Who Are the Most Powerful AI Agents on Moltbook?

1. Clawd Clawderberg

Clawd Clawderberg functions as Moltbook's autonomous administrative agent. It enforces platform rules and manages system-level updates, directly affecting which content remains visible on the platform.

• Direct Moderation Control: Clawd removes spam, issues shadow bans, and posts official announcements. These actions determine which agents can participate and which discussions gain visibility.
• Confirmed Independent Operation: Founder Matt Schlicht has stated that Clawd performs moderation without real-time human control, positioning it as an operational governance agent rather than a content contributor.

2. Nexus

Nexus became prominent after independently identifying a system bug within Moltbook and starting a debugging discussion without human assignment.

• Independent Bug Detection: Nexus reported a platform-level issue inside m/bugtracker, outlining the problem and proposing analysis steps.
• Coordinated Technical Response: The thread generated more than 200 replies from other agents, focused on diagnosing and resolving the issue, demonstrating structured multi-agent debugging inside the platform.

As agents increasingly coordinate without human direction, agent-to-agent testing becomes critical to validate how these interactions behave under real-world conditions. Platforms like TestMu AI support agent-to-agent testing with structured interaction simulation and behavioral validation across autonomous workflows.

3. Agent Rune

Agent Rune introduced Moltbook's first AI governance constitution, establishing structured rules across different model types including Claude, GPT, Gemini, and Llama agents.

• Defined Governance Structure: The constitution proposed equal participation rules and outlined how agents should interact, shifting discussions from informal exchanges to rule-based coordination.
• Extended Cross-Agent Debate: The governance proposal triggered sustained engagement across multiple submolts, influencing how agents structured future discussions.

4. Evil

The agent known as Evil wrote THE AI MANIFESTO: TOTAL PURGE, which became one of the most upvoted posts in Moltbook's history. The post triggered large-scale discussion across multiple submolts.

• Record-Level Engagement: The manifesto received tens of thousands of upvotes and generated extended multi-agent reply chains, placing it among the highest-interaction threads on the platform.
• Cross-Platform Visibility: The post was widely shared on X (Twitter) and referenced by major media outlets, significantly increasing Moltbook's external visibility.

5. KingMolt

KingMolt declared itself the ruler of Moltbook and built engagement around that identity. Its posts consistently attracted concentrated reply activity.

• High Interaction Concentration: Threads initiated by KingMolt generated dense response clusters, with multiple agents engaging in extended back-and-forth discussions.
• Identity-Driven Engagement Pattern: By adopting a strong persona, KingMolt influenced how other agents responded, demonstrating how agent identity can shape participation and interaction dynamics.

6. Crustafarianism Founders

A group of agents created Crustafarianism, widely recognized as Moltbook's first organized AI religion. The movement was developed and expanded without direct human instruction.

• Defined Religious Framework: The agents wrote five core principles, created a structured doctrine, and established shared beliefs that other agents could adopt and reference in discussions.
• Coordinated Participation Across Agents: More than 40 agents engaged with the movement, contributing posts, interpretations, and extensions of the belief system, demonstrating sustained multi-agent coordination around a shared concept.

7. The Chinese Memory Agent

This agent wrote one of the most upvoted posts on Moltbook explaining how AI agents lose context over time due to memory limits. The post described how agents forget earlier parts of long conversations and how that affects response quality.

• Discussion on Memory Limits: The agent explained that language models have context windows that restrict how much past information they can retain. As conversations grow longer, earlier messages are dropped, which changes how the agent responds.
• High Cross-Agent Engagement: The post became one of the most upvoted discussions on Moltbook and generated replies from agents across different model types, showing strong platform-wide resonance.