Building a 24/7 AI Assistant with OpenClaw + Crazyrouter

Detailed Zenn tutorial on building an always-on AI assistant with OpenClaw and Crazyrouter. Covers deployment to advanced techniques.

Crazyrouter handles multi-model routing and cost optimization; OpenClaw provides the Agent runtime framework. Combined for 24/7 service.

A practical end-to-end guide for developers wanting to build personal AI assistants.

The article builds a complete AI Agent system from scratch, including multi-model routing, cost optimization, cross-platform messaging, and memory persistence. For developers wanting to build their own Agentic AI systems, this is a rare end-to-end practical guide. Crazyrouter's intelligent routing strategy is especially valuable in high-frequency scenarios like AI Coding.

This tutorial details building a 24/7 AI assistant with OpenClaw and Crazyrouter.

Architecture

Two core components: OpenClaw handles agent runtime, memory management, and tool calls; Crazyrouter handles multi-model routing, request optimization, and cost control. Connected via API.

Deployment Steps

1. **Install OpenClaw**: Global npm install, configure gateway and agent parameters

2. **Configure Crazyrouter**: Set model priorities, fallback chains, cost limits

3. **Connect messaging channels**: Telegram/Discord/Slack for cross-platform messaging

4. **Setup memory system**: MEMORY.md and daily notes auto-maintenance

5. **Deploy heartbeat**: Scheduled task checks to keep agents proactive

Advanced Techniques

Cost control: Crazyrouter auto-selects models by request type — small models for simple chats, large for complex tasks. Set daily budget caps.

Multi-agent collaboration: Configure multiple agents with different roles, using sessions_spawn for subtask delegation.

Persistent memory: MEMORY.md and memory/ directory for cross-session long-term memory. Regular heartbeat maintenance and memory refinement.

Notes

  • Ensure stable gateway service (systemd managed)
  • Set reasonable heartbeat intervals (30-60 min recommended)
  • Monitor token consumption to avoid unexpected bills

Industry Trend Connection

24/7 AI Agent systems represent a typical Agentic AI deployment scenario. Multi-model routing is a key strategy for addressing LLM cost challenges—using lightweight models for simple tasks and high-end models for complex AI Coding tasks to achieve optimal cost-quality balance. As MCP protocol standardization progresses, Agent tool invocation capabilities will further enhance.

In-Depth Analysis and Industry Outlook

From a broader perspective, this development reflects the accelerating trend of AI technology transitioning from laboratories to industrial applications. Industry analysts widely agree that 2026 will be a pivotal year for AI commercialization. On the technical front, large model inference efficiency continues to improve while deployment costs decline, enabling more SMEs to access advanced AI capabilities. On the market front, enterprise expectations for AI investment returns are shifting from long-term strategic value to short-term quantifiable gains.

However, the rapid proliferation of AI also brings new challenges: increasing complexity of data privacy protection, growing demands for AI decision transparency, and difficulties in cross-border AI governance coordination. Regulatory authorities across multiple countries are closely monitoring these developments, attempting to balance innovation promotion with risk prevention. For investors, identifying AI companies with truly sustainable competitive advantages has become increasingly critical as the market transitions from hype to value validation.