Running Claude Code in a Docker container isn’t just a development curiosity — it’s increasingly the recommended way to work with AI coding agents in a way that’s both powerful and secure. Docker published an official guide this week walking through the full workflow: local model execution with Docker Model Runner, real-world tool connections via MCP servers, and securing agent autonomy inside isolated sandboxes.

This guide synthesizes that walkthrough into a practical tutorial for developers who want to get running quickly.

Why Run Claude Code in Docker?

Before the how-to, a quick why:

  • Isolation: Claude Code can execute code, run commands, and interact with your filesystem. Running it in a container limits what it can actually touch.
  • Local models: Docker Model Runner lets you run inference locally, keeping your code and context off Anthropic’s servers entirely.
  • Reproducibility: Container-based setups are portable across machines and team members.
  • MCP integration: Docker’s networking model makes it easy to run MCP servers as companion containers that Claude Code can connect to.

Prerequisites

  • Docker Desktop (v4.35+) or Docker Engine + Docker Compose
  • A machine with at least 16GB RAM for meaningful local model inference (32GB+ recommended)
  • Claude Code CLI installed (npm install -g @anthropic-ai/claude-code or via the official installer)

Step 1: Enable Docker Model Runner

Docker Model Runner is Docker’s local model execution runtime, available in Docker Desktop 4.35+. To enable it:

# Check Docker Desktop version
docker --version

# Enable Docker Model Runner via CLI
docker model run --help

If docker model is not available, update Docker Desktop to the latest version.

Pull a local model (Llama 3.2 3B is a good starting point for constrained hardware; use a larger model if you have the RAM):

docker model pull llama3.2:3b

For production-quality code assistance, use a larger model:

docker model pull llama3.1:70b  # Requires ~40GB RAM

Step 2: Configure Claude Code to Use a Local Model

Claude Code supports OpenAI-compatible API endpoints. Docker Model Runner exposes one at http://localhost:12434/engines/v1.

Create a .claude-code.json configuration file:

{
  "model": "llama3.1:70b",
  "api_base": "http://localhost:12434/engines/v1",
  "api_key": "docker-model-runner"
}

Start Claude Code with this configuration:

claude-code --config .claude-code.json

Claude Code will now route inference through Docker Model Runner instead of Anthropic’s API. All processing stays local.

Step 3: Set Up MCP Servers as Companion Containers

MCP (Model Context Protocol) servers let Claude Code interact with real-world systems — filesystems, databases, APIs, browsers. Running them as Docker containers keeps them isolated and manageable.

Create a docker-compose.yml:

version: "3.9"
services:
  mcp-filesystem:
    image: anthropics/mcp-server-filesystem:latest
    volumes:
      - ./workspace:/workspace:rw
    environment:
      - MCP_ROOT=/workspace

  mcp-postgres:
    image: anthropics/mcp-server-postgres:latest
    environment:
      - DATABASE_URL=postgresql://user:pass@db:5432/mydb
    depends_on:
      - db

  db:
    image: postgres:16
    environment:
      - POSTGRES_USER=user
      - POSTGRES_PASSWORD=pass
      - POSTGRES_DB=mydb

Start the MCP servers:

docker compose up -d

Configure Claude Code to connect to them in your .claude-code.json:

{
  "mcp_servers": [
    {
      "name": "filesystem",
      "url": "http://localhost:3001"
    },
    {
      "name": "postgres",
      "url": "http://localhost:3002"
    }
  ]
}

Step 4: Run Claude Code in a Sandboxed Container

For maximum isolation, run Claude Code itself inside a container with limited filesystem access:

FROM node:22-alpine

RUN npm install -g @anthropic-ai/claude-code

WORKDIR /workspace

# Only mount the workspace — no access to host home directory
VOLUME ["/workspace"]

ENTRYPOINT ["claude-code"]

Build and run:

docker build -t claude-code-sandbox .

docker run -it \
  --rm \
  -v $(pwd)/workspace:/workspace \
  --network host \
  claude-code-sandbox \
  --config /workspace/.claude-code.json

The --network host flag allows Claude Code to reach the MCP servers running on localhost. If you want stricter network isolation, use Docker networking to create a dedicated network and connect all containers to it instead.

Step 5: Verify the Setup

Once everything is running, test the integration:

# Inside the Claude Code sandbox, ask it to use an MCP tool
"List the files in /workspace and read the contents of any .md files"

If Claude Code successfully reads files via the MCP filesystem server, your setup is working.

For the database connection:

"Connect to the postgres database and describe the schema of the mydb database"

Security Considerations

The containerized setup dramatically limits what can go wrong, but a few things to keep in mind:

  • Volume mounts: Only mount directories you’re willing to let Claude Code modify. Never mount your home directory or system directories.
  • MCP server permissions: Configure MCP servers with least-privilege — a filesystem server that only sees ./workspace can’t accidentally delete your dotfiles.
  • Local model vs. API: With Docker Model Runner, your code and context stay local. If you use the Anthropic API backend instead, remember that your code is sent to Anthropic’s servers.
  • Network access: If Claude Code has network access (via --network host or an attached Docker network), it can make outbound HTTP requests. Restrict this if your use case doesn’t require it.

This setup gives you a powerful, reproducible, and meaningfully isolated environment for Claude Code — exactly what production-quality agentic coding demands.

Sources

  1. Run Claude Code with Docker — Docker Official Blog

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