agenticmemory-sdk

v1.0.0 suspicious
6.0
Medium Risk

Official Python SDK for Agentic Memory — 3-tier persistent memory for AI agents. Short-term recall, semantic search, and auto-summarised knowledge in one API.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network, shell, obfuscation, and credential handling but raises concerns due to its newness, lack of maintainer details, and absence of a GitHub repository.

  • metadata risk due to newness and lack of maintainer information
  • absence of a GitHub repository
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no immediate risk of command injection or similar attacks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package appears suspicious due to its newness, lack of maintainer information, and absence of a GitHub repository.

🔬 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: tyga.cloud>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 agenticmemory-sdk 
Create a personal knowledge management application called 'AgenticNote' using the 'agenticmemory-sdk' Python package. This app will serve as a powerful tool for organizing and retrieving information from various sources, such as notes, articles, and books. The goal is to create a seamless experience where users can input textual data, which is then processed and stored in a structured manner, allowing for efficient retrieval and summarization of key information.

Step 1: Set up the environment
- Install Python and necessary libraries including 'agenticmemory-sdk'.
- Initialize a virtual environment and install dependencies.

Step 2: Design the User Interface
- Develop a simple but intuitive command-line interface (CLI) for interacting with the application.
- Implement basic commands for adding new entries, searching existing ones, and viewing summaries.

Step 3: Implement Data Input and Storage
- Use 'agenticmemory-sdk' to store user-provided text data in a persistent and searchable format.
- Ensure that the application supports different types of inputs, such as plain text, URLs, and file paths.

Step 4: Utilize Core Features of 'agenticmemory-sdk'
- Leverage short-term recall capabilities to quickly retrieve recent entries.
- Implement semantic search functionality to find relevant information based on context.
- Enable automatic summarization of long-form content for easy review.

Suggested Features:
- Integration with common note-taking services like Evernote or Google Keep.
- Support for multimedia attachments and their associated metadata.
- Enhanced security measures to protect sensitive information.
- Export options for backing up data or sharing with other platforms.

The final product should allow users to efficiently manage their digital knowledge base, making it easier to retain and recall important information.