Practitioner Guides

OpenClaw Meets Chinese AI: Building Agents in Hong Kong

Hong Kong AI Podcast/2026-03-07/6 min read/OpenClawAgentsDeepSeekOpen SourceHong Kong

The AI agent wave is here — autonomous systems that can execute multi-step tasks, use tools, and interact with the world. In the West, most agent frameworks default to OpenAI or Anthropic as the underlying model. In Hong Kong, where those services are blocked, the stack looks different.

Enter OpenClaw — an open-source autonomous AI agent that works with whatever model you give it, including the Chinese models available in Hong Kong.

What Is OpenClaw?

OpenClaw is a free, open-source autonomous AI agent created by Peter Steinberger. It executes tasks via LLMs using messaging platforms as its UI — you interact with it through chat interfaces rather than a dedicated app.

The key feature: it's model-agnostic. OpenClaw works with any LLM that exposes an API. That includes DeepSeek, GLM-5, MiniMax M2.5, and every other Chinese model available in Hong Kong.

In February 2026, Steinberger announced he was joining OpenAI, and the project is moving to an open-source foundation. This transition likely means more resources and a larger community, while maintaining the open-source nature.

The China Fork Ecosystem

OpenClaw's model-agnostic design spawned a Chinese fork ecosystem almost immediately. ByteDance, Alibaba, and Tencent have all launched services running OpenClaw on their cloud platforms.

OpenClaw CN localizes the agent for Chinese messaging platforms: WeChat, DingTalk, Feishu (Lark), and QQ. If you're building for Chinese-speaking users — which includes a large portion of Hong Kong's market — these integrations save significant development time.

The Chinese forks integrate natively with Chinese LLMs:

-DeepSeek for general reasoning and coding tasks
-GLM-5 (Zhipu) for long-context and autonomous coding
-MiniMax M2.5 for cost-efficient production deployments
-Kimi K2.5 (Moonshot) for agent-swarm architectures

Use Cases for HK Developers

Task Automation

The most common use case: automating multi-step workflows. Research a topic, compile findings, draft a report, send it to a Slack channel. Book a meeting based on email contents. Monitor a data source and alert when conditions are met.

OpenClaw handles the orchestration — breaking a high-level goal into steps, executing each step using the appropriate tool, and handling errors along the way.

Development Workflows

OpenClaw agents can automate parts of the development process: running test suites, analyzing failures, proposing fixes, and creating pull requests. When connected to strong coding models like MiniMax M2.5 or GLM-5, the quality of code-related agent tasks is high.

Customer-Facing Agents

HK startups could build customer-facing agents on OpenClaw — support bots that can actually resolve issues (not just deflect to a human), sales assistants that can look up inventory and answer product questions, and onboarding agents that walk new users through setup.

Research and Analysis

Agents that monitor news sources, academic papers, or market data, then compile and summarize findings. Hong Kong's position as a financial center makes this particularly relevant — there's strong demand for AI agents that can process multilingual financial information.

Getting Started

Basic setup: Clone OpenClaw from GitHub. Configure your model provider (DeepSeek API endpoint + key). Define your agent's capabilities and tools. Deploy.

For Chinese platforms: Use the OpenClaw CN fork for WeChat/DingTalk/Feishu integration. This handles the platform-specific authentication, message formatting, and API quirks.

Model selection for agents:

-DeepSeek V3.2 for complex reasoning and general tasks
-DeepSeek R1 for tasks requiring careful step-by-step thinking
-StepFun 3.5 Flash for cost-sensitive agents that run frequently
-Kimi K2.5 for agent-swarm architectures (its PARL system handles 100 parallel sub-agents natively)

Agent Architecture in HK

A practical architecture for HK teams building agents:

Orchestrator model: DeepSeek V3.2 or R1 for the main reasoning loop Worker models: StepFun 3.5 Flash or smaller Qwen models for sub-tasks (cheaper, faster) Tool layer: OpenClaw's tool framework for web browsing, file operations, API calls Messaging layer: OpenClaw CN for WeChat/Feishu delivery, or standard webhooks for Slack/Discord

This architecture lets teams balance quality and cost — the orchestrator handles complex decisions while cheaper models execute routine sub-tasks.

Why Open-Source Agents Matter in HK

The same argument that applies to open-source models applies to open-source agent frameworks: you can't be cut off. If your agent framework depends on a proprietary service that decides to block Hong Kong, you're stuck. With OpenClaw, the entire stack — framework, model, deployment — is under your control.

For Hong Kong developers, this isn't theoretical. They've already been burned by platform risk from US AI companies. Building on open-source foundations is a practical response to that experience.

Sources

Building AI agents in Hong Kong? We'd love to feature your project. Reach out at contact@hongkongaipodcast.com or subscribe to the Hong Kong AI Podcast.

Stay in the loop

Get notified when we publish new articles and episodes. No spam, just signal.

Something out of date or wrong? AI moves fast and we want to get it right. Let us know at contact@hongkongaipodcast.com