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You can run OpenClaw on your own computer and keep its files there, but that does not automatically make every interaction offline or private. Hosted AI providers receive prompts sent to their APIs, and cloud chat platforms receive the messages routed through them. A more private setup depends on the whole path: where the Gateway runs, which model and interface you use, what can reach the network, and who can operate the system.
What do “local,” “offline,” and “private” mean in OpenClaw?
These terms describe different properties. OpenClaw’s Gateway is the long-running process that owns channel connections and the WebSocket control plane. By default, its sessions, memory files, configuration, and workspace are stored on the Gateway host. The location of that host therefore determines where OpenClaw’s own state lives. OpenClaw’s documentation on where things live also makes the key distinction explicit: “No: OpenClaw’s own state is local, but external services still see what you send them.”
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- Local: OpenClaw’s state, or the model inference, is on a device you control. Those are separate choices: a local Gateway can still call a hosted model.
- Offline: The workflow does not depend on network services during ordinary use. A local model alone is not enough if messages still travel through a cloud chat service.
- Private: Data is exposed only to the people and services you intend to trust. A local install can still expose information through its model provider, chat channel, plugins, tools, or operators.
OpenClaw’s state directory can contain configuration, SQLite state, provider state, and credential files, so it should be treated as sensitive. Configuration and workspace can live outside a source checkout under the user’s OpenClaw state directory; that keeps personal files separate from repository updates. The documentation describes these locations and data flows.
Can OpenClaw run locally and offline without sending data to cloud services?
It can be configured to avoid a hosted model API and cloud chat service in a particular workflow, but local inference by itself does not make the entire installation offline. OpenClaw supports integration with local-model services, including Ollama; the model then runs on the machine rather than sending prompts to a hosted model API. However, if you interact through a service such as Telegram, Slack, WhatsApp, or Discord, that platform still handles channel traffic and stores message data on its servers. A local model keeps prompts on the machine while channel traffic still goes through the channel provider. OpenClaw’s FAQ explains this distinction.
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For a workflow intended to remain disconnected during ordinary use, the components to consider are a local Gateway, a locally available model runtime, and a local interface. Restrict outbound networking to services you intentionally need. This describes a privacy-oriented design goal, not a guarantee that every OpenClaw feature works in an air-gapped environment; the available documentation does not establish universal offline compatibility.
Choose the setup by its data path
Decide separately where inference runs, how you communicate with the agent, how the Gateway is reached, and who shares access. The combinations have different privacy consequences:
| Setup choice | What stays local | What may leave the device |
|---|---|---|
| Local Gateway with hosted model | Gateway state on its host | Prompts routed to the hosted model provider; channel messages handled by the selected platform |
| Local Gateway with local model and cloud chat channel | Gateway state and model prompts | Channel traffic handled by the cloud platform |
| Local Gateway, local model, and local interface | Gateway state, inference, and interaction can remain on local devices | Other enabled features or intentional network connections may still communicate externally; check the services you configure |
| Remote access through a private tunnel | Gateway can remain on its host and bound to loopback | Remote access depends on the tunnel service or SSH connection; this is not the same as an entirely offline workflow |
The table describes the data-flow implications, not a guarantee that every feature behaves identically across configurations. A host that runs only the Gateway has different hardware needs from one that also runs model inference. OpenClaw’s reviewed documentation does not establish a universal model recommendation, minimum RAM requirement, or tested inference speed.
How to keep the Gateway from becoming a public entry point
For local-only use, OpenClaw recommends binding the Gateway to loopback with gateway.bind="loopback". Its security guidance warns against exposing the interface directly to the public internet; do not bind it to 0.0.0.0 or put a public reverse proxy in front of it. Review findings from the security audit before changing network access. OpenClaw’s security guidance describes risky configurations surfaced by openclaw security audit.
If you need remote access, keep the Gateway loopback-bound and use an SSH tunnel or Tailscale, together with strong Gateway authentication. OpenClaw’s network guidance covers network exposure and access patterns. A private tunnel limits how the Gateway is reached; it does not remove the need to protect credentials or decide which remote users are trusted.
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Who should be allowed to use one Gateway?
OpenClaw treats authenticated Gateway callers as trusted operators. Its security model does not recommend sharing one Gateway among mutually untrusted people. Separate sessions or memory do not turn a shared host into a per-user authorization boundary. If users belong to different trust groups, use separate OS users, hosts, or Gateways rather than relying on conversation separation. The security guidance explains this trust boundary.
Also treat models as untrusted principals: prompt or content injection can influence agent behavior. Security depends on host trust, authentication, tool policy, sandboxing, and execution approvals—not merely on whether the model runs locally. Plugins are trusted code too: extensions load in-process and can run with the Gateway process’s OS privileges. Install only plugins you trust, and pin trusted IDs where appropriate. OpenClaw’s security guidance covers models, tools, and plugins.
Harden the host and installation
- Run
openclaw security auditand review its findings before exposing the Gateway. - Keep local-only access on loopback; use a private tunnel and strong authentication for remote access.
- Protect the OpenClaw state directory and credentials as sensitive data.
- Separate Gateway instances or OS users when operators do not share a trust boundary.
- Limit tools and execution permissions to what the workflow needs, and assess plugins as code running with process-level privileges.
- If deploying with Docker, follow OpenClaw’s recommendations to use the non-root image user, enable read-only mode where possible, and drop capabilities. These measures harden a deployment; they do not guarantee its security.
What hardware and software does a local setup require?
As of the OpenClaw installation documentation accessed October 7, 2026, the listed Node.js requirements are 24.16+ or 26.1+, with Node 26 recommended and Node 24 identified as the supported LTS line. The installation paths cover macOS, Linux, and Windows; desktop companions can provision a local Gateway. These requirements can change, so check the current official installation guide before installing.
OpenClaw also publishes a Raspberry Pi installation guide, making a Pi a documented option for a dedicated Gateway host. That guide does not establish that a Raspberry Pi 5—or any specific board—is suitable for running a particular local language model. Do not assume Gateway hosting and model inference have the same hardware requirements.
Quick Recap
A practical decision checklist
- Choose where inference happens. A hosted provider receives prompts routed to its API; a local runtime keeps model prompts on the machine.
- Choose the interaction channel. A cloud messaging service remains an external recipient even when the model is local. Use a local interface when you want to avoid that channel dependency.
- Choose how the Gateway is reachable. Keep it on loopback for local use. For remote access, use a private tunnel and strong authentication rather than public exposure.
- Define the trust boundary. Decide which people can operate the Gateway, access its host, install plugins, and approve tool execution. Separate users or systems when trust is not shared.
- Review the network and tools. Audit the configuration, limit outbound connections and capabilities to what you intend, and treat extensions as privileged code.
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