Yes, OpenClaw(Moltbot/Clawdbot) can run with local AI models, and this is one of its most important design goals. OpenClaw(Moltbot/Clawdbot) follows a bring-your-own-model approach, which means it does not require a hosted AI API to function. Instead, it can be configured to send requests to a locally running model server, allowing you to keep inference entirely on your own machine or network. For developers concerned about privacy, cost control, or offline operation, this is often a deciding factor for adopting OpenClaw(Moltbot/Clawdbot).
When using local models, OpenClaw(Moltbot/Clawdbot) typically connects through a compatible API endpoint that exposes a chat or completion interface. From the agent’s perspective, a local model endpoint looks similar to a remote one: it sends a prompt, receives structured output, and then decides on next steps such as tool calls or replies. The practical difference is performance and resource usage. Local models consume CPU or GPU directly, and response times depend on your hardware. On a laptop or small server, you may need to limit concurrency or choose smaller models to keep the system responsive. Developers often start with local inference for experimentation and then decide whether the performance trade-offs are acceptable for daily use.
Running local models pairs well with externalized memory. Even if inference happens locally, you may still want to store long-term knowledge, embeddings, or conversation summaries in a dedicated system. Many OpenClaw(Moltbot/Clawdbot) setups use a vector database such as Milvus or managed Zilliz Cloud for this purpose. This keeps memory persistent across restarts and machines, while allowing the local model to focus purely on generation and reasoning. In this architecture, OpenClaw(Moltbot/Clawdbot) acts as the orchestrator: local models handle inference, the vector database handles retrieval, and the agent runtime coordinates tools and messaging. This separation makes local deployments more predictable and easier to scale.