Multi-agent systems: LangGraph vs CrewAI vs Autogen in 2026

Comparing LangGraph, CrewAI, and Autogen boils down to picking between three distinct mental models: explicit graph, role hierarchy, and group chat. This post takes apart each paradigm with canonical code, and solves the same real pipeline (investigate → draft → validate) three times — once per framework — so the pattern that fits your team surfaces on its own. See also: the complete guide to the mcp model context protocol, which frames the context any multi-agent system uses to swap tools.

Key takeaways

• Gartner reports a 1,445% surge in multi-agent system inquiries between Q1 2024 and Q2 2025, per its 2026 trends report^[1]; the category went from curiosity to budget in eighteen months.
• LangGraph models orchestration as an explicit state machine: pure nodes, typed edges, shared state with TypedDict and optional reducers.
• CrewAI models orchestration as a role-based hierarchy: each agent has role, goal, and backstory; tasks chain via context, and a Crew runs the process.
• Autogen, in its v0.4 rewrite, models orchestration as a group chat: AssistantAgents wired into RoundRobinGroupChat or SelectorGroupChat taking turns under a configurable selector.
• All three are production-ready in 2026; the decision depends on the cost of fine-grained control, readability for non-technical stakeholders, and the level of auditing the case demands.
• The observability layer is orthogonal to the framework: OpenTelemetry’s GenAI conventions work equally well with the three, as detailed in agent observability with OTel GenAI.

What problem a multi-agent system solves

A single agent with tool use solves more than people expect. The question is when it stops. Three signals push toward multi-agent: the single prompt blows past reasonable limits (system + few-shot examples + static RAG already scrape 10k tokens and you lose coherence), responsibilities pull in opposite directions (investigating, drafting, and validating have conflicting criteria), or you need real parallelism (three independent searches that later fold together). In any of those, splitting the problem into specialised agents under an orchestrator pays the coordination overhead.

The market notices. Gartner records a 1,445% growth in multi-agent system inquiries between Q1 2024 and Q2 2025 — one of the steepest accelerations the firm has measured in a recent technology category, per its 2026 trends report^[1]. What matters for builders isn’t the headline figure but the consequence: three mental models dominate the conversation — graph, hierarchy, and chat — and each has a mature reference framework behind it.

From here, the three models are easier to see in code. What follows are three canonical examples — no real LLM calls, just the orchestration glue — and at the end, the same pipeline solved three times.

LangGraph: explicit graph, fine control

LangGraph treats orchestration as a directed graph: you define typed state, register nodes that mutate it, and declare transitions as edges. The special START and END nodes mark entry and exit, conditional edges go through add_conditional_edges, and a final compiler returns an invocable object. The LangGraph docs^[2] formalise the pattern: StateGraph(State) to build, compile() to seal, invoke(initial_state) to run.

# langgraph_minimal.py
from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END


def add_messages(left: list, right: list) -> list:
    return left + right


class State(TypedDict):
    messages: Annotated[list, add_messages]
    context: str


def analyze(state: State) -> dict:
    return {"context": "analyzed"}


def chatbot(state: State) -> dict:
    return {"messages": [f"Reply to: {state['messages'][-1]}"]}


graph = StateGraph(State)
graph.add_node("analyze", analyze)
graph.add_node("chatbot", chatbot)
graph.add_edge(START, "analyze")
graph.add_edge("analyze", "chatbot")
graph.add_edge("chatbot", END)

app = graph.compile()
print(app.invoke({"messages": ["Hi"], "context": ""}))

The operational payoff is control. Because transitions are explicit, debugging a flow that drifts is about finding which edge condition broke; there’s no implicit selector hiding the answer. The penalty is the curve: you have to think like a distributed-systems engineer, design the state shape, and grasp reducers (Annotated[list, add_messages] won’t show up in every CV). For deterministic flows with branching, parallelism, and Postgres / Redis checkpointer persistence, the cost amortises early.

Three patterns pull LangGraph forward in production. First, per-thread persistence: with an InMemorySaver or a Postgres checkpointer, each invoke ties back to its prior conversation thread and supports resumption after a process crash. Second, the cyclic graph: add_conditional_edges("validator", router, {"ok": END, "retry": "writer"}) closes a validation loop with explicit, log-auditable criteria. Third, composition: an entire sub-graph can be wrapped as a node in a parent graph, which helps separate responsibilities without entangling the state shape.

CrewAI: teams with roles, fast productivity

CrewAI starts from a different intuition: if a human would describe the work as “a researcher searches, a writer drafts, a validator reviews”, let the code read the same. The three primitives are Agent, Task, and Crew, and the org-chart metaphor lets a product manager review a crew without asking for translation. Per-parameter detail lives in the CrewAI docs^[3]. To go deeper specifically on CrewAI, the dedicated intro at crewai teams of agents covers tools and hierarchical processes.

# crewai_minimal.py
from crewai import Agent, Task, Crew, Process

researcher = Agent(
    role="Researcher",
    goal="Find relevant technical sources on the given topic",
    backstory="Documentalist with fifteen years in technical publications.",
)

writer = Agent(
    role="Writer",
    goal="Turn research into clear prose for a technical audience",
    backstory="Writer who has published in engineering magazines.",
)

investigate = Task(
    description="Research topic X and return five sources with citation.",
    expected_output="List of five sources with URL and one-line summary.",
    agent=researcher,
)

draft = Task(
    description="Write an 800-word article based on the research.",
    expected_output="Markdown article with intro, three sections, and close.",
    agent=writer,
    context=[investigate],
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[investigate, draft],
    process=Process.sequential,
)
print(crew.kickoff(inputs={"topic": "vector databases in 2026"}))

CrewAI’s sweet spot is fast prototypes and demos where readability beats millimetre control. Process.sequential resolves linear chains in one call; Process.hierarchical adds an LLM manager that delegates and rewrites plans. The hidden cost is debugging: when a crew loops or comes up short, control logic lives inside prompts rather than edges, and tracing the why takes longer than in an explicit graph. See also: the pgvector RAG in production guide if you’ll wire the crew into a real RAG store.

Two concrete details are worth fixing from the first prototype. First, expected_output is not decoration: the model reads it and treats it as a contract; if you leave it vague (“a good article”), validation downstream will surface drifts that are hard to explain. Second, dependencies between tasks are declared with context=[previous_task], and that’s what feeds the result into the next prompt — there are no global variables and no implicit data passing. A third note for non-English-first teams: role, goal, and backstory can be written in any language without penalty as long as the chosen model is multilingual; the framework imposes no language.

Autogen: agent conversation, flexibility

Autogen launched in 2023 around the “agent conversation” pattern and, after the v0.4 rewrite shipped through 2024–2025, its canonical 2026 form lives in the autogen_agentchat package. Each agent is an AssistantAgent with its system_message, the model client comes from autogen_ext, and agents group into a team — RoundRobinGroupChat, SelectorGroupChat — that decides who speaks each turn. The official Autogen docs^[4] cover the available selectors and the async pattern.

# autogen_minimal.py
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_ext.models.openai import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient(model="gpt-4o")

planner = AssistantAgent(
    name="planner",
    model_client=model_client,
    system_message="Propose plans as numbered steps.",
)
critic = AssistantAgent(
    name="critic",
    model_client=model_client,
    system_message="Critique the plan and suggest concrete improvements.",
)

team = RoundRobinGroupChat(
    [planner, critic],
    termination_condition=MaxMessageTermination(max_messages=4),
)


async def main() -> None:
    async for msg in team.run_stream(task="Plan to migrate to OTel GenAI."):
        print(msg)


asyncio.run(main())

Autogen shines in open-ended reasoning where you don’t know up front which expert is needed, in debates between complementary roles (writer + critic), and where a human can join as another agent without ceremony. Its price is traceability: the flow emerges from the conversation, so auditing depends on the instrumentation you add. OpenTelemetry GenAI conventions fit nicely here as a cross-cutting layer that’s framework-agnostic.

The choice between RoundRobinGroupChat and SelectorGroupChat neatly captures the spectrum. Round-robin is predictable: the order of agents in the list is the speaking order. Selector is flexible: you can pass a selector_func that decides who speaks each turn, or let an intermediate LLM act as moderator. The practical rule that has worked through 2025–2026 is to start with round-robin and MaxMessageTermination to bound cost, and only migrate to selector once the heuristic for who speaks is clear. The common trap is shipping LLM selectors without a well-defined termination condition and discovering the team loops silently while burning tokens.

Real case: research + drafting + validation pipeline

The same pipeline solved three times. Three stages — investigate, draft, validate — chained in order, with no real LLM calls (replaced with stubs that show only orchestration glue). The point is to see, side by side, what a team has to write in each framework.

# === LangGraph ===
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END


class S(TypedDict):
    topic: str
    research: str
    draft: str
    valid: bool


def investigate(s: S) -> dict:
    return {"research": f"sources({s['topic']})"}


def draft(s: S) -> dict:
    return {"draft": f"article from {s['research']}"}


def validate(s: S) -> dict:
    return {"valid": "sources" in s["research"]}


g = StateGraph(S)
g.add_node("investigate", investigate)
g.add_node("draft", draft)
g.add_node("validate", validate)
g.add_edge(START, "investigate")
g.add_edge("investigate", "draft")
g.add_edge("draft", "validate")
g.add_edge("validate", END)
pipeline_lg = g.compile()

# === CrewAI ===
from crewai import Agent, Task, Crew, Process

inv = Agent(role="Researcher", goal="List sources", backstory="Doc.")
wri = Agent(role="Writer", goal="Write article", backstory="Journalist.")
val = Agent(role="Validator", goal="Verify sources", backstory="Editor.")

t1 = Task(description="Research {topic}", expected_output="List", agent=inv)
t2 = Task(description="Draft article", expected_output="MD", agent=wri, context=[t1])
t3 = Task(description="Validate sources", expected_output="OK/KO", agent=val, context=[t2])

pipeline_cw = Crew(agents=[inv, wri, val], tasks=[t1, t2, t3], process=Process.sequential)

# === Autogen v0.4 ===
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_ext.models.openai import OpenAIChatCompletionClient

mc = OpenAIChatCompletionClient(model="gpt-4o")
investigator = AssistantAgent("investigator", model_client=mc, system_message="Investigate.")
drafter = AssistantAgent("drafter", model_client=mc, system_message="Draft.")
validator = AssistantAgent("validator", model_client=mc, system_message="Validate.")

pipeline_ag = RoundRobinGroupChat(
    [investigator, drafter, validator],
    termination_condition=MaxMessageTermination(max_messages=6),
)
# asyncio.run(pipeline_ag.run(task="vector databases 2026"))

Three ways of saying the same thing, three different trade-offs. LangGraph makes order explicit on edges; CrewAI makes it explicit in context=[...] between tasks; Autogen pushes it to the team selector and trusts the model for each agent’s role. The stricter the order and the more critical the audit, the further left on that list you should land. For observability glue common to all three, see the Anthropic SDK tutorial and the sibling GenAI observability with OTel — traces are orthogonal to the framework.

Summary table: when to pick each

These three rows aren’t absolute truths; they’re the first cut. A team already living in LangChain will probably prefer LangGraph even on a simple flow; a product team that needs to iterate with marketing will probably reach for CrewAI even with rough edges; a research team mixing agents and humans in the loop will probably end up on Autogen even when auditing hurts.

The question that pays best before picking is not “which framework is best” but “which property must I preserve no matter how the flow evolves”. If the answer is bit-exact reproducibility with external auditing, LangGraph drops off the shelf on its own. If it’s iteration speed with non-technical stakeholders, CrewAI wins on readability. If it’s exploration of reasoning spaces where the conversation is the product, Autogen is the natural pick. The observability layer — OTel traces, per-agent cost metrics, per-turn latency dashboards — sits on top of all three with the same set of GenAI semconv attributes and gets reused without rewriting when you switch frameworks, which is what shaves the riskiest part off the initial decision.

Conclusion

The three frameworks solve the same problem in three different languages, and the right choice depends on who reads the code and what tests the system has to pass. LangGraph pays its learning curve with fine control and auditable edges; CrewAI pays its simplicity with murkier debugging but great readability; Autogen pays its flexibility with traceability you instrument yourself. If you have to bet on one without further context, start with CrewAI to validate the mental model with stakeholders, prototype the critical logic in LangGraph once the flow stabilises, and reserve Autogen for the cases where conversation between roles is the product itself.

Follow us on jacar.es for more on agent orchestration, MCP, GenAI observability, and real patterns from the multi-agent ecosystem in 2026.

1. 2026 trends report
2. LangGraph docs
3. CrewAI docs
4. official Autogen docs