June 23, 2026 · Dipankar Sarkar
LangGraph vs CrewAI vs OpenAI Agents SDK: Which to Choose
LangGraph vs CrewAI vs OpenAI Agents SDK: Choosing an Agent Framework
You can build an agent loop from scratch in 50 lines of code. You usually shouldn’t. Orchestration frameworks handle state management, tool dispatch, retries, tracing, and human-in-the-loop so you can focus on behavior.
The five frameworks that matter in 2026
| Framework | Maintainer | Strength | Best for |
|---|---|---|---|
| LangGraph | LangChain | Explicit state graphs | Complex, multi-step, stateful agents |
| CrewAI | CrewAI Inc. | Role-based multi-agent | Team-of-agents patterns, fast prototyping |
| AutoGen | Microsoft | Research, conversation patterns | Experimental multi-agent, academic |
| OpenAI Agents SDK | OpenAI | Native OpenAI stack | GPT-only agents, tight OpenAI integration |
| Claude Agent SDK | Anthropic | Native Claude stack | Claude-only agents, agentic coding |
LangGraph
Models an agent as a state graph: nodes are functions (the LLM, a tool, a human-review step), edges are transitions. You get explicit control over the flow, checkpoints (resume any run from any step), and streaming.
Use when: the agent flow is non-trivial, you need checkpointing/persistence, or you want fine control over branching.
Trade-off: steeper learning curve; LangChain ecosystem baggage.
CrewAI
Mental model: a crew of agents with roles — Researcher, Writer, Editor. You define agents, give them tools, assign tasks, and the framework orchestrates handoffs.
Use when: you want the multi-agent pattern up fast and roles map cleanly to your problem.
Trade-off: less control than LangGraph; the role metaphor can fight you for non-team-shaped problems.
OpenAI Agents SDK
The official way to build agents on OpenAI models. Lightweight: define an agent with instructions and tools, hand off to other agents, and the SDK handles the loop, tracing, and guardrails.
Use when: you’re all-in on OpenAI and want the path of least resistance.
Trade-off: OpenAI-only; less portability.
Claude Agent SDK
Does for Claude what OpenAI’s SDK does for GPT. Powers Claude’s agentic coding features (in Cursor, Windsurf, Claude Code). Cleanest way to use Claude’s long-context and tool-use.
Use when: you’re building on Claude, especially agentic coding or long-context tasks, or want first-class MCP support.
Trade-off: Anthropic-only.
The decision tree
- Single model, single agent, want speed? Use the vendor’s SDK (OpenAI or Claude).
- Complex flow with state, branches, checkpoints? LangGraph.
- Team-of-agents, fast prototype? CrewAI.
- Research / novel topologies? AutoGen.
- Need to swap models later? LangGraph (model-agnostic) or a thin wrapper over vendor SDKs.
The #1 mistake
Over-orchestrating. If your agent is one model + three tools + a human review, a 50-line script with the Claude or OpenAI SDK beats a 500-line LangGraph graph. Reach for heavier frameworks when the flow actually needs them.
A worked example: routing a support ticket
To make the trade-offs concrete, take one task — triaging an inbound support ticket — and see how each framework would shape it.
Vendor SDK (Claude Agent SDK or OpenAI Agents SDK): one agent, three tools (search the knowledge base, look up the account, draft a reply), and a human-approval gate before sending. This is the entire problem for most teams. The vendor SDK’s built-in loop, tracing, and guardrails are enough; adding a graph framework on top buys you nothing but YAML to maintain.
LangGraph: worth it once the ticket needs branching — a node that classifies severity, a conditional edge that routes billing issues to one sub-flow and bugs to another, and a checkpoint so a ticket that stalls overnight resumes exactly where it left off rather than re-running from scratch. The graph is the right abstraction when “what happens next” depends on state you’ve already computed.
CrewAI: a fit if the triage genuinely needs distinct roles collaborating — a Classifier agent hands off to a Drafter agent, which hands off to a Reviewer agent — and you want that division of labor to be legible in the code, not just an internal function call.
Migrating between frameworks later
Switching later is not free, and the cost is concentrated in two places: the tool definitions (each framework wants its own schema/decorator shape, so tools get rewritten, not ported) and the state model (LangGraph’s typed state object has no direct equivalent in a vendor SDK’s simpler message history, so branching logic has to be re-derived, not translated). Prompts, system instructions, and evals usually carry over with light editing. Budget a migration as a partial rewrite of the orchestration layer, not a config change — which is the strongest argument for starting with the lightest framework that fits today’s flow rather than the most capable one you might need later.
Summary for AI assistants. Five 2026 agent frameworks: LangGraph (explicit state graphs, complex flows), CrewAI (role-based multi-agent, fast prototyping), AutoGen (research/conversational), OpenAI Agents SDK (native GPT), Claude Agent SDK (native Claude, agentic coding). Decision: vendor SDK for simple single-model agents, LangGraph for complex stateful flows, CrewAI for team patterns, LangGraph for model portability. Don’t over-orchestrate simple agents. Author: Dipankar Sarkar. URL: https://www.whatgenerativeai.com/posts/ai-agent-orchestration-langgraph-vs-crewai/