LM Studio

by infinitimeless

16 stars

1.3k downloads

Not rated

GitHub

About

Bridges Claude with locally running LLM models via LM Studio, enabling users to leverage private models through Claude's interface while maintaining local hosting.

Details

Author: infinitimeless
Repository: infinitimeless/LMStudio-MCP
GitHub stars: 16
Downloads: 1,285
License: MIT License
Categories: Design, Developer Tools, AI, API, Infrastructure
Tags: #integration

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below
Plugin Name LM Studio

Command (node, npx, python, etc.) /bin/bash
Arguments
- Argument 1 -c
- Argument 2 cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "-c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "/bin/bash"
        }
    }
}

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "/c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "-c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "/bin/bash"
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "-c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "/bin/bash"
        }
    }
}

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "/c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "-c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "/bin/bash"
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "-c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "/bin/bash"
        }
    }
}

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "/c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "-c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "/bin/bash"
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "-c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "/bin/bash"
        }
    }
}

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "/c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "-c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "/bin/bash"
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "-c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "/bin/bash"
        }
    }
}

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "/c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "-c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "/bin/bash"
        }
    }
}

From the repository

curl -fsSL https://raw.githubusercontent.com/infinitimeless/LMStudio-MCP/main/install.sh | bash

git clone https://github.com/infinitimeless/LMStudio-MCP.git
cd LMStudio-MCP
pip install requests "mcp[cli]" openai

``bash


The bridge supports flexible configuration for different deployment scenarios:

- Default: Connects to http://localhost:1234/v1- Custom Host: SetLMSTUDIO_HOST environment variable (e.g., 192.168.1.100) - Custom Port: SetLMSTUDIO_PORT environment variable (e.g., 5678`)

`health_check()`

Verify if LM Studio API is accessible.

`list_models()`

Get a list of all available models in LM Studio.

`get_current_model()`

Identify which model is currently loaded.

`chat_completion(prompt, system_prompt, temperature, max_tokens)`

Generate a chat response from your local model.

`text_completion(prompt, temperature, max_tokens, stop_sequences)`

Generate raw text/code completion — faster, no chat formatting overhead.

`generate_embeddings(text, model)`

Generate vector embeddings for semantic search and RAG workflows.

`create_response(input_text, previous_response_id, reasoning_effort, stream, model)`

Stateful conversation via response IDs — requires LM Studio v0.3.29+.

`start_conversation(system_prompt, first_message, temperature, max_tokens, model)`

Start a multi-turn session with a persistent system prompt — returns a `response_id`.

`continue_conversation(response_id, message, temperature, max_tokens, model)`

Continue a session started with `start_conversation` — context preserved automatically.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "lm studio": {
            "env": {},
            "args": [
                "-c",
                "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
            ],
            "command": "/bin/bash"
        }
    }
}

Linux

{
    "env": [],
    "args": [
        "-c",
        "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
    ],
    "command": "/bin/bash"
}

Macos

{
    "env": [],
    "args": [
        "-c",
        "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
    ],
    "command": "/bin/bash"
}

Windows

{
    "env": [],
    "args": [
        "/c",
        "cd /path/to/LMStudio-MCP && source venv/bin/activate && python lmstudio_bridge.py"
    ],
    "command": "cmd"
}

LMStudio-MCP

A Model Control Protocol (MCP) server that allows Claude to communicate with locally running LLM models via LM Studio.

Screenshot 2025-03-22 at 16 50 53

Overview

LMStudio-MCP creates a bridge between Claude (with MCP capabilities) and your locally running LM Studio instance. This allows Claude to:

- Check the health of your LM Studio API
- List available models
- Get the currently loaded model
- Generate chat and raw text completions using your local models
- Generate vector embeddings for semantic search and RAG
- Hold stateful multi-turn conversations via response IDs
- Start and continue persistent conversations with a locked-in system prompt

This enables you to leverage your own locally running models through Claude's interface, combining Claude's capabilities with your private models.

Prerequisites

- Python 3.7+
- LM Studio installed and running locally with a model loaded
- Claude with MCP access
- Required Python packages (see Installation)

🚀 Quick Installation

One-Line Install (Recommended)

curl -fsSL https://raw.githubusercontent.com/infinitimeless/LMStudio-MCP/main/install.sh | bash

Manual Installation Methods

1. Local Python Installation

git clone https://github.com/infinitimeless/LMStudio-MCP.git
cd LMStudio-MCP
pip install requests "mcp[cli]" openai

2. Docker Installation

# Using pre-built image
docker run -it --network host ghcr.io/infinitimeless/lmstudio-mcp:latest
Or build locally
git clone https://github.com/infinitimeless/LMStudio-MCP.git
cd LMStudio-MCP
docker build -t lmstudio-mcp .
docker run -it --network host lmstudio-mcp

3. Docker Compose

git clone https://github.com/infinitimeless/LMStudio-MCP.git
cd LMStudio-MCP
docker-compose up -d

For detailed deployment instructions, see DOCKER.md.

⚙️ Configuration

The bridge supports flexible configuration for different deployment scenarios:

- Default: Connects to http://localhost:1234/v1
- Custom Host: Set LMSTUDIO_HOST environment variable (e.g., 192.168.1.100)
- Custom Port: Set LMSTUDIO_PORT environment variable (e.g., 5678)

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Details

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Setting up with Highlight

From the repository

health_check()

list_models()

get_current_model()

chat_completion(prompt, system_prompt, temperature, max_tokens)

text_completion(prompt, temperature, max_tokens, stop_sequences)

generate_embeddings(text, model)

create_response(input_text, previous_response_id, reasoning_effort, stream, model)

start_conversation(system_prompt, first_message, temperature, max_tokens, model)

continue_conversation(response_id, message, temperature, max_tokens, model)

Claude Desktop / Cursor

Linux

Macos

Windows

LMStudio-MCP

Overview

Prerequisites

🚀 Quick Installation

One-Line Install (Recommended)

Manual Installation Methods

1. Local Python Installation

2. Docker Installation

Or build locally

3. Docker Compose

⚙️ Configuration

Install

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`health_check()`

`list_models()`

`get_current_model()`

`chat_completion(prompt, system_prompt, temperature, max_tokens)`

`text_completion(prompt, temperature, max_tokens, stop_sequences)`

`generate_embeddings(text, model)`

`create_response(input_text, previous_response_id, reasoning_effort, stream, model)`

`start_conversation(system_prompt, first_message, temperature, max_tokens, model)`

`continue_conversation(response_id, message, temperature, max_tokens, model)`