🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate network risk due to potential data transmission to external URLs, and its newness and limited maintenance history raise concerns about reliability.

Moderate network risk due to POST requests to external URLs.
New package with limited maintenance history.

Per-check LLM notes

Network: Network calls to external URLs with POST methods could indicate legitimate functionality like reporting usage statistics or sending alerts, but also potential unauthorized data transmission.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is new and maintained by an account with limited activity, suggesting potential unreliability but no clear signs of malicious intent.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

Test runner config found: pyproject.toml
1 test file(s) detected (e.g. test_agentos.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (6530 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

155 type-annotated function signatures detected in source

○ Low Multiple Contributors 2.0

Single-author or unverifiable project

1 unique contributor(s) across 10 commits in Roxmix/agentos-mcp
Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

-Secret"] = secret req = urllib.request.Request(url=url, data=data, headers=headers, method="POST")
ders, method="POST") with urllib.request.urlopen(req, timeout=10) as resp: status = resp.stat
webhook_secret req = urllib.request.Request( url=settings.webhook_url, d
POST" ) with urllib.request.urlopen(req, timeout=5) as resp: status = resp.s
rompt}] } async with httpx.AsyncClient(timeout=30.0) as client: resp = await client.post(

✓ 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

Repository Roxmix/agentos-mcp appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "AgentOS Contributors" 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 agentos-mcp

Create a personalized news recommendation system using the 'agentos-mcp' package. This application will leverage the persistent cognitive layer provided by 'agentos-mcp' to manage and enhance the decision-making process of an AI agent responsible for recommending news articles to users based on their interests and past interactions. The system should be capable of learning from user feedback over time to improve its recommendations.

Steps to Build the Application:
1. Set up the development environment with Python and install the 'agentos-mcp' package along with other necessary dependencies like a web framework (Flask/Django), and libraries for handling data (Pandas/Numpy).
2. Design the database schema to store user profiles, news articles, and interaction logs. Ensure that the schema supports the persistent storage capabilities offered by 'agentos-mcp'.
3. Implement a basic web interface using your chosen web framework where users can log in, view recommended news articles, and provide feedback (e.g., thumbs up/down).
4. Develop the AI agent using 'agentos-mcp' as its cognitive layer. This agent should be able to analyze user behavior, preferences, and historical data to generate personalized news recommendations.
5. Integrate the AI agent into the web application so that it dynamically updates the news recommendations based on real-time user interactions.
6. Incorporate a feedback loop mechanism within the application that allows the AI agent to learn from user feedback and refine its recommendation algorithm over time.
7. Test the application thoroughly, focusing on the accuracy and relevance of the news recommendations as well as the performance of the AI agent in adapting to new user data.
8. Deploy the application on a cloud platform (AWS/GCP/Azure) for wider accessibility.

Suggested Features:
- User authentication and personalization
- Real-time news recommendation generation
- Interactive feedback system for users
- Adaptive learning algorithm based on user engagement
- Analytics dashboard for monitoring recommendation performance

Utilizing 'agentos-mcp':
- Use 'agentos-mcp' to create and manage the AI agent's cognitive state, ensuring that the agent can persistently store and retrieve information about user preferences and behaviors across sessions.
- Leverage 'agentos-mcp's ability to support continuous learning by allowing the agent to update its knowledge base with each piece of user feedback received through the application.
- Explore advanced features of 'agentos-mcp', such as integrating multiple agents if needed, or enhancing the cognitive layer with more sophisticated machine learning models.