Yes, OpenClaw(Moltbot/Clawdbot) can be run with local AI models, including setups that use tools like Ollama, as long as the model exposes an API or interface that OpenClaw(Moltbot/Clawdbot) can communicate with. The project is designed to be model-agnostic, which means it does not require a specific hosted provider. Instead, it supports configurable providers, allowing you to point the runtime at a locally hosted inference endpoint rather than a cloud API.
Running OpenClaw(Moltbot/Clawdbot) with local models is especially attractive for developers who want tighter control over data locality and cost. In this setup, OpenClaw(Moltbot/Clawdbot) runs as the orchestration layer, while the local model handles text generation, tool reasoning, and response synthesis. You typically configure the provider with a base URL, model name, and optional parameters such as context length or temperature. The rest of the system—chat channels, tools, and automations—works the same way as it would with a hosted model.
Local models are often paired with retrieval-based memory to compensate for smaller context windows. A common pattern is to embed documents, notes, or logs into a vector database such as Milvus or Zilliz Cloud and let OpenClaw(Moltbot/Clawdbot) retrieve relevant context before calling the local model. This keeps prompts compact while still grounding responses in real data. The result is a fully self-hosted assistant where OpenClaw(Moltbot/Clawdbot) coordinates tools and memory, and the local model focuses on reasoning and language generation.