Multi-agent systems use more tokens than single agents. Each agent in a crew makes its own API calls, adds context, and generates output. The orchestration overhead can double or triple your costs compared to a single-agent approach.

Here’s what multi-agent orchestration actually costs.

The cost problem

A single agent processing a task:

• 1 API call
• 2K input tokens, 1K output tokens
• Cost: $0.024 (at Sonnet 5 pricing)

A 3-agent crew processing the same task:

• 3 API calls (one per agent)
• Plus context sharing between agents
• Plus orchestration overhead
• Total: 6K input tokens, 3K output tokens
• Cost: $0.072 (3x the single agent)

Multi-agent systems trade cost for capability. The question is whether the capability improvement justifies the cost increase.

Framework comparison

CrewAI

Architecture: Role-based agents with sequential or hierarchical processes.

Token overhead:

• Context sharing: ~500 tokens per agent handoff
• Role definitions: ~200 tokens per agent
• Process coordination: ~300 tokens per task

Typical cost multiplier: 2-4x vs single agent

from crewai import Agent, Task, Crew

# Define agents (each adds ~200 tokens of context)
researcher = Agent(role="Researcher", goal="Find relevant information")
writer = Agent(role="Writer", goal="Write clear documentation")
reviewer = Agent(role="Reviewer", goal="Ensure quality and accuracy")

# Define tasks
research_task = Task(description="Research the topic", agent=researcher)
write_task = Task(description="Write the document", agent=writer)
review_task = Task(description="Review the output", agent=reviewer)

# Create crew
crew = Crew(agents=[researcher, writer, reviewer], tasks=[research_task, write_task, review_task])
result = crew.kickoff()

Cost breakdown (typical task):

Agent	Input Tokens	Output Tokens	Cost
Researcher	2,500	1,500	$0.040
Writer	3,000	2,000	$0.050
Reviewer	2,500	500	$0.030
Total	8,000	4,000	$0.120

vs Single agent: $0.024. CrewAI multiplier: 5x.

AutoGen

Architecture: Conversational agents with flexible topologies.

Token overhead:

• Conversation history: accumulates with each turn
• Agent definitions: ~150 tokens per agent
• Message passing: ~100 tokens per message

Typical cost multiplier: 2-5x vs single agent

from autogen import AssistantAgent, UserProxyAgent

# Define agents
assistant = AssistantAgent("assistant", llm_config=llm_config)
reviewer = AssistantAgent("reviewer", llm_config=llm_config)
user_proxy = UserProxyAgent("user_proxy", human_input_mode="NEVER")

# Multi-turn conversation
user_proxy.initiate_chat(assistant, message="Task description")
# Assistant responds, reviewer comments, assistant revises...

Cost breakdown (typical task):

Turn	Input Tokens	Output Tokens	Cost
Initial request	2,000	1,500	$0.035
Reviewer feedback	3,500	500	$0.040
Revision	4,000	2,000	$0.060
Total	9,500	4,000	$0.135

vs Single agent: $0.024. AutoGen multiplier: 5.6x.

LangGraph

Architecture: Graph-based agent workflows with state management.

Token overhead:

• State serialization: ~200 tokens per node
• Edge conditions: ~100 tokens per transition
• Context passing: ~300 tokens per handoff

Typical cost multiplier: 2-3x vs single agent

from langgraph.graph import StateGraph

# Define graph
workflow = StateGraph(AgentState)
workflow.add_node("researcher", research_agent)
workflow.add_node("writer", write_agent)
workflow.add_node("reviewer", review_agent)
workflow.add_edge("researcher", "writer")
workflow.add_edge("writer", "reviewer")

# Execute
result = workflow.invoke({"input": "Task description"})

Cost breakdown (typical task):

Node	Input Tokens	Output Tokens	Cost
Researcher	2,200	1,500	$0.037
Writer	2,800	2,000	$0.048
Reviewer	2,500	500	$0.030
Total	7,500	4,000	$0.115

vs Single agent: $0.024. LangGraph multiplier: 4.8x.

Comparison

Framework	Cost Multiplier	Setup Complexity	Flexibility	Best For
CrewAI	5x	Easy	Moderate	Role-based teams
AutoGen	5.6x	Moderate	High	Research, conversations
LangGraph	4.8x	Hard	High	Complex workflows

Reducing multi-agent costs

1. Use cheaper models for simple agents

Not every agent needs Sonnet 5. Use Luna ($0.20/$1.20) for simple tasks and escalate to Sonnet 5 or Opus 5 only for complex reasoning.

# CrewAI: different models per agent
researcher = Agent(role="Researcher", llm=ChatOpenAI(model="gpt-5.6-luna"))
writer = Agent(role="Writer", llm=ChatOpenAI(model="claude-sonnet-5"))
reviewer = Agent(role="Reviewer", llm=ChatOpenAI(model="gpt-5.6-luna"))

2. Minimize context passing

Don’t pass full conversation history between agents. Pass summaries or structured data instead.

def summarize_for_next_agent(output, max_tokens=500):
    """Summarize agent output before passing to next agent."""
    summary_prompt = f"Summarize this in {max_tokens} tokens: {output}"
    return call_model(summary_prompt, max_tokens=max_tokens)

3. Use single agents when possible

Not every task needs multiple agents. Use single agents for simple tasks and reserve multi-agent for complex workflows.

4. Cache common operations

If multiple agents need the same information (e.g., codebase context), fetch it once and share.

# Cache codebase context
codebase_context = get_codebase_context()  # Fetch once

# Share across agents
researcher = Agent(context=codebase_context)
writer = Agent(context=codebase_context)

5. Set token limits per agent

Prevent any single agent from consuming excessive tokens.

agent = Agent(
    role="Researcher",
    max_tokens=2000,  # Limit output tokens
    max_iterations=5,  # Limit reasoning steps
)

My take

Multi-agent orchestration costs 3-6x more than single-agent approaches. The cost is justified when:

• The task genuinely benefits from specialization
• Quality improvement outweighs cost increase
• The task is complex enough to warrant multiple perspectives

For simple tasks, single agents are more cost-effective. Don’t use multi-agent because it’s cool. Use it because the task demands it.

Start with single agents. Add agents only when you hit quality ceilings. And always use cheaper models for simple agents in the crew.

FAQ

How much more do multi-agent systems cost?

3-6x more than single-agent approaches, depending on the framework and task complexity. CrewAI and AutoGen tend to be higher (5-6x), LangGraph is more efficient (4-5x).

Is multi-agent worth the cost?

For complex tasks (code review, research, content pipelines): yes, the quality improvement justifies the cost. For simple tasks (Q&A, classification): no, single agents are more cost-effective.

How do I reduce multi-agent costs?

Use cheaper models for simple agents, minimize context passing, cache common operations, set token limits, and use single agents when possible.

Which framework is cheapest?

LangGraph (4.8x multiplier) is slightly cheaper than CrewAI (5x) and AutoGen (5.6x). But the difference is small compared to the cost of the underlying model calls.

Can I mix models in a multi-agent system?

Yes. Use Luna for simple agents and Sonnet 5 or Opus 5 for complex agents. This can reduce costs by 50-70% compared to using the same expensive model for all agents.

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