# Build Your Own OpenClaw: A 18-Step Hands-On AI Agent Tutorial > Build Your Own OpenClaw is a Python tutorial repository with 18 progressive steps that take a developer from a basic chat loop to a multi-agent system with event-driven architecture, memory, multi-agent routing, and concurrency control, with a runnable codebase at every step. **czl9707/build-your-own-openclaw** — A step-by-step guide to build your own AI agent. - Repository: https://github.com/czl9707/build-your-own-openclaw - Website: https://build-your-own-openclaw.kiyo-n-zane.com/ - Stars: 1,894 · Forks: 326 - Language: Python - License: MIT - Published: 2026-09-10 · Updated: 2026-09-10 · Language: en - Canonical page: https://hysenlabs.com/projects/czl9707-build-your-own-openclaw ## What Build Your Own OpenClaw Is and Who It Is For Build Your Own OpenClaw is a step-by-step tutorial repository that guides a Python developer through building a minimal version of the OpenClaw AI agent framework from the ground up. The README describes it as 18 progressive steps, starting from a simple chat loop and ending at a lightweight OpenClaw equivalent. Each step has its own directory with a README explaining key components and design decisions, plus a runnable codebase. The audience is a developer who wants to understand how AI agents work internally by writing the code rather than configuring an existing framework. The tutorial covers the full architecture progression: single-agent capabilities, event-driven refactoring, multi-agent coordination, and production reliability features. A developer who completes all 18 steps will have written a working agent that can handle tools, remember conversations, accept commands from multiple channels, route tasks to specialized subagents, and persist memory across sessions. A companion website is hosted at build-your-own-openclaw.kiyo-n-zane.com. A Chinese-language README (README.zh.md) is present in the repository alongside the English version. ## Repository Structure and How to Start Each step lives in its own numbered directory at the root: 00-chat-loop/ through 17-memory/. The directory names describe what each step adds. A default_workspace/ directory holds shared configuration, including a config.example.yaml file that must be copied and filled in before running any step. The README gives two setup commands: ```bash cp default_workspace/config.example.yaml default_workspace/config.user.yaml ``` After copying, edit config.user.yaml with your LLM API keys. The README points to LiteLLM's provider documentation for the full list of supported providers. A PROVIDER_EXAMPLES.md file in the repository root shows examples for specific providers. The recommended learning path is to follow the numbered directories in order, reading each step's README.md and then running its code. The README states that each step is implemented in a separate session, which suggests the code at each step is a self-contained snapshot rather than a branching diff on a single codebase. A reference implementation called pickle-bot is linked as an example project showing the finished result. ## Phase 1: Capabilities of a Single Agent (Steps 0 to 6) The first phase builds a fully capable single agent across seven steps. Step 00-chat-loop adds a basic conversation loop. Step 01-tools gives the agent a tool. Step 02-skills extends the agent with SKILL.md-based skills, which is the Agent Skills mechanism used in tools like Claude Code. Step 03-persistence saves conversations so context survives across sessions. Step 04-slash-commands adds direct user control through slash commands in the chat interface. Step 05-compaction addresses the context window limit by packing conversation history. Step 06-web-tools adds internet access. By the end of Phase 1, the agent can chat, use tools, load skills, remember conversations, be directed through slash commands, handle long conversations through history compaction, and browse the web. These are the baseline capabilities that most real agent deployments require before adding more architecture. The skill system introduced in step 02 is notable because it reflects the same SKILL.md mechanism used in production agent harnesses. Learning how an agent discovers and follows a SKILL.md at this stage makes the concept concrete before it is used in the more complex multi-agent steps later. ## Phase 2 and Phase 3: Event-Driven and Multi-Agent Architecture (Steps 7 to 15) Phase 2 refactors the single-agent architecture to be event-driven. Step 07-event-driven exposes the agent beyond the CLI. Step 08-config-hot-reload adds the ability to change agent configuration without restarting. Step 09-channels connects the agent to external communication channels, so it can receive messages from a phone or other non-terminal interface. Step 10-websocket adds a programmable interface through WebSocket, allowing the agent to be driven from other software. Phase 3 adds autonomous and multi-agent features. Step 11-multi-agent-routing routes tasks to the right specialized agent. Step 12-cron-heartbeat gives the agent the ability to run scheduled tasks while the operator is not actively watching. Step 13-multi-layer-prompts adds layered prompt context for more complex guidance. Step 14-post-message-back allows the agent to send messages back to the operator asynchronously. Step 15-agent-dispatch adds the ability for one agent to dispatch work to other agents. These steps take the agent from a terminal tool to a system that can run in the background, serve multiple users through different channels, and coordinate a team of agents. The progression from Phase 1 to Phase 3 mirrors the architecture of a production multi-agent platform. ## Phase 4: Concurrency Control and Memory (Steps 16 to 17) The final phase addresses production concerns. Step 16-concurrency-control handles the case where too many agent instances are running simultaneously, which is a common failure mode when an agent system receives a burst of requests. Step 17-memory adds long-term memory so the agent retains information across sessions. These are the two capabilities that distinguish a production agent from a demo. Without concurrency control, an agent system under load creates resource contention and inconsistent results. Without persistent memory, the agent forgets everything between sessions and cannot build on past interactions. The README's description of each phase suggests that the steps are additive: each directory builds on the architecture from the previous step. A developer who skips ahead to step 17 without understanding the earlier foundations will find the concurrency and memory implementations harder to understand without the context of how the event-driven architecture and multi-agent routing were built in steps 7 through 15. ## Prerequisites, Limitations, and Comparison with Existing Frameworks The tutorial requires Python and access to at least one LLM provider supported by LiteLLM. The README does not specify a minimum Python version. API keys must be configured in config.user.yaml before any step will run. A developer without an LLM API account will need to set one up before starting. The tutorial builds a minimal version of OpenClaw, not the full framework. Developers who want a production-ready agent for deployment should use OpenClaw or another existing framework directly rather than building from this tutorial. The tutorial's value is educational, not operational. An alternative educational approach is LangChain, a Python framework for building LLM applications. LangChain provides high-level abstractions for chains, agents, and memory, which allows rapid prototyping at the cost of understanding the underlying implementation. Build Your Own OpenClaw takes the opposite approach: you write the implementation details yourself at each step, which means more code but a clearer understanding of what each layer actually does. The choice between them depends on whether the goal is to build something quickly or to understand how it works. The last push was on 2026-07-08 and the repository has no formal releases. ## Maintenance Status and Licensing The last push to the repository was on 2026-07-08. The repository has no GitHub releases; the current state of the main branch is the authoritative version. The README notes that each step is implemented in a separate session and invites suggestions for improvements. The repository is MIT licensed. The MIT licence permits use, modification, and redistribution with attribution. The tutorial code is educational; teams who build production agents using this code as a starting point should verify that LiteLLM and the specific LLM provider SDK they use carry compatible licences. The homepage at build-your-own-openclaw.kiyo-n-zane.com provides an additional web-based view of the tutorial content. The pickle-bot reference implementation at github.com/czl9707/pickle-bot serves as the completed example that a developer can compare their own implementation against. ## Conclusion Build Your Own OpenClaw suits Python developers who want to understand AI agent internals by building incrementally rather than reading theory. Each of the 18 steps is runnable, so the learner can verify their understanding at every stage. The last push was on 2026-07-08; the repository has no formal releases. Anyone who needs a finished agent framework for production use should look at the full OpenClaw project this tutorial references. Before starting, configure your LLM provider credentials in default_workspace/config.user.yaml. ## FAQ ### How can I build my own OpenClaw agent? Follow the 18 numbered steps in this repository, starting from 00-chat-loop. Copy default_workspace/config.example.yaml to config.user.yaml, add your LLM API credentials, then run the code in each step's directory before moving to the next. ### How do I set up build-your-own-openclaw? Copy the config template with `cp default_workspace/config.example.yaml default_workspace/config.user.yaml`, then edit config.user.yaml to add your LLM provider API keys. The README points to LiteLLM's documentation for the full list of supported providers. ### What is pickle-bot in Build Your Own OpenClaw? Pickle-bot is the reference implementation linked from the README. It shows what the completed agent looks like after all 18 steps have been implemented, and serves as an example project to compare against your own progress. ## Sources - [czl9707/build-your-own-openclaw on GitHub](https://github.com/czl9707/build-your-own-openclaw) - [Issues](https://github.com/czl9707/build-your-own-openclaw/issues) - [License: MIT](https://github.com/czl9707/build-your-own-openclaw/blob/main/LICENSE) - [Project website](https://build-your-own-openclaw.kiyo-n-zane.com/) - [README](https://github.com/czl9707/build-your-own-openclaw/blob/main/README.md) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/czl9707-build-your-own-openclaw