autogen-goodmem

v0.1.0 suspicious
4.0
Medium Risk

GoodMem memory and tools for the AutoGen agent framework.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has legitimate network calls and does not exhibit signs of obfuscation, shell execution, or credential harvesting. However, the inactive repository and new maintainer raise concerns about its legitimacy.

  • Inactive or new maintainer
  • Unlocated repository
Per-check LLM notes
  • Network: The detection of network call patterns is common and could be legitimate if the package requires internet access for functionality.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository is not found and the maintainer seems to be new or inactive, which raises some concerns.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3260 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

  • 29 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • d: self._client = httpx.AsyncClient(timeout=self._timeout, verify=self._verify_ssl) retu
✓ 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 score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "PAIR Systems" 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 autogen-goodmem 
Your task is to develop a Python-based mini-application named 'MemoryMaster' using the 'autogen-goodmem' package. This application will serve as a tool for managing and optimizing memory usage in various Python scripts and applications. Your goal is to create a user-friendly interface where users can input their Python code and receive suggestions on how to improve its memory efficiency.

Key Features:
1. **Code Input**: Users should be able to paste their Python code into the application.
2. **Memory Analysis**: Use 'autogen-goodmem' to analyze the memory usage of the provided code. Identify any inefficiencies or areas that could benefit from optimization.
3. **Optimization Suggestions**: Provide actionable recommendations to the user on how they can reduce memory usage in their code.
4. **Visual Reports**: Generate visual reports showing the memory usage before and after applying the suggested optimizations.
5. **Integration Testing**: Allow users to run their optimized code within the application to see real-time improvements in memory usage.
6. **Save & Share**: Enable users to save their optimized code and share it via email or download as a file.

How to Utilize 'autogen-goodmem':
- Import and initialize the necessary classes from 'autogen-goodmem' at the start of your application.
- Use these classes to create an instance of a memory optimizer which will handle the analysis and suggestion generation processes.
- Implement functions that interact with the user interface to accept input, display results, and allow interaction with the memory optimizer.
- Ensure that the application can handle various types of Python code and provide relevant feedback based on the specific context of the code.

Your final product should demonstrate a clear understanding of how to use 'autogen-goodmem' to enhance the performance of Python applications by reducing unnecessary memory usage.

💬 Discussion Feed

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