A deep dive into configuring OpenClaw, the local-first personal AI assistant. Learn how to set up Anthropic models, secure your gateway, and connect channels...
Starting With a Local-First Foundation
OpenClaw is built around the idea that your personal AI assistant should run close to you, not on someone else's schedule. Before you touch any model settings, decide where the assistant will live: a machine you control, with its configuration and state stored on disk rather than in a remote account. This is what makes the setup predictable — you can read the config, version it, and reproduce it if the host ever changes.
Treat the config file as the single source of truth. Keep it in a location you back up, and avoid scattering settings across environment variables and inline flags. When something misbehaves later, having one authoritative file makes the difference between a two-minute fix and an afternoon of guessing.
Setting Up Anthropic Models
Connecting OpenClaw to Anthropic models comes down to two things: supplying credentials and choosing which model handles which job. Store your API key outside the config file itself — reference it through an environment variable or a secrets file so the config can be shared or committed without leaking access. OpenClaw reads the key at startup, so a missing or malformed value shows up immediately rather than mid-conversation.
Match the model to the task instead of defaulting everything to the largest one. A capable, higher-reasoning model earns its place on planning, code, and multi-step tool use; a faster, lighter model is the better fit for quick classification, routing, or short replies. If OpenClaw lets you assign models per channel or per task type, use that — it keeps latency low where you want speed and reserves the heavier model for work that actually benefits from it.
Securing the Gateway
The gateway is the entry point that accepts requests and forwards them to the assistant, which makes it the part most worth hardening. Left open, it becomes an unauthenticated proxy to your model credentials. Lock it down before you connect anything to it.
- Bind the gateway to localhost or a private interface unless you have a concrete reason to expose it, and put a reverse proxy with TLS in front of any public endpoint.
- Require authentication on every route — a token or key the assistant checks — so no anonymous client can drive your models.
- Keep credentials out of the config and out of logs; scrub anything that echoes request bodies containing keys.
- Apply rate limits so a misconfigured client or a leaked token can't run up unbounded usage.
Test the closed door as deliberately as the open one. Confirm that a request without valid credentials is actually rejected, not merely undocumented, before you route real traffic through it.
Connecting Channels
Channels are how you reach the assistant — a chat surface, a command line, a messaging integration, or a custom client hitting the gateway. Add them one at a time and verify each end to end before moving on. It is far easier to confirm that a single channel routes correctly than to debug three at once when replies stop arriving.
Give each channel only the access it needs. A channel meant for quick questions doesn't need the same tools or model as one wired into your development workflow. Scoping permissions per channel keeps a compromised or noisy integration from reaching the parts of your setup that matter most, and it makes the whole configuration easier to reason about as you add more surfaces over time.