Multi-agent frameworks all let several LLM agents work together, but each one chooses a different metaphor for how control passes between them.
**CrewAI** is role based. You define agents like a Researcher, a Writer, and a Reviewer, give each a goal, and assign tasks. The crew runs the tasks in order and agents delegate work to each other based on their roles. It reads like staffing a small team.
AutoGen is conversation based. Agents sit in a shared GroupChat and a moderator decides who speaks next. The solution emerges from a back and forth dialogue, the way a real meeting works. It is flexible, but the flow is implicit in the conversation.
LangGraph is graph based. You draw the workflow as an explicit directed graph: nodes are steps, edges are transitions, and conditional edges branch on a shared typed state. Control flow is something you can see and reason about, not something hidden inside a chat.
The simplest way to remember it: CrewAI gives agents job titles, AutoGen puts them in a chat room, and LangGraph draws the flowchart.
All three are multi-agent orchestration frameworks, but they differ in their core coordination model. **CrewAI** uses role-based delegation. You define agents as personas like Researcher or Writer, assign tasks, and the crew runs them with agents handing work to each other by role.
AutoGen uses conversational coordination. Agents share a GroupChat, and a moderator selects who speaks each turn. The answer emerges from a multi-party dialogue rather than a predefined path. This is flexible for open-ended problems, but the control flow is implicit.
LangGraph uses an explicit state machine. The workflow is a directed graph with typed shared state, where nodes are steps and conditional edges route control based on that state. Loops, branches, and retries are first-class graph constructs you can inspect.
The practical split is control flow visibility. CrewAI optimises for fast role-based setup, AutoGen for emergent conversation, and LangGraph for explicit branch-heavy workflows. Pick by how much deterministic control you need. If the path branches on intermediate results and you must trace why an agent ran, the graph model wins. If you want a quick team of specialists, roles are faster to express.
Quick reference
TL;DR
CrewAI coordinates through role-based delegation, AutoGen through a moderated group chat, and LangGraph through an explicit state graph with conditional edges.
Imagine three ways to run a team project. In the first, you hand each person a job title and a task, and they pass work down the line. The researcher gathers facts, the writer drafts, the reviewer checks. That is CrewAI. In the second, everyone sits in one chat room and a facilitator decides who speaks next, so the work emerges from conversation. That is AutoGen. In the third, you draw a flowchart on the wall with arrows and decision diamonds, and the team follows the arrows, branching based on what they find at each box. That is LangGraph. All three get the same kind of work done, but the way control passes between people is completely different. The first leans on roles, the second on dialogue, and the third on an explicit map of who runs when.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.
A single agent with a few tools already solves the task end to end. Multi-agent coordination adds latency, cost, and failure modes with no payoff here.
The workflow is a fixed deterministic pipeline that never branches on results. A plain DAG or LCEL chain is cheaper and simpler than any agent framework.
You need the lightest possible dependency footprint and full control. A hand-rolled loop avoids framework lock-in and the abstraction overhead each one carries.
Hard real-time latency budgets under a few hundred milliseconds. Multi-party turns and graph routing each add LLM round trips that miss tight SLAs.
Quick scan · 2–4 min
Everything important, quickly.
Name each framework's coordination model in one line, then contrast control flow visibility, show how each handles a new branch, and close by mapping each to the task shape it fits, with the graph model winning for audited branch-heavy work.
Real-world examples
Real products, models, and research that use this idea.
1A research-report pipeline in CrewAI uses a Researcher agent to gather sources, a Writer to draft, and a Reviewer to fact-check, with tasks chained in sequence.
2AutoGen powers code-generation assistants where a coder agent, an executor agent, and a critic agent debate fixes in a GroupChat until tests pass.
3LangGraph backs LangChain's prebuilt ReAct and supervisor agents, modelling tool loops and multi-agent handoffs as conditional edges over typed state with checkpointing.
4Teams pairing Claude Opus 4.7 or GPT-5.5 with LangGraph get replayable traces and human in the loop interrupts for audited, branch-heavy enterprise workflows.
Interviewer follow-ups
What an interviewer would ask next. Try answering before peeking at the approach.
QHow does adding a new conditional branch differ across CrewAI, AutoGen, and LangGraph?
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QWhy do audited or compliance-heavy workflows tend to converge on the graph model?
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QHow would you bound cost in each framework to stop a runaway multi-agent run?
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Don't say this
Red flags and common mistakes that signal junior thinking. Click to expand.
Most common mistake
Treating the three as interchangeable wrappers. They differ in their core coordination model, and that choice drives how you debug, branch, and control the workflow.