arcadedb-embedded

v26.6.1 safe
4.0
Medium Risk

ArcadeDB embedded multi-model database with bundled JRE - no Java installation required

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all checks with no network calls, shell executions, or credential issues. The only concern is incomplete maintainer metadata, but this alone does not indicate malicious intent.

  • Low risk scores in all categories
  • Incomplete maintainer metadata
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.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's author information is incomplete and they may be new or inactive, which raises some concern but not enough to suggest high risk.

📦 Package Quality Overall: Low (4.6/10)

○ 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://docs.humem.ai/arcadedb/
  • Detailed PyPI description (6145 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 100 commits in ArcadeData/arcadedb
  • 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: humem.ai>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ArcadeData/arcadedb 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 arcadedb-embedded 
Create a social networking application called 'FriendLink' using the ArcadeDB embedded multi-model database. This application will allow users to create profiles, connect with friends, and share posts. Utilize the 'arcadedb-embedded' package to handle all data storage needs without requiring a separate Java installation.

Steps to complete the project:
1. Set up a virtual environment for your Python project.
2. Install the 'arcadedb-embedded' package within the virtual environment.
3. Design the schema for storing user profiles, friendships, and posts using ArcadeDB's graph and document capabilities.
4. Implement functions to create, read, update, and delete user profiles.
5. Develop functionality to establish and manage friendships between users.
6. Allow users to post messages or status updates and associate them with their profiles.
7. Create a feature where users can view their friend's posts on a timeline.
8. Ensure all operations are performed efficiently and securely.
9. Write tests to validate the functionality of each component.
10. Document your code and provide instructions for setting up and running the application.

Suggested Features:
- User authentication and authorization to ensure secure access.
- Real-time notifications when a new friend request is received or a friend posts something.
- Search functionality to find other users based on name or interests.
- Ability to categorize posts into different types such as photos, videos, or text.
- Analytics dashboard showing statistics about user engagement and growth.

How to utilize 'arcadedb-embedded':
- Use the database's graph model to represent connections between users (friendships).
- Leverage the document store to maintain detailed user profiles and post content.
- Take advantage of ArcadeDB's indexing and querying capabilities to optimize performance for real-time applications.
- Explore advanced features like full-text search and geospatial queries to enhance user experience.

💬 Discussion Feed

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