🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is flagged as suspicious due to its untraceable repository and lack of maintainer history, despite showing no signs of obfuscation or credential harvesting.

Metadata risk due to untraceable repository
Lack of maintainer history

Per-check LLM notes

Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
Metadata: The package shows signs of being potentially malicious due to the lack of maintainer history and an untraceable repository.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

e in events] with httpx.Client(timeout=10) as client: resp = 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

Email domain looks legitimate: agentbills.app>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" 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 agentbills

Create a fully-functional mini-application named 'AI Fleet Budget Manager' using the Python package 'agentbills'. This application will serve as a financial oversight tool for managing the costs associated with running multiple AI agents across different cloud platforms. The goal of the application is to provide real-time cost tracking, budget alerting, and automated shutdown capabilities for AI agents that exceed their allocated budgets.

**Core Features:**
1. **Real-Time Cost Tracking:** Integrate with 'agentbills' to monitor the costs incurred by each AI agent in real time. Display this information in a user-friendly dashboard.
2. **Budget Alert System:** Set up a system where users can define budgets for their AI agents. When an agent approaches or exceeds its budget, send alerts via email or SMS.
3. **Automated Shutdown:** Implement a feature that automatically shuts down AI agents if they exceed their budget thresholds. Ensure that this action can be undone manually by the user.
4. **Historical Cost Analysis:** Provide users with the ability to view historical cost data and generate reports. Use 'agentbills' to analyze past spending patterns and suggest budget adjustments.
5. **Multi-Cloud Support:** Allow users to manage AI agents across different cloud providers (AWS, Azure, GCP). Utilize 'agentbills' to handle the complexities of cost aggregation from various platforms.

**Implementation Steps:**
1. Set up a Flask backend to handle API requests and integrate 'agentbills' for cost monitoring and budget enforcement.
2. Develop a React frontend that displays real-time cost data and allows users to set budgets and configure alerts.
3. Implement a database to store user settings, budget allocations, and historical cost data.
4. Connect to cloud provider APIs to gather cost information and control AI agent instances.
5. Test the application thoroughly, ensuring that all features work as expected under various conditions.
6. Deploy the application on a cloud platform like Heroku or AWS.

Use 'agentbills' throughout the development process to streamline the integration of cost intelligence and budget enforcement functionalities into your application.