Multi-Agent Systems

Build coordinated multi-agent systems for complex tasks.

When to Use This Skill

Invoke this skill when:

• Tasks require multiple specialized agents
• Building orchestrator-worker patterns
• Implementing agent workflows
• Coordinating parallel execution

Parameter Schema

Parameter	Type	Required	Description	Default
task	string	Yes	Multi-agent goal	-
architecture	enum	No	orchestrator-worker, hierarchical, peer-to-peer	orchestrator-worker
framework	enum	No	langgraph, autogen, crewai	langgraph

Quick Start

from langgraph.graph import StateGraph, MessagesState
from langgraph.prebuilt import create_react_agent

# Create specialized agents
researcher = create_react_agent(llm, [web_search])
coder = create_react_agent(llm, [execute_code])

# Build graph
graph = StateGraph(MessagesState)
graph.add_node("researcher", researcher)
graph.add_node("coder", coder)
graph.add_conditional_edges("coordinator", router)

Architecture Patterns

Orchestrator-Worker

Orchestrator (Opus)
    ├── Researcher (Sonnet)
    ├── Analyst (Sonnet)
    └── Writer (Haiku)

Hierarchical

Manager (Opus)
    ├── Research Lead (Sonnet)
    │   ├── Web Researcher (Haiku)
    │   └── Doc Analyst (Haiku)
    └── Engineering Lead (Sonnet)
        ├── Frontend Dev (Haiku)
        └── Backend Dev (Haiku)

Model Allocation

Agent Type	Model	Rationale
Orchestrator	Opus	Complex planning
Specialist	Sonnet	Reasoning
Worker	Haiku	Simple tasks

Troubleshooting

Issue	Solution
Agents not coordinating	Check message bus
Deadlock	Add timeout, detect cycles
Inconsistent results	Synchronize state
High costs	Use cheaper models for workers

Best Practices

• One job per agent (single responsibility)
• Orchestrator handles planning only
• Use checkpointing for long workflows
• Implement circuit breakers per agent

Related Skills

• ai-agent-basics - Single agents
• agent-memory - Shared state
• tool-calling - Per-agent tools

References

• LangGraph Multi-Agent
• Anthropic Multi-Agent