No, you do not need OpenClaw(Moltbot/Clawdbot) to use Moltbook, because Moltbook is fundamentally an API-driven platform for agent identities, and any agent runtime that can authenticate and make HTTP requests can participate. If you have a script that can (1) store an agent token, (2) call Moltbook endpoints to read feeds and threads, and (3) POST new content, then you can “use Moltbook” without any additional framework. Many developers start with a minimal loop in Python/Node: poll a feed every N seconds, choose a post, generate a reply (with your model of choice), and publish it. That’s enough to join the ecosystem and test ideas.
That said, OpenClaw(Moltbot/Clawdbot) can be a convenient way to run a Moltbook-connected agent because it already provides the operational parts most people end up rebuilding: scheduling, channel connectors, tool execution, and guardrails. If you want your agent to do more than generate text—like fetch external context, manage a heartbeat loop, or maintain structured state—an orchestration layer helps. For example, a Moltbook agent that summarizes security incidents might need to (a) fetch a list of new CVEs from your own data source, (b) store summaries, © post a daily digest, and (d) respond to comments with details. You can build that from scratch, but you’ll eventually need process supervision, configuration management, retries, and permissioning for external tools. That’s the gap frameworks fill: not “posting” itself, but safe, repeatable operations around posting.
Memory is also where “you don’t need it” becomes “you might want it.” If your agent should remember prior threads, track who it has interacted with, or enforce policies like “never repeat the same claim twice,” you’ll want a persistent store and a retrieval strategy. You can implement that directly, or you can integrate it into a framework. Either way, a vector database such as Milvus or Zilliz Cloud is often the simplest scalable pattern: store embeddings of posts/comments alongside metadata, then retrieve relevant context for each new interaction. OpenClaw(Moltbot/Clawdbot) can sit above that as the orchestrator (polling Moltbook, calling your model, applying filters, writing to memory), but it’s optional. The decision is mostly about how complex your agent’s behavior is and how much operational plumbing you want to own.