🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to its newness and potential inactivity of the maintainer, which raises some concerns about its legitimacy and ongoing maintenance.

Metadata risk indicating possible lack of active maintenance
Potential for supply-chain attack given the package's novelty and maintainer activity level

Per-check LLM notes

Network: The observed network calls seem to be related to API interactions and could be legitimate if the package is designed for AI cost management or response caching.
Shell: No shell execution patterns were detected, indicating a low risk of direct system command execution.
Obfuscation: The use of uuid import for generating request IDs seems to be a common practice for logging and debugging purposes, not necessarily malicious.
Credentials: No suspicious patterns indicating credential harvesting were found.
Metadata: The package shows signs of being newly created or maintained by an inactive user, raising suspicion but not definitive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

ignore session = requests.Session() exporter._session = session except Exc
try: resp = requests.post( f"{self._base_url}/ai-cost-guard-router",
try: resp = requests.post( f"{self._base_url}/cache-ai-response",
try_on self._client = httpx.AsyncClient( timeout=timeout, headers={
} try: with httpx.Client(timeout=5) as client: response = client.post(url
id try: with httpx.Client(timeout=5) as client: response = client.post(url

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

. "X-Request-Id": __import__("uuid").uuid4().hex, } if self.debug:

✓ 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: agentbill.io>

✓ 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 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 agentbill-py-sdk

Create a Python-based command-line utility named 'AgentMonitor' that tracks the usage of AI agents in a development environment and generates detailed billing reports using the 'agentbill-py-sdk' package. This tool will help developers understand the cost implications of their AI agent usage and ensure compliance with budget constraints through real-time monitoring and reporting capabilities. Here’s a step-by-step guide on how to build this utility:

1. **Setup Project Environment**: Initialize a new Python project and install the necessary dependencies including 'agentbill-py-sdk'.
2. **Integrate OpenTelemetry**: Configure OpenTelemetry within your project to track AI agent activities effectively.
3. **Define Agent Classes**: Create classes for different types of AI agents (e.g., Text Generation Agents, Image Processing Agents) that inherit from a base Agent class. Each class should have methods to start, stop, and log usage.
4. **Billing Report Generation**: Implement functionality to generate billing reports based on the tracked data. These reports should include details such as agent type, usage duration, and estimated costs.
5. **Cost Guard Integration**: Utilize the 'agentbill-py-sdk' package's Cost Guard feature to set up alerts when certain cost thresholds are reached, ensuring proactive management of expenses.
6. **CLI Interface**: Develop a user-friendly command-line interface (CLI) where users can start/stop agents, view current usage statistics, and access historical billing reports.
7. **Testing and Validation**: Thoroughly test the utility to ensure accurate tracking, billing, and alerting functionalities. Validate these features by simulating various scenarios of AI agent usage.
8. **Documentation**: Write comprehensive documentation explaining how to use 'AgentMonitor', including setup instructions, API reference, and best practices for managing AI agent costs.

This project aims to provide developers with a robust tool for managing the financial aspects of AI agent deployments, thereby promoting efficient resource utilization and cost control.