arandu

v0.14.3 suspicious
5.0
Medium Risk

Long-term memory for AI agents — wisdom acquired through experience. Extraction, entity resolution, reconciliation, and retrieval.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package arandu v0.14.3 has minimal direct security risks but exhibits signs of potential unreliability due to missing author details and a non-existent git repository.

  • absent author information
  • non-existent git repository
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands, which is typical for most Python packages.
  • 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 package shows some red flags with an absent author and a non-existent git repository, indicating potential unreliability.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 10 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://pe-menezes.github.io/arandu/
  • Detailed PyPI description (3637 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

  • 245 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

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: gmail.com>

✓ 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 arandu 
Create a mini-application called 'WisdomBot' that leverages the 'arandu' package to store and retrieve wisdom from a simulated AI agent's long-term memory. WisdomBot will allow users to input experiences, which are then processed and stored as knowledge. Users can later query this knowledge base using natural language queries to retrieve relevant experiences and insights.

Step 1: Set up the project environment by installing the 'arandu' package and any other necessary dependencies such as a database connector if 'arandu' requires one.

Step 2: Design the user interface for WisdomBot. This could be a simple command-line interface or a more complex web-based interface depending on your preference and skills.

Step 3: Implement the functionality to accept user inputs. These inputs represent new experiences or pieces of wisdom that the AI agent has encountered. Use 'arandu' to extract key entities from these inputs, resolve any ambiguities in the data, and reconcile it with existing knowledge.

Step 4: Develop the storage mechanism for these experiences. 'arandu' should handle the persistence of this information, possibly through indexing or other mechanisms designed to enhance retrieval efficiency.

Step 5: Create a query system that allows users to ask questions about the stored wisdom. Utilize 'arandu's retrieval capabilities to find the most relevant responses based on the user's query.

Suggested Features:
- A scoring system for relevance of retrieved experiences.
- An option for users to rate the accuracy or usefulness of retrieved wisdom, which could feed back into improving future retrievals.
- A visual representation of the connections between different pieces of wisdom to help users understand the context and relationships between them.
- Integration with a calendar or timeline feature to show when certain pieces of wisdom were acquired or updated.

Ensure that the application demonstrates the full potential of 'arandu', showcasing its ability to manage long-term memory effectively and provide valuable insights through complex queries.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!