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Fatimazahra Brmiki
Fatimazahra Brmiki

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Running AI Coding Agents Locally with Ollama and Claude Code

#programming #ai #tutorial #beginners

Can an AI coding agent analyze your project, create files, implement features, fix bugs, and run commands directly on your own machine, without a monthly AI subscription?

I have been exploring how to build a local AI-powered development environment with Ollama and Claude Code, and I want to share the setup process with other developers, especially students and beginners interested in AI-assisted coding.

The idea is simple: run an AI model locally, connect it to a coding agent, and use it to work on real software projects. Below is a step-by-step guide for Windows users.

1. Understand the Tools

Ollama runs AI models locally on your computer.

Claude Code is an AI coding agent that can inspect a codebase, edit files, execute commands, and work through multi-step development tasks.

Local integration connects a compatible model to an agent so you can move beyond asking questions and start working on actual project files.

Important distinction: using Claude Code with Ollama does not mean Anthropic's Claude model is running locally. Ollama runs the local model, while Claude Code provides the agent interface. Compatibility depends on the integration and the model you choose.

2. Install Ollama

Download Ollama for Windows: https://ollama.com/download

After installation, open PowerShell and verify the installation:

ollama --version
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Check which models are already installed:

ollama list
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If you do not have a model yet, download a coding model. For example:

ollama pull qwen3.5:0.8b
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Other available models can be found here: https://ollama.com/search

Once the download is complete, launch the model:

ollama run qwen3.5:0.8b
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Try a simple prompt:

"Write a Java method that checks whether a string is a palindrome, and explain how it works."

If the model responds, your local AI model is running. To exit the interactive session:

/bye
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Screenshot 1: PowerShell showing the Ollama version, installed model, and its response.

3. Install Claude Code

Install Claude Code using the official Windows installation command:

irm https://claude.ai/install.ps1 | iex
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Follow the installation instructions. Official documentation: https://docs.anthropic.com/en/docs/claude-code/overview

Important: installing Claude Code and Ollama does not automatically connect them. Claude Code normally uses Anthropic's supported services and authentication. To use a local Ollama model, you need a compatible integration and configuration. Ollama's documented integration instructions are available here: https://docs.ollama.com/integrations/claude-code

4. Connect Ollama to the Coding Agent

First, confirm that your local model is available:

ollama list
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Then follow Ollama's current Claude Code integration instructions to check the required version and configure the agent. The documented launch command is:

ollama launch claude
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Follow the prompts to select a supported model and complete the setup. Once configured, navigate to your project directory, replacing the example path with the actual path to your project:

cd "D:\MQL\S3\AI skills\TP1\biblio"
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Launch the agent:

ollama launch claude
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The agent should start in your project context, with access to the tools enabled by your integration and permissions.

Model compatibility matters. Not every local model supports tool calling or works correctly with every agent. If the model only returns text instead of editing files, check the model's compatibility and the agent's available tools.

Screenshot 2: The terminal showing the project path and the coding agent starting.

5. Test It on a Real Project

Suppose you are working on a Java, Spring Boot, React, or Node.js project. Instead of asking the model to return code in the chat, give the agent a task that requires actual changes.

Example prompt:

"Analyze the structure of this project. Identify the main components and explain how they interact. Then create a README.md file containing the project description, technologies used, installation steps, and commands to run the application. Create the file directly in the project and verify that it exists."

You can also experiment with the following tasks.

Generate files

"Create a Java utility class for validating email addresses and add unit tests for valid and invalid inputs."

Implement a feature

"Add CRUD operations for products using the existing Spring Boot architecture. Create the necessary classes, update the tests, and explain the changes."

Fix bugs

"Analyze the authentication functionality, identify potential bugs, and implement appropriate fixes. Run the relevant tests."

Understand an existing codebase

"Explore this repository and explain how the controllers, services, repositories, and database interact."

The objective is to let the agent inspect your project, plan its work, modify files, and execute commands, rather than simply generate code snippets.

Screenshot 3: The agent creating or modifying files and running tests.

6. Use It with IntelliJ IDEA

You can keep IntelliJ IDEA as your main development environment and use the agent through its integrated terminal. A simple workflow:

  1. Open your project in IntelliJ IDEA.
  2. Open the integrated terminal.
  3. Navigate to the project root directory.
  4. Launch your configured coding agent.
  5. Describe the task you want to complete.
  6. Review the changes in IntelliJ IDEA and run your tests.

Note: ollama run qwen3.5:0.8b starts a conversation with the model. It does not automatically provide filesystem access or the ability to execute project tasks. For that, you need an agent integration with the appropriate tools and permissions.

7. Why Run AI Locally?

  • No per-request inference fees for models running on your own hardware.
  • Offline inference after the model is downloaded, provided the rest of your workflow does not require an internet connection.
  • Greater control over the models and development environment you use.
  • Privacy advantages when prompts and source code remain on your machine.
  • A strong learning opportunity for exploring local AI, automation, and agentic development.

Limitations: local models consume your computer's memory and processing power, and their speed and coding capability depend on your RAM, CPU, GPU, and model size. Also, verify your integration's behavior before assuming that all data stays local.

Final Thoughts

AI-assisted development is more than asking a chatbot to write a function. Coding agents can help developers navigate entire projects and complete multi-step tasks when connected to the right tools.

My key takeaway: a local model generates responses, while a coding agent turns those capabilities into a development workflow. Ollama makes experimenting with local AI more accessible, and a compatible agent can turn that local model into a practical development assistant.

Resources

Question for developers: Have you tried running local AI models for software development? Which model gives you the best results on your machine?

Ollama #ClaudeCode #LocalAI #ArtificialIntelligence #SoftwareEngineering #OpenSource #AI #Coding #DeveloperTools

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