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

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Quick start

  1. Install LM Studio (desktop) or llmster (headless), then start the local server:
curl -fsSL https://lmstudio.ai/install.sh | bash
  1. Start the server
Make sure you either start the desktop app or run the daemon using the following command: 
lms daemon up

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.
  1. OpenClaw requires an LM Studio token value. Set LM_API_TOKEN:
export LM_API_TOKEN="your-lm-studio-api-token"
If LM Studio authentication is disabled, use any non-empty token value: 
export LM_API_TOKEN="placeholder-key"
For LM Studio auth setup details, see LM Studio Authentication.
  1. Run onboarding and choose LM Studio:
openclaw onboard
  1. In onboarding, use the Default model prompt to pick your LM Studio model.
You can also set or change it later: 
openclaw models set lmstudio/qwen/qwen3.5-9b
LM Studio model keys follow a author/model-name format (e.g. qwen/qwen3.5-9b). OpenClaw model refs prepend the provider name: lmstudio/qwen/qwen3.5-9b. You can find the exact key for a model by running curl http://localhost:1234/api/v1/models and looking at the key field.

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Non-interactive onboarding

Use non-interactive onboarding when you want to script setup (CI, provisioning, remote bootstrap): 
openclaw onboard \
  --non-interactive \
  --accept-risk \
  --auth-choice lmstudio
Or specify base URL or model with API key: 
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
--custom-model-id takes the model key as returned by LM Studio (e.g. qwen/qwen3.5-9b), without the lmstudio/ provider prefix. Non-interactive onboarding requires --lmstudio-api-key (or LM_API_TOKEN in env). For unauthenticated LM Studio servers, any non-empty token value works. --custom-api-key remains supported for compatibility, but --lmstudio-api-key is preferred for LM Studio. This writes models.providers.lmstudio, sets the default model to lmstudio/<custom-model-id>, and writes the lmstudio:default 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.

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Configuration

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Explicit configuration

{
  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,
          },
        ],
      },
    },
  },
}

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Troubleshooting

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LM Studio not detected

Make sure LM Studio is running and that you set LM_API_TOKEN (for unauthenticated servers, any non-empty token value works): 
# Start via desktop app, or headless:
lms server start --port 1234
Verify the API is accessible: 
curl http://localhost:1234/api/v1/models

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Authentication errors (HTTP 401)

If setup reports HTTP 401, verify your API key:
  • Check that 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, use any non-empty token value for LM_API_TOKEN.

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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.