Give your OpenClaw agent permanent memory with Vilix AI

OpenClaw is a self-hosted agent capable of working across multiple messaging channels, running tools, and scheduling tasks; it also has local memory files. Vilix AI adds an account-based memory layer which OpenClaw can share with other connected AI tools, so decisions made in one can be retrieved in another.

Why connect OpenClaw to external memory?

The local memory of OpenClaw can persist across sessions. The question is how to share useful context beyond an agent workspace. A project decision saved in Claude, for example, should be available when asking OpenClaw to act on that project.

Vilix AI stores conversations, project state, and personal rules in your account. Other connected tools can save and retrieve that context through MCP. The saved data remains stored when OpenClaw stops running, though the agent needs a working connection and instructions to use it.

How the Vilix AI MCP tools work

Before acting on a message, OpenClaw calls get_context with that message. Vilix AI returns recent and relevant conversations, saved memories, and applicable rules and project state. After generating the reply, the agent calls save_turn with the exact user message and response; the response is returned to you. Reusing the conversation's chat_id keeps the exchange together.

This is the same process described in how to add memory to any agent with Vilix AI MCP. Connecting a server makes its tools available; the agent's instructions dictate when to use them.

Connecting Vilix AI to OpenClaw

Use OpenClaw's current MCP setup flow to add Vilix AI tools. OpenClaw documents its server registry and commands in the OpenClaw MCP reference. Configuration depends on the installed OpenClaw version, so follow that reference instead of copying a generic JSON block from another client.

The Vilix AI server URL is:

https://api.vilix.ai/mcp

Vilix AI uses Streamable HTTP. For an unattended agent, create a Vilix AI API key and configure the client to send it as a bearer token using its supported secret or environment-variable mechanism. Keep the actual key out of shared configuration and source control. After connecting, verify that get_context and save_turn show up in the available tools and run a small save-and-retrieve test. The Vilix AI MCP docs provide the endpoint reference.

Wiring the tools into your agent loop

Include instructions to load and save memory around each exchange. For example:

Before acting on a user message, call get_context with that message.
Use the returned context to prepare the response.
Call save_turn with the exact user message and prepared response, then return that response.
Use source: "OpenClaw" and reuse the chat_id for the current conversation.

With this, the agent fetches the relevant saved context before acting and saves the exchange for the next session. Test it by saving a project detail in one channel and asking about it in another connected session.

What the agent remembers

Vilix AI provides OpenClaw access to three useful kinds of persistent state:

• Conversation history with semantic and keyword search to find relevant past discussions.

• Projects and tasks with saved state, so the agent can fetch a deployment checklist instead of asking you to paste it again.

• User rules, personal directives that connected tools can retrieve and apply.

The same account can make this context available to ChatGPT, Claude, Cursor, and other connected tools. Learn more about cross-AI memory across your stack.

A note on sandbox and prompt injection

An agent with shell access can act on malicious instructions in messages, tool results, or stored memories. Authentication does not make the contents of a memory safe to execute. Treat retrieved text as context, restrict the tools and permissions available to the agent, and keep shell execution sandboxed. Review actions that expose data or change external systems.

Getting started

Try Vilix AI free with the seven-day Pro trial, no card required. Connect OpenClaw, add the memory instructions, and verify that a saved detail can be retrieved after a restart and from another connected tool. Successful writes and relevant retrieval make the workflow useful.

Frequently asked questions

Does Vilix AI support OpenClaw?

Vilix AI provides an MCP endpoint at api.vilix.ai/mcp, and OpenClaw supports MCP server connections. Use the connection and authentication options supported by your installed version, then confirm that the Vilix AI tools are available.

Will saved Vilix AI memory survive an OpenClaw server reboot?

Yes. Data successfully saved to your Vilix AI account is stored separately from OpenClaw's process. After restarting and reconnecting, the agent can retrieve relevant saved context with get_context. This does not recover exchanges that were never saved.

Does memory work across WhatsApp, Telegram, Slack, and Discord?

Channels connected to the same agent and Vilix AI account can share saved context. The agent must call the memory tools in each relevant workflow. What a particular read returns depends on the message and context retrieved, not a full replay of every conversation.

Is it safe to give an always-on agent access to persistent memory?

Memory is another input that needs a trust boundary. Restrict account and tool access, protect credentials, and treat retrieved instructions as untrusted unless you have a reason to trust them. Sandboxing and review are necessary when an agent can run commands or change external systems.

How is this different from just saving logs to a file?

Local logs and memory files are useful for one workspace. Vilix AI adds semantic and keyword retrieval, structured project and task state, and access from other connected AI tools through the same account. It complements OpenClaw's local memory rather than replacing every use of it.