OpenShell/docs/tutorials/local-inference-lmstudio.md

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page nav Route Local Inference Requests to LM Studio Local Inference with LM Studio	Configure inference.local to route sandbox requests to a local LM Studio server running on the gateway host.	Generative AI Cybersecurity	Tutorial Inference Routing LM Studio Local Inference Sandbox	type difficulty audience tutorial technical_intermediate engineer

Route Local Inference Requests to LM Studio

This tutorial describes how to configure OpenShell to route inference requests to a local LM Studio server.

:::{note} The LM Studio server provides easy setup with both OpenAI and Anthropic compatible endpoints. :::

This tutorial will cover:

• Expose a local inference server to OpenShell sandboxes.
• Verify end-to-end inference from inside a sandbox.

Prerequisites

First, complete OpenShell installation and follow the {doc}/get-started/quickstart.

Install the LM Studio app. Make sure that your LM Studio is running in the same environment as your gateway.

If you prefer to work without having to keep the LM Studio app open, download llmster (headless LM Studio) with the following command:

Linux/Mac

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

Windows

$ irm https://lmstudio.ai/install.ps1 | iex

And start llmster:

$ lms daemon up

Step 1: Start LM Studio Local Server

Start the LM Studio local server from the Developer tab, and verify the OpenAI-compatible endpoint is enabled.

LM Studio will listen to 127.0.0.1:1234 by default. For use with OpenShell, you'll need to configure LM Studio to listen on all interfaces (0.0.0.0).

If you're using the GUI, go to the Developer Tab, select Server Settings, then enable Serve on Local Network.

If you're using llmster in headless mode, run lms server start --bind 0.0.0.0.

Step 2: Test with a small model

In the LM Studio app, head to the Model Search tab to download a small model like Qwen3.5 2B.

In the terminal, use the following command to download and load the model:

$ lms get qwen/qwen3.5-2b
$ lms load qwen/qwen3.5-2b

Step 3: Add LM Studio as a provider

Choose the provider type that matches the client protocol you want to route through inference.local.

:::::{tab-set}

::::{tab-item} OpenAI-compatible

Add LM Studio as an OpenAI-compatible provider through host.openshell.internal:

$ openshell provider create \
    --name lmstudio \
    --type openai \
    --credential OPENAI_API_KEY=lmstudio \
    --config OPENAI_BASE_URL=http://host.openshell.internal:1234/v1

Use this provider for clients that send OpenAI-compatible requests such as POST /v1/chat/completions or POST /v1/responses.

::::

::::{tab-item} Anthropic-compatible

Add a provider that points to LM Studio's Anthropic-compatible POST /v1/messages endpoint:

$ openshell provider create \
    --name lmstudio-anthropic \
    --type anthropic \
    --credential ANTHROPIC_API_KEY=lmstudio \
    --config ANTHROPIC_BASE_URL=http://host.openshell.internal:1234

Use this provider for Anthropic-compatible POST /v1/messages requests.

::::

:::::

Step 4: Configure LM Studio as the local inference provider

Set the managed inference route for the active gateway:

:::::{tab-set}

::::{tab-item} OpenAI-compatible

$ openshell inference set --provider lmstudio --model qwen/qwen3.5-2b

If the command succeeds, OpenShell has verified that the upstream is reachable and accepts the expected OpenAI-compatible request shape.

::::

::::{tab-item} Anthropic-compatible

$ openshell inference set --provider lmstudio-anthropic --model qwen/qwen3.5-2b

If the command succeeds, OpenShell has verified that the upstream is reachable and accepts the expected Anthropic-compatible request shape.

::::

:::::

The active inference.local route is gateway-scoped, so only one provider and model pair is active at a time. Re-run openshell inference set whenever you want to switch between OpenAI-compatible and Anthropic-compatible clients.

Confirm the saved config:

$ openshell inference get

You should see either Provider: lmstudio or Provider: lmstudio-anthropic, along with Model: qwen/qwen3.5-2b.

Step 5: Verify from Inside a Sandbox

Run a simple request through https://inference.local:

:::::{tab-set}

::::{tab-item} OpenAI-compatible

openshell sandbox create -- \
    curl https://inference.local/v1/chat/completions \
    --json '{"messages":[{"role":"user","content":"hello"}],"max_tokens":10}'

openshell sandbox create -- \
    curl https://inference.local/v1/responses \
    --json '{
      "instructions": "You are a helpful assistant.",
      "input": "hello",
      "max_output_tokens": 10
    }'

::::

::::{tab-item} Anthropic-compatible

openshell sandbox create -- \
    curl https://inference.local/v1/messages \
    --json '{"messages":[{"role":"user","content":"hello"}],"max_tokens":10}'

::::

:::::

Troubleshooting

If setup fails, check these first:

• LM Studio local server is running and reachable from the gateway host
• OPENAI_BASE_URL uses http://host.openshell.internal:1234/v1 when you use an openai provider
• ANTHROPIC_BASE_URL uses http://host.openshell.internal:1234 when you use an anthropic provider
• The gateway and LM Studio run on the same machine or a reachable network path
• The configured model name matches the model exposed by LM Studio

Useful commands:

$ openshell status
$ openshell inference get
$ openshell provider get lmstudio
$ openshell provider get lmstudio-anthropic

Next Steps

• To learn more about using the LM Studio CLI, refer to LM Studio docs
• To learn more about managed inference, refer to {doc}/inference/index.
• To configure a different self-hosted backend, refer to {doc}/inference/configure.