Can Flame use LM Studio?

Yes. Flame supports LM Studio as a local AI provider, alongside Ollama and compatible endpoints.

Can I use LM Studio for local coding help in Flame?

Yes. LM Studio can be configured for Flame’s model-powered surfaces, including chat, inline completion, and focused AI actions, with project-aware context controlled by Flame permissions.

Do LM Studio model requests stay local?

When Flame is configured to use a local LM Studio endpoint, model requests go to that local endpoint. Other connected providers and services have their own configured boundaries.

Can I use LM Studio alongside cloud models?

Yes. Flame supports local providers, cloud providers, and full coding-agent runtimes, so you can choose the provider per task or AI surface.

LM Studio integration

Use LM Studio local models in your development workspace

LM Studio can provide the local model endpoint. Flame supplies the project context and the development workspace around it: files, Git, terminals, tests, debugging, and review.

Download Flame See the workflow

The developer problem

A local endpoint is more useful when it can work with the project in front of you.

Running a model locally gives developers control over the endpoint and model selection, but it should not force them to move project context into a separate chat interface. Files, diffs, test output, and terminal errors are part of a coding conversation.

Flame connects LM Studio as a local AI provider. That lets developers use their local model with project-aware AI surfaces while keeping Git, terminals, testing, debugging, and code review in the same desktop workspace.

Try it on a real task

What LM Studio enables in Flame

Connect

Load a model in LM Studio, start its compatible inference server, and configure the endpoint and model in Flame's AI settings.

Example task

Ask a local model to explain a private utility function using only the files you select.

Inspect the result

Review the explanation beside the source, then use the same configured endpoint for supported AI surfaces when the model fits the task.

What to expect

LM Studio is a model endpoint, not a native Claude Code or Codex runtime. Context size, tool support, memory use, and quality depend on the model and server. Other connected services keep their own data paths.

A connected workflow

A local LM Studio coding workflow

01
Configure the local endpoint
Add LM Studio in Flame’s AI settings and select an available local model for the AI surface where it fits.
02
Bring in the codebase context
Use selected files, diffs, terminal output, test results, and browser state as scoped context instead of manually copying fragments to another application.
03
Work with local developer tools
Keep your terminal, test runner, debugger, API client, and Git review in the project workspace while you use the model.
04
Choose the right provider per task
Use LM Studio for a local-model task, then select a cloud provider or a full coding-agent runtime when another job requires it.

How it works in Flame

LM Studio in Flame, with clear boundaries

A local provider in Flame

Flame supports LM Studio alongside Ollama and compatible endpoints for model-powered surfaces such as chat, inline completion, and focused AI actions.

Local requests stay on the host

When configured with a local LM Studio endpoint, Flame sends model requests to that local endpoint. This does not automatically make external services, browser resources, or every configured provider local.

Use real development context

Scoped AI context can include selected files, diffs, terminal output, test results, and browser state, while the project’s Git, terminal, test, and debugging tools remain in Flame.

Keep provider choice flexible

LM Studio can coexist with Ollama, cloud APIs, and Flame’s full coding-agent runtimes. Changing models does not require changing the projects, branches, or review workflow around them.

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