LangGraph vs CrewAI: Which Orchestration Framework in 2026

LangGraph vs CrewAI: which orchestration framework to pick

LangGraph and CrewAI answer the same question, how do multiple agents coordinate, in opposite ways. LangGraph is a low-level graph runtime with no built-in coordination pattern: you wire the graph yourself. CrewAI splits the problem into Crews, autonomous agent teams that divide work among themselves, and Flows, deterministic start and listen steps that wrap those Crews in a fixed pipeline.

LangGraph vs CrewAI comes down to control versus convention: LangGraph gives you full control over every edge in the graph but no preset shape to start from, while CrewAI gives you two ready-made shapes, Crews for loose agent collaboration and Flows for strict sequencing, at the cost of working inside its own abstractions. Neither is a strict upgrade of the other; they solve different amounts of the same problem.

LangGraph's model

LangChain's own docs describe LangGraph as "very low-level, and focused entirely on agent orchestration", a runtime for mixing deterministic, hand-coded steps with LLM-driven agentic steps in the same graph. It ships no preset multi-agent pattern. Instead, LangChain publishes separate reference packages for the shapes teams build most often: langgraph_swarm, where agents dynamically hand off control to one another based on specialization and the system remembers which agent was last active, and langgraph_supervisor, a supervisor pattern that LangChain's own reference now says it recommends implementing directly via tool calls rather than through the library, for tighter control over context engineering. A separate handoffs guide covers the same transfer-of-control idea, implemented as single-agent middleware or as multiple agent subgraphs. LangGraph's docs list Klarna, Uber and J.P. Morgan as users of the runtime.

CrewAI's model

CrewAI splits coordination into two concepts. Crews are teams of role-playing autonomous agents with tasks, memory and tools, run under a sequential or hierarchical process, where the agents themselves have latitude over how work divides between them. Flows are a separate, event-driven layer built from start, listen and router steps, with explicit state management, designed to coordinate coding tasks and whole Crews as steps inside a larger, deterministic pipeline. CrewAI's GitHub repository describes the project as a framework for orchestrating role-playing autonomous AI agents, built to work standalone from LangChain. Its docs also treat MCP integration as a first-class way to give a Crew's agents tools over stdio, SSE or streamable HTTP transports.

The practical difference

A LangGraph app is a graph you draw: nodes are steps, edges decide what runs next, and multi-agent behavior is something you build on top with your own routing logic or LangChain's swarm and supervisor packages. A CrewAI app starts from a higher level: you describe agents' roles and tasks, choose a process, and CrewAI's Crew runtime handles the turn-taking, with Flows available when you need a deterministic wrapper around that autonomy. Teams that already want a hand-built graph, or that need behavior no preset covers, tend to reach for LangGraph. Teams that want a working multi-agent team faster, and are comfortable working inside CrewAI's Crew and Flow abstractions, tend to reach for CrewAI.

Where the other three frameworks fit

LangGraph and CrewAI are not the only two answers. Three more frameworks are common alternatives, and each ships a different amount of built-in structure.

Microsoft Agent Framework

Microsoft's orchestration layer reached 1.0 on 8 July 2026, stable in both the Python agent-framework-orchestrations package and .NET. The orchestrations page, last updated 25 August 2026, lists five built-in patterns: Sequential, Concurrent, Handoff, Group Chat and Magentic, a pattern where a manager agent dynamically coordinates specialists. In Magentic, a MagenticBuilder takes a set of participant agents and a manager agent; the manager turns the task into a plan, assigns work each round, checks a progress ledger, and can reset and replan if the team stalls, all inside guardrails set as constructor arguments: max_round_count, max_stall_count and max_reset_count.

OpenAI Agents SDK

The OpenAI Agents SDK (v0.22.0 on GitHub, 29.3k stars) coordinates agents mainly through handoffs: one agent delegates a task to another, with a customer-support example in the docs where separate agents handle order status, refunds and FAQs. A handoff is created with the handoff() function, which accepts an input_type to define the data passed to the receiving agent and an input filter to trim conversation history. The SDK's orchestration guide splits coordination into orchestrating via LLM, where an agent decides on its own when to hand off, and orchestrating via code, where your own logic picks the next agent.

Google Agent Development Kit (ADK)

ADK's multi-agent workflows start from three template workflow agents: SequentialAgent runs sub-agents in a fixed order, ParallelAgent runs sub-agents concurrently, and LoopAgent repeats a sub-agent sequence for iterative work. Google shipped ADK TypeScript 2.0 as a general-availability release with graph workflow support, bringing the TypeScript SDK's workflow capabilities in line with ADK's Python, Go, Java and Kotlin SDKs.

What's new (as of 8 September 2026)

Microsoft's five orchestration patterns went stable at 1.0 across Python and .NET on 8 July 2026, ending a period where teams chose a pattern partly around which SDK had shipped it first. Google shipped ADK TypeScript 2.0 as general availability, adding graph workflow support to the TypeScript SDK. LangChain's own supervisor reference now steers teams toward implementing the supervisor pattern directly with tool calls instead of through the langgraph_supervisor library, for tighter control over what context each agent sees.

Key takeaways

• LangGraph ships no built-in multi-agent pattern by design; CrewAI ships two, Crews for autonomous teams and Flows for deterministic pipelines.
• Microsoft Agent Framework's five orchestration patterns became stable 1.0 in both Python and .NET on 8 July 2026.
• The OpenAI Agents SDK (v0.22.0) coordinates agents mainly through handoffs, created with the handoff() function.
• Google ADK's three template workflow agents are now consistent across Python, TypeScript, Go, Java and Kotlin since ADK TypeScript 2.0 GA.
• LangChain's reference docs now recommend building the supervisor pattern directly with tool calls rather than through the langgraph_supervisor library.

FAQ

Is LangGraph better than CrewAI?

Neither is strictly better; they trade off differently. LangGraph gives full control over a hand-built graph with no preset multi-agent shape, which suits teams that need behavior no built-in pattern covers. CrewAI gives two ready-made shapes, Crews and Flows, which gets a working multi-agent team running faster inside CrewAI's own abstractions.

Can CrewAI and LangGraph be used together?

The frameworks are built to work standalone; CrewAI's own repository describes it as independent from LangChain. Teams that want LangGraph's low-level control with CrewAI-style role definitions would need to build that integration themselves, since neither framework's docs describe an official combined mode.

What is the difference between a handoff and agents-as-tools?

In a handoff, control of the conversation transfers to the receiving agent, which continues the turn. In agents-as-tools, the calling agent keeps control and treats the other agent as a function call whose result it uses. The OpenAI Agents SDK documents both as its two core coordination patterns.

Does CrewAI support MCP servers?

Yes. CrewAI's docs describe MCP integration over stdio, SSE and streamable HTTP transports as a first-class way to give a Crew's agents tools. The OpenAI Agents SDK and Google ADK document their own MCP support as well.

Most teams do not pick one orchestration framework forever. The OpenAI Agents SDK listing on AI Agents Listing is one place to check what else builds on top of it, and the wider agents directory covers the rest of the field these frameworks connect to.