LMStudioClient.jl

Thin Julia client for LM Studio's native REST API.

It supports:

• model listing
• loaded model listing
• model download
• model load
• model unload
• server status
• non-streaming chat
• stateful chat with ChatSession
• streaming chat via stream_chat

Use LMStudioClient.jl when you want LM Studio-native model management and chat flows rather than a higher-level prompting abstraction.

Prerequisites

• Julia 1.12+
• A running LM Studio server
• By default, Client() connects to http://127.0.0.1:1234

Before you run the examples below, confirm the exact model identifier in LM Studio itself. If you use the lms CLI, lms ls shows installed models and lms ps shows loaded models.

Installation

Repo-local Development

Clone the repository first, then run Julia from the repository root:

git clone <your-repo-url>
cd LMStudioClient

using Pkg
Pkg.develop(path=".")

Scratch Environment

For a scratch environment outside the repo:

using Pkg
Pkg.activate("lmstudio-scratch")
Pkg.develop(path="/absolute/path/to/LMStudioClient")
Pkg.instantiate()

Quickstart

Make sure the LM Studio server is already running before you execute the example below. If you use the lms CLI, one way to start it is:

lms server start -p 1234 --bind 127.0.0.1

using LMStudioClient

client = Client()

status = server_status(client)
println("reachable=$(status.reachable), authenticated=$(status.authenticated)")

models = list_models(client; domain=:llm)
if isempty(models)
    println("No LLM models installed.")
else
    println(first(models).key)
end

# Replace this with a model identifier that exists in your LM Studio setup.
job = download_model(client, "google/gemma-4-e2b")
wait_for_download(client, job; timeout=1800)

load_model(client, "google/gemma-4-e2b"; context_length=8192)

response = chat(client; model="google/gemma-4-e2b", input="Reply with the single word BLUE.")

for item in response.output
    if item isa MessageOutput
        println(item.content)
    end
end

Streaming Chat

using LMStudioClient

client = Client(
    base_url=get(ENV, "LMSTUDIO_BASE_URL", "http://127.0.0.1:1234"),
)

# Replace the model name with one that exists in your LM Studio setup.
# Confirm the exact identifier in LM Studio first. If you use the CLI,
# `lms ls` shows installed models and `lms ps` shows loaded ones.
model = get(ENV, "LMSTUDIO_MODEL", "google/gemma-4-e2b")

for event in stream_chat(client; model=model, input="Say hello")
    if event isa MessageDeltaEvent
        print(event.content)
    elseif event isa StreamErrorEvent
        @warn "stream error" event.error
    elseif event isa ChatEndEvent
        println()
    end
end

The streamed text chunks arrive in MessageDeltaEvent(content::String).

More Docs

• Getting Started
• Model Management
• Streaming

Relationship To PromptingTools.jl

PromptingTools.jl and LMStudioClient.jl live at different layers. PromptingTools.jl is a higher-level prompting interface, while LMStudioClient.jl provides thin access to LM Studio's native REST API for model management and native chat flows.

PromptingTools.jl is often the easier fit when you mainly want:

• one-shot generation through OpenAI-compatible APIs
• embeddings
• client-managed multi-turn chat
• prompt templates and REPL ergonomics
• higher-level extraction or classification helpers

LMStudioClient.jl is the better fit when you need:

• LM Studio-native model download, load, unload, and listing
• server-side stateful chat via response_id and ChatSession
• native streaming event types via stream_chat
• LM Studio-specific load options such as context_length and flash_attention
• download job polling with statuses such as downloading, completed, and already_downloaded

Building The Docs Locally

julia --project=docs -e 'using Pkg; Pkg.instantiate()'
julia --project=docs docs/make.jl

Then open docs/build/index.html.