agentmesh_message_bus

v3.7.0 suspicious
4.0
Medium Risk

A lightweight, broker-agnostic message bus designed specifically for AI Agents

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network activity, shell execution, and obfuscation. However, the metadata risk score is moderately high due to the author having only one package, which raises some suspicion about its legitimacy.

  • Low risk in network, shell, and obfuscation activities.
  • Moderate risk from author's limited presence in the repository.
Per-check LLM notes
  • Network: No network calls suggest normal behavior unless specific functionality requires external communication.
  • Shell: No shell executions indicate the package is not executing system commands which reduces risk.
  • Obfuscation: No obfuscation patterns detected, suggesting legitimate use.
  • Credentials: No credential harvesting patterns detected, indicating no immediate risk of secret theft.
  • Metadata: The author has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/microsoft/agent-governance-toolkit#readme
  • Detailed PyPI description (7414 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 152 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 14 unique contributor(s) across 100 commits in microsoft/agent-governance-toolkit
  • Active community — 5 or more distinct contributors

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository microsoft/agent-governance-toolkit appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Microsoft Corporation" 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 agentmesh_message_bus 
Develop a real-time chat application for AI agents using the 'agentmesh_message_bus' package. This application will enable seamless communication between multiple AI agents across different platforms or services without needing a centralized broker. Here’s a detailed breakdown of the project requirements and steps:

1. **Project Setup**: Begin by setting up your development environment. Ensure you have Python installed along with the 'agentmesh_message_bus' package.
2. **Application Design**: Design your application to support two main components: the AI Agent and the Message Bus. The AI Agent will send/receive messages, while the Message Bus will facilitate message routing and delivery.
3. **Message Types**: Define different types of messages such as 'Text', 'Image', and 'File'. Each type should be handled differently based on its content.
4. **Agent Communication**: Implement functionality allowing AI agents to register themselves with the message bus upon startup and deregister when they shut down. Agents should be able to subscribe to specific topics or channels and receive messages accordingly.
5. **Real-Time Communication**: Utilize 'agentmesh_message_bus' to ensure all communications are in real-time. Messages should be delivered instantly to subscribed agents without delays.
6. **Error Handling**: Implement robust error handling mechanisms to deal with any network issues, message loss, or other potential problems during communication.
7. **Security Measures**: Since this application involves AI agents communicating sensitive data, incorporate basic security measures like message encryption and authentication for agents.
8. **User Interface**: Although not mandatory, consider developing a simple web-based UI to monitor the status of agents and message flow. This will help in debugging and demonstrating the application's capabilities.
9. **Testing & Documentation**: Thoroughly test the application under various scenarios to ensure reliability and performance. Document your setup process, codebase, and any usage instructions for others who might want to use or extend your application.