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LM Studio runs llama.cpp (GGUF) or MLX models locally, as a GUI app or the headless llmster daemon. For install and product docs, see lmstudio.ai.

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Quick start

1

Install and start the server

Install LM Studio (desktop) or llmster (headless), then start the server:
lms server start --port 1234
Or run the headless daemon:
lms daemon up
If using the desktop app, enable JIT for smooth model loading; see the LM Studio JIT and TTL guide.
2

Set an API key if auth is enabled

export LM_API_TOKEN="your-lm-studio-api-token"
If LM Studio authentication is disabled, leave the API key blank during setup. See LM Studio Authentication.
3

Run onboarding

openclaw onboard
Choose LM Studio, then pick a model at the Default model prompt.
Change the default model later: 
openclaw models set lmstudio/qwen/qwen3.5-9b
LM Studio model keys use an author/model-name format (e.g. qwen/qwen3.5-9b); OpenClaw model refs prepend the provider: lmstudio/qwen/qwen3.5-9b. Find the exact key for a model by running the command below and looking at the key field: 
curl http://localhost:1234/api/v1/models

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Non-interactive onboarding

openclaw onboard --non-interactive --accept-risk --auth-choice lmstudio
Or specify base URL, model, and API key explicitly: 
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. Pass --lmstudio-api-key (or set LM_API_TOKEN) for authenticated servers; omit it for unauthenticated servers and OpenClaw stores a local non-secret marker instead. --custom-api-key is still accepted for compatibility, but --lmstudio-api-key is preferred. This writes models.providers.lmstudio and sets the default model to lmstudio/<custom-model-id>. Providing an API key also writes the lmstudio:default auth profile. Interactive setup can additionally prompt for a preferred load context length and applies it across the discovered models it saves to config.

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Configuration

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Streaming usage compatibility

LM Studio doesn’t always emit an OpenAI-shaped usage object on streamed responses. OpenClaw recovers token counts from llama.cpp-style timings.prompt_n / timings.predicted_n metadata instead. Any OpenAI-compatible endpoint resolved as a local endpoint (loopback host) gets this same fallback, which covers other local backends such as vLLM, SGLang, llama.cpp, LocalAI, Jan, TabbyAPI, and text-generation-webui.

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Thinking compatibility

When LM Studio’s /api/v1/models discovery reports model-specific reasoning options, OpenClaw exposes matching reasoning_effort values (none, minimal, low, medium, high, xhigh) in model compat metadata. Some LM Studio builds advertise a binary UI option (allowed_options: ["off", "on"]) while rejecting those literal values on /v1/chat/completions; OpenClaw normalizes that binary shape to the six-level scale before sending requests, including for older saved config that still has off/on reasoning maps.

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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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Disabling preload

LM Studio supports just-in-time (JIT) model loading, loading models on first request. OpenClaw preloads models through LM Studio’s native load endpoint by default, which helps when JIT is disabled. To let LM Studio’s JIT, idle TTL, and auto-evict behavior own model lifecycle instead, disable OpenClaw’s preload step: 
{
  models: {
    providers: {
      lmstudio: {
        baseUrl: "http://localhost:1234/v1",
        api: "openai-completions",
        params: { preload: false },
        models: [{ id: "qwen/qwen3.5-9b" }],
      },
    },
  },
}

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LAN or tailnet host

Use the LM Studio host’s reachable address, keep /v1, and make sure LM Studio is bound beyond loopback on that machine: 
{
  models: {
    providers: {
      lmstudio: {
        baseUrl: "http://gpu-box.local:1234/v1",
        apiKey: "lmstudio",
        api: "openai-completions",
        models: [{ id: "qwen/qwen3.5-9b" }],
      },
    },
  },
}
lmstudio automatically trusts its configured endpoint for model requests, including loopback, LAN, and tailnet hosts (except metadata/link-local origins). Any custom/local OpenAI-compatible provider entry gets the same exact-origin trust. Requests to a different private host or port still require models.providers.<id>.request.allowPrivateNetwork: true; set it to false to opt out of the default trust.

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Troubleshooting

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LM Studio not detected

Make sure LM Studio is running: 
lms server start --port 1234
If authentication is enabled, also set LM_API_TOKEN. Verify the API is reachable: 
curl http://localhost:1234/api/v1/models

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Authentication errors (HTTP 401)

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