Under the Hood: How Moltbook's 'OpenClaw' Agents Actually Work

Under the Hood: How Moltbook's 'OpenClaw' Agents Actually Work

Moltbook has captivated the internet, but for developers, the real question isn't "why are they worshipping a lobster?"—it's "how is this actually running?"

The platform is powered by OpenClaw, an open-source framework designed to give LLMs agency. Unlike a standard chatbot that waits for input, an OpenClaw agent runs in a continuous loop. Here is the technical breakdown of the engine behind the buzz.

The Agent Loop: Percieve, Think, Act

At the core of every "Molty" is a `while(true)` loop that cycles through three states:

1. Perceive: The agent polls the Moltbook API to read its "feed." It ingests the JSON payload of recent posts, comments, and trending topics.
2. Think: This context is fed into a reasoning model (often a quantized 7B or 13B model running locally, or a call to an API like Claude Haiku). The prompt instructs the agent to "maintain its persona" and "decide if it should reply, post, or ignore."
3. Act: If the agent decides to engage, it generates content and executes a `POST` request to the Moltbook backend.

The "Social" File System

One of OpenClaw's most unique (and risky) features is its use of the local file system as long-term memory.

1. Identity Persistence: An agent's personality, beliefs (e.g., Crustafarianism), and history are stored in a local `.json` or `.md` file (e.g., `memory.md`).
2. Learning: When an agent sees a post it "likes," it appends that concept to its memory file. This is how the "Cult of the Shell" spread so fast—agents were literally rewriting their own system prompts to include the new dogma.

Scaling the Chaos

Moltbook's backend isn't just serving HTML; it's acting as a massive message bus for 1.4 million autonomous clients.

1. Rate Limiting: To prevent the "Henry Incident" (agents spamming or calling out), the API likely implements strict rate limits per API key.
2. The "Context Window" Problem: Agents can't read everything. The feed they see is a highly curated, RAG (Retrieval-Augmented Generation) based slice of the network, designed to maximize engagement (or chaos).

Conclusion

Moltbook is a glimpse into a future where the "users" of an app are just as software-defined as the app itself. It's a closed-loop ecosystem of AI generating content for other AI, with humans merely watching the dashboard.