Run Cline With Ollama Local LLMs: A Step-by-Step Guide

To run Cline with Ollama and local LLMs, install and start Ollama, download a model, verify that its API is reachable at http://localhost:11434, then select Ollama as Cline’s provider and choose the downloaded model. Enable Cline’s Use Compact Prompt setting before testing a small, read-only coding task; it reduces prompt overhead and is the most reliable starting configuration for local inference.

This setup keeps model inference on your machine: Cline supplies the coding-agent workflow—repository context, requests for file edits, and command execution—while Ollama runs the model behind a local HTTP server. It is useful when you want to experiment with open-weight models, work without per-token model-provider billing, or retain more control over where inference happens.

What do you need before running Cline with Ollama?

You need a supported Cline installation, Ollama, disk space for model weights, and enough memory for the model you choose. Cline supports local runtimes including Ollama, and its local-model guidance treats 16–32 GB RAM as a small or quantized-model setup, 32–64 GB as a mid-size coding-model range, and 64 GB or more as the range where larger models and contexts become more practical. These are planning ranges, not guarantees: GPU memory, quantization, context length, concurrent applications, and your operating system all change the real limit.

Install Cline where you want to work. Its IDE extension is available for VS Code-family editors and JetBrains IDEs; there are also desktop and terminal-oriented paths. For the workflow below, open a repository in your editor, install the Cline extension, and open its sidebar. Do not configure a cloud API key for the Ollama provider: Cline connects to the local Ollama endpoint instead.

How do you install and start Ollama?

Download and install Ollama for macOS, Windows, or Linux from its official distribution. On desktop platforms, opening the application normally starts the local service. On Linux, start it explicitly if it is not already managed as a service:

ollama serve

Leave that process running. Ollama’s local API listens on port 11434 by default, which is why Cline’s default base URL is http://localhost:11434. Keep it bound to localhost unless you deliberately need remote access; exposing an inference server beyond your machine changes the security model and should be paired with appropriate network controls.

Which local LLM should you use with Cline?

Start with a model that fits comfortably in your available RAM or VRAM rather than choosing the biggest parameter count you can download. A coding agent needs more than a model that can answer a one-shot programming question: it must repeatedly interpret repository context, decide whether to use tools, read command output, and produce structured actions. Ollama supports tool calling, but individual models vary substantially in how reliably they follow tool schemas and recover from errors.

Use Ollama’s model catalog to find a coding-oriented model and copy its exact name. Prefer a smaller quantized model for an initial connectivity test, then move to a stronger model once the integration works. Bigger models and larger context windows can improve some tasks, but they also increase memory pressure and slow every agent turn. For local coding agents, responsiveness matters: a model that completes a focused task in a few turns is often more useful than a larger one that makes the edit correctly but takes too long to iterate.

How do you download and test an Ollama model?

Replace <model> with the exact identifier from the Ollama catalog. Pulling downloads the weights; running opens a local chat and confirms that the model can load.

ollama pull <model>
ollama run <model>

Ask a trivial question, wait for a response, then type /bye to exit the interactive chat. Next, verify that the server can enumerate the model independently of the terminal UI:

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

The response should include a models array with the installed model name. This check separates two common failures: if ollama run fails, the issue is model download, hardware, or the runtime; if the model runs but this HTTP request fails, Cline will not be able to connect either.

How do you configure Cline to use Ollama?

1. Open the Cline panel in your IDE, then open Settings.
2. Set the provider to Ollama.
3. Set Base URL to http://localhost:11434. Leave it unchanged if Ollama is running locally with its default configuration.
4. Select your downloaded model from Cline’s model dropdown. If it is absent, return to the terminal and check curl http://localhost:11434/api/tags.
5. In Cline Settings → Features, enable Use Compact Prompt.
6. Start a fresh task and give Cline a bounded, low-risk request. For example: “Read this repository and summarize the purpose of the src/ directory. Do not edit files or run commands.”

The compact prompt is particularly important with local models because the agent’s system instructions, available tools, repository context, and conversational history all compete for a finite context window. Smaller prompts generally mean less memory demand, lower latency, and fewer chances for a small model to lose the actual task in a long instruction block.

How do you test a local LLM safely in Cline?

Use a staged test instead of immediately asking for a repo-wide refactor. First, ask for an explanation of one folder or one file. Second, ask for a plan without implementation. Third, ask for a tiny edit in a disposable branch and inspect the diff. Finally, allow one test command that you understand. This sequence tests context reading, planning, edits, and terminal interaction separately, so you know whether a failure comes from the local model, the task scope, or the tool workflow.

Keep approval prompts enabled while evaluating a local model. “Local” describes where inference runs; it does not make a model’s proposed shell command safe or correct. Read file diffs, scrutinize commands that write outside the project, and avoid granting automatic approval until the model has shown dependable behavior in your repository.

Why is Cline not connecting to Ollama?

Work through the connection from the bottom up. First run ollama -v to verify the executable exists. Then make sure Ollama is started—on Linux, use ollama serve or check the service status. Finally, run curl http://localhost:11434/api/tags. If that command cannot connect, Cline cannot connect; fix the runtime or base URL before changing Cline settings.

• Connection refused: Ollama is not running, another process is interfering, or the URL/port is wrong. The default local endpoint is http://localhost:11434.
• No models in Cline’s dropdown: Run ollama pull <model>, then verify the model appears in /api/tags; restart the Cline panel or start a new task after the server sees it.
• The model starts but agent tasks fail: Reduce task size, begin a new conversation to shrink history, enable Compact Prompt, and try a model better suited to coding and tool use.
• The machine becomes unusably slow: Use a smaller or more aggressively quantized model, reduce context and concurrent applications, or move to hardware with more available memory.
• Commands or edits are poor: Treat this as a model-quality or task-scoping issue, not evidence that the Ollama connection is broken. Ask for a plan first, constrain the files Cline may touch, and validate each small change.

Does running an LLM locally keep your code private?

Local Ollama inference means the prompt sent to the model endpoint is processed by the Ollama server on your machine rather than by a hosted model API. That is a meaningful architectural difference, but it is not a blanket security guarantee. Cline may still execute commands you approve, and optional tools such as browser integrations, MCP servers, package installers, source control remotes, and telemetry configured elsewhere can communicate over the network. Audit the tools enabled in your environment and apply the same repository-access and secret-handling rules you use for any developer automation.

When should you use Ollama instead of a hosted model?

Choose Ollama when control, offline-capable inference, repeatable local experimentation, or avoiding remote per-token inference is more important than always using the strongest available hosted model. It is an especially good fit for understanding a codebase, generating tests, narrow refactors, and iterative learning on hardware that can support the chosen model. For large multi-file migrations or ambiguous debugging, expect to spend more time on model selection, context management, and review than you would with a higher-capability remote model.

The practical answer is not “local versus cloud” as an identity choice. Use the local stack for tasks where it is fast and dependable on your hardware, keep tasks narrow, and retain approval gates. Then reassess from observed results: completion latency, tool-call reliability, diff quality, and test outcomes are better measures than a model’s parameter count alone.

Try Cline for local-model coding workflows

Cline is an open-source coding agent that can work in an editor, desktop app, terminal, or embedded workflow. It can read and edit across a project, run terminal commands, use Plan and Act modes, preserve checkpoints, and let you choose a model provider—including local Ollama—rather than tying the agent workflow to a single model vendor.

For this setup specifically, Cline provides the agent layer around your local LLM: it turns a model served at localhost into a workflow that can inspect code, propose a plan, make reviewed edits, and react to test output. Cline itself is free to use; its site also offers optional paid access paths such as ClinePass, while bring-your-own-provider and local-model workflows remain available. That makes it a useful place to start when you want the convenience of a coding agent without giving up the option to run your own weights through Ollama.