Best Way to Use Claude Code Offline Without Internet Access

Claude Code is powerful, but you don’t always need an internet connection to get work done. Whether you’re on a flight, working in a secure facility, or just want to avoid latency, this guide shows you how to use Claude Code effectively offline.

Understanding Claude Code’s Offline Capabilities

Claude Code operates in a hybrid mode—it can function with limited connectivity, but some features require internet access while others work entirely locally. The key is knowing which components work offline and how to configure your environment accordingly.

When Claude Code initializes, it loads skills from your local filesystem. These skills are just Markdown files with YAML front matter, so they’re always available locally. The real question is whether you’re using local or remote models for inference.

Using Local Models for Offline Inference

The most important decision for offline Claude Code usage is your choice of model. Claude Code supports multiple model backends, including local models through Ollama and similar providers.

Setting Up Ollama for Local Inference

Ollama lets you run large language models locally on your machine. Here’s how to configure Claude Code to use Ollama:

# Install Ollama first
brew install ollama

# Pull a capable model for offline use
ollama pull llama3.3

# Claude Code uses the ANTHROPIC_BASE_URL and model env vars to point to Ollama
export ANTHROPIC_BASE_URL=http://localhost:11434
export ANTHROPIC_MODEL=llama3.3

Once configured, Claude Code will use your local model instead of Anthropic’s API. This means all your coding assistance happens on your machine—no network requests required.

Practical Example: Offline Code Review

Here’s a practical workflow for offline code review using local models:

# Start Claude Code in offline mode
claude

# Create a focused skill for code review that works locally
# In your skill, specify local model preferences:
---
name: Code Review
description: Perform thorough code reviews
---

You are an expert code reviewer. Analyze the provided code for:
1. Security vulnerabilities
2. Performance issues
3. Code quality problems
4. Best practices violations

Offline Skills: Your Local Knowledge Base

Skills are the heart of Claude Code’s extensibility, and they work perfectly offline since they’re stored locally. The key is building a skill library that doesn’t depend on web resources.

Creating Self-Contained Offline Skills

When writing skills for offline use, avoid referencing external URLs or APIs that require internet access. Instead, embed all necessary information directly in the skill:

---
name: Docker Commands
description: Common Docker commands for offline reference
---

# Docker Commands Reference

## Container Management

### List Running Containers
```bash
docker ps

List All Containers (Including Stopped)

docker ps -a

Start a Container

docker start <container_name>

Image Management

List Images

docker images

Remove Unused Images

docker image prune -a

This skill works entirely offline—no Docker Hub lookups or external documentation needed.

## Self-Hosted MCP Servers for Offline Tooling

Model Context Protocol (MCP) servers extend Claude Code's capabilities. For offline use, you need self-hosted servers that don't require external APIs.

### Running Local MCP Servers

Many MCP servers work offline once installed. Here's how to set up a local file server:

```bash
# Install a local filesystem MCP server
npm install -g @modelcontextprotocol/server-filesystem

# Configure Claude Code to use it
claude mcp add filesystem /path/to/your/projects

This gives Claude Code access to your local files without any network dependency.

Offline Database Tools

For database work offline, use local database servers:

# Start a local PostgreSQL instance
docker run -d -p 5432:5432 -e POSTGRES_PASSWORD=local postgres:16

# Use a local MCP server for database access
claude mcp add postgres "host=localhost port=5432 dbname=mydb"

Optimizing Your Workflow for Offline Use

Pre-Caching Essential Skills

Before going offline, prepare your skills by copying the relevant .md files into your project’s .claude/ directory (or ~/.claude/ for global availability):

# Skills are .md files — copy them into place before going offline
mkdir -p .claude
cp ~/my-skills/code-review.md .claude/
cp ~/my-skills/docker-helper.md .claude/
cp ~/my-skills/git-commands.md .claude/

# Skills are local files — no network required to use them offline

Project-Specific Offline Configurations

Create a .claude/ directory in your project with offline-ready configuration:

mkdir -p .claude/skills
mkdir -p .claude/mcp

# Create project-specific skill
echo '---
name: Project Helper
description: Project-specific assistance
---

# Project Help

This skill knows about our specific codebase.
Use this when answering questions about our project.' > .claude/skills/project.md

Offline-Friendly Project Patterns

For teams working offline, establish these patterns:

Local-first documentation: Keep all docs in the repo
Embedded examples: Include code examples in skills rather than linking
Local linters and formatters: Don’t rely on cloud-based tools
Offline package caches: Mirror npm/pip registries locally

Limitations to Understand

While Claude Code works well offline, be aware of limitations:

Model quality: Local models are generally less capable than Claude’s API
No web search: Looking up documentation online won’t work
No live updates: Skills won’t refresh until you’re back online
Limited MCP servers: Some servers require cloud APIs

Best Practices for Offline Success

Prepare before going offline: Test your setup while connected
Keep skills focused: Smaller, self-contained skills are more reliable offline
Use local models wisely: Save local model capacity for complex tasks
Document offline workflows: Create skills that document your offline procedures
Sync when back online: Regularly sync skills and configurations

Conclusion

Claude Code can be a powerful offline coding assistant with the right setup. By using local models through Ollama, building self-contained skills, and running MCP servers locally, you can maintain productivity even without internet access. The key is preparation—set up your offline environment while connected, and you’ll never be stuck without your AI coding assistant.

Start by installing Ollama and pulling a model, then create a few offline-ready skills for your common tasks. Once you’ve established your offline workflow, you’ll wonder how you ever worked without it.

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