Based on the file structure and the typical architecture of such tools, here is a complete README.md file you can use for the repository.

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LMStudioClient

LMStudioClient is a modular Python client designed to interact with LM Studio's local server. It provides a structured way to build agentic applications by wrapping LLM API calls and offering a native interface for tool usage (function calling).

🚀 Features

• Easy Integration: Connects seamlessly to LM Studio's local inference server (default: http://localhost:1234/v1).
• Tool/Function Calling: Includes a BaseTool system to create custom tools that your LLM can execute.
• Modular Design: Separates client logic (BaseClient) from implementation (LMStudioClient), making it easy to extend.
• Agent-Ready: Designed to facilitate building local AI agents that can interact with their environment.

📂 Project Structure

• main.py: The entry point for running the client or testing the agent loop.
• lm_studio_client.py: The core client implementation specific to LM Studio's API.
• base_client.py: Abstract base class defining the standard interface for LLM interaction.
• base_tool.py: Base class for defining custom tools/functions.
• tools/: Directory containing specific tool implementations.

🛠️ Prerequisites

1. LM Studio: Download and install LM Studio.
2. Local Server: Open LM Studio, load a model, and start the Local Server (typically on port 1234).
3. Python 3.8+

📦 Installation

Clone the repository:

git clone https://github.com/KatVolts/LMStudioClient.git
cd LMStudioClient

Install dependencies (assuming a requirements.txt exists, otherwise install requests or openai):

pip install requests openai

💻 Usage

1. Basic Chat

You can use the client to send simple prompts to your local model.

from lm_studio_client import LMStudioClient

# Initialize the client (points to localhost:1234 by default)
client = LMStudioClient(base_url="http://localhost:1234/v1")

# Send a message
response = client.chat("What is the capital of France?")
print(response)

2. Creating & Using Tools

The power of this library lies in its tool support. You can define a new tool by inheriting from BaseTool.

Define a Tool (tools/my_tool.py):

from base_tool import BaseTool

class CalculatorTool(BaseTool):
    def __init__(self):
        super().__init__(name="calculator", description="Useful for performing math calculations.")

    def run(self, expression):
        # specific logic for the tool
        return eval(expression)

Run the Agent (main.py):

from lm_studio_client import LMStudioClient
from tools.my_tool import CalculatorTool

def main():
    # 1. Setup Client
    client = LMStudioClient()

    # 2. Register Tools
    calc_tool = CalculatorTool()
    client.register_tool(calc_tool)

    # 3. Chat with Tool support
    user_input = "What is 25 * 4?"
    
    # The client handles the tool selection and execution automatically (logic depends on implementation)
    response = client.chat_with_tools(user_input)
    
    print(f"Agent: {response}")

if __name__ == "__main__":
    main()