anymodel-py

v0.6.3 suspicious
4.0
Medium Risk

OpenRouter-compatible LLM router with unified batch support. Route requests across OpenAI, Anthropic, and Google with a single API.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate network risks due to external API calls and shows signs of potential low activity and insufficient maintainer information, raising concerns about its legitimacy and maintenance.

  • Moderate network risk due to external API calls
  • Potential low activity and lack of maintainer information
Per-check LLM notes
  • Network: The package makes network calls to external APIs which may indicate legitimate functionality but could also suggest potential unauthorized data transfer.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of potential low activity and lack of maintainer information, which could indicate a less reputable source.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 16 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (7824 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 256 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 29 commits in probeo-io/anymodel-py
  • Single author but highly active (29 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • rom OpenRouter...") req = urllib.request.Request(OPENROUTER_API, headers={"User-Agent": "anymodel-py/
  • anymodel-py/build"}) with urllib.request.urlopen(req, timeout=30) as resp: if resp.status !=
  • e: self._client = httpx.AsyncClient( base_url=ANTHROPIC_API_BASE,
  • e: self._client = httpx.AsyncClient( base_url=GEMINI_API_BASE, h
✓ 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: probeo.io>

✓ 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 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 anymodel-py 
Create a versatile question-answering mini-application using the 'anymodel-py' package. This application will serve as a user-friendly interface where users can input their queries and receive responses from various LLMs (Language Learning Models) such as OpenAI, Anthropic, and Google, all through a unified API provided by 'anymodel-py'. Here’s a detailed plan on how to develop this application:

1. **Setup Project Environment**: Begin by setting up your Python development environment. Install necessary packages including 'anymodel-py', Flask for web serving, and any other dependencies.
2. **Design User Interface**: Create a simple yet effective HTML/CSS frontend for user interaction. Ensure it has fields for query input and displaying the response.
3. **Backend Development**: Develop the backend logic using Flask. Integrate 'anymodel-py' to route questions to different LLMs based on predefined rules or user choice.
4. **Implement Core Functionality**: Write functions that handle HTTP POST requests from the frontend, process these requests using 'anymodel-py' to get answers from the chosen LLM, and return the answer back to the frontend.
5. **Enhance Features**:
   - Allow users to select which LLM they want to use for their query.
   - Implement a feature to track and display the time taken for each response.
   - Include error handling for cases when the LLMs are not responding.
6. **Testing & Deployment**: Thoroughly test the application locally and then deploy it to a cloud service like Heroku or AWS.
7. **Documentation**: Prepare comprehensive documentation explaining how to install, configure, and use the application.

This project aims to demonstrate the versatility and ease of use of 'anymodel-py' while providing a practical tool for anyone needing quick access to powerful language models.

💬 Discussion Feed

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