agentmesh_memory

v3.7.0 suspicious
5.0
Medium Risk

Public Preview — Episodic Memory Kernel for AI agent experience storage

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk in terms of network usage, shell execution, and code obfuscation. However, the metadata risk score is elevated due to the maintainer's new or inactive account and lack of proper identification.

  • Metadata risk due to maintainer's new or inactive account
  • Lack of proper author name
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network interaction for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious shell command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, which raises some suspicion.

📦 Package Quality Overall: Medium (6.4/10)

◈ 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 (1157 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 57 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

Email domain looks legitimate: microsoft.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository microsoft/agent-governance-toolkit appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 agentmesh_memory 
Create a Python-based interactive learning companion named 'MemoryMentor' that leverages the 'agentmesh_memory' package to enhance user interaction through episodic memory management. MemoryMentor will serve as an educational tool that not only assists users in learning new information but also retains and retrieves past interactions to personalize future sessions. Here’s a detailed breakdown of what your application should achieve:

1. **User Profile Creation**: Allow users to create profiles where they can input their interests, goals, and preferred learning styles. This data will be stored and used to tailor subsequent interactions.
2. **Learning Sessions**: Implement sessions where users can ask questions or seek explanations on various topics. MemoryMentor should be able to provide answers based on pre-fed knowledge and also learn from these interactions.
3. **Episodic Memory Storage**: Utilize the 'agentmesh_memory' package to store details of each session, including the questions asked, answers provided, and any feedback given by the user. This will enable MemoryMentor to recall past interactions and use them to inform future sessions.
4. **Personalized Recommendations**: Based on the episodic memory stored, MemoryMentor should be capable of offering personalized content recommendations to users. These could range from additional reading materials to relevant videos or podcasts.
5. **Feedback Loop**: Integrate a system where users can rate the usefulness of the information provided during each session. Use this feedback to improve the quality of responses over time.
6. **Progress Tracking**: Enable users to track their progress in different areas by visualizing their learning journey using graphs and charts.
7. **Integration with External Knowledge Sources**: Optionally, allow MemoryMentor to integrate with external knowledge bases or APIs to fetch up-to-date information when required.

To utilize the 'agentmesh_memory' package effectively, focus on its ability to manage episodic memories. This includes storing and retrieving contextually rich data about user interactions, which is crucial for personalization and enhancing the learning experience. Ensure that the application demonstrates how episodic memory improves the overall interaction between the user and MemoryMentor.