Configuring Ollama Models in Cline via OpenAI-Compatible Provider

Integrating Ollama's locally hosted models into Cline enhances your development workflow by combining Cline's autonomous coding capabilities with the efficiency of local AI models. This guide provides a comprehensive walkthrough on configuring Cline to utilize Ollama models through an OpenAI-compatible provider setup.

Prerequisites

Before proceeding, ensure you have the following:

1. Cline Installed Cline is an autonomous coding agent designed for Visual Studio Code (VSCode). If you haven't installed it yet, download and set it up from the official Cline website.

2. Ollama Installed and Running Ollama is an open-source tool that allows users to run large language models (LLMs) on their local systems. Ensure Ollama is installed and the server is running on your local machine. By default, Ollama operates on http://127.0.0.1:11434/v1. (Ollama Website)

Step-by-Step Configuration

1. Launch VSCode and Access Cline Settings

• Open Visual Studio Code.
• Navigate to the Extensions panel and ensure Cline is installed and active.
• Click on the Cline icon or use the command palette (Ctrl + Shift + P or Cmd + Shift + P on Mac) to access Cline's settings.

2. Configure the OpenAI-Compatible Provider

• In Cline's settings, locate the API Provider section.

• From the dropdown menu, select "OpenAI Compatible."

• Input the following details:

  yaml
  Wrap|Copy
   Base URL: http://127.0.0.1:11434/v1
   API Key: (any placeholder, as Ollama does not require verification)
  

3. Specify the Model ID

• In the same settings panel, specify the model you intend to use.

• To list available models in your Ollama setup, run the following command in your terminal:

  sh
  Wrap|Copy
   ollama list
  

• For instance, to utilize the deepseek-r1:8b model, set the Model ID to:

  yaml
  Wrap|Copy
   Model ID: deepseek-r1:8b
  

• Ensure that the model is available and properly configured in your Ollama setup. (Ollama Model Library)

4. Save and Test the Configuration

• After entering the necessary details, save your settings.
• Initiate a coding task or query within Cline to verify that it's correctly interfacing with the Ollama model.
• If configured properly, Cline will process your requests using the specified local model.

Benefits of This Integration

• Enhanced Performance: Running models locally reduces latency, leading to faster response times during development tasks.
• Data Privacy: Processing data locally ensures that sensitive information remains within your infrastructure, addressing potential security concerns associated with cloud-based solutions.
• Cost Efficiency: Utilizing local models eliminates the need for external API calls, potentially reducing costs associated with third-party services.

Conclusion

By configuring Cline to work with Ollama's locally hosted models through an OpenAI-compatible provider setup, you can create a powerful and efficient development environment. This integration leverages the strengths of both tools, offering a seamless and responsive coding assistant experience.

For more information on available models and their capabilities, visit the Ollama Model Library.