LangGraph vs CrewAI vs Microsoft Agent Framework (2026)
Pick LangGraph when you need explicit control over an agent's flow, state, checkpoints and human approval steps. Pick CrewAI when you want to prototype a role-based multi-agent setup quickly. Pick Microsoft Agent Framework when your team works in .NET or Python on Azure and wants Microsoft's supported path, which succeeds Semantic Kernel and brings in AutoGen-style orchestration, and remember that plain code with a provider SDK is still a strong option for simple agents.
Checked October 2026. Re-verify before choosing.
Comparison
| LangGraph | CrewAI | Microsoft Agent Framework | |
|---|---|---|---|
| Model | Graph of nodes and edges with shared state | Crews of role-based agents with tasks | Agents and workflows SDK |
| Control over flow | High, explicit | Medium | High |
| State and checkpoints | Built in, persistent checkpointers | Lighter | Built in |
| Human approval | Interrupts in the graph | Supported, less central | Supported |
| Languages | Python, JavaScript | Python | .NET, Python |
| Best fit | Production agents needing control | Fast multi-agent prototypes | Microsoft-centric enterprises |
| Status | Active, part of the LangChain ecosystem | Active | Successor to Semantic Kernel and AutoGen |
How each one thinks
LangGraph models an agent as a state machine: you define nodes (steps), edges (transitions, including loops) and the state passed between them. That makes pauses, approvals and resuming after a crash explicit, at the cost of more code up front.
CrewAI models a team: agents with roles and goals, assigned tasks, and a process that coordinates them. It's quick to get several agents working together, and harder to control exactly what happens at each step.
Microsoft Agent Framework brings Semantic Kernel's enterprise features and AutoGen's multi-agent patterns into one SDK, with Azure integration.
When to choose which
- You need pause, approve and resume, or recovery after crashes: LangGraph or Microsoft Agent Framework.
- You need a demo of several cooperating agents this week: CrewAI.
- Your organisation standardises on Azure and .NET: Microsoft Agent Framework.
- One agent, two tools: plain code.
What to test before committing
Build the same small agent in your top two choices and check: how you'd add an approval step, how state survives a restart, what a trace of one run looks like, and how hard it is to see the exact prompt the model received.
From the floor
In the DevFix auto agent build, we used LangGraph, built on top of LangChain, because the agent needed stateful loops that write code, run it, check errors and fix them until tests pass, and in a separate workshop we used CrewAI to build a multi-agent system, where the goal was understanding how agents with different roles work together.