Why OpenClaw is Trending Now and Why You Shouldn't Feel Anxious About It

In the period leading up to the Lunar New Year, OpenClaw (also known as ClawdBot or MoltBot) became incredibly popular, to the point where it even drove a significant increase in Mac Mini sales.

What is OpenClaw? Simply put, it is a new form of Agent. This Agent runs on your computer and can manipulate your files, websites, and accounts. You can send it messages through IM (Instant Messaging) applications like Slack to command it to perform tasks.

Compared to Manus, which gained popularity in early 2025, Manus is a cloud-based service where you give it a single-sentence task and it completes it automatically. OpenClaw, however, runs on your own machine and has access to all your data and task context, making it more like an all-purpose butler and assistant.

In this article, I want to discuss why OpenClaw became popular at this specific moment and why you shouldn't feel anxious because of it.

Why OpenClaw Became Popular at This Moment

Like Manus, OpenClaw is an Agent application. It is clear that the capabilities of Agent applications are evolving step-by-step. Whenever there is a sudden leap in capability, a viral application is bound to emerge.

OpenClaw's success is due to several prerequisites coming together at this point in time, pushing Agent capabilities to a new level.

I have categorized these prerequisites into three main points:

1. Continuous enhancement of model capabilities
2. Maturity of Agent frameworks
3. The emergence of the Skills protocol

Continuous Enhancement of Model Capabilities

The improvement of any Agent is inseparable from the capabilities of the underlying model. Over the past two years, Large Language Model (LLM) capabilities have been steadily increasing. However, two significant enhancements have occurred recently:

First is Claude Opus 4.5. This is an Opus model with a massive improvement in price-performance ratio—it costs only 1/3 of Opus 4.1 while offering better performance. Many Agent tasks will see a huge boost in effectiveness when using Opus 4.5.

Second is the rise of open-source models like Kimi K2 and GLM 4.6, which have reached a "usable" level. Many people use Claude Code in combination with these open-source models. While they may be slightly inferior to Claude models in terms of performance, they win on price and availability.

Current OpenClaw users are generally divided into two camps: those using the most powerful Claude Opus models to pursue the best results regardless of budget, and those using open-source models to balance costs, which is sufficient for a daily assistant. Regardless of the approach, if you went back six months, you wouldn't find a model with a comparable price-performance ratio.

Maturity of Agent Frameworks

A year ago, building an Agent was considered a difficult task that required careful design of structures, tools, and workflows. Many people used frameworks like LangGraph to orchestrate Agent processes.

Later, with the popularity of Claude Code and its design philosophy, people realized that an Agent is essentially a tool-calling loop. Other complex structures are, to some extent, redundant. As long as the model is provided with sufficient tools, current models can reliably use them, observe, reflect, and complete tasks.

This has greatly simplified Agent development. For simple tasks, the Deep Agents framework from LangChain is basically enough. Alternatively, you can use the Claude Agent SDK to easily build your own Agent system. When the framework is mature and simplified, the barrier to implementing and verifying ideas is significantly lowered.

OpenClaw is based on an Agent framework called Pi. The Pi framework is an extremely streamlined framework containing only four core tools: read, write, edit, and bash. This is a product of the maturity of Agent frameworks. Based on such a mature framework, OpenClaw is able to achieve powerful and stable functionality.

The Emergence of the Skills Protocol

While the recent popularity of "Skills" involves some hype, it does solve problems that were previously unsolvable; it isn't just a marketing concept. The emergence of the Skills protocol filled the final gap for OpenClaw's success.

In OpenClaw, Skills are like "plugins." When you need to expand an Agent's capabilities, the recommended approach is to install Skills.

Let's look at how OpenClaw would expand without Skills. Would the MCP protocol work? According to current usage patterns, one might add 30 to 50 MCP Servers, giving the Agent around 500 to 1,000 tools. Such a large number of tools results in one thing: terrifying context consumption.

The model might consume tens or even hundreds of thousands of context tokens just upon startup. A typical model (like Opus 4.5) has a 200K context window, but generally, once the context exceeds 60%, the model's capability declines, entering the "dumb zone." In this scenario, the model hits a context limit after just a few exchanges, which is fatal for Agent performance. The "all-purpose assistant" persona of OpenClaw would cease to exist.

With Skills, only a few dozen words of description enter the context when they are not loaded. Therefore, you can install dozens of Skills without worry; most will not start and will simply sit in the skill list. Only the Skills actually required for a task are loaded, which is highly efficient for context usage. This allows everyone to build and publish Skills to the community, and users can install different Skills based on their needs without worrying about side effects.

This is the most important feature of Skills: Progressive Disclosure. OpenClaw's use case is the "sweet spot" for Skills.

Why You Don't Need to Be Anxious About OpenClaw

The hype surrounding OpenClaw, coupled with praise from various quarters, makes it seem as though AI tools have evolved to an entirely new level—as if you will fall far behind the times if you don't use this tool or buy your own Mac Mini.

In the AI era, this FOMO (Fear Of Missing Out) mentality is very common. Therefore, after analyzing why OpenClaw is popular, I want to explain why you shouldn't be anxious.

Compared to Claude Code, OpenClaw does not actually represent a unique evolution in capability. Roles like local butler or all-purpose assistant have always been within Claude Code's reach. When Claude Code was popular six months ago, I wrote articles sharing various ways to use it beyond coding. In reality, there is no longer a significant difference between a programming Agent and a general-purpose Agent. Since it runs on your own computer, it naturally functions as a local butler.

OpenClaw simply makes these processes smoother, adds a few capabilities, and introduces cool usage methods, such as commanding it via an IM app. This is more of an extraordinary innovation in usage scenarios rather than an evolution in technical capability.

Once you understand this, you realize that most FOMO regarding AI products is self-inflicted. As AI becomes more powerful, the barrier to learning and using it will decrease. What you need to care about is not how to master these tools, but how to equip yourself with innovative thinking so you can play a uniquely human role in the AI era.

While OpenClaw is popular, it is currently in an awkward state: for the average person, the barrier to deploying it is actually quite high. This gives social media influencers an opportunity to create anxiety, making it seem like you've fallen behind if you haven't deployed it. In reality, OpenClaw is still a toy for geeks. You just need to wait patiently, and consumer products with lower barriers to entry will appear. It won't be too late to experience it then.

If you can't figure out OpenClaw, try playing with Claude Code or OpenCode instead. You will soon find that they are all magically converging toward the same form.