🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity such as network calls, shell execution, obfuscation, or credential risks. The metadata risk is slightly elevated due to the maintainer's limited history, but overall, it appears safe.

No network calls detected
Maintainer has only one package

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 immediate risk of command injection or similar attacks.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
Metadata: The maintainer has only one package, indicating a potentially new or less active account, but no other suspicious flags are present.

🔬 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: copilotkit.ai

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Ran Shem Tov" 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 ag-ui-langgraph

Develop a language learning mini-app that leverages the AG-UI protocol for LangGraph using the 'ag-ui-langgraph' Python package. This app will facilitate interactive language practice by allowing users to input sentences or phrases in their target language, receive real-time feedback on grammatical accuracy, and explore related vocabulary through an intuitive graphical interface.

Step 1: Set up the project environment.
- Install necessary Python packages including 'ag-ui-langgraph'.
- Ensure your development environment supports Python 3.x.

Step 2: Design the user interface.
- Create a clean, user-friendly interface where users can type sentences or phrases in their target language.
- Integrate buttons or menus for selecting different languages supported by the 'ag-ui-langgraph' package.

Step 3: Implement core functionalities.
- Use 'ag-ui-langgraph' to analyze the grammatical structure of user inputs.
- Display instant feedback on any errors found in the sentence structure.
- Highlight correct usage patterns and suggest improvements.

Step 4: Enhance user experience.
- Add a feature to visualize the relationships between words in the sentence using the graph capabilities provided by 'ag-ui-langgraph'.
- Allow users to click on specific words to see definitions, synonyms, and examples in context.

Step 5: Incorporate additional learning tools.
- Include a section that suggests similar sentences for practice based on the user's input.
- Provide a quiz mode where users can test their knowledge on grammar rules and vocabulary.

Step 6: Test and refine.
- Conduct thorough testing to ensure all features work as intended.
- Gather user feedback and make adjustments to improve usability and effectiveness.

By following these steps, you'll create a valuable tool for anyone looking to improve their language skills in a structured and engaging way.