OpenClaw in Google Antigravity vs GCP: Pros, Cons, and Deployment Guide

Introduction

OpenClaw is a popular open-source AI agent framework (formerly known as Clawdbot/Moltbot) that acts as a “personal AI assistant” with direct system access . It can automate tasks like coding, web browsing, emailing, and more by plugging into large language models (LLMs) and various tools on your system  . Given its autonomy and power, OpenClaw has seen explosive growth – surpassing 100,000 GitHub stars in months  – but also raised serious security concerns (e.g. plaintext credentials, risky tool use)  .

Google Antigravity is a new agentic development IDE from Google (currently in free preview) designed for AI-driven coding workflows . It combines an AI-powered code editor with an agent manager to let autonomous coding agents plan and execute tasks across editor, terminal, and browser surfaces  . Antigravity is powered by Google’s models (Gemini 3, etc.) and allows developers to spawn and observe multiple agents in a controlled environment . In essence, Antigravity provides an IDE “home base” for agents, with generous access to Google’s AI backends during the preview  .

This guide will analyze the advantages and disadvantages of using OpenClaw within Google’s Antigravity IDE vs. on Google Cloud Platform (GCP). We’ll compare key factors like compatibility, performance, scalability, and usability for non-developers, and note potential limitations in each context. A comparative summary table is included for quick reference. Finally, we provide a step-by-step guide to the safest, most user-friendly way to deploy OpenClaw on GCP, prioritizing ease of setup and security for those who may not be cloud experts.

Using OpenClaw in Antigravity vs. on GCP – A Comparison

Compatibility and Integration
• Antigravity: Integration with Models and Tools. OpenClaw can interface with Antigravity primarily by using Google’s LLMs (like Gemini) as its AI backend. During OpenClaw’s setup, users can select “Google Antigravity OAuth” as the model provider, allowing OpenClaw to connect to Gemini via the user’s Google account . In practice, this means OpenClaw can leverage cutting-edge models (Gemini 3 Pro/Flash, etc.) available in Antigravity’s platform . This integration lets OpenClaw run with Google’s AI without requiring an OpenAI or Anthropic API key, which is a big plus if you have access to Antigravity’s free quotas. However, the integration is somewhat unofficial – essentially treating Antigravity’s model endpoint as just another model provider. There have been reports of compatibility hiccups; for example, changes in Antigravity’s API have caused errors (“This version of Antigravity is no longer supported”) requiring community-patched forks of OpenClaw to fix . Also, using Gemini via OpenClaw may violate terms if abused – one user even had their Antigravity account temporarily banned after heavy use via OpenClaw . In short, basic compatibility is there (OpenClaw’s author clearly anticipated Antigravity support), but it’s not as smooth or officially supported as using OpenClaw with its native APIs. OpenClaw itself doesn’t run “inside” Antigravity – you still install OpenClaw on a machine (your local PC or a server) – so deeper integration (like Antigravity’s GUI for agent orchestration) isn’t automatically available. You’ll primarily be using Antigravity’s AI brain with OpenClaw, rather than its full agent workspace tools.
• GCP: Flexible Self-Hosting. On Google Cloud Platform, OpenClaw is fully compatible with standard compute services. Since OpenClaw is essentially a Node.js application (with Docker support), you can run it on a Compute Engine VM or container with no special modifications. GCP’s infrastructure is “perfectly suited” for hosting OpenClaw instances . In particular, a Linux VM on Compute Engine can host OpenClaw similarly to any VPS setup – you have full control to install Node or use the official Docker image . OpenClaw supports using Vertex AI (Google’s cloud ML) as a model provider as well, which means on GCP you could configure it to use your own Vertex GPT-3.5/4 or PaLM APIs instead of external services . There’s also an ecosystem of OpenClaw “skills” (plugins) that integrate with GCP services – for example, community skill packs exist to let OpenClaw run gcloud commands or manage GCP resources . In general, compatibility on GCP is broad and direct: you’re essentially self-hosting OpenClaw on cloud VMs as you would on-prem, with no artificial restrictions. The downside is that Google Cloud hasn’t (as of early 2026) provided a one-click OpenClaw service . Unlike some other providers (DigitalOcean, Alibaba, etc.) who rolled out one-click installers or templates  , on GCP you must set it up manually (or via community scripts). In summary, OpenClaw runs reliably on GCP’s VMs or Kubernetes, and it can integrate with GCP’s AI and services – but you as the user have to handle that integration and installation yourself.

Performance and Scalability
• Antigravity: Performance tied to Preview Environment. When using OpenClaw with Antigravity, the heavy LLM computation is done by Google’s cloud (Gemini, etc.), so OpenClaw’s own performance mainly depends on how quickly it can communicate with the Antigravity service. Latency is generally low (Google’s infrastructure is fast), but some users have noted that Antigravity’s platform itself can be slow or unstable under load. Early adopters reported serious performance issues – slow agent responses and frequent errors – in the Antigravity IDE . This suggests that while the underlying models are powerful, the IDE environment might throttle or queue agent tasks, especially when many users are on the free preview. OpenClaw running via Antigravity might also be limited by any usage quotas (rate limits) imposed on Antigravity’s API. On the plus side, OpenClaw doesn’t consume your local CPU for AI inference in this setup – Gemini is doing the thinking – so even a modest machine can run the OpenClaw runtime fine. However, Antigravity itself isn’t designed for high-throughput or scaled deployments; it’s a development tool. You likely can’t run many concurrent OpenClaw agents via one Antigravity session – it’s more for interactive, single-user scenarios. Scalability is therefore limited. If you needed OpenClaw to handle numerous tasks or users at once, Antigravity’s IDE would not be the right venue. It’s best suited for individual productivity or prototyping. Additionally, if Antigravity’s web service goes down or your account loses access, your OpenClaw agent loses its “brain.” In summary, performance of the LLM is excellent (Gemini is highly capable), but the overall responsiveness may suffer from the preview IDE’s constraints. There’s minimal ability to scale out beyond what the Antigravity interface allows – you can spawn multiple agents in Antigravity, but it’s meant for orchestrating a few coding agents, not running a persistent fleet of personal assistants.
• GCP: Scalable and Configurable. Running OpenClaw on GCP gives you full control of resources, so performance can be tuned to your needs. The OpenClaw runtime itself is lightweight – most of the heavy “thinking” still happens via API calls to Claude, GPT, or other models  . Even a micro VM (1 vCPU, 1GB RAM) can surprisingly handle the agent’s coordination tasks because the VM mostly waits on network responses from the LLM provider . (In fact, GCP’s e2-micro free tier instance with burstable 2 vCPU often suffices for a trial run .) For better performance, you can simply choose a larger machine type – e.g. an e2-medium (2 vCPU, 4GB) is a good starting point to ensure OpenClaw has enough memory for tools like headless browsers, etc . GCP allows vertical scaling (bigger VMs with more CPU/RAM, or GPUs if you decide to run local models) and horizontal scaling (multiple instances or containers). For example, an enterprise could run several OpenClaw agents on a GKE Kubernetes cluster, each serving a different team – something impossible under the single-user Antigravity IDE. Network performance on GCP is excellent and you can deploy your VM in a region close to you for low latency . Scalability is only limited by your budget and management overhead. You could even integrate with GCP’s load balancers or Pub/Sub if you tried to distribute tasks among agents. In practice, most users just need one instance, but GCP gives the headroom to grow. Another aspect is persistent state: OpenClaw uses a local SQLite DB to store conversation history and context, which improves its long-term memory . On GCP, you should use a Persistent Disk for your VM (the default) so that this state isn’t lost on reboot. You can snapshot disks or use Cloud Storage for backups of this data – enabling reliability and easy recovery that scale with your needs. The main performance bottleneck on GCP might be the cost and rate limits of the LLM APIs (OpenAI/Anthropic) that your agent calls, not the VM itself. One must watch out for network egress costs too: heavy use of external APIs with large context windows can chew through the free 100GB egress quickly . In summary, GCP offers robust performance (you aren’t stuck waiting for a free service) and true scalability for OpenClaw, from a tiny free-tier VM up to a distributed architecture – but you as the user have to configure and pay for those resources appropriately.

Usability and Accessibility for Non-Developers
• Antigravity: Built for Developers (or Ambitious Hobbyists). By nature, Antigravity is an IDE – a development environment – so it assumes a certain comfort with coding workflows. Using OpenClaw within Antigravity isn’t a plug-and-play “app” experience; it likely requires fiddling with configurations and understanding both the IDE and OpenClaw’s setup. For a non-developer, the learning curve can be steep. On one hand, Antigravity provides a slick UI for agent development that could guide semi-technical users through creating and monitoring agents. On the other hand, to even use OpenClaw with it, you must install OpenClaw (likely via command line) and then perform an OAuth flow to tie into Antigravity’s models . The Antigravity interface itself, while powerful, can be overwhelming – it has an editor view, terminals, an agent manager, etc., which might confuse someone who isn’t a programmer. Non-developers might also lack access to Antigravity in the first place if it’s restricted or requires joining a preview. In terms of day-to-day use, Antigravity doesn’t provide a friendly chatbot UI for OpenClaw; you would still interact with OpenClaw through its normal channels (like a messaging app or its Terminal UI) even if the underlying model is Gemini. Essentially, Antigravity helps with coding and orchestrating agents, but it’s not designed as a consumer-facing app platform. Another consideration is that troubleshooting issues might be harder – if something goes wrong with the Gemini integration, a non-dev user might not know whether the issue lies with OpenClaw or Antigravity. To Google’s credit, Antigravity emphasizes trust and verification (it encourages agents to show their work and verify results) , which could mitigate some risky behavior for users. However, those features mainly apply to agents built within Antigravity’s system. If you’re a non-developer just trying to “get an AI assistant working,” running OpenClaw via Antigravity is not the most straightforward path. It’s more suited to developers experimenting with agent-driven coding. In short, usability for novices is limited: Antigravity is powerful but complex, and using OpenClaw with it demands technical setup. Most non-dev users would find it easier to interact with OpenClaw through its chat interface on Telegram/WhatsApp once it’s running somewhere – an IDE doesn’t add much value for them.
• GCP: DIY Hosting with Some Help – Steeper Setup, Smooth Usage. Deploying OpenClaw on GCP will require some technical steps upfront, which can be challenging for non-developers, but after setup the usage can be made quite friendly. There is no avoiding that cloud deployment is more complex than a local install – new users face pitfalls with creating projects, handling billing, setting up VM instances, and configuring networking  . The process involves using the GCP Console or CLI, dealing with SSH or startup scripts, and understanding basic Linux admin tasks. This can be daunting if you’ve never launched a cloud VM before. In fact, guides explicitly warn that GCP’s IAM and VPC settings can trip up beginners (e.g. forgetting to allow firewall ports, leading to “why can’t I connect” confusion)  . On the bright side, the community has produced step-by-step tutorials and even one-line install scripts to simplify this. The official OpenClaw website offers a one-line installer that “works everywhere” by installing Node.js and all dependencies for you  . Using such a script on a GCP VM greatly lowers the difficulty – it automates the setup so you don’t have to manually configure each component. Third-party resources like OpenClaw Experts and others provide GCP-specific walkthroughs (creating a VM, running Docker or the install script, etc.)  . For a non-developer willing to follow instructions, it’s feasible to get OpenClaw running on GCP in under an hour by copying recommended settings. Once the OpenClaw instance is running, using it can be very user-friendly: OpenClaw has a web Control UI and a chat interface. After installation, you can access a browser-based dashboard (the “Control UI”) that shows the agent’s status and lets you issue commands or view output . More commonly, you’d connect OpenClaw to a messaging app like Telegram, which gives a simple chat interface on your phone to converse with your AI agent  . For a non-developer end-user, interacting via Telegram or WhatsApp feels natural – it’s like texting a smart assistant. All the complexity of what the agent is doing on the VM is hidden behind conversational commands (“/start”, asking questions, etc.). In terms of ongoing maintenance, non-technical users might struggle with keeping the VM updated or secure (we’ll address safety in the next section). Anecdotally, journalists and tech bloggers have managed to install and use OpenClaw on cloud VMs but noted that configuration and keeping it running can be a headache if you’re not experienced . If you want to avoid command-line entirely, there are emerging “managed OpenClaw” services (like OpenClawd.ai, V2 Cloud hosting, etc.) that for a fee will handle deployment and give you a one-click web console  . On GCP itself, no such fully-managed service exists yet, so you are responsible for the VM. The good news is that once it’s set up properly, daily usage is quite easy – you chat with it, or use the web UI – and you don’t need to be a developer to benefit from its automations (many non-programmers are already using OpenClaw as a personal assistant after initial help with setup  ). To summarize, GCP deployment has a higher upfront complexity for non-devs, but with guided steps it’s doable; after that, OpenClaw’s user-facing experience (chat/web UI) is quite accessible to anyone.

Security and Limitations
• Antigravity: Sandboxed Model, Less System Access by Default. One advantage of using OpenClaw with Antigravity is that the LLM (Gemini) is hosted by Google and does not run on your machine. This means the model itself can’t directly abuse your system – it only can do what OpenClaw’s runtime allows via tool calls. However, remember that OpenClaw’s runtime still executes on your computer (or wherever you installed it) even if its “brain” is Gemini. So OpenClaw will happily carry out destructive commands if instructed, regardless of using Antigravity or not. The difference is that, when following Google’s lead, Antigravity’s built-in agents likely have more guardrails. With OpenClaw, you essentially remove those guardrails by giving the model system-level tools. In Antigravity, Google’s agent manager might restrict certain actions or require verification steps for code changes . OpenClaw doesn’t have such fine-grained enterprise safety – it’s “insecure by default” according to Gartner’s analysis  . Running OpenClaw via Antigravity doesn’t change that fundamental risk; it only changes the model source. In fact, Gartner explicitly warns enterprises to block OpenClaw and not be lulled by the impressive demo, because it ships without authentication and stores secrets in plaintext  . In the Antigravity scenario, some limitations act as double-edged swords: since Antigravity is not a production environment, you are less likely to connect OpenClaw to sensitive corporate data (it’s a dev tool, not plugged into your production servers), which could be a good thing. But if you do connect personal accounts (email, etc.), those credentials are still at risk if the OpenClaw agent misbehaves. Antigravity also likely times out or stops if you close the IDE, meaning OpenClaw wouldn’t run 24/7 unless you keep it open. This limits potential damage (the agent isn’t running constantly unless you explicitly keep it active), but also limits usefulness for things like background monitoring. Another limitation is you’re subject to Google’s platform: if they update Antigravity or change the API, your OpenClaw might break (as seen by people needing patched forks to adapt to new Gemini auth flows) . In summary, Antigravity doesn’t magically make OpenClaw safe – you still must trust an autonomous agent with system access, which carries risk. The best practice, as always, is to run it in a non-critical, isolated environment and avoid giving it credentials or permissions you can’t afford to lose. Antigravity itself is ephemeral and not meant for production, so consider it a safer playground for experimentation rather than a secure deployment. Keep in mind that any files OpenClaw touches on your machine (through Antigravity or not) could be altered or deleted if instructions go awry. You’ll need to supervise and set boundaries just as you would on GCP – the difference is simply that Antigravity’s limitations somewhat constrain how and where you might use OpenClaw.
• GCP: Full Responsibility for Security (Isolation Strongly Advised). Deploying OpenClaw on GCP gives you the power – and the responsibility – to secure it. Security is a critical concern when running OpenClaw in any cloud or local environment. You are essentially giving an AI the keys to a server. Gartner’s blunt warning applies here: OpenClaw “is not enterprise software” and should only be run in isolated, non-production VMs with throwaway credentials . The good news is GCP makes it easy to create an isolated VM just for OpenClaw. You should never run OpenClaw on a server that also holds sensitive data or other critical services. Spin up a dedicated Compute Engine VM (and consider using a separate GCP project for it) . This way, even if the agent does something crazy, it’s contained. Use GCP’s firewall to restrict access – for instance, only open the minimum required ports (perhaps 22 for SSH, maybe 80/443 if you need web access) and keep others closed  . When setting up the VM, run OpenClaw under a normal user account (not as root) , so that even the commands it executes have limited privileges. GCP’s Identity and Access Management (IAM) can be leveraged to give the VM a very scoped role – e.g., if you integrate with other GCP services or APIs, use a service account with only the necessary permissions  . Also, avoid storing any sensitive API keys or secrets on the VM in plain text; you can use Google Secret Manager to hold those and fetch them at runtime . One big risk in self-hosting is exposure of the interface: by default, OpenClaw’s web UI has no login – if you expose port 3000 or 80 to the internet, anyone could connect and potentially interact with your agent. The safest approach is actually not to expose the web UI at all (you don’t truly need it once Telegram/Discord is connected). If you do want browser access, set up HTTPS and some authentication. For example, you could put Nginx or Caddy as a reverse proxy in front of OpenClaw, with SSL (Let’s Encrypt) and basic auth enabled  . GCP’s load balancer with IAP (Identity-Aware Proxy) is another enterprise-grade way to require Google login to access the site (though that’s overkill for most). In terms of limitations on GCP, these mostly come from the agent itself and cost management. OpenClaw can rack up significant API costs – some users report hundreds of dollars in Claude/OpenAI bills per month with active usage . Running it on GCP adds cloud compute costs on top. Be sure to set budget alerts on your GCP project  and monitor for unexpected usage (if the agent goes into a loop, it could continuously hit an API). Reliability of the deployment is in your hands: you should implement backups for the SQLite database (so you don’t lose memory)  and plan for updates (OpenClaw is evolving rapidly with frequent fixes). Regularly update the OpenClaw software to get security patches – e.g., a recent update fixed a critical one-click RCE vulnerability . If you’re not attentive to these, your instance could be left vulnerable. Despite these concerns, a properly locked-down VM on GCP significantly reduces risk compared to running OpenClaw on your personal PC. If something goes wrong, you can delete the VM and start fresh. Your personal files and networks stay untouched. This isolation is why many view cloud deployment as the sensible way to use OpenClaw despite the hassle  . To summarize, on GCP you must actively secure and monitor your OpenClaw instance – use the principle of least privilege, network isolation, and never assume the agent won’t make a mistake. With those precautions, GCP can be one of the safer ways to enjoy OpenClaw’s benefits (certainly safer than running it on a work laptop with all your data!). Always treat the agent as if it could misinterpret a command and “rm -rf” the wrong directory – and set up your environment such that even if it tries, the blast radius is minimal  .

Pros and Cons Summary Table

To recap the comparison, below is a table highlighting key pros and cons of using OpenClaw in Google Antigravity vs. on Google Cloud Platform:

Environment Pros (Advantages) Cons (Disadvantages)
OpenClaw in Google Antigravity • Seamless access to Google’s AI models (Gemini 3, etc.) via Antigravity OAuth, enabling powerful code-centric LLM capabilities with no API costs  .• Integrated agent development tools – Antigravity provides an IDE with agent orchestration and verification features, useful for coding-related tasks and ensuring agent output is reviewed for trust  .• No infrastructure setup – you don’t need to manage servers or cloud deployments; Antigravity is a hosted platform, simplifying the compute aspect (ideal for quick experimentation).• Google-backed environment – the agent runs in a context designed by Google, which may offer some guardrails (e.g. context on verification, easier integration with Google services through the IDE). • Complex setup / limited support – OpenClaw’s integration with Antigravity is unofficial and can break with updates . Requires technical steps (installing OpenClaw locally and linking accounts) not trivial for novices.• Performance constraints – Antigravity (in preview) can be slow or error-prone under load . Agents may run sluggishly and you’re subject to preview rate limits and outages (mixed reliability).• Not scalable or persistent – Antigravity is built for single-user dev sessions, not 24/7 agent operation. It’s not suitable for multiple concurrent users or long-running autonomous tasks (closing the IDE or losing connection stops the agent).• Developer-oriented – The IDE environment is overwhelming for non-developers. There’s no simple chat UI in Antigravity for OpenClaw; you still interact via CLI or messaging apps, so the IDE adds little for a regular end-user.• Potential ToS issues – Using Antigravity’s free model access for an autonomous agent could violate terms if abused. Heavy use might result in account flags or bans .
OpenClaw on Google Cloud Platform (GCP) • Full control & flexibility – You choose the VM specs, region, and can configure the environment to suit your needs (e.g. allocate more CPU/RAM for heavy tasks, attach GPUs or large disks, etc.). No artificial limits on usage beyond your own quotas .• Scalability – Can support multiple agents or instances. Easy to upgrade resources or deploy additional instances for load. Suitable for continuous 24/7 operation and multi-user access if needed (you control how it’s exposed).• Better isolation – Running on a dedicated cloud VM keeps the agent away from your personal machine and network. This sandboxing contains risks; Gartner recommends this isolation as essential for OpenClaw .• Integration with GCP services – Can utilize Vertex AI for models, Cloud Storage for backups, Secret Manager for credentials, etc. OpenClaw can be configured to use GCP’s AI and other cloud APIs securely  .• Persistent and autonomous – The agent can run continuously on a server, performing background tasks (cron-like scheduling, monitoring, etc.) without needing an active user session, which unlocks more “assistant” use cases (true 24/7 personal AI)  . • More complex setup – Requires cloud knowledge: creating a project, launching a VM, SSHing, installing software, and securing it. Common pitfalls (firewall rules, IAM, etc.) can frustrate inexperienced users  . No one-click installer from Google, so setup is manual (though community guides help).• Maintenance burden – You are responsible for updates, security patches, uptime, and cost management. Configuring HTTPS, backups, and monitoring requires additional effort (or expertise). If something breaks, you must troubleshoot the Linux server or OpenClaw app itself .• Cost – Running a VM 24/7 and making frequent API calls will incur costs. Even a small instance plus moderate Claude/ChatGPT usage can add up (e.g. ~$0.50 per task as some estimate) . Without careful monitoring and use of free tiers/credits, you could get unexpected bills .• Security risks if misconfigured – An improperly secured instance could be compromised (especially since OpenClaw had known vulnerabilities ). Exposing its interface or leaving credentials on the VM can lead to serious breaches  . Non-experts might make mistakes in this area, so following best practices is critical (least privilege, etc.).• Learning curve for non-devs – While using the agent via chat is easy, the process of deploying it on GCP might be too technical for some. They may require step-by-step guidance or third-party assistance to get started, which could be a barrier.

Table: Key advantages and disadvantages of using OpenClaw within Google’s Antigravity IDE vs. deploying it on Google Cloud Platform. (Antigravity is great for quick access to Google’s AI and development tools, but is limited in stability and scope; GCP offers power and scalability, but demands more setup and responsibility.)    

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Deploying OpenClaw on GCP: Safe and User-Friendly Installation Guide

If you decide that running OpenClaw on Google Cloud Platform is the right choice (for the control, scalability, and isolation it offers), the following guidance will help you set it up as safely and easily as possible. This section focuses on a beginner-friendly approach to installation and usage on GCP, emphasizing minimal technical fuss and strong security practices. We’ll cover a step-by-step setup on a Compute Engine virtual machine, options for a graphical interface or chat control, automation tips, and managed service alternatives to simplify maintenance.

Overview: The easiest way for most users to deploy OpenClaw on GCP is to create a single VM (virtual machine) on Compute Engine and run OpenClaw there, using the official one-line installer or a Docker container. This provides a persistent “server” for your AI agent. You can then interact with OpenClaw through a web UI or (more securely) via messaging apps from your phone. We’ll walk through the process:

Step 1: Set Up a Google Cloud Project and VM
1. Create a GCP Project & Enable Compute: If you haven’t already, create a new project in the Google Cloud Console for this deployment (e.g. “OpenClaw-Assistant”). Make sure billing is enabled (new accounts get $300 free credit which is plenty for testing). Enable the Compute Engine API for your project . It’s a good idea to use a dedicated project just for OpenClaw so you can apply separate permissions and monitor costs easily .
2. Launch a Compute Engine VM: Navigate to Compute Engine > VM Instances and click “Create Instance.” For a smooth experience, choose an Ubuntu Linux 22.04 LTS image (a common, supported environment). Select a machine type – for starters an e2-medium (2 vCPU, 4 GB RAM) is recommended  , as OpenClaw can use around 1–2 GB RAM when active. (You can use an e2-micro for very light usage or testing, since the heavy AI work is offloaded , but it might struggle if you load many skills or run browser automation.) Under Firewall, check the boxes to “Allow HTTP traffic” and “Allow HTTPS traffic”  – this will automatically create firewall rules to permit web access to your agent on ports 80/443, which you’ll need for the web UI or for messaging webhooks. Choose a region close to you for low latency (e.g. us-central1 or others near your location) . Finally, give the VM a name (e.g. “openclaw-vm”) and click Create.
• Security tip: Consider setting up an SSH key for the VM or use the Google Cloud Shell SSH button (which is one-click). The VM’s default network settings will block all ports except SSH unless you allowed HTTP/HTTPS as above . So initially, it’s locked down – we will open specific ports in later steps as needed. Using a brand new VM ensures a clean environment for OpenClaw, reducing the chance of interference with other processes.

Step 2: Install OpenClaw on the VM

Once your VM is running, you’ll install OpenClaw itself. You have two primary options:
• Option A: Use the One-Line Installer (easiest). OpenClaw provides a convenient installation script that sets up everything for you (installs Node.js, the OpenClaw package, etc.). To use it, connect to your VM via SSH. In the Cloud Console, you can hit the “SSH” button next to your VM instance to open a browser-based terminal into the VM . Once you have a shell on the VM, run the following command:

curl -fsSL https://openclaw.ai/install.sh | bash

This one-liner will download and execute the official install script. It detects your OS (Ubuntu) and installs all prerequisites automatically  . It may take a few minutes as it installs Node.js and then the OpenClaw npm package. You might be prompted for confirmation during the script – follow any on-screen instructions. After completion, OpenClaw will be installed globally on the system (or under your user). The script might even launch the initial OpenClaw configuration TUI (Terminal User Interface) automatically. If not, you can start it by running the command openclaw or openclaw launch.
Using the one-line installer is highly recommended for non-developers, because it removes the need to manually configure Node or Docker. It’s essentially “OpenClaw in one step.” The script is designed to work on Linux and will get the latest stable version for you .

• Option B: Use Docker (containerized deployment). If you’re familiar with Docker or prefer an isolated container, you can run OpenClaw in Docker. First, on the VM install Docker:

sudo apt update && sudo apt install -y docker.io docker-compose

(On a new Ubuntu VM, this will install Docker Engine.) Then add your user to the “docker” group: sudo usermod -aG docker $USER and re-login or run newgrp docker  . Now you can pull and run the OpenClaw image. The OpenClaw Experts guide suggests:

docker pull openclaw/openclaw:latest

docker run -d --name openclaw --restart unless-stopped \
  -p 80:3000 -e OPENCLAW_API_KEY=<your-api-key> openclaw/openclaw:latest

This will start OpenClaw in a detached container, mapping port 3000 (the agent’s web UI/API port) to port 80 on the VM, and set an environment variable like an API key if needed . (If you don’t have an API key yet or want to configure via the TUI, you can omit the -e and do setup interactively later.) Using --restart unless-stopped ensures the container (and your agent) restarts automatically on VM reboot or if it crashes. Docker is nice because it encapsulates all dependencies, but note that you might need to bind mount a volume if you want to preserve data (the container’s SQLite DB) across restarts. For simplicity, you could just use the host filesystem (the default in this run command will keep the DB inside the container – if you destroy the container, you’d lose memory). For beginners, the one-line installer (Option A) might be simpler to grok, but Docker (Option B) is a solid choice for long-term maintainability.

Choose one of the above methods. When done, verify that OpenClaw is running. If using the script/TUI, you should see the OpenClaw interface or prompts in your SSH terminal. If using Docker, you can run docker ps to ensure the container is up, and maybe curl http://localhost inside the VM to check it responds (it should return some HTML or a message).

Step 3: Initial Configuration and Onboarding

OpenClaw has an interactive onboarding process that runs on first launch (either in the Terminal UI or via the Control UI). This is where you’ll set up the AI model, communication interface, and any initial skills. Here’s how to handle it for a smooth start:
• Model Setup: OpenClaw will ask which model provider to use. You can use OpenAI (GPT-4/GPT-3.5), Anthropic Claude, Google Gemini via Antigravity, or even local models like Ollama. Since we’re focusing on GCP deployment, you might choose the provider you have credentials for. For example, if you have an OpenAI API key, select “OpenAI” and paste your key when prompted. If you have an Anthropic Claude token, choose that. What about Vertex AI? – If you want to use Google’s Vertex AI models (like PaLM APIs), OpenClaw supports it, but you should have a service account JSON ready. The simplest route for beginners is often OpenAI or Claude (as many have those keys). If you did use the Antigravity/Gemini option, it will open a browser for OAuth – you can actually do this by copying a URL and pasting it in your local browser (since the VM likely has no GUI). Given this is a GCP deploy, let’s assume you pick a straightforward API model like Claude or GPT to start. Note: If you skip model setup in the wizard (“Skip for now”), OpenClaw will let you configure it later. But the agent won’t be able to do much until a model is connected. Provide whatever API keys or auth info is needed during this step (the TUI will prompt you). This is a one-time thing.
• Communication Interface: Next, OpenClaw will ask how you want to chat with your bot. It supports Telegram, WhatsApp, Discord, iMessage, Slack, etc.  . A very popular choice is Telegram, because it’s easy to set up a bot and has a nice UI. To use Telegram: choose that option, then follow the prompts. Typically, you’ll talk to the @BotFather on Telegram to create a new bot (sending /newbot and following instructions to get a bot token) . Once BotFather gives you the token, paste it into the OpenClaw prompt in the VM . OpenClaw will connect and finalize the link. (Make sure to type /start in your new bot chat on Telegram to activate it .) If done right, OpenClaw’s console will confirm the bot is linked. Alternative: If you prefer, you could use the web Control UI at this point instead – OpenClaw shows a local URL (something like http://127.0.0.1:3000) in the console . Since we mapped port 80, you can actually go to http://<VM External IP>/ in your browser to see the Control UI dashboard (assuming you allowed HTTP firewall). This UI can also be used to finish setup, but it might not be fully secure externally yet (we address that soon). For onboarding, using the TUI in SSH or the Telegram method is fine.
• Skills and API Keys: OpenClaw may prompt you to configure “skills” (plugins) and additional API keys during onboarding  . For your first time, it’s okay to skip optional skills – you can always install them later. The wizard might list some popular skills (email, web search, etc.); you can press “Skip for now” to proceed without installing any . Similarly, it may ask for API keys for services like Google APIs, OpenWeather, etc., to enable certain skills. If you have them handy, you can enter them, but otherwise say “No” or skip – the agent will simply not be able to use those specific tools until configured . Keeping it minimal initially is less overwhelming.
• Persona Setup: Finally, OpenClaw will “hatch” the bot, asking you a few questions to personalize it – e.g. what is the bot’s name, what should it call you, any personality profile you want  . This is straightforward: you might name it “Claudia” or “Assistant” and give your name or nickname. It will then generate a greeting confirming its identity and readiness . Congrats – your OpenClaw instance is live!

At this point, you should be able to go to your Telegram chat with the bot (or whichever interface you set) and have a conversation. For example, saying “Hello” or asking “What can you do?” should elicit a response from OpenClaw. The Codecademy tutorial shows that once connected, you can chat naturally and issue commands via chat like searching the web or managing files, and the agent will comply  .

Step 4: Secure Access (HTTPS and Auth)

Now that the functionality is in place, it’s important to secure the setup for ongoing use, especially if you plan to use the web UI or leave the agent running continuously. A few sub-steps here:
• Limit Exposure: If you are primarily using Telegram or another chat app to interact with OpenClaw, you actually don’t need to expose any web port to the internet. The OpenClaw bot connects out to Telegram’s API (polling or webhook), and that’s how it gets commands. In this case, you could even remove the firewall rule for port 80/443 and only keep port 22 (SSH) open. This is the safest arrangement: no one can directly hit your agent’s HTTP endpoint if it’s closed off. Consider this approach if security is a top priority and you don’t require the web dashboard.
• Enable HTTPS (if using Web UI): If you do want to use the web Control UI remotely (say, to monitor or input commands from a browser), set up HTTPS. You have two main options on GCP:
1. Use a Reverse Proxy on the VM: An easy method is to install Caddy or NGINX on the VM and let it handle HTTPS. For example, Caddy can automatically fetch and renew Let’s Encrypt certificates. You’d need a domain name (even a free one or DDNS) pointing to your VM’s external IP. Then you can create a simple Caddyfile:

yourassistant.example.com {
  reverse_proxy localhost:3000
}

Caddy will serve your OpenClaw UI on https://yourassistant.example.com with a valid cert . This is simpler and cheaper than using GCP’s load balancer for a single VM. If using NGINX, you’d similarly proxy and use Certbot for certs . The OpenClaw Experts guide on “gateway with HTTPS” details these steps . Make sure to also configure a basic auth or at least a long random password on the OpenClaw side if possible (OpenClaw might not natively support auth on the UI yet, so limiting by network or adding your own auth is wise).

2. Use a Cloud Load Balancer (advanced): GCP’s HTTPS Load Balancer can sit in front of the VM. You’d reserve a static IP, set up a target pointing to your instance, and provision a Google-managed SSL certificate for your domain  . This provides a Google-trusted endpoint and can leverage Cloud Armor, etc. However, it’s a bit complex and can incur extra costs. For most personal use, the VM-level proxy is sufficient and easier.
Whichever route, once HTTPS is set, you’ll access the UI via https://yourdomain. The connection will be encrypted – preventing eavesdropping – and you can then comfortably control the agent from a browser if needed.

• Harden the VM: Apply basic security hardening to your VM. Ensure auto-upgrades are enabled for security patches (on Ubuntu, you can turn on unattended upgrades). Consider installing Fail2Ban or Google’s OS Config agent to protect SSH. Disable password authentication for SSH (use key auth only). These standard steps keep the VM itself secure from unauthorized access. Also, rotate any credentials you gave to OpenClaw (tokens, API keys) periodically, and revoke anything you aren’t using . Since OpenClaw stores them in config files, if an attacker ever got in, those could be compromised. Rotating keys limits the window of abuse.
• Monitor and Log: GCP allows you to enable Stackdriver Logging/Monitoring on your VM. It might be overkill for a hobby project, but it can be useful to catch anomalies. At minimum, watch your OpenClaw console or logs. OpenClaw is quite verbose about what it’s doing. If you see it calling tools you didn’t expect, investigate. You can also log conversations if that helps auditing (though be mindful of privacy – those logs will contain the content of your interactions).

By this stage, you have a fully functional OpenClaw deployment on GCP, reachable via secure channels. You can chat with your AI agent from your phone, and it will execute tasks on its VM environment. The VM is locked down to minimize risks to you or others.

Step 5: Usage Tips for Non-Developers

Now it’s time to actually use OpenClaw to make your life easier! Here are some usage and management tips, especially geared towards non-developers:
• Utilize Natural Language: You don’t need to know coding to use OpenClaw – you converse with it in plain English (or your language). For example, you can ask: “Could you find the latest news on AI and save summaries to a file?” and it will attempt to perform web searches and create a document  . It’s like having a very smart, scripting-savvy assistant. Always double-check its outputs, though. It will cite sources for information it gathers (as shown in the Markdown file example with sources ), which is helpful.
• Leverage Messaging Platforms: If you connected via Telegram/WhatsApp, treat it like chatting with a colleague. You can send files to the bot (perhaps a CSV or log) and ask it to analyze them – the bot can read attachments from chat if configured. You can also set up channels or groups where the bot is a member to integrate it into team workflows (be careful with permissions in that case). Many users find controlling OpenClaw from a smartphone extremely convenient, effectively turning it into a personal Siri/Google Assistant on steroids that can actually use apps and accounts on your behalf  .
• Use the Control UI for Insight: The web Control UI (at your VM’s address) provides a console view of what the agent is doing. This can be very insightful if you want to see the sequence of tool calls or any errors. It’s essentially a dashboard with the conversation history and options to tweak settings. Non-developers can use it to, say, install a new skill from the ClawHub marketplace with a click, or to stop the agent if it’s stuck in a loop.
• Skill Installation (when comfortable): OpenClaw’s power grows when you add “skills” – plugins that let it interface with more tools (email, calendars, cloud APIs, smart home devices, etc.). There is a Skills Marketplace called ClawHub. You can browse available skills (there are hundreds, but beware not all are safe – stick to popular, verified ones)  . To install a skill, you typically run openclaw skill install <skill-name> in the terminal or ask the bot via chat “Install the X skill.” For instance, to give it Gmail access, you’d install an email skill and provide OAuth credentials. The process for each skill varies, but documentation is usually on the skill’s page. Caution: Only install skills you truly need and from trusted sources, as they execute code in your agent’s context  . As a non-dev, if you’re unsure about a skill’s safety, ask the community or avoid it.
• Automation and Schedules: OpenClaw can run tasks on a schedule (it has a concept of “heartbeats” or can use cron skill). For example, you might schedule it to check your calendar every morning and message you a summary, or to monitor a website for updates. Setting this up might require a bit of configuration (some tasks the agent can autonomously decide to do if you give it permission to be proactive  ). Always explicitly approve such behaviors and test them. The ability for continuous background operation is a big plus of the GCP deploy – just be careful that automation doesn’t go haywire (maybe start with read-only or notification tasks, not things like “auto-purchase items” until you fully trust it).
• Updates: OpenClaw is evolving quickly. Check the GitHub or community forums periodically for new releases. Updating if you used the script is usually npm update -g openclaw or rerunning the script. With Docker, you’d pull the newer image and recreate the container. Updates often include bug fixes and security patches  , so don’t fall too far behind. Before updating, stop the agent and back up the openclaw.db file (the SQLite history) just in case. Most updates are smooth, but it’s good practice.
• Cost Management: Since this is GCP, keep an eye on your costs. The VM we chose (e2-medium) costs a few tens of dollars per month if run 24/7 (roughly ~$20-25/mo in some regions). API calls (OpenAI/Claude) will likely dominate your expenses if you use the agent a lot – monitor those usage on their respective dashboards. If your use is infrequent, you can stop the VM when not needed (you won’t pay for CPU while it’s stopped, just a few cents for the static IP and storage). You can automate VM shutdown at night using a Cloud Scheduler job or a simple script. Alternatively, consider using Spot VM instances for cost savings – some have run OpenClaw on GCP’s spot VMs nearly free by coupling it with free model tiers , though spot VMs can be preempted (restarted) at any time. For a non-critical personal agent, that might be acceptable and can save money. In any case, configure a budget alert in GCP so you get an email if, say, costs exceed $50.

Managed and Simplified Alternatives

If all of the above still sounds too intimidating, you should know there are emerging options to use OpenClaw without deep technical work:
• OpenClawd (Hosted OpenClaw): OpenClawd.ai is a third-party service that launched to provide one-click OpenClaw deployment with built-in security . Essentially, they host the agent for you (on their cloud, which could be GCP or others under the hood). You sign up, click a button to deploy an OpenClaw instance, and get a web UI to interact with it . They emphasize added security measures (patching known vulnerabilities, isolating each instance). This is a paid service (since they cover the infra and management), but it completely removes the infrastructure barrier for non-developers. It’s like having your own OpenClaw without ever opening a terminal. The trade-off is you must trust OpenClawd (with your data, keys, etc.) and it might limit some customization. However, for many users this is by far the easiest route – essentially turning OpenClaw into a SaaS offering you just sign up for . If you prioritize ease and are less concerned about controlling the environment, a managed host like this could be ideal.
• Cloud Marketplace Images: Keep an eye on GCP Marketplace – as of early 2026, Google hasn’t published an official OpenClaw image , but the rapid adoption means it’s possible that community VM images or click-to-deploy solutions will appear. Other cloud providers already did (DigitalOcean’s 1-Click installer, etc.  ). If GCP publishes one, it would let you deploy via a wizard where all the steps we did are automated. You’d just fill in a form (VM size, API keys) and get a running instance. In absence of that, the manual steps we provided are your best bet.
• Railway or Cloud Run (PaaS options): The OpenClaw Experts guide notes that Railway.app can deploy OpenClaw with “zero infrastructure management” – you link the GitHub repo, it auto-builds and runs it for you in the cloud . That’s a developer-friendly platform-as-a-service. You still have to configure environment variables (API keys, etc.), but you skip managing VMs directly. On Google Cloud, an analogous approach could be trying Cloud Run (a serverless container service). In theory, you could containerize OpenClaw and run it on Cloud Run. However, Cloud Run instances shut down when idle and have a max request timeout, so keeping an agent running persistently is tricky – not to mention needing a stable webhook for Telegram which Cloud Run can handle, but the stateless nature may complicate the agent’s long loops. Some advanced users might attempt it, but it’s not straightforward or officially supported. We mainly mention it because as the ecosystem matures, it’s plausible someone will craft a Cloud Run-friendly version or a Terraform script to set everything up. For now, a single VM is simpler and more predictable.
• Community Support: Finally, remember that the OpenClaw community is very active (check Reddit, Discord “Friends of the Crustacean”  , etc.). If you hit a roadblock, you can likely find help or a guide from others who’ve done it. There are already beginner-friendly tutorials (e.g. a Codecademy article and freeCodeCamp tutorial for OpenClaw) that provide stepwise instructions and even videos  . Don’t hesitate to use those resources – the goal is to lower the barrier so that even non-developers can safely run their own “AI coworker.”

Safety Best Practices Recap

Before we conclude, a quick recap of safety must-dos when deploying OpenClaw on GCP (or anywhere), since this is crucial:
• Isolate the Agent: Run it in its own VM or container, not on a shared production server . This limits damage and exposure.
• Least Privilege: Do not give the OpenClaw VM any more access than necessary. If it doesn’t need to call GCP APIs, don’t attach broad IAM roles. If it’s just doing local tasks, it might not need any GCP service account at all. And run the process as a normal user, not root .
• Secure Credentials: Never paste API keys or secrets into config files in plaintext if you can help it . Use environment variables or secret managers. And definitely don’t leave things like cloud admin keys on the VM.
• Monitor Agent Actions: Especially early on, keep an eye on what the agent is doing. OpenClaw will follow instructions exactly, even dangerous ones, so be mindful of your own commands. If you say “clean up my files” without specifying, it might delete more than you intended . Always specify scope (“in folder X”) and perhaps use the simulation mode if you’re unsure.
• Update Frequently: Stay up-to-date with OpenClaw releases to get security patches (e.g. fixes for the RCE flaw etc.) . The project is new, so vulnerabilities are being found and fixed on an ongoing basis.
• Backup Data: Backup your OpenClaw’s memory (the SQLite DB) and any important files it creates. This way if something gets corrupted (by bug or by the agent), you can restore. GCP’s persistent disk snapshots are an easy way to do this.
• Have an “Emergency Stop”: If the agent starts doing something unintended (like spamming an API or deleting wrong files), you should know how to intervene. This could be as simple as typing /stop in the chat (if implemented), or killing the process via SSH. In the worst case, stop the VM from the cloud console – that’s a sure way to halt it. You can always restart it later. Gartner humorously said the approach to OpenClaw might be “kill it with fire” if it misbehaves  – hopefully you’ll never need to, but it’s good to have the option ready.

By following this guide, you should have a solid, user-friendly OpenClaw setup running on Google Cloud – one that harnesses the power of autonomous AI agents while minimizing the typical risks and complexities. You’ll be able to chat with your AI assistant via familiar apps and have it perform truly helpful tasks across email, web, coding, and more, all while your data stays under your control on your GCP instance. Non-developers have successfully done this and found it “magical”   – but they also emphasize doing it carefully. Used wisely, OpenClaw on GCP can feel like “the future is already here” , giving you a glimpse of what personal AI agents can do, without needing to rely on a big tech company’s closed platform. Good luck, and stay safe while exploring this new frontier of AI!