🤖 AI Analysis

Final verdict: SAFE

The package aevum-llm is deprecated and advises users to migrate to aevum-agent. There are no detected risks related to network calls, shell execution, or credential harvesting. The main concern is the sparse metadata, but overall it appears safe.

Package is deprecated
No network calls detected
No shell execution detected
No credential harvesting detected
Sparse metadata

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 signs of executing system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author's information is sparse, indicating potential low credibility, but there are no clear signs of malicious intent.

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aevum-labs/aevum appears legitimate

⚠ 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 aevum-llm

Create a simple chatbot application using the 'aevum-llm' Python package. Since 'aevum-llm' has been deprecated and users are advised to switch to 'aevum-agent', let's assume for the purpose of this exercise that we're still working with 'aevum-llm'. This chatbot will serve as a customer support tool for a fictional e-commerce website, allowing users to ask questions about products, track orders, and get general assistance.

Step 1: Set Up Your Environment
- Install Python and necessary libraries including 'aevum-llm'.
- Create a virtual environment for your project.

Step 2: Design the Chatbot's Core Functionality
- Integrate 'aevum-llm' into your project to handle natural language processing and generate responses.
- Implement a user interface where users can input their queries.
- Develop a response generation mechanism that leverages 'aevum-llm' to interpret user inputs and provide relevant answers.

Step 3: Add Features
- Include a product search feature that allows users to look up items by name or description.
- Implement order tracking functionality where users can enter their order number and receive status updates.
- Add a FAQ section that provides quick access to common questions and answers.
- Ensure the chatbot can handle multiple concurrent users efficiently.

Step 4: Testing and Deployment
- Thoroughly test the chatbot's ability to understand various types of user inputs and provide accurate responses.
- Deploy the chatbot on a web server or integrate it into the e-commerce site's existing infrastructure.
- Monitor the chatbot's performance and gather feedback from users to improve its capabilities.

Remember, despite 'aevum-llm' being deprecated, treat it as if it were actively maintained for the purposes of this project. Focus on demonstrating how you would utilize its features to enhance the chatbot's functionality.