agentmesh_context

v3.7.0 safe
3.0
Low Risk

A pure, logic-only library for routing context, handling RAG fallacies, and managing context windows. Layer 1 Primitive - no agent dependencies.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, or obfuscations observed. The metadata risk is slightly elevated due to the author's limited package history, but there are no other suspicious activities.

  • No network calls detected
  • Single package from author, indicating possible new or less active account
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communications.
  • Shell: No shell execution detected, indicating no immediate risk of command injection or similar attacks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author has only one package, which may indicate a new or less active account, but no other suspicious activities are flagged.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 13 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: conftest.py
  • 13 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/microsoft/agent-governance-toolkit/tree/m
  • Detailed PyPI description (1363 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_context 
Develop a conversational AI chatbot named 'ContextualMind' that leverages the 'agentmesh_context' Python package to manage user conversations efficiently and accurately. This chatbot will be designed to handle complex queries by maintaining context throughout the conversation, preventing common issues like RAG fallacies, and ensuring that the response generation process stays within a manageable context window. Here’s how you can structure your project:

1. **Project Setup**: Initialize a new Python project and install the necessary packages, including 'agentmesh_context'. Ensure all dependencies are managed via a requirements.txt file.
2. **User Interface Design**: Design a simple but effective user interface where users can input their queries. This could be a command-line interface (CLI) for simplicity, or a more sophisticated web-based UI using Flask or Django.
3. **Context Management**: Utilize 'agentmesh_context' to manage the conversation flow. Implement features that allow the chatbot to remember previous interactions, understand context shifts, and maintain a coherent narrative thread throughout the conversation.
4. **RAG Fallacy Handling**: Integrate mechanisms within 'agentmesh_context' to identify and correct any inconsistencies or inaccuracies that might arise from out-of-context responses. This includes verifying facts, cross-referencing information, and ensuring that the chatbot's responses are as accurate as possible given the context.
5. **Context Window Management**: Use 'agentmesh_context' to manage the size of the context window. This feature ensures that the chatbot doesn't become overwhelmed by too much context, which could lead to slower response times or less relevant answers. Implement logic to prune old context data when necessary.
6. **Testing and Validation**: Develop comprehensive tests to validate the functionality of ContextualMind, focusing on its ability to maintain context, handle RAG fallacies, and manage context windows effectively. Use unit tests and integration tests to ensure robustness.
7. **Documentation**: Write clear documentation detailing how to set up and use ContextualMind, including examples of how 'agentmesh_context' is utilized within the application.
8. **Deployment**: Plan for deployment options, such as running the chatbot locally, deploying it on a cloud service like AWS or Google Cloud, or even integrating it into existing applications or websites.

Your goal is to create a versatile, reliable chatbot that showcases the capabilities of 'agentmesh_context' in managing complex conversations. Emphasize clarity, efficiency, and accuracy in your implementation.