agentguard-spend-hermes

v0.1.4 safe
3.0
Low Risk

AgentGuard Spend plugin for Hermes Agent (Nous Research). One-step install of local spend caps + signed audit logs for every Anthropic/OpenAI call your Hermes agent makes. No proxy, prompts never leave your machine.

🤖 AI Analysis

Final verdict: SAFE

The package appears safe based on the analysis notes, with low risks across all categories except for metadata where it scores a moderate 5 due to its newness and lack of maintainer details.

  • No network or shell risks detected
  • Low obfuscation and credential risks
  • Metadata risk due to newness and missing maintainer details
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access to function properly.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some red flags due to its newness and lack of maintainer details, but there's no concrete evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: agentguard.run>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository NousResearch/hermes-agent appears legitimate

⚠ 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 agentguard-spend-hermes 
Create a mini-application called 'LocalSpendAudit' that leverages the 'agentguard-spend-hermes' package to ensure secure and locally monitored usage of AI services like Anthropic and OpenAI within a Hermes Agent environment. This application will allow users to set up local spend caps and enable signed audit logs for all AI interactions, ensuring that sensitive data remains on their own machines without ever being transmitted through external proxies.

### Core Features:
1. **Local Spend Cap Management**: Users should be able to define and manage spend limits for different AI services. This includes setting initial caps, viewing current spending status, and adjusting these limits as needed.
2. **Signed Audit Logs**: Every interaction with an AI service must be logged locally with a digital signature to verify authenticity and prevent tampering.
3. **Real-time Monitoring**: Provide real-time alerts when the spend cap is nearing its limit, helping users stay informed about their usage and costs.
4. **User Interface**: Develop a simple, intuitive user interface that allows users to interact with the application easily. Consider both command-line and graphical interfaces.
5. **Integration with Hermes Agent**: Ensure seamless integration with the Hermes Agent framework, making it easy for developers to incorporate LocalSpendAudit into their existing projects.

### Steps to Build the Application:
1. **Setup Environment**: Begin by setting up a Python development environment and installing the necessary dependencies, including 'agentguard-spend-hermes'.
2. **Define Data Models**: Create data models for storing user settings, spend information, and audit logs. These should be designed to work efficiently with the package's requirements.
3. **Implement Core Functionality**: Write the core logic for managing spend caps and generating signed audit logs using the functionalities provided by 'agentguard-spend-hermes'.
4. **Develop User Interface**: Design and implement a user-friendly interface that allows users to view their spending, adjust limits, and monitor their AI interactions.
5. **Testing and Validation**: Rigorously test the application to ensure all features work as expected, especially focusing on security and data integrity aspects.
6. **Documentation**: Prepare comprehensive documentation detailing how to use the application, integrate it with other systems, and troubleshoot common issues.
7. **Deployment**: Package the application for deployment, considering options like Docker containers for ease of use across different environments.

This project aims to demonstrate the power and flexibility of the 'agentguard-spend-hermes' package while providing a practical tool for enhancing privacy and control over AI service usage.