🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with no signs of malicious activity. The moderate metadata risk score is due to the maintainer's low level of engagement, but this alone is insufficient to conclude any malicious intent.

No shell execution or credential harvesting detected
Low obfuscation risk
Moderate metadata risk due to low maintainer activity

Per-check LLM notes

Network: The use of httpx.AsyncClient suggests network requests are part of the package's functionality, likely for communication with a server. This is not inherently suspicious but should be reviewed for destination and purpose.
Shell: No shell execution patterns detected, indicating no immediate risk from command execution within the package.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
Credentials: No credential harvesting patterns detected, suggesting the package does not pose a risk for stealing secrets or credentials.
Metadata: The maintainer has only one package and lacks PyPI classifiers, suggesting low effort or inactivity.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

e) self._client = httpx.AsyncClient(**client_kwargs) async def aclose(self) -> None:

✓ 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 4.0

2 maintainer concern(s) found

Author "Agent Control Team" 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 agent-control-server

Develop a mini-application called 'AgentGuard' which acts as a comprehensive dashboard for managing and evaluating AI agents using the 'agent-control-server' Python package. This application should allow users to create, monitor, and assess various AI agents in real-time, providing insights into their performance and behavior. Here are the key features and steps to implement the project:

1. **Setup Environment**: Ensure you have Python installed along with the 'agent-control-server' package. Set up a virtual environment for your project.
2. **Create a Web Interface**: Develop a user-friendly web interface where users can interact with the AI agents. This could be done using Flask or Django for backend and HTML/CSS/JavaScript for frontend.
3. **Integration with 'agent-control-server'**: Use 'agent-control-server' to establish a server that manages the AI agents. This includes setting up endpoints for controlling agent actions, retrieving agent statuses, and evaluating their performance metrics.
4. **Agent Management Features**:
- **Create Agents**: Allow users to define new AI agents with customizable parameters.
- **Monitor Agents**: Display real-time status updates of each agent including activity logs and current tasks.
- **Evaluate Performance**: Provide tools to analyze agent performance over time, including visual graphs and numerical data.
5. **Security Measures**: Implement basic security measures such as user authentication and authorization to ensure only authorized users can control the agents.
6. **Testing & Documentation**: Thoroughly test all functionalities and document the setup process, usage instructions, and any API documentation for 'agent-control-server'.

The goal is to create a versatile tool that not only showcases the capabilities of 'agent-control-server' but also serves as a practical solution for managing AI agents in a controlled environment.