AI Analysis
Final verdict: SAFE
The package shows minimal signs of potential risk, with only a moderate network risk that is likely due to its intended functionality rather than malicious intent.
- Moderate network risk due to external communication
- Low risk in other categories including shell execution, obfuscation, and credential management
Per-check LLM notes
- Network: The presence of network calls suggests the package communicates externally, which could be legitimate but should be reviewed for unexpected or insecure behavior.
- Shell: No shell execution patterns detected, indicating lower risk for direct system command injection.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
encode("utf-8") req = urllib.request.Request( url, data=data,POST", ) with urllib.request.urlopen(req, timeout=timeout) as resp: return 20
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 AgentBudget/agentbudget appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Sahil Jagtap" 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 agentbudget
Create a real-time budget monitoring tool for AI agent sessions using the 'agentbudget' Python package. Your task is to develop a mini-application that allows users to set a budget for their AI session costs and receive notifications when they approach or exceed their spending limit. This application will be particularly useful for developers who want to control the financial aspect of their AI projects without compromising on performance. **Steps to Develop the Application:** 1. **Setup Environment**: Install necessary packages including 'agentbudget', any required AI libraries (e.g., transformers), and other utilities such as Flask for web services if needed. 2. **Define Budget Parameters**: Allow users to input their desired budget amount and specify the type of AI tasks they plan to perform (e.g., text generation, image processing). 3. **Integrate 'agentbudget'**: Use 'agentbudget' to monitor and enforce the budget during AI task execution. Ensure that the application can dynamically adjust the AI model's parameters to stay within the budget constraints. 4. **Notification System**: Implement a system that sends alerts (via email, SMS, or in-app notifications) when the user is approaching or has exceeded their budget limit. 5. **User Interface**: Develop a simple yet effective UI where users can manage their budgets and view their current spending status. Consider using React or Vue.js for the frontend. 6. **Testing and Deployment**: Test your application thoroughly to ensure it accurately monitors costs and enforces budgets. Deploy your application to a cloud service like AWS or Heroku for public access. **Suggested Features**: - Detailed spending reports showing how much was spent on each task. - Historical data tracking to help users understand past spending patterns. - Customizable alerts based on different thresholds (e.g., 80%, 90% of budget). - Integration with popular AI models from Hugging Face. - Multi-user support allowing teams to share budgets and track costs together.