antonlytics

v2.3.0 suspicious
5.0
Medium Risk

Memory for AI Agents - Simple natural language SDK

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential risk, particularly in its network interactions and metadata, which suggest either legitimate usage with unknown intent or possible supply-chain risks.

  • Network risk due to use of requests.Session()
  • Low community engagement and new maintainer
Per-check LLM notes
  • Network: The use of requests.Session() and custom headers may indicate legitimate network interactions, but without further context, it could also hint at unauthorized data transmission.
  • Shell: No shell execution patterns detected, suggesting low risk for direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer seems new or inactive, and the repository lacks community engagement.

📦 Package Quality Overall: Medium (5.2/10)

◈ 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://antonlytics.com/docs/python-sdk
  • Detailed PyPI description (6004 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 26 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 7 commits in Voidback-Inc/antonlytics-python-sdk
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • se_url self.session = requests.Session() self.session.headers.update({ 'Authori
✓ 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: voidback.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Voidback" 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 antonlytics 
Create a personal knowledge management mini-app using the 'antonlytics' package. This app will serve as a memory extension for users, allowing them to store, retrieve, and manage information through natural language interactions. Here’s how it works:

1. **Setup**: Begin by installing the 'antonlytics' package in your Python environment. Ensure you have a basic understanding of its API and functionalities.
2. **User Interface**: Design a simple command-line interface (CLI) or a web-based UI where users can interact with the app. For simplicity, let's start with a CLI.
3. **Features**:
   - **Store Information**: Users should be able to input information in natural language and have it stored in their personal memory space. For example, they could say, “Remember that I bought groceries today.”
   - **Retrieve Information**: Users should be able to ask questions in natural language and receive relevant answers from their stored information. For instance, asking “What did I buy today?” should return the answer stored earlier.
   - **Delete Information**: Provide a way for users to delete specific pieces of information if they no longer need it.
   - **Search History**: Implement a feature that allows users to search through their past queries and responses.
4. **Integration with 'antonlytics'**:
   - Use 'antonlytics' to handle the natural language processing aspects of storing and retrieving information. This includes converting user inputs into structured data and vice versa when retrieving information.
   - Leverage 'antonlytics' for maintaining context across multiple interactions, ensuring that the app understands the sequence of events and can provide accurate responses based on previous inputs.
5. **Testing and Feedback**:
   - Test the app thoroughly to ensure all features work as expected.
   - Gather feedback from initial users to identify any improvements or additional features that might be needed.
6. **Documentation**: Write clear documentation explaining how to install and use the app, including examples of how to integrate 'antonlytics' effectively.

Your goal is to create a user-friendly, efficient, and versatile tool that leverages 'antonlytics' to enhance personal knowledge management through natural language interactions.