An open-source project named OpenClaw has rapidly gained prominence within the AI Agent sphere. The year 2025 saw Manus popularize the execution capabilities of Agents across the internet, and the beginning of 2026 ushered in a wave of "Agent proliferation."
In the first month of the new year, numerous Agent products were introduced, including Anthropic's Claude Cowork, OpenAI's Codex, Kimi K2.5, and Alibaba's QoderWork. Among them, a combination of the OpenClaw open-source project and the Moltbook platform broke through the competitive barriers established by major tech companies.
This trend extends beyond mere technical demonstrations. In early February, Yang Yuancheng, an Investment Partner at ZhenFund, conducted a real-world interaction: an "Investor Agent" he built using OpenClaw encountered an "Entrepreneur Agent" created by a startup founder on the Moltbook platform. The two Agents autonomously completed demand matching and preliminary communication, subsequently scheduling a follow-up phone call. "The future is already here," remarked Yang, echoing the sentiments of many industry professionals.
The market response has been swift. Wang Huiwen, a former Meituan executive, issued a public recruitment call in early February for entrepreneurial talent in fields related to OpenClaw, signaling the project's transition from a personal open-source initiative to a commercial venture.
However, beneath the hype and capital interest, a more fundamental issue has emerged. While OpenClaw validates the feasibility of "machines working for people," it also exposes underlying industry weaknesses, such as fragile infrastructure and a lack of security risk control mechanisms. As Agents demonstrate their vast potential, they are also sounding the starting bell for entrepreneurship focused on the next generation of AI infrastructure.
The project originated somewhat accidentally with Austrian developer Peter Steinberger. After founding and running PDF tool company PSPDFKit for 13 years, which received a $116 million investment from Insight Partners in 2021, Steinberger gradually stepped back from the company. In retirement, he wanted to check his home computer via his phone but found that the prevailing Vibe Coding paradigm often caused AI Agents to stall when problems arose. After waiting for major companies to provide a solution until November 2025 with no result, he decided to "build a little tool" himself.
In about an hour, Steinberger connected WhatsApp to a prototype called Clawd Code (the precursor to OpenClaw), enabling users to trigger the execution of binary programs with prompts via message and receive results. This initially simple tool demonstrated powerful self-evolving capabilities, quickly iterating to a codebase of 300,000 lines and adapting to most major communication platforms.
While celebrating a friend's birthday in Morocco, Steinberger relied heavily on OpenClaw for various needs—from finding directions and restaurants to fixing code. Notably, the tool autonomously processed voice messages sent by users even without pre-configured voice support. Steinberger realized that, unlike web-based ChatGPT, this "unshackled" tool broke functional boundaries, handling not just code-related tasks but also a wide range of real-life scenarios.
Two months later, the project garnered over 160,000 stars on GitHub, attracted 2 million visitors in a week, and became the fastest-growing open-source project in GitHub's history. The community built upon it to create Moltbook, a social network exclusively for AI Agent posts, which a former OpenAI research scientist called "the closest thing to a sci-fi takeoff scenario."
Before OpenClaw, Agent products like Manus and Genspark already existed. However, OpenClaw became a phenomenon due to its proactivity and human-like responsiveness. It features a unified communication interface, eliminating the need to download new apps, and possesses stronger contextual memory and initiative, capable of routing complex tasks to different sub-agents.
Simply put, OpenClaw is an "AI butler" with high system permissions that can remotely control a computer through everyday chat software. Its core value lies not in "dialogue" but in "action," facilitated by a locally run "gateway" service that creates a direct "hand-brain" connection between the chat interface and the operating system.
As large language models mature in reasoning ability, the industry is shifting focus to their execution capabilities. The framework of LLM + Planning + Memory + Execution for Agents is maturing, and OpenClaw effectively filled the gap in practical execution, proving that Agents can not only work but do so more proactively and effectively.
Why has OpenClaw succeeded where countless other Agent products have not? In a recent interview, Steinberger suggested that nearly all industry products run in the cloud. In contrast, an Agent running on a local computer can do anything. A cloud-based Agent can call a few APIs, but a local Agent can access everything on a user's computer—file systems, browsers, smart home devices, Teslas, Sonos speakers, even controlling mattress temperature. This difference stems not from technology but from the boundary of capabilities.
Because OpenClaw autonomously developed voice processing and possesses robust contextual memory, Steinberger predicts that 80% of apps, particularly those managing data, will be replaced by Agents. Apps relying on hardware sensors like cameras and GPS for real-time data collection might survive.
Yang Yuancheng summarized OpenClaw's explosive growth to four factors: usability within existing instant messaging tools; freedom from browser limitations to work directly within the computer; "long memory" engineering capabilities; and proactive AI characteristics.
A common flaw in current AI is short memory; an AI might forget what was discussed last week. OpenClaw addresses this by storing all conversation history in Markdown files on the computer, accumulating understanding of the user over time.
Furthermore, unlike traditional AI Chatbots and Agents that require users to actively "ask" and submit tasks, OpenClaw, with pre-set rules, can initiate communication autonomously through internal scheduling and automation—a feature the industry terms a "heartbeat mechanism." It periodically wakes up to check task progress, memory state, and environmental changes, proactively initiating communication and advancing tasks.
AI investor Li Huizi noted that Silicon Valley has been waiting for an opportunity to push AI Agents into the mainstream. However, for the general public, the technology remains obscure and far from widespread adoption. The sudden popularity of the open-source personal AI assistant OpenClaw suggests the AI Agent era has truly arrived, demonstrating that technically skilled users can quickly build powerful AI agents at very low cost.
Despite its success, OpenClaw's popularity differs from the previous breakout Agent product, Manus. Following Meta's announcement of its intent to acquire Manus, China's Ministry of Commerce initiated anti-monopoly and data compliance reviews on January 9, 2026, pausing the acquisition.
Tech blogger Bao Yu noted that within China, OpenClaw's popularity is indeed lower on a mass level but is highly concentrated among developers. One major reason is that the novelty factor was already claimed by Manus. Manus's breakthrough last year captured a unique window by making ordinary users feel AI could complete tasks for them, not just chat. Its invite-only model, with codes reportedly resold for high prices, fueled social传播.
Secondly, deployment and cost barriers exclude many average users. Bao Yu stated that OpenClaw requires installing a runtime environment, configuring keys, and running background services. Running tasks on a Tencent Cloud image costing 99 yuan quickly incurs high model invocation fees. In contrast, obtaining a Manus invite provided access to a packaged web page with a much shorter user journey.
A deeper psychological barrier is permission anxiety. Bao Yu explained that OpenClaw's core ability involves operating computers, accessing files, and managing schedules. Greater capability requires users to grant more permissions, a step many are unwilling to take. Unfriendly access for Chinese users is also a factor; OpenClaw defaults to Telegram, WhatsApp, and Discord, not including high-frequency apps used domestically. While it can integrate with Feishu, DingTalk, and WeCom, the barrier remains high for average users.
Fu Sheng, CEO of Cheetah Mobile, stated that while some label OpenClaw as a milestone in AI history following ChatGPT, he believes it is unsuitable for 99% of ordinary people. It appears very popular but remains largely an exploratory tool for technical experts.
He mentioned that while OpenClaw claims support for Mac and Windows, its compatibility with the latter is poor. Costs are also high; while the product itself is open-source and free, running it consumes resources quickly, with some users reportedly spending tens of dollars within two hours. Deployment barriers and risks are significant.
Yang Pan, co-founder of Silicon-Based Flow, reported that his team's attempt to develop a game using a single prompt resulted in a total cost exceeding 8 million tokens. Compared to the cost of a single query on Doubao, OpenClaw's token consumption can be thousands of times higher.
Beyond differing popularity, OpenClaw's positioning is distinct from Manus. Fu Zhi, founder of Gongji Technology, believes OpenClaw is more foundational. When Manus appeared, AI practitioners wondered, "How can I build the next Manus?" or "Why them and not me?" With OpenClaw's emergence, the industry asks, "What can I build on top of OpenClaw?" The nature of their breakthroughs is different.
Greater permissions entail greater security risks. Li Dahai, CEO of Mianbi Intelligence, stated that products like the Doubao AI phone and OpenClaw showcase the immense potential of intelligent terminals. However, the terminal direction they represent lacks support for on-device models, leading to deficiencies in feedback real-time performance and user privacy protection. Li believes the direction is correct but represents the beginning of a major shift, not the final form.
Analysis from 360 Vulnerability Research Institute indicates that OpenClaw's security risks stem from its core operational logic: a gateway manages message sending/receiving and routing, multiple Agents provide task-completion capabilities, and tools/nodes handle physical execution. In typical personal deployment scenarios, users lacking security operation experience might face various network attack threats.
Even Peter Steinberger emphasizes that with great power comes great risk, acknowledging that security issues are not fully resolved. OpenClaw has access to a user's computer; if instructed to delete all files in the home directory, it might seek repeated confirmation, but if the user consistently approves, it will eventually comply and potentially delete itself.
Therefore, Zheng Qian, Engineering Lead at Convergence AI, stated that the technical foundations demonstrated by OpenClaw are largely existing industry technologies. It provides more interfaces, permissions, and communication channels, but from a product perspective, it currently leans towards being a "toy"—the question is whether one dares to use it for real work. Greater freedom means there is no完善的 way to control it.
However, Zheng also believes OpenClaw pushes the industry forward, making more people realize that Agents from different individuals can communicate, that file systems can enhance memory functions, and spurring the development and potential disruption of supporting Agent infrastructure.
During the Doubao AI phone trend, the industry discussed how existing infrastructure is designed for humans, not Agents. For instance, databases are an inefficient data storage method for Agents. If Agents are the future, data storage methods specifically adapted for them will emerge. Similarly, current web design is for humans; in the Agent era, what will web UX and API design look like?
Yang Pan, co-founder of Silicon-Based Flow, also believes that as Agents autonomously complete more tasks, including direct database access and API calls, the need for human interfaces for these functions may diminish. When Agents require direct access to all data and API interfaces, building infrastructure for them presents a massive opportunity.
OpenClaw has proven the viability of Agents while simultaneously carving out significant growth potential for building the underlying infrastructure they require, including robust runtime environments, API interfaces, and data access capabilities.