How to Run Cline With a Local LLM Using Ollama

Yes—you can run Cline with a local LLM through Ollama: install Ollama, pull a capable coding model, make sure its local server answers on http://localhost:11434, then choose Ollama as Cline’s provider. You won’t need an API key for that connection, but you will need realistic expectations about your hardware and the quality of the model you choose.

What you’re actually wiring together

Cline is the coding-agent interface: it reads your workspace, proposes edits, runs commands when allowed, and keeps a task-oriented conversation going. Ollama is the local model runtime. Cline sends prompts and tool results to Ollama; Ollama runs the model on your CPU, GPU, or Apple Silicon and returns tokens to Cline.

That separation matters when debugging. If Cline can’t see a model, it’s usually an Ollama process, URL, or model-download problem—not a mysterious agent problem. If Cline can see the model but produces weak plans, malformed edits, or loops, the connection is fine and the model is the limiting factor.

Set up the local runtime first

Install Ollama for your operating system, then open a terminal and pull a coding-oriented model. Ollama’s own documentation currently calls out qwen3-coder as a strong local option for coding tasks, so it’s a sensible first model to test.

ollama pull qwen3-coder
ollama run qwen3-coder

The first command downloads the model. The second command is both a quick smoke test and a way to start it interactively. Ask it something small, such as “Explain what a TypeScript discriminated union is,” and make sure you receive a response before involving Cline.

Next, verify the HTTP API that Cline will use. Ollama normally listens on port 11434 locally. This command lists models installed in the runtime:

curl http://localhost:11434/api/tags

You should get JSON containing a models array. If the request fails, start or restart Ollama before changing anything in Cline. If it succeeds but your intended model is absent, repeat ollama pull with the exact model name and tag you plan to use.

Point Cline at Ollama

Open Cline inside VS Code, go to its settings or provider configuration, and select Ollama. Set the base URL to http://localhost:11434, then choose the model you pulled from the model dropdown. Cline’s local-model guide uses this same provider and URL flow.

Enable Use Compact Prompt in Cline’s Features settings while you’re testing local inference. Agent prompts can become large because Cline needs room for its instructions, your request, file contents, diffs, tool calls, and command output. A compact prompt gives a smaller local model a more realistic chance of retaining the task rather than spending its context window on framework overhead.

Now start with a bounded request in a small repository. Don’t begin with “refactor this monorepo.” Instead, ask Cline to inspect one module, explain a failing test, or add coverage for a single function. You’re checking four things: the model follows the plan, reads relevant files, proposes a coherent patch, and recovers when a test fails.

Choose the model based on the job and the machine

The setup is identical for every Ollama model; the experience is not. Smaller quantized models may be pleasant for explanation, navigation, and narrow edits, but coding-agent work stresses instruction following and tool use much more than ordinary chat. A model that writes a nice standalone function can still struggle to update three files, interpret test output, and avoid undoing its own work.

Use your first few tasks to find the largest model your machine can run without making each agent turn unbearable. Cline’s local-model guidance gives a useful rough starting point: 16–32 GB of RAM for small or quantized setups, 32–64 GB for mid-sized coding models, and 64 GB or more for larger models or larger context windows. Available GPU memory, model quantization, and the context size you configure all change the real answer.

If replies become slow after Cline has inspected a few files, reduce the scope before immediately blaming the model. Start a new task, ask it to work on one package, avoid pasting logs that don’t matter, and keep command output focused. Context is a resource: every irrelevant token competes with the code and requirements the model needs to remember.

Use a safer approval posture while you learn

Running the model locally changes where inference happens. It does not make an autonomous coding agent harmless. Cline can read files, edit files, execute terminal commands, use browser access, and call configured MCP tools depending on the permissions you grant it.

For a new local model, begin with project-file reads enabled and keep edits, terminal commands, browser access, and MCP access approval-gated. Review its proposed changes and commands. Once it has earned some trust on a throwaway branch, selectively enable routine actions such as test commands. Don’t turn on broad auto-approval merely because you aren’t paying per token.

Troubleshoot the failures in the right order

1. Cline cannot connect: run curl http://localhost:11434/api/tags. If it fails, Ollama is not reachable at the configured URL.
2. The model is not in Cline’s dropdown: run ollama pull <exact-model-name>, then reload Cline or reopen provider settings.
3. The model responds but Cline is poor at agent tasks: try a stronger coding model, use Compact Prompt, shorten the task, and begin a fresh task when the conversation has accumulated too much context.
4. Your machine becomes unresponsive or generations crawl: choose a smaller or more aggressively quantized model, lower the context window, and close other GPU or memory-heavy applications.
5. Cline proposes risky commands: leave command approval on, work in a Git branch, and treat local inference as separate from tool permissions.

Once this works, your repeatable loop is simple: pull a model, test it directly with Ollama, connect Cline to the localhost endpoint, and benchmark it on a real small task from your own codebase. That last step matters more than model chatter online. The right local model is the one that can inspect, change, and verify your particular code at a speed you’ll actually tolerate.