agent-library

v0.13.2 suspicious
5.0
Medium Risk

Multi-modal knowledge library with vector and full-text search for text, code, images, and PDFs

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to shell interaction and metadata concerns, though no direct evidence of malicious activity was found.

  • Shell risk from system-specific clipboard tool interaction
  • Unknown author and lack of GitHub repository
Per-check LLM notes
  • Network: The network call pattern is likely for legitimate HTTP requests, possibly for fetching resources or updates.
  • Shell: Shell execution patterns indicate the package interacts with system-specific clipboard tools, which could be benign but warrant further investigation into its purpose.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The package has some red flags such as an unknown author and a lack of a GitHub repository, indicating potential unreliability.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • e: self._client = httpx.Client(timeout=120.0) logger.info("Initialized embeddin
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • editor = _get_editor() subprocess.run([editor, path], check=False) def _copy_to_clipboard(text:
  • form == "darwin": subprocess.run(["pbcopy"], input=text.encode(), check=True) elif sy
  • tform == "linux": subprocess.run( ["xclip", "-selection", "clipboard"],
  • tform == "win32": subprocess.run(["clip"], input=text.encode(), check=True) else:
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: arcade.dev>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 agent-library 
Create a comprehensive document management and retrieval system using the 'agent-library' Python package. This system will allow users to upload and manage various types of documents including text files, code snippets, images, and PDFs. Users should be able to perform both vector-based and full-text searches across all stored data to retrieve relevant information efficiently. The application should support the following key functionalities:

1. **Document Upload**: Allow users to upload multiple file types including text (.txt), code (.py, .js), images (.jpg, .png), and PDF (.pdf). Each uploaded document should be indexed for both vector and full-text search capabilities.
2. **Search Functionality**: Implement a robust search feature where users can input a query and receive results based on relevance. The search should be capable of understanding natural language queries and returning the most relevant documents. Additionally, enable advanced search options like filtering by file type and date range.
3. **Visualization and Preview**: For each type of document, provide a preview function. Text and code documents should display a snippet of the content, while images should show a thumbnail, and PDFs should offer a page preview.
4. **User Interface**: Develop a clean, intuitive web interface using Flask or Django for easy interaction. Ensure that the UI supports drag-and-drop uploads, quick search functionality, and user-friendly navigation.
5. **Security and Privacy**: Implement basic security measures such as user authentication and authorization to ensure that only authorized users can access and modify their documents.
6. **Performance Optimization**: Optimize the system to handle large volumes of data efficiently. Consider implementing caching mechanisms and optimizing database queries to speed up search times.

To achieve these functionalities, you will need to utilize the 'agent-library' package for indexing and searching documents. Specifically, leverage its multi-modal capabilities to support different file types and its ability to perform both vector and full-text searches. Document your implementation process thoroughly and include instructions for setting up the environment and running the application.