Deploy and Host openclaw-railway on Railway

openclaw-railway is a Docker-based Railway template that deploys OpenClaw as an internal-only service. It builds and runs via Docker, keeps the service unexposed to the public internet, and ensures the agent workspace is created and configured correctly on startup.

About Hosting

Railway builds the included Docker image and runs it as a private service (no public ingress by default). On startup, the container verifies and prepares the agent workspace (required folders, paths, and permissions) so the environment is immediately usable. You typically only need to set the required environment variables and (if you want persistence) attach a volume so the agent workspace survives restarts and redeploys.

Why Deploy

Deploy this when you want a repeatable, low-maintenance way to run OpenClaw on Railway without dealing with servers or manual provisioning. The template handles a consistent Docker runtime, avoids accidental public exposure, and bootstraps the agent workspace so agents can operate immediately with the expected directory structure and permissions.

Dependencies for

This template is intended for deployments where OpenClaw needs a predictable containerized environment and a correctly initialized agent workspace. Optional dependencies depend on what your OpenClaw setup uses (e.g., external APIs, storage, or a database). If your workflow is purely self-contained, you may not need anything beyond Railway + Docker.

Deployment Dependencies

• Railway project with permission to deploy services
• Dockerfile (included in the template) and a container-based deploy on Railway
• Environment variables required by your OpenClaw setup (keys, model provider creds, etc.)
• Workspace storage (Railway volume or equivalent) if the agent workspace must persist across restarts

Common Use Cases

• Run OpenClaw privately for internal tooling or team workflows
• Create isolated deployments per environment (dev/staging) with consistent workspaces
• Test agents safely in a containerized runtime without exposing endpoints publicly
• Deploy a repeatable baseline for experimentation where workspace setup “just works”