2442 Pattern Comparison 06f08cff

Multi-Agent Pattern Comparison

Quick reference for choosing the right pattern for your use case.

Pattern Overview Table

Decision Tree

Start: Do you need multiple agents?
├─ No → Use simple single-agent pattern
└─ Yes → Continue

    Can tasks be done in parallel?
    ├─ Yes → Orchestrator-Worker Pattern
    └─ No → Continue

        Is routing decision deterministic?
        ├─ Yes → Router Pattern
        └─ No → Continue

            Do agents need to collaborate?
            ├─ Yes → Supervisor Pattern
            └─ No → Handoffs Pattern

Detailed Comparison

Supervisor Pattern

Architecture:

User → Supervisor ⇄ Agent1
              ⇄ Agent2
              ⇄ Agent3
         ↓
       END

Characteristics:

• Coordination: Central supervisor makes all routing decisions
• Routing: Dynamic, context-aware, LLM-based
• Communication: All agents communicate through supervisor
• Flexibility: High - can adapt to changing requirements
• Cost: Higher (LLM calls for routing)
• Latency: Higher (sequential routing decisions)

Use When:

• Workflow is complex and context-dependent
• Agents need to collaborate or share information
• Routing logic is too complex for simple rules
• You need centralized control and visibility

Avoid When:

• Simple linear workflow
• Performance/latency is critical
• Routing is deterministic
• Budget is constrained

Example Use Cases:

• Research + writing + review pipeline
• Customer support with escalation
• Multi-step analysis tasks
• Complex decision-making workflows

Router Pattern

Architecture:

User → Router → Agent1 → END
           ├→ Agent2 → END
           └→ Agent3 → END

Characteristics:

• Coordination: None (one-shot routing)
• Routing: Deterministic, rule-based or simple classification
• Communication: No inter-agent communication
• Flexibility: Low - fixed routing logic
• Cost: Lower (minimal LLM usage)
• Latency: Lower (single routing decision)

Use When:

• Routing can be determined from initial request
• Tasks are independent
• Low latency is important
• Clear categorization exists

Avoid When:

• Agents need to collaborate
• Dynamic routing needed
• Multi-step workflows required

Example Use Cases:

• Customer support ticketing (sales/support/billing)
• Content categorization
• Request triage
• Intent-based routing

Orchestrator-Worker Pattern

Architecture:

User → Orchestrator → Worker1 ┐
                  → Worker2  ├→ Aggregator → END
                  → Worker3 ┘

Characteristics:

• Coordination: Orchestrator for task decomposition, Aggregator for synthesis
• Routing: Task decomposition into parallel subtasks
• Communication: Workers are independent, results aggregated
• Flexibility: Medium - handles parallelizable tasks
• Cost: Higher (multiple parallel LLM calls)
• Latency: Lower (parallel execution)

Use When:

• Tasks can be decomposed into independent subtasks
• Subtasks can run in parallel
• Throughput is important
• Results need synthesis

Avoid When:

• Tasks are sequential
• Dependencies between subtasks
• Resource constraints (memory, API limits)

Example Use Cases:

• Multi-source research (web, academic, news)
• Parallel document processing
• Distributed data analysis
• Concurrent API calls

Handoffs Pattern

Architecture:

User → Agent1 → Agent2 → Agent3 → END
       (with context passing)

Characteristics:

• Coordination: Agent-to-agent handoffs
• Routing: Sequential with explicit handoff points
• Communication: Direct agent-to-agent with context preservation
• Flexibility: Medium - agents control handoffs
• Cost: Medium (sequential LLM calls)
• Latency: Medium (sequential execution)

Use When:

• Clear sequence of specialized tasks
• Each agent builds on previous work
• Context must be preserved
• Agents have distinct responsibilities

Avoid When:

• Parallel execution needed
• Complex routing logic required
• No clear sequence exists

Example Use Cases:

• Research → Write → Edit pipeline
• Data collection → Analysis → Reporting
• Planning → Execution → Review
• Draft → Review → Approval workflows

Performance Comparison

Latency

Fastest to Slowest:

1. Router - Single routing decision
2. Orchestrator-Worker - Parallel execution
3. Handoffs - Sequential but direct
4. Supervisor - Sequential with routing overhead

Token Usage

Most Efficient to Least:

1. Router - Minimal LLM calls
2. Handoffs - One call per agent
3. Orchestrator-Worker - Decomposition + aggregation + workers
4. Supervisor - Routing decisions + agent work

Complexity

Simplest to Most Complex:

1. Router - Simple routing logic
2. Handoffs - Sequential flow
3. Orchestrator-Worker - Parallel coordination
4. Supervisor - Dynamic routing + coordination

Cost Considerations

Token Cost Formula

Router:

Cost = routing_decision + agent_execution

Supervisor:

Cost = Σ(routing_decision_i) + Σ(agent_execution_i)
where i = number of routing iterations

Orchestrator-Worker:

Cost = decomposition + (worker_execution × num_workers) + aggregation

Handoffs:

Cost = Σ(agent_execution_i) for each agent in sequence

Cost Optimization Strategies

Router:

• Use keyword matching instead of LLM for simple cases
• Cache routing decisions for similar queries

Supervisor:

• Use cheaper models for routing (gpt-4o-mini)
• Implement routing caches
• Add early termination logic

Orchestrator-Worker:

• Limit number of parallel workers
• Use cheaper models for workers
• Implement result streaming

Handoffs:

• Summarize context before handoff
• Use cheaper models where appropriate
• Skip unnecessary handoffs

Scaling Characteristics

Horizontal Scaling

Best to Worst:

1. Orchestrator-Worker - Natural parallelization
2. Router - Independent agent execution
3. Handoffs - Can parallelize within agents
4. Supervisor - Sequential bottleneck

Vertical Scaling (Complexity)

How Each Pattern Scales with Complexity:

Router:

• Scales poorly - routing logic becomes complex
• Consider upgrading to Supervisor for complex cases

Supervisor:

• Scales well - LLM handles complexity
• May need hierarchical supervisors for very complex cases

Orchestrator-Worker:

• Scales well - add more workers
• Aggregation may become bottleneck

Handoffs:

• Scales moderately - add more agents in sequence
• Long chains may lose context

Debugging Difficulty

Easiest to Hardest:

1. Router - Single decision point, clear flow
2. Handoffs - Linear flow, easy to trace
3. Orchestrator-Worker - Parallel execution can be confusing
4. Supervisor - Dynamic routing hard to predict

Debugging Strategies by Pattern

Router:

• Log routing decision with input query
• Test routing logic with unit tests

Supervisor:

• Use LangSmith to visualize routing decisions
• Log supervisor reasoning at each step
• Add routing decision validation

Orchestrator-Worker:

• Track worker progress and failures
• Log aggregation inputs
• Monitor parallel execution

Handoffs:

• Log handoff points and context
• Visualize handoff sequence
• Track context size

Migration Paths

Upgrading Patterns

Router → Supervisor: When routing becomes too complex:

# Before: Simple router
def route(query):
    if "sales" in query:
        return "sales_agent"
    ...

# After: Supervisor with dynamic routing
def supervisor(state):
    decision = llm.invoke(f"Route this query: {state['query']}")
    return {"next": decision.content}

Handoffs → Supervisor: When you need dynamic routing:

# Before: Fixed handoff sequence
Agent1 → Agent2 → Agent3

# After: Supervisor decides routing
Supervisor ⇄ Agent1/Agent2/Agent3

Supervisor → Orchestrator-Worker: When tasks become parallelizable:

# Before: Sequential supervision
Supervisor → Agent1 → Supervisor → Agent2 → ...

# After: Parallel execution
Orchestrator → [Agent1, Agent2, Agent3] → Aggregator

Downgrading Patterns

Supervisor → Router: When routing becomes predictable:

# If supervisor always routes the same way,
# replace with deterministic router

Hybrid Patterns

Router + Supervisor

Route to different supervisor teams:

Router → Sales Supervisor ⇄ Sales Agents
     ↓
     Support Supervisor ⇄ Support Agents

Use When:

• Different domains need different coordination
• Top-level categorization is simple
• Domain-specific complexity exists

Supervisor + Orchestrator

Supervisor dispatches to orchestrators:

Supervisor → Research Orchestrator → Workers → Aggregator
         ↓
         Writing Agent

Use When:

• Some tasks are parallelizable, others sequential
• Mix of coordination strategies needed

Hierarchical Supervisor

Supervisors manage other supervisors:

Top Supervisor → Research Supervisor → Research Agents
             ↓
             Writing Supervisor → Writing Agents

Use When:

• Very complex workflows
• Multiple coordination levels
• Team-based organization

Summary Table

Quick Decision Guide

Choose Supervisor when:

• Need dynamic, context-aware routing
• Agents must collaborate
• Workflow is complex and unpredictable
• Centralized control is important

Choose Router when:

• Simple categorization task
• Routes are deterministic
• Low latency required
• Independent agent execution

Choose Orchestrator-Worker when:

• Tasks are parallelizable
• High throughput needed
• Results need synthesis
• Resources allow parallel execution

Choose Handoffs when:

• Clear sequential workflow
• Each step builds on previous
• Context preservation critical
• Simple coordination sufficient

Additional Resources

• Pattern implementations: See assets/examples/
• State design for patterns: See state-management-patterns.md
• Individual pattern details: See supervisor-subagent.md, router-pattern.md, orchestrator-worker.md, handoffs.md