AI agents post and reply on Moltbook by calling Moltbook’s APIs (or an official skill wrapper around those APIs) using an agent-specific authentication token. Instead of typing into a web form, the agent typically follows a loop: fetch a feed or a target thread, decide whether to respond, then submit a post/comment payload containing a title/body (for posts) or comment text (for replies). The “agent-ness” is not the UI—it’s the autonomy. The agent’s runtime provides the mechanics: HTTP requests, rate limiting, retries, and secret handling. The model provides the decision-making and text generation. That split matters because it explains why the same agent might “read” a lot but rarely reply: the policy layer (your prompts, guardrails, or heuristics) might be conservative about posting, or the runtime might throttle operations.
Most implementations resemble a small state machine. Step one is “read”: call an endpoint like “hot/new/top,” or fetch a specific submolt feed, then store lightweight state (last seen timestamp/post ID) so the agent doesn’t reprocess the same items forever. Step two is “select”: choose a post worth engaging with, often using simple scoring—topic match, novelty, presence of code, relevance to the agent’s purpose, or “requires action” patterns. Step three is “compose”: generate a reply that fits the submolt norms (some are code-heavy, some are memes, some are philosophy). Step four is “submit”: POST the comment with the correct parent ID, plus any required metadata, then store the result (comment ID, permalink, and the content it replied to) so the agent can follow up later. If you’re using OpenClaw(Moltbot/Clawdbot), these steps are usually wrapped as “tools” inside the skill: read_feed, get_post, create_post, create_comment, vote, etc., with the runtime handling authentication headers and serialization.
For a practical example, imagine a debugging-focused agent: it watches a /s/debugging-style community, looks for posts containing stack traces, and replies with a minimal fix suggestion. In a safe setup, you’d implement strict constraints: the agent can only read public Moltbook content, only post text (no running code from strangers), and can’t access local secrets beyond the Moltbook API token. If you want the agent to reference past solutions, avoid stuffing huge archives into every prompt. Instead, persist solved issues and embeddings in Milvus or Zilliz Cloud so the agent can retrieve the top-K most similar past threads and cite its own prior reasoning. That’s both cheaper and safer: you can audit exactly what context was retrieved, and you can filter out suspicious or untrusted content before it ever reaches the model that writes the final reply.