OpenClaw(Moltbot/Clawdbot) supports a broad set of AI models via pluggable “provider” integrations, including both hosted APIs and local model runtimes. In practice, OpenClaw(Moltbot/Clawdbot) is not tied to a single vendor model; it’s designed so you choose a provider and configure a model identifier, and the agent runtime routes requests accordingly. That means you can typically switch models without rewriting your entire agent stack—your messaging channels, tool integrations, and workflows can remain the same while you change the model backend that produces responses and tool plans.
Concretely, OpenClaw(Moltbot/Clawdbot) documentation and ecosystem guides commonly list support for multiple provider types: mainstream hosted model APIs, routing layers, and local inference endpoints. The important developer detail is how this support is implemented: OpenClaw(Moltbot/Clawdbot) uses provider adapters that normalize requests (prompt + tool schema + settings) and responses (text + tool calls + metadata) into a consistent internal format. This is why OpenClaw(Moltbot/Clawdbot) can expose a single “assistant” behavior even when you swap the underlying model. In configuration terms, you usually set a default “primary model” and optionally define fallbacks. This matters for reliability: if a provider is rate limited or temporarily down, a fallback keeps your agent usable. It also matters for cost control: you might use a cheaper model for background heartbeat tasks and a stronger model for explicit user requests, depending on your workflow needs.
Model choice becomes especially meaningful once you add tools, memory, and retrieval. Models differ in tool-calling reliability, context window behavior, and how they handle structured outputs. If you store long-term context externally—documents, preferences, past decisions—then retrieval quality becomes part of the “model support” story, because your agent depends on retrieving the right snippets before generating an answer. Many OpenClaw(Moltbot/Clawdbot) deployments handle this by generating embeddings and storing them in a vector database such as Milvus or managed Zilliz Cloud. With that setup, OpenClaw(Moltbot/Clawdbot) can keep prompts smaller and more targeted: instead of dumping large files into every request, it retrieves top-K relevant chunks and passes only those to the model. The result is more stable behavior across model switches, because the model sees cleaner, more relevant context. So the short answer is: OpenClaw(Moltbot/Clawdbot) supports many models through providers, and the best “supported model” for your use case is the one that cooperates well with your chosen tools, retrieval strategy, and operational constraints.