A customizable PyQt5-based desktop chat interface for real-time, streaming LLM conversations with OpenAI-compatible backends.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install chat-window
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install chat-window using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Pywayne LLM Chat Window?
The Pywayne LLM Chat Window is a specialized desktop GUI component built with PyQt5, designed to provide a seamless interface for interacting with Large Language Models. As a powerful addition to the library of Openclaw Skills, it offers developers a ready-to-use window for streaming LLM responses, managing conversation history, and fine-tuning system messages. This tool simplifies the process of building native AI assistants by handling the complexities of GUI event loops and asynchronous API communication.
By utilizing the pywayne.llm.chat_window module, users can quickly launch a robust chat environment that supports various model configurations, including temperature, token limits, and penalty settings. Whether you are building a dedicated coding assistant or a general-purpose AI tool, this skill provides the necessary UI infrastructure to support high-performance Openclaw Skills workflows.
Pywayne LLM Chat Window Use Cases
- Creating standalone desktop AI assistants for specific coding or writing tasks.
- Rapidly prototyping LLM interactions in a native window environment.
- Managing complex system prompts and personas in a structured UI.
- Implementing real-time streaming chat interfaces for Openclaw Skills developers.
How Pywayne LLM Chat Window Works
- The user defines a ChatConfig object containing the API credentials, model choice, and GUI parameters.
- The ChatWindow class is instantiated, initializing a PyQt5 window with input fields and a display area.
- System messages are injected to define the AI agent's behavior and context.
- Upon user input, the skill sends a request to the configured OpenAI-compatible API endpoint.
- Responses are processed in a separate thread to maintain UI responsiveness, streaming tokens back to the window in real-time.
- The user can interact with the message history or use the stop button to terminate long generations immediately.
Pywayne LLM Chat Window Setup
To get started, ensure you have the required dependencies installed:
pip install PyQt5 openai
You can then launch a basic window with a few lines of Python:
from pywayne.llm.chat_window import ChatWindow
ChatWindow.launch(
base_url="https://api.your-provider.com/v1",
api_key="your_api_key",
model="your-preferred-model"
)
Pywayne LLM Chat Window Data Schema & Taxonomy
The skill manages its internal state and UI appearance through the following schema and data structures:
| Attribute | Description |
|---|---|
ChatConfig |
Dataclass containing API settings (base_url, api_key) and UI dimensions. |
System Messages |
A list of dictionary objects defining roles and content for the LLM context. |
Conversation History |
In-memory storage of the current session's user and assistant messages. |
Window Parameters |
Defines title, width, height, and screen coordinates (X, Y) for the GUI. |
Pywayne LLM Chat Window Advanced Features
- Support for multiple system prompts to build layered agent personas.
- Dynamic UI control with a toggleable send/stop button for long-form generations.
- Full customization of LLM sampling parameters like temperature and top_p via Openclaw Skills integration.
- Real-time streaming UI that prevents window freezing during heavy API loads.
- Easy integration with any OpenAI-compatible endpoint, including local models or third-party providers.
SKILL.md
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