Published on March 17, 2026
Alibaba Releases Open-Source Tool for AI Agents That Runs on Low-End Hardware
Alibaba has open-sourced the HiClaw and CoPaw bundle – a lightweight solution for AI agents that consumes significantly less memory and runs locally.
Products 3 – 5 minutes min readOne of the most significant challenges in running AI agents is their high resource requirements. For a model to function properly, it needs either a powerful machine or the cloud. Alibaba has attempted to solve this problem and recently open-sourced a bundle called OpenClaw – two tools, HiClaw and CoPaw, that together allow AI agents to run with significantly lower memory consumption.
What Is OpenClaw and What Is It For?
AI agents are more than just language models that answer questions. They are systems capable of performing tasks: opening a browser, clicking buttons, filling out forms, and following links. Simply put, such an agent can do what a person typically does on a computer – but automatically.
For an agent to understand what's happening on the screen, it needs to 'see' the interface in some way. Typically, this is done by taking screenshots and feeding them to the model, but images consume a lot of memory and require powerful hardware. This is where HiClaw offers a different approach.
HiClaw: Fewer Images, More Structure
Instead of feeding heavy screen images to the model, HiClaw translates the visual content of an interface into structured text. It's something like a page layout: a button here, an input field there, a link over here. The model reads this layout and understands what it's working with – without needing to process a full-fledged screenshot.
The result is a significant reduction in memory consumption. This allows agents to run on devices that were previously unsuitable for such tasks.
CoPaw: The Agent That Doesn't Lose the Thread
The second part of the bundle is CoPaw. It is a task management system for the agent. In short, it helps the agent not to 'forget' what it's doing and to execute complex, multi-step instructions sequentially without losing context.
When an agent operates in a browser or an application interface, tasks are rarely limited to a single action. It needs to navigate to a page, find the right element, enter data, and confirm – all in a sequence. CoPaw ensures that this chain of actions isn't broken.
Why This Is Interesting Right Now
The field of AI agents is actively developing: more and more companies and developers are looking to automate routine tasks using models. But most serious solutions either require powerful infrastructure or work only through the cloud – which raises questions of cost and privacy.
OpenClaw is designed for local execution. This means the agent can run directly on your computer without sending data to third-party servers. For corporate tasks or simply for those who don't want to share data with the cloud, this is fundamentally important.
The open-source code adds another advantage: developers can study its architecture, adapt it to their needs, and integrate it into their own products.
Who Is This Primarily For?
If you're a developer and want to try running an AI agent without the cloud and without a top-tier GPU, OpenClaw could be an interesting starting point. The HiClaw + CoPaw bundle lowers the barrier to entry and makes experimenting with agents more accessible.
For a broader audience, this is more of a signal about the direction the industry is heading: AI agents are gradually ceasing to be an exclusively cloud-based story and are starting to 'fit' onto standard hardware. This changes both where they can be applied and who can use them.
What Remains Unknown
It's still hard to say how well OpenClaw will handle real-world tasks compared to more 'heavyweight' solutions. Reducing memory consumption is great, but the question of the agent's accuracy and reliability in complex scenarios remains open. How it will perform in practice – only time and the community that starts working with it will tell.
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How This Text Was Created
This material is not a direct retelling of the original publication. First, the news item itself was selected as an event important for understanding AI development. Then a processing framework was set: what needs clarification, what context to add, and where to place emphasis. This allowed us to turn a single announcement or update into a coherent and meaningful analysis.
We openly show which models were used at different stages of processing. Each performed its own role — analyzing the source, rewriting, fact-checking, and visual interpretation. This approach maintains transparency and clearly demonstrates how technologies participated in creating the material.
The neural network studies the original material and generates a coherent text
Correction of errors, inaccuracies, and ambiguous phrasing
Generating a textual prompt for the visual model
Generating an image based on the prepared prompt
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