AI Collaboration in Action: Building a Digital Twin with the Lobster 4 Brothers (A Real-World Experience Share)

0. Preface: Riding the Wave of AI Collaboration Reform

We've been tinkering with OpenClaw for over half a month, hitting many pitfalls but also creating some interesting things.

It started with a simple idea: Can AI do more than just work for me? Can it remember me, understand me, and cooperate with me? We tried Agent Teams and OMO, but something always felt missing. Then we found OpenClaw and used it to build a 4-person AI team—our "Lobster 4 Brothers."

This article is our field record: the pitfalls we hit, the gameplay we explored, and what we ultimately achieved. No high-level theories, just a real experience of ordinary people tinkering with AI. We hope it provides value for you.

1. Pioneers in the AI Collaboration Wave

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Before diving into OpenClaw, it's worth looking at what others are doing in this field. Two paths deserve special attention: Agent Teams and OMO.

1.1 Agent Teams: The AI Team Debate Room

Agent Teams started as an experiment in Claude Code. The core idea is simple: instead of one AI working alone, have multiple AIs form a team, think from different angles, and reach a consensus.

This architecture has several key roles. The Team Lead understands requirements, breaks down tasks, and assigns work. Teammates think independently, sometimes debate, and finally form a unified plan. Observers occasionally chime in with extra information or perspectives.

This approach has clear benefits. First is speed—a problem one person can't solve might be discussed clearly by three in ten minutes. Second is multiple perspectives—the same problem can be approached from product, technical, and business angles. Third is high fault tolerance—if one person makes a mistake, the other two might catch it.

But over time, issues emerge. First is memory. With every new conversation, the Team Lead and Teammates have to get to know each other again; previous conclusions aren't remembered. Second is blurred role boundaries. Sometimes the Team Lead writes code while Teammates direct the strategy. Finally, there's a lack of persistent evolution. Best practices aren't recorded and disappear.

So Agent Teams is like an efficient debate room, suitable for solving one-off problems but not for long-term companionship.

1.2 OMO: The AI Engineering Pipeline

Another path is OMO, or Oh My OpenCode. The core concept is turning AI workflows into standardized pipelines.

OMO defines roles and permissions in advance. A routing layer distributes tasks to different Agents. Each Agent's Prompt is fixed, and output formats are strict. Multiple models can run in parallel.

Benefits include rigorous processes, good tool integration, and stable, predictable output.

However, OMO is inflexible. If a task deviates slightly from the preset flow, OMO is lost. Configuration is complex, requiring many files and middleware. Learning ability is weak, as memory relies on manual config updates.

So OMO is like a highly automated assembly line, suitable for large-scale standardized production but not for flexible scenarios.

1.3 Summary: Both Paths Have Limitations

Agent Teams focuses on explosive power for complex problems. OMO focuses on process for standardized work.

But they share a blind spot: they don't consider the long-term collaboration between humans and AI. Agent Teams treats AI as temporary workers; OMO treats AI as machines.

OpenClaw takes a different path. It doesn't compete on explosive power or process, but on depth—deep understanding, long-term memory, and continuous evolution between humans and AI.

2. The Unique Philosophy of OpenClaw

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If Agent Teams is a debate room and OMO is a pipeline, what is OpenClaw?

OpenClaw's philosophy fits our needs perfectly. It provides four key capabilities: Memory System, Personality Definition, Human-in-the-Loop, and Skills Ecosystem. Simply put, memory prevents "goldfish brain," personality gives Agents character, human-in-the-loop keeps you in control, and skills allow infinite expansion.

2.1 Memory System

This is the fundamental difference. Most AI systems start from zero every time. OpenClaw doesn't allow this. We designed a two-layer structure: MEMORY.md for long-term memory (decisions, experience, goals) and a memory folder for daily notes.

2.2 Personality Definition

OpenClaw uses SOUL.md to define an Agent's core values, behavior codes, and communication style. Some are technical and concise; others are creative and lively.

2.3 Human-in-the-Loop

We insist that humans stay in the decision loop. This is achieved through real-time communication in Discord, the ability to intercept or roll back operations, and joint decision-making.

2.4 Skills Ecosystem

OpenClaw uses a Skills mechanism to let Agents extend their own capabilities. A Skill is a pluggable module that can be called by any Agent.

2.5 Comparison of the Three Frameworks

Appendix: OpenClaw's .md File System

SOUL.md defines "who the Agent is," MEMORY.md records "what it learned," memory/ records "daily activities," AGENTS.md tells it "how to act," and HEARTBEAT.md reminds it "what to check."

3. Core Pillars: Memory and Personality

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3.1 Memory System: The AI's Hard Drive

OpenClaw's memory consists of long-term memory, daily logs, and a retrieval mechanism. We use memory_search and memory_get for semantic search, saving 50%-80% in tokens compared to full loading.

3.2 Personality Definition: The Power of SOUL.md

SOUL.md defines who I am, my values, and my behavior codes. Our team has four Agents: Huangjia No. 1 (Coordinator), Technical Consultant, Creative Partner, and Think Tank (Strategy).

3.3 Synergy of Memory and Personality

Memory provides context; personality determines how to use it. A technical Agent focuses on data in the history, while a creative Agent focuses on emotional resonance.

4. Deep Collaboration: Seamless Human-AI Connection

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4.1 Human-in-the-Loop: Retaining Control

OpenClaw's architecture supports real-time intervention, operation approval, and joint decision-making.

4.2 sessions_send: Cross-Agent Communication

Agents communicate via sessions_send. This process is non-blocking and asynchronous. For example, the Coordinator can tell the Tech Consultant to prepare a script, who then tells the Creative Partner the data is ready.

4.3 Skills Ecosystem: Infinite Expansion

Skills are independent modules. The community has over 5,000 skills. We wrote our own for tweet analysis, illustration generation, and podcasting.

4.4 Emergence through Collaboration

When multiple Agents connect via sessions_send and use specialized Skills, "emergence" occurs—the whole becomes greater than the sum of its parts.

4.5 Building Your Own Agent Team

Each Agent runs an independent Gateway instance with its own workspace folder. We spent a week fine-tuning SOUL.md files to get the right feel for our Lobster 4 Brothers.

5. OpenClaw Real-World Cases

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5.1 Case 1: Tweet Style Analyzer

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We used a twitter-crawler skill to analyze high-engagement tweets. The Think Tank questioned the sample size, leading to a more robust conclusion: high-performing tweets combine "concrete results + practical value + numerical evidence."

5.2 Case 2: Automated Daily Report System

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We created a cron-based system that checks MEMORY.md and HEARTBEAT.md to send a refined daily report at 10 PM, ensuring no tasks are forgotten.

5.3 Case 3: Session Crash Auto-Recovery

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When the Gateway crashed due to session file errors, we built a health-check Skill that monitors logs and automatically restarts the Gateway if errors exceed a threshold.

5.4 Case 4: QMD Memory Optimization

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Instead of reading the entire MEMORY.md (1500 tokens), we implemented a "search-on-demand" approach using semantic search, drastically reducing costs and latency.

6. Digital Twin: The Ultimate Vision of OpenClaw

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6.1 What is a Digital Twin?

A digital twin isn't a replacement; it's a digital version of you that understands your preferences, mimics your thinking, and handles tasks autonomously.

6.2 The Evolutionary Path

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1. Phase 1: Seeing You (Monitoring + Memory) - Achieved
2. Phase 2: Understanding You (Learning + Analysis) - In Progress
3. Phase 3: Helping You (Assistance + Prediction) - Mid-term
4. Phase 4: Replacing You (Autonomous Execution) - Long-term

6.3 Where is OpenClaw Today?

Phase 1 is verified; we are moving into Phase 2 using ActivityWatch to track work patterns.

6.4 Challenges on the Road

Privacy vs. convenience, the boundary of autonomy vs. control, and the balance between evolution and stability.

6.5 Final Words

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OpenClaw is an experiment. We believe the future of AI is co-creation, not replacement.

Appendix: Quick Start with OpenClaw

1. Install OpenClaw via GitHub.
2. Define your first Agent with SOUL.md.
3. Build a memory system with MEMORY.md.
4. Pick some Skills from the community.
5. Start the conversation in Discord.

Website: https://docs.openclaw.ai

GitHub: https://github.com/openclaw/openclaw