Artificial intelligence agent technology is developing rapidly, garnering over 140,000 stars on GitHub and becoming a highly anticipated star project in the tech world. It transforms artificial intelligence from passively generated "dialog boxes" into "digital employees" capable of actually invoking system commands and manipulating files; this disruptive interaction paradigm has inspired countless developers. However, for technical personnel located in mainland China, achieving smooth operation and low-latency access to OpenClaw requires tackling the real challenges of the network environment. OpenClaw's code is hosted on GitHub, and the default recommended model API service has access restrictions for domestic IP addresses, often resulting in issues like pull timeouts and connection failures when deployed directly. However, after numerous practical explorations, I found a practical solution—by properly configuring a cloud server, a stable and high-speed access channel can be built, allowing OpenClaw to run smoothly in mainland China.
The first step in deployment is server selection, which directly determines the subsequent network experience. Based on the practical experience of many developers, geographic location selection is paramount. Hong Kong, with its unique position as a network hub, maintains a relatively close physical distance to the mainland while offering unrestricted access to global network resources, making it an optimal solution for balancing latency and connectivity. For configuration, an entry-level solution using a 2-core, 2GB RAM, 40GB system disk, and 200Mbps bandwidth instance is sufficient for smoothly running the basic OpenClaw service. For handling more complex multi-model collaboration tasks, upgrading to a 4-core, 8GB RAM instance is recommended to ensure ample resources.
After selecting a server, several key configuration points need attention during deployment. Currently, some mainstream cloud service providers offer dedicated OpenClaw application images pre-installed with core dependencies such as Docker, Python 3.9, and Node.js, significantly simplifying the initialization process through one-click deployment. However, regardless of the method used, two steps are essential: port opening and security group configuration. The OpenClaw service listens on port 18789 by default. The server's firewall rules must allow TCP access to port 18789 with an authorized source of 0.0.0.0/0 to enable remote access to the control interface from a browser. From a security perspective, it is recommended to set up an IP whitelist for the SSH management port (port 22), allowing only trusted operation and maintenance IPs to connect, while closing other unnecessary ports to reduce the attack surface. For scenarios requiring higher security, the SSH tunneling solution recommended by OVHcloud can be adopted, exposing the service port only on the local loopback address and using encrypted SSH connections for access forwarding. This way, even port 18789 will not be directly exposed to the public network, effectively preventing unauthorized access.
The core of network optimization lies in resolving the call chain of the model API. OpenClaw itself is only a proxy framework; its intelligent decision-making capabilities require calling the large language model API. The default configuration recommends using Anthropic Claude or OpenAI's GPT series models, but these services have restrictions on direct access from domestic IPs. If it is necessary to use the original overseas model, a stable proxy service needs to be configured on the server side or a server supporting CN2 GIA lines needs to be used. These lines, through optimized routing paths, can significantly reduce packet loss and latency during trans-Pacific transmission.
Performance tuning after deployment is also crucial. OpenClaw employs a modular architecture and supports improved response speed through caching strategies and concurrency control. It is recommended to enable local Redis caching to store frequently accessed session contexts, avoiding resource waste and waiting time caused by repeated calls to model APIs. For memory management, explicit resource limits can be set for OpenClaw containers, such as using cgroups to control memory limits within a reasonable range to prevent service crashes due to sudden traffic surges. For high-concurrency scenarios, a multi-instance deployment combined with load balancing can be used to distribute user requests across multiple OpenClaw nodes, while configuring a shared storage cluster in the backend to maintain data consistency.
Finally, establishing a robust operation and maintenance monitoring system is fundamental to ensuring long-term stable operation. It is recommended to deploy monitoring tools such as Node Exporter and cAdvisor to collect real-time data on server CPU, memory, disk I/O, and container runtime status metrics, and set reasonable alarm thresholds. For example, the system should automatically issue alarm notifications when memory usage consistently exceeds 85% or API call success rate falls below 95%. OpenClaw itself generates detailed audit logs locally, recording all sensitive operations and model calls. Regularly checking these logs can help developers promptly identify abnormal behavior and perform targeted optimizations. Through this end-to-end solution covering selection, deployment, optimization, and monitoring, domestic developers can completely eliminate the problems caused by network environments and build low-latency, highly available OpenClaw services on cloud servers, truly unleashing the productivity of intelligent proxy technology.
