How to Run a Claude Code-like AI Programming Assistant Locally (100% Free & Fully Private)

Have you ever wished for a powerful AI programming assistant like Claude Code but worried about code privacy, API costs, or simply wanted to work in an offline environment? Today, we’ll walk through the steps to deploy a fully functional AI coding agent entirely on your own computer. The entire process requires no internet connection, incurs no cloud service fees, and guarantees 100% privacy for all your code and data.

This article details how to use open-source tools and models to build a local AI partner capable of reading files, editing code, and executing terminal commands.

Why Choose Local Deployment for an AI Programming Assistant?

Before diving into the technical details, let’s clarify the core value of this approach. Running an AI programming assistant locally offers three main advantages:

  1. Absolute Privacy: Your source code, project structure, and even the AI’s suggestions are processed entirely on your device. Not a single piece of data is sent to external servers.
  2. Zero-Cost Operation: No paying for expensive API calls. Deploy once, use indefinitely. The only “cost” is your local computer’s computational resources.
  3. Offline Availability: Whether you’re on transportation with unstable internet or in a high-security internal network environment, your AI assistant is always on standby.

This setup is ideal for developers, privacy-conscious power users, open-source enthusiasts, and anyone who wants an AI collaborator that can truly understand and manipulate a local file system.

Preparation and Core Tools

To achieve this goal, we need two core components:

  1. An “engine” capable of hosting large language models locally.
  2. An “agent” program that can call this engine and possesses file operation and command execution capabilities.

In the following steps, we will install and configure them separately.

Image: Schematic of the Ollama application interface, showing its simple background operation mode

Step 1: Build the Local “Brain” – Install Ollama

Ollama is a powerful open-source tool that makes downloading, running, and managing large language models locally exceptionally simple. Think of it as a local “model server” responsible for loading AI models and communicating with external applications (like our coding agent).

Installation Process:

  1. Go to the official Ollama website to download the installer. It supports macOS, Windows, and Linux systems.
  2. Run the installer. After installation, Ollama typically runs silently as a background service. You can find its icon in the system tray or menu bar.

With Ollama installed, it’s like an empty “brain container,” waiting for us to inject it with “intelligence.”

Choose the Right AI Model

Ollama itself doesn’t contain a model. We need to select an open-source model proficient in programming tasks for it. The model choice largely depends on your computer’s performance, especially the amount of memory (RAM).

Here are some recommendations for devices with different performance levels:

Device Performance Recommended Model Key Characteristics
High-Performance System
(Sufficient RAM, e.g., 32GB+)		 qwen3-coder:30b This is a larger parameter model, generally offering deeper and more accurate performance in code generation, understanding, and debugging. Requires more computational resources.
Mid-Range System
(Mainstream configuration, ~16GB RAM)		 qwen2.5-coder:7b Strikes a good balance between performance and resource consumption. An excellent model optimized for programming, powerful enough for most development tasks.
Low-Resource or Entry-Level Device
(e.g., 8GB RAM)		 gemma:2b Small parameter size, fast runtime, very low hardware requirements. While not as capable as larger models, it can still handle many basic code completion and explanation tasks.

How to Download the Model?
After choosing a model, we download it locally using a simple terminal command. Open your terminal (PowerShell or CMD on Windows) and use the following format:

ollama run <model-name>

For example, if you decide to use the qwen2.5-coder:7b model, type:

ollama run qwen2.5-coder:7b

The first time you execute this command, Ollama will start downloading the corresponding model files from the network. The download progress will be shown in the terminal. Once downloaded, it automatically enters an interactive chat interface, meaning the model has loaded successfully. You can press Ctrl+D to exit this chat interface. The model itself is now ready and waiting to be called.

Image: Screenshot of the ollama run qwen2.5-coder:7b command running in a terminal

Step 2: Install the “Hands and Feet” – The Claude Code Agent

Now, the “brain” is ready. Next, we need to install the “agent program” that allows this brain to get to work. This agent is responsible for understanding your natural language instructions, translating them into file reading, modification, and terminal command execution.

The installation of this agent is also done via the terminal.

  • On macOS or Linux systems, open the terminal and run:

    curl -fsSL https://claudecode.com/install.sh | bash
  • On Windows systems, open PowerShell as Administrator and run:

    irm https://claudecode.com/install.ps1 | iex

These installation scripts will automatically handle the necessary downloads and configuration. After installation, verify success with:

claude --version

If the terminal displays a version number, congratulations, the agent is installed successfully.

An Important Note: If you were previously using the official Anthropic Claude service and logged into an account, you may need to log out within the terminal to ensure the configuration smoothly switches to local mode.

Step 3: Critical Configuration – Point the Agent to the Local Brain

This is the most crucial part of the entire setup. By default, the claude command tries to connect to Anthropic’s official cloud service. We need to use environment variables to explicitly tell it: “Please talk to Ollama on your local machine.”

We need to set two core environment variables:

  1. Set the Base Connection URL: Tell the Claude Code agent where the API service is on your local machine. Ollama defaults to serving at http://localhost:11434.

    export ANTHROPIC_BASE_URL="http://localhost:11434"
  2. Provide a “Pass”: Although the local service doesn’t need a real API key, the agent program still requires this parameter to start. We just need to provide any string; the convention is to use "ollama".

    export ANTHROPIC_AUTH_TOKEN="ollama"

Additional Recommendation (Privacy Enhancement): You can also set a variable to explicitly prohibit the agent from sending any non-essential diagnostic or usage data, ensuring absolute privacy.

export CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1

Are These Settings Temporary?
Using the export command directly in the terminal sets environment variables temporarily, valid only for the current terminal session. If you want them to be permanent, you need to add these three lines to your shell configuration file (e.g., ~/.bashrc, ~/.zshrc) or the system environment variables on Windows.

Image: Screenshot of commands setting environment variables in a terminal

Step 4: Hands-On Test – Launch Your Private Programming Assistant

All components are in place. Now it’s time to see the results. Let’s launch this fully local AI programming assistant and complete a real task.

  1. Launch the Agent:
    First, navigate to any of your project directories via the terminal. Then, use the claude command to launch the agent, specifying the local model you want to use with the --model parameter.

    claude --model qwen2.5-coder:7b

    After execution, you should see the terminal interface change, displaying a welcome message from the local model, indicating a successful connection.

  2. Execute Your First Task:
    Now, you can give it instructions as if talking to a human assistant. For example, type:

    “Create a simple ‘Hello World’ static website.”

    Next, you’ll witness something amazing:

    • The Claude Code agent will analyze the files in your current directory.
    • It might generate HTML, CSS, or JavaScript files.
    • It will execute terminal commands like touch index.html, echo ‘<html>...’ > index.html, or python -m http.server.
    • All of this happens in real time on your screen, with no network requests happening in the background.

Throughout the process, you can observe file creation, content writing, and even the launch of a local server. This is a fully functional, completely private AI programming assistant at work.

Image: Screenshot of Claude Code running and interacting with a user in a terminal interface

Frequently Asked Questions (FAQ)

Q1: Is this local version of “Claude Code” exactly the same as the official Claude Code?
A: Not exactly. The official Claude Code is a proprietary product developed by Anthropic. What we’ve implemented here is a local AI coding agent with similar file operation and command execution capabilities, assembled using open-source tools (Ollama) and open-source code models. You can think of it as an open-source alternative. The core experience is similar, but the underlying “brain” (the model) is different.

Q2: How powerful does my computer need to be to run this?
A: This primarily depends on the model you choose. Smaller models like gemma:2b can run smoothly on a standard laptop with 8GB of RAM. Larger models like qwen3-coder:30b require 32GB or more RAM to run well. It’s recommended to start with a mid-range model like qwen2.5-coder:7b.

Q3: Besides programming, what else can this local assistant do?
A: Its core capabilities are determined by the underlying language model. The “Coder” series models you choose are trained on code, so they perform strongest on programming tasks. However, language models themselves also possess general knowledge Q&A, text summarization, creative writing, and other abilities. Its file operation and command execution functions are provided by the Claude Code agent program, primarily designed around development workflows.

Q4: Do I need to re-download the model and set environment variables every time I use it?
A: No. Model files are stored locally after the first download. If environment variables are configured for “permanent effect,” that also only needs to be done once. Afterward, you simply need to run claude --model <model-name> within your project folder to start the assistant.

Q5: If something goes wrong during the process, how do I troubleshoot?
A: Check the following points in order:

  1. Is Ollama running? Check the system tray or use the command ollama serve to ensure the service is started.
  2. Is the model downloaded? Try ollama list to see the list of downloaded models.
  3. Are environment variables set correctly? Run echo $ANTHROPIC_BASE_URL (Linux/Mac) or echo %ANTHROPIC_BASE_URL% (Windows) in the terminal to verify.
  4. Did you specify the correct model name? Ensure the model name after claude --model matches the one in Ollama.

Summary and Outlook

Through these four steps, you have successfully brought a powerful AI programming assistant onto your own computer. It is no longer a cloud service requiring an internet connection and charging per use, but has become a truly yours, always-available offline tool.

This localization deployment model represents a significant trend: enjoying the powerful capabilities of AI while firmly retaining control and privacy. Whether for working in restricted network environments, for security considerations regarding core code assets, or as an exploration of open-source technology by developers, this solution provides a solid and feasible path.

Now, you can confidently let your AI assistant deeply access your projects, try more complex instructions like “refactor this module,” “add tests for this function,” or “analyze the errors in this log file.” All the thinking and operations are completed inside your quietly running machine—secure, private, and free.