AI Analysis
Final verdict: SUSPICIOUS
The package shows minimal risk in terms of network, shell, and obfuscation activities, but the lack of a discoverable repository and limited maintainer activity raises concerns about its legitimacy and long-term support.
- Repository not found
- Maintainer has few packages
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 malicious shell command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
- Metadata: The repository is not found, and the maintainer has few packages, indicating potential risk.
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-eval-geval
Create a Python-based mini-application named 'EvaluatorBot' that leverages the 'agentforge-eval-geval' package to assess the quality of responses generated by various large language models (LLMs). This application will serve as a tool for developers and researchers to evaluate different LLMs against specific criteria, such as coherence, relevance, factual accuracy, and creativity. The application should include the following key components and functionalities: 1. **User Interface**: Develop a simple command-line interface (CLI) that allows users to input their evaluation criteria and select which LLMs they wish to test. 2. **Evaluation Criteria Setup**: Allow users to define custom evaluation criteria based on G-Eval metrics provided by the 'agentforge-eval-geval' package. These criteria could include aspects like logical consistency, factual correctness, and adherence to ethical guidelines. 3. **LLM Response Generation**: Integrate the ability to query multiple LLMs with a given prompt or set of prompts. Ensure that the application supports popular LLM APIs such as OpenAI's API and Anthropic's Claude API. 4. **Scoring Mechanism**: Implement a scoring system that evaluates each response against the defined criteria using the 'agentforge-eval-geval' package. Display the scores in an easily understandable format. 5. **Report Generation**: Enable the generation of detailed reports summarizing the evaluation results. Reports should include visualizations like charts and graphs to help compare performance across different LLMs. 6. **Customizability**: Make the application customizable so that users can add new evaluation criteria or modify existing ones without needing to rewrite the entire application. 7. **Documentation**: Provide comprehensive documentation explaining how to install and use the application, including examples of how to integrate it into other projects or workflows. Your task is to design and implement the 'EvaluatorBot' application from scratch, ensuring it effectively utilizes the 'agentforge-eval-geval' package's capabilities for evaluating LLM-generated content. Focus on creating a user-friendly experience while also allowing for deep customization and flexibility.