In This Article
Introduction
Every time you send a message to ChatGPT, Claude, or Gemini, that message travels to a server operated by a large technology company. It's processed by AI models running on that company's hardware. The response travels back to you. And somewhere in that process, your conversation may be logged, stored, reviewed by humans for safety evaluation, used to train future models, or subject to a government request. These practices vary by provider and are evolving — but the fundamental fact is the same: your conversation leaves your control the moment you hit send.
OpenClaw's local-first architecture makes a different choice. Your conversations, your memory files, your configuration, and your history live on your hardware. Understanding what this means in practice — what actually stays local, what still leaves your machine, and how to achieve the strongest privacy guarantees — is essential for anyone choosing OpenClaw specifically for privacy or compliance reasons.
What Data Sovereignty Means
Data sovereignty means that you — not an infrastructure provider, not an AI company, not a cloud storage vendor — control where your data lives, who can access it, how long it's retained, and whether it's used for any purpose beyond your own use. It means that your data is subject to your laws and your policies, not the privacy policies of a corporation whose terms of service you agreed to without reading.
For individuals, data sovereignty may mean simply knowing that your private conversations, your business strategies, your health concerns, and your financial information aren't being logged by a third party. For businesses, it has additional implications: GDPR compliance may require that customer data not leave certain jurisdictions; attorney-client privilege may require that legal discussions not be disclosed to third parties; competitive intelligence may require that strategic plans not be accessible to any infrastructure provider.
OpenClaw's local-first design was a deliberate architectural choice to support data sovereignty. Every default points toward local storage: memory files are on your filesystem, logs are on your filesystem, configuration is on your filesystem. The flow is reversed from cloud AI: the AI comes to your data rather than your data going to the AI.
What Data Stays Local
These data categories never leave your machine in a standard OpenClaw deployment:
Memory files: Your entire memory directory — PROFILE.md, project files, preferences, contact context, decision logs — lives on your local filesystem. No cloud sync occurs unless you explicitly configure one. The LLM providers never see the content of your memory files as a corpus; they only see specific excerpts that are included as context in individual API calls.
Configuration and credentials: Your OpenClaw configuration (which channels are configured, which agents are running, which Skills are enabled) and your stored credentials (stored in the local keyring) never leave your machine.
Heartbeat task definitions: Your HEARTBEAT.md file — which tells the agent what to monitor and what actions to take proactively — is local. No party other than your configured LLM provider (via the API call context) sees this file.
Application logs: OpenClaw's detailed activity logs are stored locally. Action logs, reasoning traces, error logs — all local. You control retention policy and can delete them on any schedule.
Conversation history (beyond the current session): Past conversation history that the agent references from memory files is stored locally. The agent may include summaries of relevant past conversations in API calls as context, but the full historical record never leaves your machine.
What Data Leaves Your Machine
Being precise about what does leave your machine is equally important. In a standard OpenClaw configuration using cloud LLM APIs:
Your messages (current session): The messages you send in the current conversation are sent to the LLM API as part of each inference call. These are subject to the LLM provider's privacy policy and data handling terms.
Relevant memory excerpts: The agent includes relevant excerpts from memory files in API calls to provide context to the LLM. If you're discussing your Q1 product roadmap, the agent may include relevant excerpts from your roadmap memory file in the API call. These excerpts leave your machine and are processed by the LLM provider.
Tool call inputs and outputs: When the agent executes Skills (web browsing, email checking, code execution), the inputs and outputs of those tool calls are included in the conversation context sent to the LLM API.
Heartbeat task context: When the agent processes heartbeat tasks, the task instructions and any relevant memory context are sent to the LLM API as part of the inference call.
The practical implication: the LLM provider sees snapshots of relevant context from individual conversations, but never the complete, accumulated memory corpus. They see individual API calls, not a persistent profile.
Comparison to Cloud AI Providers
| Data Category | Cloud AI (ChatGPT, Claude) | OpenClaw (cloud model) |
|---|---|---|
| Your messages | Stored on provider servers | Sent to API, not persistently stored |
| Conversation history | Stored indefinitely on provider | Stored locally only |
| Memory/profile | Stored on provider (if feature exists) | Stored locally, never uploaded |
| Used for training | Potentially (varies by provider/settings) | Governed by API provider terms |
| Data jurisdiction | Provider's jurisdiction | Your jurisdiction (local) + API provider |
The key difference is persistence and scope. Cloud AI providers store your complete conversation history on their infrastructure. OpenClaw stores your complete history locally; only the current conversation context leaves your machine.
Achieving a True Air Gap
For deployments requiring complete data sovereignty with zero external data transmission, OpenClaw supports local-only inference via Ollama. In this configuration, no data of any kind leaves your network:
- Configure OpenClaw to use Ollama as the LLM provider (set in config.yaml)
- Run Ollama locally with your chosen local model (Mistral, Llama, Phi, etc.)
- All LLM inference happens on your own hardware
- No API calls to external services
- Complete data sovereignty
The tradeoff is capability. Local models as of 2026 are meaningfully less capable than frontier cloud models for complex reasoning tasks. For simple automation, scheduling, and structured data tasks, the capability gap is manageable. For complex analysis, nuanced writing, or sophisticated reasoning, the gap is significant.
A hybrid approach — local model for routine, lower-stakes tasks; cloud model for complex tasks after explicit review — balances privacy and capability. Configure this in OpenClaw using separate agent instances or the model routing configuration.
Frequently Asked Questions
Do the LLM providers use my API call content to train their models? This varies by provider and API tier. Many providers explicitly exclude API traffic from training data, but read the current terms of service for each provider you use. Enterprise API agreements typically include explicit training exclusion clauses.
Is OpenClaw GDPR-compliant? OpenClaw's local-first architecture supports GDPR compliance, but compliance is your responsibility. You must ensure that any personal data processed through the agent is handled in accordance with GDPR requirements, including appropriate legal basis for processing, retention limits, and data subject rights procedures.
What happens if my machine is confiscated or accessed by authorities? All your OpenClaw data (memory files, logs, configuration) is accessible on the machine. Encrypt your storage (FileVault on macOS, BitLocker or LUKS on Windows/Linux) to protect data at rest. This is a local machine security question, not an OpenClaw-specific one.
Can I use OpenClaw with no internet connection at all? Yes, using Ollama for local inference and only enabling Skills that don't require internet access. This is a genuine air-gapped deployment suitable for classified or fully air-gapped environments.
Wrapping Up
OpenClaw's data sovereignty model is meaningfully different from cloud AI providers — your accumulated memory, history, and configuration are yours, stored on your hardware, under your control. The API calls to LLM providers represent the necessary boundary where some data must leave your machine to leverage cloud model capabilities. For deployments where even API call data must remain local, Ollama provides a complete local inference solution. Understanding this architecture precisely — what stays local, what leaves, and how to minimize external exposure — allows you to make informed decisions about where OpenClaw fits in your privacy-conscious information architecture.