages to make the bot more accessible to a wider audience.

The main goal of this project is to demonstrate the practical usage of the 'ag-ui-protocol' package in building interactive applications. Ensure that your implementation clearly showcases how the package is utilized in each step of the interaction process.