AI Analysis
Final verdict: SUSPICIOUS
The package is rated as suspicious due to potential network risks associated with external service interactions and the maintainer having only one package listed.
- Potential network risks from external service interactions.
- Maintainer has only one package listed.
Per-check LLM notes
- Network: Network calls to external services suggest the package may be performing actions like fetching pricing data or sending analytics, which could be legitimate but should be verified against the package's intended functionality.
- Shell: No shell execution patterns were detected.
- Metadata: The maintainer has only one package, which might indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.
Heuristic Checks
Outbound Network Calls
score 6.0
Found 4 network call pattern(s)
response = requests.get( f"{base_url.rstrip('/')}/v1/pricing",y logic""" session = requests.Session() # Retry strategy retry_strategeating project...") with httpx.Client() as client: response = client.post( fying analytics...") with httpx.Client() as client: headers = {"Authorization": f"Bearer {
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: gmail.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository agentcost-ai/agentcost-sdk appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Kushagra Agrawal" 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 agentcost
Create a Python-based dashboard application that monitors and visualizes the cost incurred by using different Large Language Models (LLMs) from various providers such as OpenAI, Anthropic, and LangChain. The application should use the 'agentcost' package to track these costs without requiring any modifications to the existing codebase that interacts with these models. ### Features: 1. **Real-time Cost Tracking**: Display the real-time cost of each API call made to the LLMs, categorized by provider and model type. 2. **Historical Cost Analysis**: Provide a feature to view historical cost data over a selected time period (e.g., daily, weekly, monthly). 3. **Cost Alerts**: Implement a system where users can set up alerts based on cost thresholds. For example, notify users if the cost exceeds a certain amount within a given timeframe. 4. **Visualization**: Include graphs and charts to visually represent the cost trends and usage patterns. 5. **Provider and Model Comparison**: Allow users to compare costs between different providers and models in a single view. 6. **User Authentication**: Ensure that each user has their own account where they can manage their cost tracking preferences and view their own cost data. 7. **Customizable Settings**: Enable users to customize settings such as alert thresholds, preferred time zones, and currency types. ### Utilizing 'agentcost': - Integrate 'agentcost' into your application to automatically track the cost of each API request made to the LLMs without changing the way you interact with these models. - Use 'agentcost' to categorize costs by provider and model, which will then be displayed in the dashboard. - Leverage 'agentcost' to retrieve historical cost data for analysis and visualization purposes. - Configure 'agentcost' to trigger alerts when predefined cost thresholds are met or exceeded.