The key difference
Both frameworks orchestrate LLM agents, but they start from opposite ends.
CrewAI starts from people. You describe agents the way you’d describe a team: a role, a goal and a backstory. You give them tasks and group them into a crew that works through the tasks in order, or hierarchically with a manager agent delegating the work. For more control, CrewAI adds Flows: event-driven workflows with structured state that can run crews as steps.
LangGraph starts from the program. You define a state object and a graph: nodes are functions, including LLM calls and tool calls, and edges decide what runs next, including conditional branches and loops. The LangGraph team calls it a “low-level orchestration framework for building stateful agents”. Durable execution, human-in-the-loop and memory are built into the runtime, so they work at any node.
Both can do the same jobs. The difference is where the control lives. CrewAI gives you a high-level model first and lets you add structure with Flows; LangGraph gives you the structure first and lets you build agents on top, or reach for its higher-level Deep Agents package.
Rule of thumb: if you’d sketch your system as an org chart, CrewAI fits. If you’d sketch it as a flowchart, LangGraph fits.
What the code looks like
CrewAI. A crew of two agents working through two tasks in order. This is excerpted from the example in CrewAI’s README:
from crewai import Agent, Crew, Process, Task
analyst = Agent(
role="Senior Market Analyst",
goal="Conduct deep market analysis with expert insight",
backstory="You're a veteran analyst known for identifying subtle market patterns",
)
researcher = Agent(
role="Data Researcher",
goal="Gather and validate supporting market data",
backstory="You excel at finding and correlating multiple data sources",
)
analysis_task = Task(
description="Analyze {sector} sector data for the past {timeframe}",
expected_output="Detailed market analysis with confidence score",
agent=analyst,
)
research_task = Task(
description="Find supporting data to validate the analysis",
expected_output="Corroborating evidence and potential contradictions",
agent=researcher,
)
crew = Crew(
agents=[analyst, researcher],
tasks=[analysis_task, research_task],
process=Process.sequential,
)
result = crew.kickoff(inputs={"sector": "tech", "timeframe": "1W"})You don’t have to define crews in Python code: crewai create crew <name> now scaffolds a JSON-first project, with agents in agents/*.jsonc and tasks in crew.jsonc (custom tools are still Python). The older Python/YAML layout is available with --classic.
LangGraph. A tool-calling agent built as a graph. This is the build step from the official quickstart; MessagesState, llm_call, tool_node and should_continue are defined earlier in that guide:
from langgraph.graph import StateGraph, START, END
agent_builder = StateGraph(MessagesState)
agent_builder.add_node("llm_call", llm_call)
agent_builder.add_node("tool_node", tool_node)
agent_builder.add_edge(START, "llm_call")
agent_builder.add_conditional_edges("llm_call", should_continue, ["tool_node", END])
agent_builder.add_edge("tool_node", "llm_call")
agent = agent_builder.compile()The contrast shows the trade-off. CrewAI hides the agent loop behind roles and tasks. LangGraph makes you write the loop: call the model, run tools if it asked for them, then go back to the model. In return you can change any part of it.
Side by side
| CrewAI | LangGraph | |
|---|---|---|
| Core abstraction | Agents, tasks and crews; Flows for event-driven control | State and a graph of nodes and edges |
| Multi-agent | Built in: crews of role-based agents, run sequentially or hierarchically under a manager agent | Built from the graph: subgraphs, supervisor patterns |
| Deterministic control | Flows (@start, @listen, @router) |
The graph itself: edges and conditional edges |
| State | Structured Flow state (Pydantic models) | Typed shared state, with checkpointing |
| Persistence | Flows: @persist (SQLite by default), at class or method level |
Built into the runtime: checkpointing, durable execution, short- and long-term memory |
| Human-in-the-loop | Flows: @human_feedback (1.8.0+); webhook-based human input on Enterprise |
Interrupt at any node, inspect or edit state, then resume |
| Languages | Python | Python and TypeScript (LangGraph.js) |
| Models | Native integrations for OpenAI, Anthropic, Gemini, Azure, Bedrock and Snowflake Cortex; others through LiteLLM | Any model you can call from Python or JS; LangChain integrations optional |
| MCP | Yes | Yes, through langchain-mcp-adapters |
| Higher-level option | — | Deep Agents, a package built on LangGraph |
| License | MIT | MIT |
GitHub activity and stars are in the table at the top of this page, refreshed daily.
Hosting and observability costs
Both frameworks are free and self-hostable. What you might pay for is each company’s platform:
| CrewAI AMP | LangSmith | |
|---|---|---|
| Free | Basic: 50 workflow executions/month, visual editor, tracing | Developer: 1 seat, 5k base traces/month, no deployments |
| Paid | Enterprise: custom pricing (SSO, RBAC, dedicated VPC) | Plus: $39/seat/month, includes one small serverless deployment; extra usage metered |
| Enterprise | On-premise or cloud | Custom; cloud, hybrid or self-hosted |
Prices are from crewai.com/pricing and langchain.com/pricing, checked October 22, 2026. LangSmith Deployment was previously called LangGraph Platform.
Who should pick which
Choose CrewAI if:
- your workflow is naturally a team of specialists (researcher, writer, reviewer) handing work to each other
- you want a working multi-agent prototype quickly, with less code
- you’re a Python shop and like the idea of defining agents in config files
Choose LangGraph if:
- you need to control exactly what happens at each step, including loops and branches
- you want checkpointing and human interrupts available at any step of the graph, not only at Flow-method boundaries
- you’re building in TypeScript, or want the same framework in Python and TypeScript
Consider both if: you like CrewAI’s crews for open-ended subtasks but need a strict outer workflow. CrewAI’s own Flows may already cover that; otherwise, a LangGraph node can call a crew.
Hands-on test
We haven’t benchmarked these frameworks against each other yet. This comparison is based on each project’s documentation, README, examples and pricing pages, linked on the CrewAI and LangGraph pages. When we add a test, this page will name the task, the framework and model versions, and the date it was run.
