agenticml-py

v0.0.2 suspicious
4.0
Medium Risk

Python SDK for the AgenticML platform. Installs as `agenticml-py`, imports as `agenticml`.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to network and metadata concerns. While there's no clear evidence of malicious intent, the unstable API and lack of repository information raise flags.

  • Network risk due to API key handling
  • Metadata risk due to sparse maintainer information
Per-check LLM notes
  • Network: The use of headers to include an API key suggests legitimate authentication but should be scrutinized for proper handling and transmission.
  • Shell: Executing git commands may be part of version control operations but could also indicate unintended system access, warranting further investigation.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository is not found and the maintainer information is sparse, indicating potential risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • d_key self.session = requests.Session() if self.api_key: self.session.headers[
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • try: completed = subprocess.run( ["git", *args], cwd=repo_ro
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: agenticml.xyz>

✓ 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 agenticml-py 
Create a personalized recommendation system using the 'agenticml-py' Python package. This system will be designed to suggest books to users based on their reading history and preferences. Here’s a detailed outline of the steps and features you need to implement:

1. **User Profile Creation**: Allow users to create profiles where they can input their favorite genres, authors, and specific book titles they enjoyed.
2. **Reading History Integration**: Users should be able to add books they have read recently or plan to read, along with ratings if available.
3. **Recommendation Engine**: Utilize 'agenticml-py' to train a model that learns from user data and suggests new books. The engine should consider both explicit inputs (like genre preferences) and implicit inputs (like reading history).
4. **Interactive Suggestions**: Implement a feature where users can interactively explore recommendations. They should be able to request more books similar to a particular author or genre.
5. **Feedback Loop**: Provide users with a way to give feedback on the recommendations, which will help improve the model over time.
6. **Visualization**: Display recommendations in a visually appealing manner, perhaps through a simple web interface or a console-based UI.
7. **Scalability Considerations**: Ensure that the system can scale to handle multiple users and a large database of books.

The 'agenticml-py' package is essential for training and deploying the recommendation model. It provides APIs for data preprocessing, model training, and prediction, making it easier to integrate machine learning into your application without deep expertise in ML frameworks.