agstack

v1.24.1 suspicious
6.0
Medium Risk

Production-ready toolkit for building FastAPI and LLM applications

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential misuse due to network risks and unreliable metadata, but lacks evidence of direct malicious intent.

  • Network risk due to potentially misspelled authentication header
  • Incomplete maintainer information and lack of associated GitHub repository
Per-check LLM notes
  • Network: The use of HTTP sessions with a potentially misspelled header ('Autho') may indicate an attempt to authenticate and could be used for unauthorized access if not properly secured.
  • Shell: No shell execution patterns detected, indicating low risk for direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious attempts to steal credentials.
  • Metadata: The package has no associated GitHub repository and the maintainer's information is incomplete, suggesting potential unreliability.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (5045 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

  • Classifier: Typing :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 236 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • self._sync_http_session = requests.Session() self._sync_http_session.headers.update({"Autho
✓ 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: xtravisions.com>

✓ 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 agstack 
Create a fully-functional mini-app using the 'agstack' package, which is designed to streamline the development of FastAPI and LLM-based applications. Your task is to build a simple yet powerful application that allows users to generate summaries of text using a pre-trained language model. Here’s a detailed breakdown of the requirements and steps to accomplish this project:

1. **Project Overview**: Develop an app named 'TextSummarizer' that accepts long-form text input from users and returns a concise summary of the content. This app will leverage 'agstack' for its robust API framework and LLM integration capabilities.

2. **Features**:
   - **User Input Interface**: Provide a user-friendly interface where users can paste or type their text content.
   - **Summary Generation**: Use a pre-trained model included in 'agstack' to generate summaries based on user input.
   - **Customizable Length**: Allow users to specify the length of the summary they desire.
   - **Real-time Feedback**: Display processing status and results in real-time.

3. **Utilizing 'agstack'**:
   - Initialize your project with 'agstack' to set up the FastAPI backend and integrate necessary LLM components.
   - Configure 'agstack' to use a specific pre-trained summarization model provided by the package.
   - Implement error handling and logging mechanisms through 'agstack' functionalities.

4. **Development Steps**:
   - Step 1: Set up your development environment and install 'agstack'.
   - Step 2: Define the FastAPI routes and endpoints for receiving user inputs and returning summaries.
   - Step 3: Integrate the selected summarization model from 'agstack' into your API.
   - Step 4: Create a frontend UI using a web framework like Streamlit or a simple HTML/CSS/JavaScript setup for interacting with the FastAPI backend.
   - Step 5: Test the application thoroughly, ensuring it handles various types of input gracefully.

5. **Deployment Considerations**:
   - Discuss potential deployment strategies, such as deploying the app on platforms like Heroku or AWS, while leveraging 'agstack' for scalable and efficient execution.

This project aims to demonstrate the ease and power of combining FastAPI with advanced LLM capabilities via 'agstack', providing a practical solution for text summarization.