LM Studio

LM Studio is a friendly yet powerful app for running open-weight models on your own hardware. It lets you run llama.cpp (GGUF) or MLX models (Apple Silicon). Comes in a GUI package or headless daemon (

). For product and setup docs, see lmstudio.ai.

Quick start

1. Install LM Studio (desktop) or
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   llmster
   (headless), then start the local server:

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curl -fsSL https://lmstudio.ai/install.sh | bash

2. Start the server

Make sure you either start the desktop app or run the daemon using the following command:

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lms daemon up

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lms server start --port 1234

If you are using the app, make sure you have JIT enabled for a smooth experience. Learn more in the LM Studio JIT and TTL guide.

3. If LM Studio authentication is enabled, set
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   LM_API_TOKEN
   :

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export LM_API_TOKEN="your-lm-studio-api-token"

If LM Studio authentication is disabled, you can leave the API key blank during interactive OpenClaw setup.

For LM Studio auth setup details, see LM Studio Authentication.

4. Run onboarding and choose
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   LM Studio
   :

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openclaw onboard

5. In onboarding, use the
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   Default model
   prompt to pick your LM Studio model.

You can also set or change it later:

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openclaw models set lmstudio/qwen/qwen3.5-9b

LM Studio model keys follow a

format (e.g. ). OpenClaw model refs prepend the provider name: . You can find the exact key for a model by running and looking at the field.

Non-interactive onboarding

Use non-interactive onboarding when you want to script setup (CI, provisioning, remote bootstrap):

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openclaw onboard \
  --non-interactive \
  --accept-risk \
  --auth-choice lmstudio

Or specify the base URL, model, and optional API key:

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openclaw onboard \
  --non-interactive \
  --accept-risk \
  --auth-choice lmstudio \
  --custom-base-url http://localhost:1234/v1 \
  --lmstudio-api-key "$LM_API_TOKEN" \
  --custom-model-id qwen/qwen3.5-9b

takes the model key as returned by LM Studio (e.g. ), without the provider prefix.

For authenticated LM Studio servers, pass

or set . For unauthenticated LM Studio servers, omit the key; OpenClaw stores a local non-secret marker.

remains supported for compatibility, but is preferred for LM Studio.

This writes

and sets the default model to . When you provide an API key, setup also writes the auth profile.

Interactive setup can prompt for an optional preferred load context length and applies it across the discovered LM Studio models it saves into config. LM Studio plugin config trusts the configured LM Studio endpoint for model requests, including loopback, LAN, and tailnet hosts. You can opt out by setting

.

Configuration

Streaming usage compatibility

LM Studio is streaming-usage compatible. When it does not emit an OpenAI-shaped

object, OpenClaw recovers token counts from llama.cpp-style / metadata instead.

Same streaming usage behavior applies to these OpenAI-compatible local backends:

• vLLM
• SGLang
• llama.cpp
• LocalAI
• Jan
• TabbyAPI
• text-generation-webui

Thinking compatibility

When LM Studio's

discovery reports model-specific reasoning options, OpenClaw preserves those native values in model compat metadata. For binary thinking models that advertise , OpenClaw maps disabled thinking to and enabled levels to instead of sending OpenAI-only values such as or .

Explicit configuration

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{
  models: {
    providers: {
      lmstudio: {
        baseUrl: "http://localhost:1234/v1",
        apiKey: "${LM_API_TOKEN}",
        api: "openai-completions",
        models: [
          {
            id: "qwen/qwen3-coder-next",
            name: "Qwen 3 Coder Next",
            reasoning: false,
            input: ["text"],
            cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
            contextWindow: 128000,
            maxTokens: 8192,
          },
        ],
      },
    },
  },
}

Troubleshooting

LM Studio not detected

Make sure LM Studio is running. If authentication is enabled, also set

:

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# Start via desktop app, or headless:
lms server start --port 1234

Verify the API is accessible:

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curl http://localhost:1234/api/v1/models

Authentication errors (HTTP 401)

If setup reports HTTP 401, verify your API key:

• Check that
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  LM_API_TOKEN
  matches the key configured in LM Studio.
• For LM Studio auth setup details, see LM Studio Authentication.
• If your server does not require authentication, leave the key blank during setup.

Just-in-time model loading

LM Studio supports just-in-time (JIT) model loading, where models are loaded on first request. Make sure you have this enabled to avoid 'Model not loaded' errors.

LAN or tailnet LM Studio host

Use the LM Studio host's reachable address, keep

, and make sure LM Studio is bound beyond loopback on that machine:

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{
  models: {
    providers: {
      lmstudio: {
        baseUrl: "http://gpu-box.local:1234/v1",
        apiKey: "lmstudio",
        api: "openai-completions",
        models: [{ id: "qwen/qwen3.5-9b" }],
      },
    },
  },
}

Unlike generic OpenAI-compatible providers,

automatically trusts its configured local/private endpoint for guarded model requests. Custom loopback provider IDs such as or are also trusted automatically; for LAN, tailnet, or private DNS custom provider IDs, set explicitly.

Related

• Model selection
• Ollama
• Local models