AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in terms of network, shell, obfuscation, and credential usage, but the metadata risk score is elevated due to missing git repository information and a new maintainer account, raising suspicion.
- Elevated metadata risk score
- New maintainer account
- Missing git repository
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external API interactions.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, suggesting low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: Suspicious due to missing git repository and new maintainer account, but no direct evidence 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
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "The AgentForge Authors" 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 agentforge-litellm
Create a mini-application named 'MultiModelQA' that serves as a question-answering system capable of handling queries from various users via a simple web interface. This application will leverage the 'agentforge-litellm' package to route questions to different large language models (LLMs) based on predefined criteria such as model availability, query type, or user preference. Here’s how you will develop it: 1. **Setup Project**: Initialize a new Python project using Flask as the web framework. Ensure you have Flask installed and set up a basic web server. 2. **Integrate agentforge-litellm**: Install the 'agentforge-litellm' package and configure it to connect to multiple LLM providers. Define functions to route incoming queries to the appropriate LLM based on certain conditions. 3. **Design User Interface**: Create a simple HTML form where users can input their questions. The form should also allow users to select a preferred LLM if they wish. 4. **Implement Query Handling**: Write backend logic to process user inputs. Use 'agentforge-litellm' to send these queries to the selected or recommended LLM. Handle responses appropriately and return them to the user. 5. **Add Error Handling**: Implement error handling to manage scenarios where a selected LLM might not be available or returns an unexpected response. 6. **Enhance Functionality**: Consider adding features like session management to track user interactions over time, sentiment analysis on user queries to better understand user satisfaction, or even integrating a feedback loop where user feedback improves future responses. 7. **Testing and Deployment**: Thoroughly test your application locally before deploying it to a cloud service like Heroku or AWS. By following these steps, you will create a versatile mini-application that demonstrates the flexibility and power of 'agentforge-litellm' in routing complex tasks to multiple AI models.