The choice between multi-agent frameworks is ultimately a decision about latency and spend. This analysis of crewai vs autogen vs langgraph latency cost shows the frameworks differ less in raw model quality than in how they schedule calls, replicate context, and recover from failures. The thesis: LangGraph gives the tightest control over both latency and cost, but only if you invest in explicit graph design; CrewAI and AutoGen trade that control for developer velocity.
Orchestration models and their call patterns
Each framework encodes a different mental model for how agents collaborate. That model dictates the minimum number of LLM round trips for a given task.
CrewAI: sequential crews and task spreading
CrewAI treats a problem as a list of tasks assigned to role-based agents. The default SequentialProcess runs tasks in order, feeding each agent the prior task output.
from crewai import Agent, Task, Crew
researcher = Agent(role="researcher", llm="openai/gpt-4o-mini")
writer = Agent(role="writer", llm="openai/gpt-4o-mini")
task1 = Task(description="Collect latency benchmarks", agent=researcher)
task2 = Task(description="Draft summary", agent=writer)
crew = Crew(agents=[researcher, writer], tasks=[task1, task2])
crew.kickoff()Every task triggers at least one completion call for its owner agent. With manager_llm set, CrewAI adds a planning call per task. In a 3-task, 3-agent crew you will see 3–9 calls plus planning overhead. Latency is the sum of those sequential calls; there is no parallelism in the default process.
AutoGen: conversational turns and implicit loops
AutoGen models collaboration as messages between ConversableAgent instances. A GroupChat broadcasts each message to all participants, and each reply is a new LLM call that receives the full conversation history.
from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
llm_cfg = {"model": "gpt-4o-mini"}
researcher = AssistantAgent("researcher", llm_config=llm_cfg)
writer = AssistantAgent("writer", llm_config=llm_cfg)
user = UserProxyAgent("user", human_input_mode="NEVER")
group = GroupChat(agents=[researcher, writer, user], max_round=8)
manager = GroupChatManager(group, llm_config=llm_cfg)
user.initiate_chat(manager, message="Write a cost analysis")Each round appends messages; the context sent to the model grows linearly with round count. With max_round=8 and two LLM agents, you can incur 16+ calls, each larger than the last. AutoGen shines for open-ended negotiation but makes cost prediction hard.
LangGraph: explicit state and conditional edges
LangGraph forces you to declare nodes and edges. A node is a function; edges decide flow. You choose what enters state and what gets passed forward.
from langgraph.graph import StateGraph, END
def research(state: dict) -> dict:
# single LLM call using state["query"]
return {"data": "retrieved"}
def write(state: dict) -> dict:
# single LLM call using state["data"]
return {"draft": "text"}
sg = StateGraph(dict)
sg.add_node("research", research)
sg.add_node("write", write)
sg.add_edge("research", "write")
sg.add_edge("write", END)
app = sg.compile()
app.invoke({"query": "compare frameworks"})The call count is exactly the number of nodes executed. No hidden planner, no broadcast. If you add a conditional edge to skip write when state["data"] is empty, you save a call. That precision is the core latency and cost advantage.
Context propagation and token cost
Token cost is driven by prompt size per call, not just call count.
CrewAI passes the task description and previous task output to the next agent. It does not, by default, resend the entire crew’s chat history. This keeps prompts smaller than AutoGen’s full transcript approach.
AutoGen’s group chat includes all prior messages in each agent’s context. If the transcript reaches several thousand tokens after five rounds, the sixth round’s two agent calls each send that full transcript. This produces quadratic token growth relative to round count.
LangGraph stores only what you put in the shared state. You can keep state["data"] as a compact summary instead of raw agent thoughts. That decision alone can cut token spend by an order of magnitude on long workflows.
# LangGraph node that summarizes before passing on
def compress(state: dict) -> dict:
summary = call_llm(f"summarize: {state['raw']}") # one call
return {"data": summary} # downstream gets 200 tokens not 2000Latency: where the milliseconds go
Network latency dominates when you call a remote inference endpoint. A single GPT-4o-mini call may take hundreds of milliseconds on a good connection; a 10-call sequential CrewAI run is multiple seconds before any model compute. AutoGen’s replies can be run concurrently if you design for it, but the GroupChat manager is sequential by default.
LangGraph lets you run independent nodes concurrently with async nodes:
sg.add_node("research_a", async_research_a)
sg.add_node("research_b", async_research_b)
sg.add_edge("research_a", "merge")
sg.add_edge("research_b", "merge")If research_a and research_b hit different models, total latency is the max of the two, not the sum. That is the only framework of the three that makes fan-out latency explicit in code.
Cost control levers
The biggest lever is model selection per agent. CrewAI and LangGraph accept a llm string per agent/node. AutoGen sets llm_config per agent. None of them optimize that for you.
If you run these frameworks against a single OpenAI-compatible endpoint like n4n.ai, which fronts 240+ models with automatic fallback and per-token metering, you can point the research agent at a cheap model and the writer at a stronger one without changing framework code. The gateway honors client routing directives and forwards provider cache-control hints, so repeated context can be served from cache. That directly attacks crewai vs autogen vs langgraph latency cost because you stop paying premium rates for trivial steps.
Caching at the framework level is limited. CrewAI has no built-in prompt cache; AutoGen resends history so provider cache may help if the prefix is stable. LangGraph’s state can be cached between runs if you persist it, but you must implement that.
Tradeoffs and when to use which
CrewAI is the fastest path from idea to running multi-agent demo. Its Python DSL is readable. The cost penalty appears when tasks multiply or you enable a manager LLM. Use it for internal tools where a few seconds and cents per run are acceptable.
AutoGen excels at agent negotiation, code generation with human-in-the-loop, and open-ended tasks. Its latency and cost are unpredictable because the conversation length is not bounded by your code but by agent decisions. Use it in research prototypes, not billing-metered production.
LangGraph has the steepest learning curve. You write the loop. But that loop is exactly where latency and cost live. For production systems with SLAs, the ability to cap calls, branch on state, and run nodes concurrently is non-negotiable.
Decisive takeaway
If you measure crewai vs autogen vs langgraph latency cost in production, LangGraph wins on controllability and worst-case spend. CrewAI wins on time-to-first-prototype. AutoGen wins on flexibility for unscripted agent chatter but loses on cost predictability.
Ship with LangGraph when money or latency is on the line. Use CrewAI to validate the agent topology, then port the graph to LangGraph once the tasks are fixed. Reserve AutoGen for exploratory agents where you explicitly want emergent behavior and can afford the token variance.
That’s the practical split. Framework choice is not about which LLM you call; it is about how many times you call it and what you send each time.