Guide

How to Configure OpenClaw Without YAML or CLI

Using ConfigClaw as your OpenClaw configuration layer

If you skim the official OpenClaw docs, configuration looks like this: a JSON or JSON5 file in ~/.openclaw/openclaw.json, a few CLI commands like openclaw onboard or openclaw configure, and a control UI. For developers, that's fine. For everyone else, editing config files and running CLI tools is usually where things fall apart.

This guide walks through what OpenClaw configuration normally involves—and then shows how ConfigClaw lets you do the same work from a visual dashboard, without writing YAML, JSON, or commands.

What OpenClaw configuration controls

Think of OpenClaw configuration as the brain and wiring of your assistant. In the main OpenClaw configuration guide, we break down the moving parts in detail. At a high level, the config decides:

  • Which AI models are available (Claude Opus, GPT-4o, Gemini, Kimi, etc.).
  • How your agents behave—their persona, memory, and default tools.
  • Which channels are enabled (Telegram, Slack, Discord, WhatsApp, and more).
  • Where OpenClaw stores state, skills, and plugins on disk.

The traditional way: config files and CLI

Out of the box, OpenClaw expects you to work directly with openclaw.json and the CLI. A typical workflow might look like:

  • Run openclaw onboard to generate an initial config.
  • Open ~/.openclaw/openclaw.json in a text editor to tweak models, channels, and behaviors.
  • Use helpers like openclaw config get and openclaw config set to adjust individual fields.
  • Restart the gateway or run openclaw doctor when something breaks.

This gives you full control—but it assumes you're comfortable editing JSON, managing environment variables, and debugging startup failures.

The no-CLI way: using ConfigClaw instead

ConfigClaw sits on top of standard OpenClaw configuration and handles the hard parts for you. Instead of editing openclaw.json, you work in three simple areas:

  • A visual **agent editor** for persona, memory, tools, and behavior—no YAML or JSON fields to memorize.
  • A **models panel** where you choose and combine models (Opus, GPT-4o, Gemini, Kimi) from dropdowns.
  • A **channels tab** where you paste tokens once and toggle channels on and off without touching env files.

Example: email + Slack assistant without touching config files

Let's say you want an OpenClaw AI agent that reads your inbox and answers Slack questions using Claude Opus, with a friendly tone and memory. Here's how that compares:

Manual OpenClaw config

  • Edit openclaw.json to define a new agent with the right model and tools.
  • Configure provider keys, model IDs, and parameters like temperature and max tokens by hand.
  • Wire up email and Slack integrations under the right channels.* keys, including webhooks and tokens.
  • Run openclaw doctor or restart until the config validates and the agent starts.

Doing the same thing with ConfigClaw

  • Create a new agent from a preset like “Email & Slack assistant”.
  • Pick Claude Opus (or Auto mode) from a dropdown—no provider JSON required.
  • Paste your email and Slack tokens into guided fields in the Channels tab and toggle them on.
  • Hit deploy—ConfigClaw writes and validates the underlying OpenClaw configuration for you.

When you still might use the CLI

If you're a developer or DevOps engineer, you might still prefer the CLI for advanced automation, CI/CD, or migrations. ConfigClaw doesn't lock you out: you can always drop down to the raw OpenClaw configuration if you need to, but you don't have to start there.

Next steps

To see the full picture of how configuration fits into the rest of the stack, start with our OpenClaw AI overview and deep-dive configuration guide. When you're ready to try this without touching config files, head to the OpenClaw setup page or join the waitlist from the homepage.