aurra

v0.8.0 suspicious
6.0
Medium Risk

Memory infrastructure for AI agents — citations, audit trails, bi-temporal versioning, BYO-LLM

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several signs of potential unreliability, including missing author information and an untraceable repository, which raises concerns about its origin and maintenance. While there are no direct indications of malicious activity, these factors combined with the moderate network risk suggest caution.

  • Metadata risk due to missing author details and untraceable repository
  • Moderate network risk associated with potential API interaction misuse
Per-check LLM notes
  • Network: The use of a session with custom headers might indicate normal API interaction but could also be used for data exfiltration.
  • 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 package shows some red flags such as a missing author name and a repository that cannot be found, indicating potential unreliability.

📦 Package Quality Overall: Low (3.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://aurra.us/docs
  • Detailed PyPI description (5328 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 27 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)

  • meout self._session = requests.Session() self._session.headers.update({ "Author
✓ 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: aurra.us>

✓ 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

  • 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 aurra 
Create a Python-based mini-application called 'MemoryMentor' which will serve as a personal knowledge management tool for users. This application should utilize the 'aurra' package to manage and maintain a comprehensive memory infrastructure for user data, including but not limited to, academic citations, audit trails of changes made to user entries, and bi-temporal versioning to track historical versions of entries. Additionally, the app should allow users to integrate their own preferred Large Language Model (LLM) for advanced text analysis and processing capabilities.

Steps to develop this application:
1. Set up the project environment and install necessary packages, including 'aurra'.
2. Design the database schema to store user information, citations, and audit trails.
3. Implement a user interface (CLI or GUI) where users can input new entries, edit existing ones, and view historical versions.
4. Integrate 'aurra' functionalities such as citation management, audit trails, and bi-temporal versioning into the application.
5. Allow users to connect their preferred LLM through the application's settings for enhanced data processing.
6. Test the application thoroughly to ensure all features work as expected.
7. Document the code and provide usage instructions for end-users.

Suggested Features:
- User authentication and authorization.
- Support for various citation styles (APA, MLA, Chicago).
- Real-time audit trails showing who made what changes and when.
- Bi-temporal versioning allowing users to revert to previous states.
- Integration with popular LLMs like OpenAI's GPT series.
- Export options for data in common formats (CSV, PDF, etc.).
- Search functionality within the application.

How 'aurra' is Utilized:
- For managing citations: Users can add, modify, and delete citations using 'aurra's citation management features.
- For maintaining audit trails: Every action performed on an entry (create, update, delete) will be logged using 'aurra', providing a transparent history of changes.
- For bi-temporal versioning: Entries will be stored with timestamps for both creation and modification, allowing users to retrieve past versions of their entries easily.
- For integrating custom LLMs: 'aurra' supports BYO-LLM, enabling users to choose their preferred model for analyzing and enhancing their entries.

💬 Discussion Feed

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