AI Analysis
Final verdict: SUSPICIOUS
The package has a moderate risk score due to low maintainer activity and poor metadata quality, despite showing no immediate signs of malicious intent.
- Metadata risk is high with low maintainer activity and poor metadata quality.
- Network interactions through requests.Session suggest legitimate use but require further scrutiny.
Per-check LLM notes
- Network: The use of requests.Session indicates the package is designed to make HTTP requests, which could be for legitimate purposes like API calls.
- Shell: No shell execution patterns detected, suggesting low risk of direct command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintainer activity and poor metadata quality, which could indicate potential risk.
Package Quality Overall: Low (4.4/10)
✦ High
Test Suite
9.0
Test suite present — 3 test file(s) found
3 test file(s) detected (e.g. test_artifacts.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (1425 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
52 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
self.session = session or requests.Session() @staticmethod def _normalize_base_url(base_url: s
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
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 8.0
4 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with agents24
Create a fully-functional mini-app named 'AgentControlCenter' that serves as a command-line interface (CLI) for managing and controlling Agents24 agents. This app will allow users to deploy, monitor, and manage multiple agents simultaneously, providing a centralized management solution for their agent-based workflows. The application should include the following features: 1. **Agent Deployment**: Users should be able to specify the type of agent they wish to deploy, provide necessary configurations, and initiate the deployment process. 2. **Agent Monitoring**: The app should display real-time status updates for each deployed agent, including health checks, active tasks, and any errors encountered. 3. **Task Management**: Users must be able to assign new tasks to agents, check the progress of ongoing tasks, and retrieve results once tasks are completed. 4. **Agent Control**: Provide functionalities such as pausing, resuming, and terminating agents based on user input. 5. **Configuration Management**: Allow users to save and load agent configurations, making it easier to manage multiple setups without manual reconfiguration. 6. **Logging & Reporting**: Implement logging to track all actions performed via the CLI and generate periodic reports summarizing agent performance and task outcomes. To achieve these features, you'll need to utilize the 'agents24' package effectively. Start by exploring its API documentation to understand how to interact with the runtime and control functions. Use the package to handle agent lifecycle management (deploy, start, stop), task execution, and monitoring operations. Ensure your implementation leverages the package's capabilities to streamline these processes and enhance usability. Additionally, consider incorporating error handling and user-friendly prompts to guide users through the various functionalities of 'AgentControlCenter'.