AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in terms of network activity, shell execution, and obfuscation. However, the metadata risk score is elevated due to the maintainer's new or inactive account and lack of proper identification, raising suspicion about potential supply-chain risks.
- Metadata risk due to new/inactive maintainer account
- Lack of proper author name
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution detected, indicating the package does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
- Metadata: The maintainer has a new or inactive account and lacks a proper author name, which could indicate 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
Email domain looks legitimate: gmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository dkondo/agent-tackle-box appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 agent-debugger
Create a comprehensive debugging tool for developers working with LangChain/LangGraph applications using the 'agent-debugger' Python package. This tool will serve as an essential aid for identifying and resolving issues within complex agent-based systems. Your task is to design and implement a fully-functional mini-application that leverages the core functionalities of the 'agent-debugger' package to provide real-time insights into the behavior of agents within these systems. Here are the steps and features you need to consider: 1. **Project Setup**: Begin by setting up your development environment with Python and installing the 'agent-debugger' package alongside any other necessary dependencies. 2. **Integration with LangChain/LangGraph**: Integrate the 'agent-debugger' with existing LangChain/LangGraph applications or create a simple mock-up system to demonstrate its capabilities. 3. **Real-Time Monitoring**: Implement real-time monitoring of agent interactions, allowing users to track the state and behavior of individual agents over time. 4. **Error Detection & Reporting**: Develop an error detection mechanism that can automatically identify potential issues in agent operations and generate detailed reports for further analysis. 5. **Visualization Tools**: Incorporate visualization tools to graphically represent agent interactions and system states, aiding in quicker understanding and diagnosis of problems. 6. **Customizable Alerts**: Enable users to set up customizable alerts based on specific conditions or thresholds related to agent performance or system health. 7. **Documentation & User Interface**: Provide comprehensive documentation and a user-friendly interface to guide users through the setup and use of the debugging tool. By completing this project, you'll have developed a valuable resource for developers looking to optimize their LangChain/LangGraph applications, showcasing the power and versatility of the 'agent-debugger' package.