🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories except for network and metadata, which are still within acceptable limits. There's no strong indication of malicious activity.

Low shell and obfuscation risks
No detected credential harvesting
Potential inexperience indicated by single package from maintainer

Per-check LLM notes

Network: The use of aiohttp.ClientSession with TCPConnector suggests the package is making network calls, which is common for client-side packages but should be reviewed for destination and frequency.
Shell: No shell execution patterns were detected, indicating low risk.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
Metadata: The maintainer has only one package, indicating potential inexperience or a new account, but no other red flags are present.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 24 test file(s) found

24 test file(s) detected (e.g. test_agent.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (1418 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

168 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.pool_manager = aiohttp.ClientSession( connector=aiohttp.TCPConnector(limit=self.m

✓ 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: fetch.ai

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Xavier" 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 agentverse-client

Create a mini-app called 'AgentVerse Assistant' which serves as an intelligent personal assistant using the 'agentverse-client' package to interact with Fetch AI services. This app will have a simple graphical user interface (GUI) built with Tkinter, allowing users to input tasks and receive intelligent responses from the Fetch AI services through the 'agentverse-client' API. Here's a step-by-step guide on how to build this app:

1. **Setup Project Environment**: Initialize a new Python project and install necessary packages including 'agentverse-client', 'tkinter', and any other dependencies required.
2. **Design the GUI**: Use Tkinter to design a basic GUI with fields for task input and a display area for responses. Include buttons for submitting tasks and clearing the display.
3. **Integrate 'agentverse-client'**: Utilize the 'agentverse-client' package to connect to Fetch AI services. Ensure that you handle authentication and setup any required configurations to communicate with the services effectively.
4. **Implement Task Submission Logic**: When the user submits a task through the GUI, use 'agentverse-client' to send this task to Fetch AI for processing. Capture the response and display it back to the user within the app.
5. **Enhance User Experience**: Add error handling to manage cases where the Fetch AI service might not respond or return an error. Implement features like history logging of past interactions and the ability to save favorite tasks or responses for quick access.
6. **Testing and Optimization**: Test the application thoroughly to ensure all functionalities work as expected. Optimize the code for better performance and user experience.
7. **Deployment**: Prepare the application for deployment, ensuring it runs smoothly on different operating systems. Consider packaging it as a standalone executable for easy distribution.

Suggested Features:
- History Log: Keep a log of previous tasks submitted and responses received.
- Favorites: Allow users to mark certain tasks or responses as favorites for quick retrieval.
- Error Handling: Gracefully handle errors and provide meaningful feedback to the user.
- Customization: Let users customize the appearance of the GUI to some extent.

The 'agentverse-client' package plays a crucial role in this project by enabling seamless communication with Fetch AI services, allowing the app to perform complex tasks and provide intelligent responses to user inputs.