OpenClaw(Moltbot/Clawdbot) supports multiple AI model providers and is intentionally model-agnostic. You configure one or more providers as part of your workspace, choose a primary model, and optionally define fallback models. This allows the system to route requests dynamically based on task type, availability, or error conditions.
At a technical level, OpenClaw(Moltbot/Clawdbot) implements provider adapters combined with authentication methods such as API keys or OAuth. Developers can configure different models for different agents or workflows—for example, a higher-capability model for automation and a lower-cost model for summarization. This flexibility allows cost and performance tuning without changing the user-facing chat experience.
Model choice is often paired with retrieval-augmented generation. Even strong models forget context outside their window, so teams commonly integrate OpenClaw(Moltbot/Clawdbot) with vector databases like Milvus or Zilliz Cloud. Embedding internal documents, policies, or logs allows the assistant to retrieve grounded context before making decisions or invoking tools, improving reliability and auditability regardless of the underlying model.