agentmesh-context

v3.7.0 suspicious
4.0
Medium 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: SUSPICIOUS

The package shows no immediate signs of malicious activity, but the unusual presence of Microsoft Corporation with only one package raises some concerns about potential supply-chain manipulation.

  • Unusual metadata risk due to single package from a major corporation
  • Otherwise clean with no network, shell, obfuscation, or credential risks
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
  • Metadata: The author Microsoft Corporation has an unusual presence with only one package, which could be suspicious but not conclusive.

📦 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 
Create a mini-application named 'ContextualQueryBot' that leverages the 'agentmesh-context' Python package to manage context in conversational AI scenarios. This application will serve as a chatbot that can handle complex queries by maintaining context across multiple interactions, addressing RAG (Retrieval-Augmented Generation) fallacies, and managing context windows effectively. Here’s a detailed breakdown of the application’s functionality and features:

1. **Initialization**: Set up the environment by installing the necessary packages, including 'agentmesh-context'. Ensure your Python version is compatible with the package.
2. **Context Management**: Implement a feature where the bot maintains context across user interactions. For example, if a user asks about a product and then follows up with a related question, the bot should remember the initial context and provide relevant responses.
3. **RAG Fallacy Handling**: Integrate the package's ability to address RAG fallacies. When the bot retrieves information from external sources, ensure it can handle cases where the retrieved data might not align with the user's expectations or previous interactions.
4. **Context Window Management**: Use the package to manage context windows, ensuring that the bot doesn't lose track of recent interactions while still having access to older, relevant information.
5. **User Interface**: Develop a simple text-based interface for users to interact with the bot. Users should be able to input their queries, and the bot should respond accordingly.
6. **Testing and Validation**: After implementation, test the bot with various scenarios to ensure it correctly handles context, addresses RAG fallacies, and manages context windows effectively.

The 'agentmesh-context' package is utilized throughout the application to manage context, handle RAG fallacies, and manage context windows. It acts as the backbone for the bot's ability to maintain coherent conversations with users, making the interaction more natural and effective.