Openclaw on freshestweb

OpenClaw Setup Guide: AGENTS.md first, then skills, memory and automations

Tue, 24 Mar 2026 19:35:00 +0100

Last updated on March 24, 2026, 7:42 PM

OpenClaw Setup Guide: AGENTS.md first, then skills, memory and automations

If OpenClaw becomes “weird” after a few days, it is rarely the model. Mostly it’s the setup: too many rules in the wrong place, automation too early, unclean roles between files and a memory that is more of a data dump than knowledge. That’s exactly where you burn time, tokens and nerves.

If your agent suddenly seems stupid: openclaw tools.profile-Fail

Mon, 16 Mar 2026 20:09:57 +0000

Last updated: March 16, 2026

You switch from the top model to a cheaper model (or slip into a fallback) and suddenly your agent seems thwarted: a lot of explanation, little execution. This feels like “model got dumber”, but it is often a different error: Capability drift due to an incorrect tool profile.

We had exactly this practical case in the OpenClaw setup: With tools.profile: messaging only messaging/sessions were available - but no exec, no file system, no browser automation. Result: The agent plans properly, but can no longer carry out its own steps.

What is a harness? Run OpenClaw agents stably

Sun, 15 Mar 2026 21:00:56 +0000

Last updated: March 15, 2026

What is a harness? The missing layer between LLM and real execution

When talking about agents, many only talk about models: GPT, Claude, Gemini, that’s it. In practice, however, another layer often decides whether your setup works stably or gets stuck in demo mode: the Harness. In this article we clarify what a harness really is, why it counts for OpenClaw workflows and how you can evaluate it for your own setup.

Avoid token eaters: The right memory setup for OpenClaw (with a concrete setup plan)

Wed, 11 Mar 2026 21:39:41 +0000

Last updated on March 16, 2026, 9:55 PM

Last updated: March 11, 2026

Avoid token eaters: The right memory setup for OpenClaw (with a concrete setup plan)

When OpenClaw becomes productive in everyday life, the same pain point almost always arises: the answers are good at first, then it becomes slow, expensive and sometimes imprecise. This is rarely due to “bad models” and almost always due to the memory and context setup. Many teams dump too much raw text into each turn, without clean retrieval, without hard limits and without condensation. This costs tokens, degrades signal-to-noise and makes automations unstable.

OpenClaw Setup: Old Computer vs. VPS (How to Start Securely)

Wed, 04 Mar 2026 22:07:28 +0000

After understanding the Agentic AI hype, the next practical question is simple: where should OpenClaw run if you want stable workflows without unnecessary risk?

This decision has bigger impact than model choice in the early phase. If runtime boundaries are unclear, every future automation inherits that instability.

1) First architecture decision: where does OpenClaw run?

Before selecting model stacks, define your runtime environment:

OpenClaw & Agentic AI: Hype vs. Reality for Digital Marketers

Tue, 03 Mar 2026 14:07:24 +0000

Agentic AI is one of the loudest buzzwords in marketing tech right now. The core idea is genuinely interesting: not just chatting with an AI assistant, but letting it execute complete task chains across tools, files, and workflows.

But there is still a big gap between a cool demo and stable daily operations. That is exactly where OpenClaw deserves a realistic review.

1) What OpenClaw already does very well

OpenClaw is strong because it does not stop at text output. It can orchestrate multi-step workflows, call real tools, and return structured outcomes.