Understanding OpenClaw by Building One(github.com)
github.com
Understanding OpenClaw by Building One
https://github.com/czl9707/build-your-own-openclaw
3 comments
Appreciated!
For me, the most difficult pieces are the pieces I haven't tried my self!
The browser operation and interaction with coding agent in this case.
I wanted to really understand how AI agents work, so I spent two weeks building one from scratch. Then I turned my learning into a step-by-step tutorial.
18 progressive steps — each adds one concept, each has runnable code. Some highlights from the journey:
- Step 0: Chat Loop — Start with the basics. Just you and the LLM, talking. - Step 1: Tools — Give your agent the ability to take actions. - Step 2: Skills — Dynamically load capabilities as needed. - Step 6: Web Tools — Agent can search and read the web. - Step 11: Multi-Agent Routing — Multiple agents, right one for the right job. - Step 15: Agent Dispatch — Agents that can collaborate with each other. - Step 17: Memory — Long-term knowledge that persists across sessions.
Each step is self-contained with a README + working code.
[https://github.com/czl9707/build-your-own-openclaw](https://github.com/czl9707/build-your-own-openclaw)
Hope this helpful! Feedback welcome.
18 progressive steps — each adds one concept, each has runnable code. Some highlights from the journey:
- Step 0: Chat Loop — Start with the basics. Just you and the LLM, talking. - Step 1: Tools — Give your agent the ability to take actions. - Step 2: Skills — Dynamically load capabilities as needed. - Step 6: Web Tools — Agent can search and read the web. - Step 11: Multi-Agent Routing — Multiple agents, right one for the right job. - Step 15: Agent Dispatch — Agents that can collaborate with each other. - Step 17: Memory — Long-term knowledge that persists across sessions.
Each step is self-contained with a README + working code.
[https://github.com/czl9707/build-your-own-openclaw](https://github.com/czl9707/build-your-own-openclaw)
Hope this helpful! Feedback welcome.
Rebuilding something yourself often reveals design decisions that aren’t obvious when you only read documentation or use the tool as a black box.
Curious what parts were the most difficult to reproduce compared to the original OpenClaw implementation.