Written by Matteo Giardino - CTO and AI Consultant.
Introduction: What are HiClaw Advanced Patterns?
If you've already used HiClaw to orchestrate simple agents, you know that the power lies in local collaboration. But in 2026, the challenge is to create architectures that aren't just "working," but resilient. HiClaw Advanced patterns define how to scale these orchestrations, transforming experimental scripts into enterprise-ready systems.
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1. The Supervisor-Worker Pattern ("Brain" Architecture)
In complex systems, avoiding overburdening the main agent is crucial. The Supervisor-Worker pattern separates control logic from execution.
Logic Implementation
The "Supervisor" agent (Brain) has one job:
- Analyze: Receive the user prompt.
- Decompose: Break the task into logical sub-tasks.
- Delegate: Send sub-tasks to specialized "Worker" agents.
# Conceptual HiClaw configuration
# Supervisor delegating specific tasks
workflow = TaskFlow(
supervisor="brain_agent",
workers=["researcher_agent", "coder_agent", "qa_agent"]
)2. Resilience via Retry Logic and Circuit Breakers
A common issue in multi-agent systems is cascading failure. If a worker fails, the supervisor needs to know how to react.
Our resilience strategy:
- Exponential Backoff: Don't give up on the first failure. HiClaw allows you to configure automatic retries.
- Circuit Breaker: If a worker doesn't respond, the supervisor must "isolate" it to prevent wasting resources on a dead component.
Integration with RAG and Vector Databases
An advanced pattern often overlooked is shared memory. Instead of sending the entire context to every agent, use a vector database (e.g., Qdrant or Chroma) via MCP (Model Context Protocol).
Each agent queries the database to retrieve only the information needed for its specific task. This drastically reduces token usage and increases execution speed.
Frequently Asked Questions (FAQ)
What is the main benefit of HiClaw Advanced?
Modularity. You can update a single "Worker" (e.g., replace the local model from Qwen to DeepSeek) without touching the supervisor logic.
Is it hard to implement a multi-agent system?
No, if you take a declarative approach. OpenClaw and HiClaw make this complexity transparent.
How do I manage compute costs?
By using local models managed via Ollama, the cost per query is effectively zero. The supervisor can decide to delegate heavy tasks to larger models only when strictly necessary.
Ready to implement HiClaw Advanced in your project? Contact me for a consultation.