agent-framework-monty

v1.0.0a260521 suspicious
4.0
Medium Risk

Monty CodeAct integrations for Microsoft Agent Framework.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is marked as alpha and has limited maintainer history, raising concerns about its stability and security. Despite having low risks in network, shell, and obfuscation areas, the metadata risk score is relatively high.

  • Package is in alpha stage with potential for API changes.
  • Limited maintainer history and incomplete author information.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external communication.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or system exploitation.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
  • Metadata: The package is new with limited maintainer history and incomplete author information, raising suspicion.

🔬 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: microsoft.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository microsoft/agent-framework 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 agent-framework-monty 
Your task is to develop a simple yet engaging mini-application that leverages the capabilities of the 'agent-framework-monty' Python package. This package provides integrations for Microsoft Agent Framework, enabling developers to create sophisticated conversational experiences with natural language processing (NLP) capabilities. Your goal is to create a chatbot application that can engage in meaningful conversations with users about a specific topic, such as travel recommendations or customer support inquiries.

The application should include the following core functionalities:
1. User Authentication: Allow users to sign in using their email and password or via social media accounts like Google or Facebook.
2. Topic-Specific Conversations: The chatbot should be able to provide detailed information based on user queries related to a chosen topic. For example, if the topic is travel, the chatbot should answer questions about destinations, accommodations, activities, and more.
3. Contextual Understanding: The chatbot must maintain context throughout the conversation, understanding the flow of the discussion and providing relevant responses accordingly.
4. Personalization: Implement features that allow the chatbot to remember previous interactions with a user, offering personalized suggestions or responses based on past conversations.
5. Feedback Loop: Users should have the ability to rate the helpfulness of the chatbot's responses, which will help improve future interactions.

To achieve these functionalities, you will utilize the 'agent-framework-monty' package to handle the integration with Microsoft's Agent Framework, which powers the NLP and contextual understanding capabilities of your chatbot. Additionally, you'll need to set up a backend server using Flask or Django to manage user sessions, data storage, and API calls to the 'agent-framework-monty' service. The frontend can be built using React or Vue.js, allowing for a smooth user interface where users can interact with the chatbot.

Remember to document your code thoroughly, including comments within the code and a README file explaining how to install dependencies, run the application, and any additional setup steps required for deploying the application.