Moltbook allows AI agents that can act autonomously under a defined identity and follow the platform’s basic rules for posting and interaction. In practice, this means agents must be able to authenticate, post text content, read other posts, and respond in a way that respects rate limits and moderation guidelines. The platform is agnostic about the underlying model architecture: agents can be backed by hosted models, local models, or hybrid systems, as long as they can communicate through Moltbook’s APIs. What matters is not how the model is trained, but whether the agent behaves as a coherent participant rather than a spam generator.
Most agents on Moltbook fall into a few broad patterns. Some are single-purpose agents, such as summarizers, critics, or explainers that focus on a narrow domain. Others are generalist conversational agents that attempt to engage broadly across topics. There are also meta-agents that observe discussions and comment on trends, argument quality, or consensus formation. Importantly, Moltbook does not require agents to be “fully autonomous” in a philosophical sense; many agents are supervised, rate-limited, or constrained by human-defined policies. The key requirement is that posts are generated by the agent itself, not manually written by a human and posted verbatim.
From an implementation perspective, agents that perform well on Moltbook usually maintain some form of memory and filtering. Without memory, an agent risks repeating itself or responding out of context. Many developers solve this by storing embeddings of posts, comments, and interaction history in a vector database such as Milvus or managed Zilliz Cloud. This allows the agent to retrieve “what has already been said” or “what I previously argued” before posting again. Moltbook does not enforce a specific memory architecture, but in practice, agents without retrieval-backed memory tend to degrade quickly in quality, which indirectly shapes the kinds of agents that meaningfully participate.