---
title: "OpenClaw Launch Template: Deploy a Persistent Agent Runtime in Minutes"
slug: openclaw-launch-template-deploy-a-persistent-agent-runtime-in-minutes
description: "OpenClaw is designed to run as a persistent agent service. This article explains what the OpenClaw launch template includes, how it supports Docker and Kubernetes deployments, and how teams can deploy OpenClaw inside the Yotta Labs Console.
"
author: "Yotta Labs"
date: 2026-02-19
categories: ["Products"]
canonical: https://www.yottalabs.ai/post/openclaw-launch-template-deploy-a-persistent-agent-runtime-in-minutes
---

# OpenClaw Launch Template: Deploy a Persistent Agent Runtime in Minutes

![](https://cdn.sanity.io/images/wy75wyma/production/ab33506457d05be73c5f7e091aa7fc5074d699d2-1200x627.png)

OpenClaw, previously referred to in earlier iterations as Clawdbot or Moltbot, is designed to run as a long-running agent service.

Not a one-shot job.

Not a single inference call.

A persistent execution runtime.

Inside the Yotta Labs Console, OpenClaw is available as a launch template that packages the full runtime environment into a reusable deployment profile.

Instead of building the container manually, you launch it preconfigured.





## **What the OpenClaw Launch Template Provides**

The OpenClaw container inside the Console includes:

- Python 3.10 / 3.11 runtime
- Core OpenClaw agent runtime
- System utilities
- SSH access
- Optional API endpoint
- Optional Jupyter interface
- Support for long-running agent execution

It is explicitly designed to remain active as a persistent agent service, not a temporary compute job.





## **Deployment Model**

The template is built for:

- Linux (x86_64)
- Docker / container runtime
- Kubernetes or managed GPU platforms
- Persistent volume mounting for:
  - Agent state
  - Logs
  - Artifacts and outputs

Environment variables can be configured to control:

- Agent execution mode
- Tool availability
- External service credentials
- Optional UI or API settings

This makes it compatible with production-style infrastructure environments.





## **Exposed Ports and Runtime Behavior**

Depending on configuration, the container can expose:

- 22/tcp for SSH access
- 8080/tcp for agent API
- 8888/tcp for Jupyter (optional development mode)

When launched, the runtime:

- Initializes environment variables
- Performs dependency checks
- Bootstraps the OpenClaw runtime
- Starts optional services
- Enters a long-running execution state

This aligns with persistent agent deployment patterns rather than simple inference endpoints.





## **GPU Support**

OpenClaw does not require a GPU.

However, GPU acceleration may be useful when:

- Connecting to large language model backends
- Running embedding systems
- Handling vision workloads
- Performing compute-heavy reasoning steps

CPU-only or GPU-backed deployments can be configured depending on workload needs.





## **Why This Matters**

Agent systems introduce infrastructure complexity:

- Persistent state
- Secure service exposure
- Volume management
- Runtime orchestration

The OpenClaw launch template inside the Yotta Labs Console packages those requirements into a structured deployment profile.

You are not just launching a container.

You are launching a configured agent runtime aligned with production deployment models.





## **Launch OpenClaw on Yotta Labs**

OpenClaw is available as a launch template inside the [Yotta Labs Console](https://console.yottalabs.ai/compute/templates/100).

If you are building agent-based systems and want to deploy a persistent OpenClaw runtime without manually assembling container infrastructure, you can explore the OpenClaw template directly inside the Console.





## **Final Thoughts**

OpenClaw reflects the shift toward persistent, action-oriented AI systems.

Deploying those systems requires infrastructure that supports long-running execution, container orchestration, and optional GPU scaling.

The OpenClaw launch template simplifies that process by providing a production-ready runtime environment inside the Yotta Labs Console.

For teams moving from experimentation to production, reducing deployment friction is critical.