Can I code with local models in Flame?

Yes. A connected local model can be used with Flame’s project-aware AI workflows while terminals, Git, tests, debugging, and review remain part of the desktop workspace.

Does using Ollama mean all of my code stays local?

Ollama handles inference locally when you use a local model. That alone does not change the behavior of other providers, connected services, browser resources, or external integrations; review each configured service and permission separately.

Can I use both Ollama and cloud models?

Yes. Flame supports local providers and cloud providers, so the model can be selected according to the task without rebuilding the surrounding development setup.

Ollama integration

Use local AI models for coding with Ollama and Flame

Ollama gives Flame a local model provider. Flame gives that model a real development environment: project context, Git, terminals, tests, debugging, and an option to use cloud providers for a different task.

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The developer problem

Choosing a local model should not mean giving up the development workspace.

Developers use local models for experimentation, model control, predictable local inference, or privacy-sensitive parts of a workflow. But a model endpoint alone does not provide a project browser, Git review, test runner, debugger, or an implementation process.

Flame can connect Ollama as a local provider while retaining those tools around the model. The provider choice is separate from the rest of the workspace, so local and cloud models can coexist according to the task.

Try it on a real task

What Ollama enables in Flame

Connect

Run Ollama on the host, download a suitable model, and select its endpoint and model in Flame's AI settings.

Example task

Summarize a small staged diff locally before you write a commit message.

Inspect the result

Inspect the generated summary against the actual changes and choose what to retain. Keep cloud models configured separately for tasks that need them.

What to expect

Local inference requires the local model and server to be available. It does not grant native runtime feature parity or make task trackers, remote URLs, and other model providers local.

A connected workflow

A local-model coding workflow

01
Connect the local provider
Add Ollama in Flame’s AI settings and choose it where a local model is appropriate for chat, completion, or compatible AI workflows.
02
Work with the project context
Use selected files, diffs, terminal output, test results, and browser state as scoped AI context instead of moving code into a separate local-model interface.
03
Verify with local development tools
Keep the terminal, tests, debugger, API client, and Git diff in the same workspace as the AI interaction.
04
Choose cloud only when it fits
Use a cloud provider or a full coding-agent runtime for another task without rebuilding the projects, branches, or review workflow.

How it works in Flame

Local AI, with precise boundaries

Code with local models

Flame supports Ollama as a local AI provider, alongside LM Studio and compatible endpoints. The model choice is configured in Flame rather than requiring a separate editor.

What stays local

When you use a local Ollama model, model inference is handled by that local provider. This does not automatically make every other Flame feature, connected integration, or external service local or private.

Use local models with the actual workspace

AI context can include selected files, diffs, terminal output, test results, and browser state under Flame’s scoped permissions; terminals, Git, tests, and debugging remain local workspace tools.

Mix local and cloud AI deliberately

Flame supports local providers as well as Anthropic, OpenAI, Gemini, OpenRouter, Bedrock, Vertex, and compatible endpoints. Use the provider that suits the job without replacing the workspace.

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