alltoken

v0.2.25 safe
3.0
Low Risk

Official Python SDK for AllToken — one API for OpenAI, Anthropic, and 100+ models.

🤖 AI Analysis

Final verdict: SAFE

The package appears safe with no evidence of malicious activities such as shell execution, obfuscation, or credential harvesting. The network risk is moderate due to its expected behavior of making API calls, but this aligns with its described functionality.

  • Moderate network risk due to expected API interactions
  • Low risks in other categories
Per-check LLM notes
  • Network: The presence of network calls to specific URLs suggests the package is intended to interact with external services, likely for API access.
  • Shell: No shell execution patterns detected, indicating low risk for direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's details are sparse, suggesting a potentially less experienced or inactive developer.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://alltoken.ai/docs
  • Detailed PyPI description (2506 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

  • Type checker (mypy / pyright / pytype) referenced in project
  • 18 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in alltoken-ai/alltoken-python
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • ) -> None: self.raw = httpx.Client( base_url=join_base_url(config.base_url, _ANTHRO
  • ) -> None: self.raw = httpx.Client( base_url=join_base_url(config.base_url, _OPENAI
✓ 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: alltoken.ai>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository alltoken-ai/alltoken-python appears legitimate

⚠ 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 alltoken 
Create a conversational AI assistant application using the 'alltoken' Python package that integrates multiple AI models from different providers into a unified interface. This application will allow users to converse with AI models in real-time, switching between different models seamlessly based on user preferences or context. The application should have a simple GUI or CLI interface for easy interaction. Here’s a detailed breakdown of the project requirements:

1. **Setup**: Install the necessary packages including 'alltoken', and any other dependencies needed for your chosen GUI/CLI framework.
2. **Authentication**: Implement authentication mechanisms to securely connect to different AI model APIs using tokens provided by 'alltoken'.
3. **Model Selection**: Provide a feature where users can select from a list of available AI models supported by 'alltoken'. The application should display the name, provider, and brief description of each model.
4. **Real-Time Interaction**: Enable real-time text-based conversation with the selected AI model through the GUI/CLI. Users should be able to type messages and receive responses immediately.
5. **Contextual Switching**: Allow users to switch between models during a conversation without losing context. For example, if a user starts a conversation with Model A and wants to continue with Model B, the application should maintain the continuity of the conversation as much as possible.
6. **Feedback Loop**: Integrate a feedback mechanism where users can rate their experience with each model after conversations. Use these ratings to suggest better-suited models for future interactions.
7. **Documentation**: Write comprehensive documentation explaining how to install and use the application, along with any additional configuration steps required for optimal performance.

Utilize the 'alltoken' package to abstract away the complexities of connecting to various AI models, focusing instead on providing a smooth and intuitive user experience. Ensure that the application is user-friendly and accessible, making it easy for both technical and non-technical users to engage with advanced AI technologies.