How to Reduce AI Token Usage in OpenClaw

Most of the advice for cutting token usage in one AI agent applies to all of them, because the underlying mechanics are the same everywhere: a model reasoning over whatever’s in its context window, and that window filling up with either useful signal or noise you didn’t mean to include. OpenClaw is no exception — the same discipline that keeps a Claude Code or Copilot session lean works here too, it just shows up in a slightly different shape given what OpenClaw actually is.

OpenClaw is a self-hosted gateway that connects messaging apps — Discord, Slack, Telegram, WhatsApp, and a handful of others — to an AI agent, so you get a personal assistant you can message from anywhere without handing your data to a hosted service. Each Gateway runs a single embedded agent runtime, with its own workspace and session store, which changes the token-usage picture slightly: you’re not just managing one chat’s context, you’re managing what that one persistent agent process accumulates across every channel it’s connected to.

1. Scope what loads by default

The agent’s workspace and bootstrap files load into every session it runs, the same way a CLAUDE.md or AGENTS.md does elsewhere. Keep that file to what the agent actually needs for the task at hand — commands, conventions, known gotchas — not a running log of everything you’ve ever told it. Anything that loads on every single interaction should earn that place.

2. Don’t connect every available tool or integration

Any tool or integration you connect typically registers its description and parameters into context whether you end up using it or not. If a task only needs two capabilities, leave the other five disconnected rather than wiring everything up “just in case.”

3. Start clean per task

A long-running session that’s drifted across three unrelated requests is carrying all three histories forward into every new message. Starting a fresh session for unrelated work keeps context focused on the thing you’re actually asking about right now.

4. Be specific about what you want read or acted on

Vague requests (“check the settings page”) tend to pull in more context than precise ones (“check the toggle in SettingsPanel”). The narrower the ask, the less the agent has to load to answer it correctly the first time.

5. Batch related requests instead of trickling them in one at a time

Sending five small, related asks one after another means each one re-sends the growing history of the previous four. If they’re related, describe them together so the agent can act on all of them in a single pass.

6. Give it structured context instead of a picture, when precision matters

For anything UI-related, a screenshot forces the model to interpret pixels and guess at the underlying structure before it can act — and a wrong guess costs you a retry, which costs you the whole conversation again. Structured context (the actual element, its styles, its position in the page) skips that guessing step entirely.

That’s the specific problem UICuts solves — point at an element in the browser and it exports the selector, computed styles, and DOM hierarchy as text, so whatever agent you’re driving gets exact input instead of a picture to guess from.

Key lessons learned

• The mechanics are the same across every AI agent: less irrelevant context in, better answers out, regardless of pricing model.
• Connect only what a task needs — unused tools and integrations are a standing cost, not a free option.
• Structured input beats a screenshot whenever precision is the point.

Related reading

If you’re comparing OpenClaw’s approach to more code-focused agents: Claude Code scopes context with CLAUDE.md and slash commands, OpenCode leans on provider/model choice, and both Cursor and Windsurf build persistent context into the editor itself. If you’re connecting MCP tools to your OpenClaw agent, the MCP-specific tactics apply directly.

Try UICuts free if UI feedback is part of your workflow, whichever agent you’re pairing it with.