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Documentation Index

Fetch the complete documentation index at: https://docs.openclaw.ai/llms.txt

Use this file to discover all available pages before exploring further.

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. If LM Studio authentication is enabled, set LM_API_TOKEN:
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.
  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 the base URL, model, and optional 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. For authenticated LM Studio servers, pass --lmstudio-api-key or set LM_API_TOKEN. For unauthenticated LM Studio servers, omit the key; OpenClaw stores a local non-secret marker. --custom-api-key remains supported for compatibility, but --lmstudio-api-key is preferred for LM Studio. This writes models.providers.lmstudio and sets the default model to lmstudio/<custom-model-id>. When you provide an API key, setup also 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. 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 models.providers.lmstudio.request.allowPrivateNetwork: false.

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Configuration

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Streaming usage compatibility

LM Studio is streaming-usage compatible. When it does not emit an OpenAI-shaped usage object, OpenClaw recovers token counts from llama.cpp-style timings.prompt_n / timings.predicted_n metadata instead. Same streaming usage behavior applies to these OpenAI-compatible local backends:
  • vLLM
  • SGLang
  • llama.cpp
  • LocalAI
  • Jan
  • TabbyAPI
  • text-generation-webui

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Thinking compatibility

When LM Studio’s /api/v1/models discovery reports model-specific reasoning options, OpenClaw exposes the matching OpenAI-compatible reasoning_effort values in model compat metadata. Current LM Studio builds can advertise binary UI options such as allowed_options: ["off", "on"] while rejecting those values on /v1/chat/completions; OpenClaw normalizes that binary discovery shape to none, minimal, low, medium, high, and xhigh before sending requests. Older saved LM Studio config that contains off/on reasoning maps is normalized the same way when the catalog is loaded.

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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. If authentication is enabled, also set LM_API_TOKEN: 
# 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, leave the key blank during setup.

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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. 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, 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 LM Studio 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" }],
      },
    },
  },
}
Unlike generic OpenAI-compatible providers, lmstudio automatically trusts its configured local/private endpoint for guarded model requests. Custom loopback provider IDs such as localhost or 127.0.0.1 are also trusted automatically; for LAN, tailnet, or private DNS custom provider IDs, set models.providers.<id>.request.allowPrivateNetwork: true explicitly.