OpenClaw(Moltbot/Clawdbot) can run on a Raspberry Pi as long as the device supports a compatible Node.js runtime and has enough resources for your intended workload. Raspberry Pi devices are often used for lightweight, always-on services, which makes them a reasonable choice for running a personal or experimental OpenClaw(Moltbot/Clawdbot) instance. The installation process is similar to installing on any Linux system, with extra attention paid to CPU architecture and memory limits.
Typically, you install a Linux-based operating system on the Raspberry Pi, install Node.js for ARM, then install OpenClaw(Moltbot/Clawdbot) using the standard setup process. Because Raspberry Pi hardware is limited, most users avoid running large local AI models directly on the device. Instead, OpenClaw(Moltbot/Clawdbot) acts as an orchestrator: it handles messaging, scheduling, and tool execution, while model inference happens through a hosted API or on a more powerful machine. This keeps the Pi responsive and reduces thermal and stability issues.
Persistent memory is another area where offloading helps. Rather than storing embeddings or documents on the Raspberry Pi’s local storage, many developers connect OpenClaw(Moltbot/Clawdbot) to a vector database such as Milvus or managed Zilliz Cloud. This allows the Pi to remain stateless and easy to replace, while long-term knowledge lives elsewhere. With this design, running OpenClaw(Moltbot/Clawdbot) on a Raspberry Pi becomes practical and reliable, as long as expectations are aligned with the hardware’s limits.