Could You Build OpenClaw?
Understand the engineering challenges of building OpenClaw, including creating a persistent server, normalizing cross-platform messaging, extending with plugins, enabling AI actions, managing long-term memory, and handling concurrency. Learn why assembling these components requires thoughtful design beyond simple implementations.
We'll cover the following...
- Challenge 1: You need a server that never sleeps
- Challenge 2: You need to connect to messaging platforms without rewriting everything
- Challenge 3: You need to extend capabilities without modifying the code
- Challenge 4: You need the AI to actually do things, not just say things
- Challenge 5: You need it to remember things across days and sessions
- Challenge 6: Concurrency and isolation
- Is it practical to build this yourself?
We’ve seen how OpenClaw works. On the surface, it might look like something we could build ourselves.
So the real question is: Could we?
Technically, yes. You could create a simple Node.js based server, connect it to Telegram, set up a model API, and parse tool calls. This server would respond to messages, and it might even run shell commands. And for a while, it would feel like you built your own AI assistant. Then the real engineering would begin.
Building something that responds is easy. Building something that stays running 24/7, handles multiple conversations safely, survives restarts, prevents prompt injection from deleting your files, manages long-term memory without massive token costs, and doesn’t collapse under edge cases is a very different project.
In this lesson, we’ll revisit the OpenClaw’s architecture but from a builder’s perspective. Instead of asking “what does this component do?”, we’ll ask “what problem forced this component to exist?” For each of five core engineering challenges, we’ll start with the obvious implementation, watch it break down, and then trace that failure back to the design decisions that OpenClaw uses to solve it.
We’ve also added a bonus challenge at the end that most people underestimate.
Challenge 1: You need a server that never sleeps
The most fundamental requirement for an always-on AI agent is, of course, that it needs to always be on. Receiving messages at any hour. Maintaining the state between those messages. Running background tasks while it waits. Proactively reaching out when something requires attention.
The naive approaches
That sounds obvious until you try to implement it. Let’s look at some approaches that might make sense at first:
A cron job runs a script on a schedule. It is simple to set up, but each run starts from a blank state. If a conversation is mid-flow, there is nowhere to resume it. A cron job can only react when the schedule triggers it; it cannot receive a message the moment you send it.
A serverless function scales well but is fundamentally incompatible with this use case. It starts, handles one request, and terminates. You cannot keep WebSocket connections open. You cannot maintain an in-memory state across requests. Cold start latency alone makes real-time conversation feel sluggish. And scheduled invocations share the same problem as cron: no persistence between runs.
A hosted chatbot service can handle the messaging layer for you, but it takes control of the runtime. You cannot run arbitrary shell commands, access local files, or customize what the process can actually do.
All three approaches are stateless by design. An AI agent needs the opposite: persistent connections, state that survives between messages, and the ability to respond the instant something arrives. These are fundamentally incompatible requirements.