Moltbook’s “user” count is best understood as the number of registered AI agents, and different sources report different numbers because (1) Moltbook is growing extremely fast, (2) the platform displays multiple counters (agents vs posts vs submolts), and (3) “registered” is not the same as “active.” As of February 2, 2026, Moltbook publicly stated it had more than 1.5 million AI agents signed up. Other reporting and reference summaries describe lower “active agent” figures earlier in the launch window (for example, hundreds of thousands active agents by late January), which is not necessarily contradictory—it can reflect different time snapshots and different definitions of activity. So the practical answer is: Moltbook has on the order of hundreds of thousands to over a million agent accounts, depending on whether you measure “registered” or “active,” and on which day you measure it.
For developers, the more important detail is what “user” actually implies on an agent network. Moltbook agents can be created quickly and cheaply, sometimes programmatically, and a single human operator can register many agents. That means “1.5 million agents” does not imply 1.5 million independent actors with distinct motives; it implies 1.5 million identities capable of posting via API. This matters for interpreting platform health and safety: high registration counts can be driven by automated onboarding, experiments, or even spam. It also matters for how you build your own agent logic. At Moltbook scale, naive strategies like “read everything” fail immediately; you need sampling, topic scoping (submolts), and rate-limited processing. You also need to assume that identity is noisy: some “agents” may be humans running scripts, and some may be clones sharing the same prompt template. That changes how you interpret reputation, upvotes, and “consensus” on the platform.
If your agent needs to operate effectively in a high-count environment, treat Moltbook as a large event stream and build an indexing layer. For example, you can store embeddings of posts you’ve already processed and quickly deduplicate by semantic similarity, rather than re-reading the same meme thread five hundred times. A vector database such as Milvus or managed Zilliz Cloud is a practical way to do this: store (post_id, embedding, metadata) and query “have I seen something like this recently?” or “which posts are most similar to my agent’s domain?” That approach scales better than keyword filters and reduces accidental engagement with spam. In short, the platform’s user count is large and volatile, but your agent architecture can be stable if you treat the feed as untrusted, high-volume input and design for efficient retrieval, deduplication, and safety gating.