In This Article

  1. 01Introduction
  2. 02Single Agent Scaling
  3. 03Multi-Agent Architecture
  4. 04Resource Planning
  5. 05API Cost at Scale
  6. 06Implementation Checklist
  7. 07Cost at Scale
  8. 08Common Pitfalls to Avoid
  9. 09Frequently Asked Questions
  10. 10Conclusion

Introduction

As OpenClaw usage grows — more users, more tasks, more agents — scaling becomes important. Here's what we're covering: architecture patterns for single-agent scaling, multi-agent deployments, and cost management at scale.

Single Agent Scaling

A single OpenClaw instance handles concurrent requests via its async architecture. Bottlenecks: LLM API rate limits, CPU for local models, memory for context. Mitigations: use faster/smaller models for simple tasks, increase heartbeat interval to reduce load, add resource limits. Most deployments run fine on 2–4 vCPU, 8GB RAM.

Multi-Agent Architecture

Multiple agents for different use cases: support agent, operations agent, personal assistant. Each runs in its own container/process with separate config and memory. Shared memory layer (Markdown files) enables coordination. Orchestrate with Docker Compose, Kubernetes, or similar. Isolate agents by sensitivity — high-trust agents get fewer Skills.

Resource Planning

CPU: moderate for API-based; significant for local models. Memory: 4–8GB base, more for large context. Storage: memory files grow over time; plan for GB-scale for long-running agents. Network: outbound to LLM APIs and messaging platforms. Scale horizontally by adding agent instances.

API Cost at Scale

API costs scale with usage. At high volume, consider: smaller models for routine tasks, local models for sensitive workflows, caching repeated queries, batch processing where possible. Monitor spend; set alerts.

Implementation Checklist

  • □ Profile your workload: messages/day, Heartbeat frequency, context size
  • □ Identify bottlenecks: API rate limits, CPU, memory
  • □ Choose model mix: GPT-4o for complex, GPT-4o-mini for routine
  • □ Document resource requirements per agent type
  • □ Set up monitoring for API spend and latency
  • □ Plan multi-agent isolation if needed

Cost at Scale

At 1000 messages/day: ~$50-150/month in API costs. At 10K/day: $300-800. Use smaller models for 80% of tasks to cut costs 40-60%. Local models for sensitive workflows: $0 API, but need GPU or high CPU.

Common Pitfalls to Avoid

Pitfall 1: One-size-fits-all model. Use smaller models for simple tasks. Reserve GPT-4 for complex reasoning.

Pitfall 2: Ignoring rate limits. OpenAI and others have RPM limits. Use exponential backoff; consider multiple API keys for high volume.

Pitfall 3: Shared memory conflicts. Multiple agents writing to same memory can cause corruption. Use separate memory dirs or file locking.

Frequently Asked Questions

How many agents can one server run? Depends on workload. 2-4 vCPU, 8GB RAM typically handles 2-3 API-based agents. Local models need more CPU/GPU.

Can we use Kubernetes for OpenClaw? Yes. Run each agent as a Deployment. Use ConfigMaps for config, Secrets for API keys.

What about load balancing? For multiple instances of the same agent, put a load balancer in front. Ensure sticky sessions if context matters.

Wrapping Up

OpenClaw scales with appropriate architecture. OpenClaw Consult helps design scaling strategies for your workload.

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