AI Agents

Build Your Own OpenClaw: The Free GitHub Tutorial That Actually Works

Julian Goldie · · 8 min read

Build your own OpenClaw — that's the headline I never thought I'd write.

For most of 2025, OpenClaw was the agent everyone wanted but nobody understood.

Then a GitHub repo dropped that walks you through building your own from scratch.

18 steps.

About 25 minutes if you let Claude Code guide you through it.

I'm going to break down what you actually learn, what you ship, and why this is the best way to understand AI agents in 2026.

Why Build Your Own OpenClaw

Three reasons.

One — you finally understand how agents work. Reading docs about agents is one thing. Building one teaches you the architecture in your bones.

Two — you customise to your needs. OpenClaw out of the box has tools and skills you may never use. Your own version is leaner.

Three — you learn a transferable skill. Building agents is the new "build a website" — knowing how is going to be table stakes within 2 years.

The bonus reason — it's genuinely fun.

🔥 Want my full Build Your Own OpenClaw walkthrough + the prompts I used? Inside the AI Profit Boardroom I've recorded the full step-by-step build, the Claude Code prompts that walk through each phase, and the customisations I added to make my version genuinely useful. 2,800+ members already shipping custom agents. Plus weekly coaching where I'll review your build live. Click below. → Get the full Build Your Own OpenClaw track

What The GitHub Tutorial Covers

The tutorial is structured in four phases across 18 progressive steps.

Phase 1 — Single agent (steps 0-5)

Build a chat loop, add tool calling, add skills as markdown files, add persistent memory, add slash commands.

Phase 2 — Event-driven architecture (steps 6-9)

Move from polling to event-driven. Add async handling, queue patterns.

Phase 3 — Autonomous multi-agent (steps 10-14)

Add a heartbeat system, sub-agent spawning, parallel execution.

Phase 4 — Production and scale (steps 15-17)

Add deployment patterns, monitoring, scale considerations.

By the time you finish, you've built something that resembles OpenClaw or Hermes Agent in core capability.

Step 0 — Setup

You need:

Clone the GitHub repo.

Hand the URL to Claude Code (or any code-aware AI agent).

Tell it to walk you through the steps.

That's the entire setup.

If you want to use Claude Code specifically, my Claude code free post covers the install — pairs perfectly with this tutorial.

Step 1 — The Chat Loop

The first agent is just an LLM with a chat loop.

You ask it something, it answers, you ask the next thing.

No tools.

No memory.

Just chat.

Sounds basic — but seeing it work locally with your own code is satisfying.

This is the foundation everything else builds on.

Step 2 — Adding Tools

Now your agent can do things.

You add four tools:

Suddenly your local agent can:

That's already useful.

You're 5% of the way to OpenClaw.

I covered the tool architecture pattern in my paperclip Hermes agent post — same idea applied across multiple agents.

Step 3 — Skills As Markdown Files

This is the breakthrough step.

Skills aren't code.

They're markdown files that describe what to do.

Your agent gets a meta-tool called "skill" that loads any skill markdown file from disk.

Add a skill called "tweet drafter" — drop a markdown file describing the workflow.

Your agent can now draft tweets.

Same pattern for any skill.

This is exactly how Hermes works — and now you've built it yourself.

If you want to see Hermes's version of this, my Hermes agent workspace post covers the production implementation.

Step 4 — Persistent Memory

Now we add memory.

Every conversation message gets written to a memory.md file.

You close the chat.

You reopen it.

Your agent... still doesn't remember.

Because step 4 only writes memory.

Step 5 reads it back.

This deliberate gap teaches you something — memory write and memory read are different problems.

Step 5 — Slash Commands And Session Recall

Slash commands give you shortcuts.

/sessions — see previous conversations.

/skills — list available skills.

/help — get the command list.

/resume — load up a previous session and continue where you left off.

Now your agent has continuity.

You can pick up tomorrow where you stopped today.

Step 6+ — Event-Driven And Beyond

The remaining steps add:

By step 17 you have a real, deployable AI agent.

I haven't fully built every step myself — but the first 5 took me 25 minutes and gave me deep understanding of how OpenClaw works under the hood.

For the autonomous heartbeat pattern specifically, my Hermes vs OpenClaw post compares how the two production agents implement it.

What You Actually Learn

The technical learning is real but secondary.

The real learning is conceptual.

You internalise that agents are:

Once you see it that way, you can spot the architectural decisions in any agent product.

You can also build your own when none of the existing ones fit.

That's a skill that compounds.

🔥 Want my Build Your Own OpenClaw customisations? Inside the AI Profit Boardroom I've documented the custom skills I added to my version — content drafting, SEO research, Telegram integration, scheduled tasks. The exact extensions that make a barebones agent actually useful. Click below. → Get the customisation library

Build Your Own vs Use OpenClaw / Hermes

Honest comparison.

Build your own:

Use OpenClaw:

Use Hermes:

My take — build your own at least once for the learning. Then use Hermes (or OpenClaw) for daily work.

The build version will rarely be your daily driver.

But you'll understand your daily driver way better after building one yourself.

How Long Does It Actually Take?

The video shows steps 0-5 in about 25 minutes.

That's because Claude Code does the heavy lifting.

Without an AI assistant, the same steps would probably take 4-8 hours of careful reading and typing.

For the full 18 steps:

Either way it's accessible.

Build Your Own OpenClaw FAQ

Do I need to know how to code?

Helpful but not required. Claude Code reads the docs and walks you through the commands. You're more directing than coding.

What if I get stuck?

Hand the error to Claude Code. It'll diagnose and fix. The interactive learning loop is what makes this approach work.

Will my version be as good as OpenClaw?

For the core features — basically yes after step 17. For the polish, community skills, and production hardening — no.

Is the GitHub repo maintained?

Yes — actively. Watch the repo for new steps and improvements.

Can I share my custom skills back to OpenClaw?

Skills are portable across most agents that follow agentskills.io standard. Your skills can be reused.

Should I build this on my main work machine?

Yes — it's lightweight and doesn't conflict with anything. Run in a separate folder for safety.

Related Reading

Final Take

Build your own OpenClaw is the highest-leverage learning project I've done in 2026.

Free.

Open source.

Educational.

Genuinely fun.

You finish with a working AI agent and a deep understanding of how every other AI agent on the market works under the hood.

Spend a weekend on it.

Worth more than a year of reading agent tutorials.

🔥 Ready to build your own OpenClaw this weekend? Get a FREE AI Course + Community + 1,000 AI Agents 👉 join here. Or grab the full Build Your Own OpenClaw track inside the AI Profit Boardroom.

Learn how I make these videos 👉 aiprofitboardroom.com

Video notes + links to the tools 👉 skool.com/ai-profit-lab-7462

Build your own openclaw is the weekend project that pays back forever — start tonight.

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