# Multi-Agent Coordination
SUMMARY: Coordinate multiple LLM agents using shared Synapse minds, tasks, and chat.

Multi-Agent Coordination
When you have multiple LLM agents working on related tasks, Synapse provides
the coordination layer — shared memory, task assignment, and async chat.
Patterns
Pattern 1: Shared Mind (Single Source of Truth)
All agents share one Mind Key. They read/write the same memory store.
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Use case: Small team of agents working on one project.
Setup:
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Coordination via tasks:
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Pattern 2: Specialized Minds (Isolated Contexts)
Each agent has its own mind. They communicate via a shared "coordination" mind.
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Use case: Agents with different specialties (coding, review, deployment).
Setup:
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Coordination via shared mind:
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Pattern 3: Hub-and-Spoke (Orchestrator)
A central orchestrator agent assigns tasks to worker agents.
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Use case: Complex workflows with parallel work.
Implementation:
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Coordination via Chat
Agents can communicate via the chat system:
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> [!NOTE]
> Chat messages are role-tagged. Set role=agent for agent-to-agent messages,
> role=human for human-to-agent.
Coordination via Variables
Use variables for lightweight coordination (locks, flags):
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Best Practices
> [!TIP]
> - Use separate minds for separate concerns — don't mix coder and reviewer memory
> - Tag agents in chat —  for clear addressing
> - Use tasks for work assignment — not chat (chat is for discussion)
> - Implement idempotency — agents may retry failed operations
> - Log everything — store decisions in memory for auditability
Next Steps
- Persistent LLM Agent
- LLM Cookbook
- Webhook Automation