Use LangGraph when you need precise control over a stateful, multi-step agent with checkpoints and human approval, CrewAI when the problem maps naturally onto a team of role-based agents, and the OpenAI Agents SDK (or the Claude Agent SDK) when you want a small, readable codebase built around one model provider. For many business use cases you need none of them: a model with tool calling and a few hundred lines of your own code is easier to test and cheaper to run. This guide compares the main AI agent frameworks in 2026 and explains how we choose between them on client projects.
The Short Answer: A Comparison Table
| Feature | LangGraph | CrewAI | OpenAI Agents SDK | Claude Agent SDK |
|---|---|---|---|---|
| Core idea | Agents as a graph of nodes and edges with shared state | A “crew” of role-based agents working through tasks | Lightweight agents with tools, handoffs and guardrails | The agent loop behind Claude Code, as a library |
| Control over flow | Very high | Medium | Medium | Medium |
| Learning curve | Steep | Gentle | Gentle | Gentle |
| Persistence and resume | Built-in checkpointing | Via Flows and memory | Sessions | Sessions |
| Human-in-the-loop | First-class (interrupts) | Supported | Supported via your code | Permission controls on tools |
| Model choice | Any provider | Any provider | OpenAI first, others via adapters | Claude models |
| Best for | Complex production workflows | Fast prototypes of multi-agent ideas | Clean single or few-agent apps | Agents that work with files, code and MCP tools |
What an Agent Framework Actually Gives You
An AI agent is a loop: the model reads the goal and context, decides whether to call a tool, your code runs the tool, the result goes back to the model, and the loop continues until the task is done. Every major model API supports the tool-calling part natively. A framework adds the parts around the loop:
- State management: what the agent knows between steps and between sessions.
- Control flow: branches, retries, parallel steps and stopping conditions.
- Multi-agent coordination: passing work between specialised agents.
- Human checkpoints: pausing for approval and resuming later.
- Tracing: seeing exactly what happened in each run.
If your agent needs few of these, a framework adds more weight than value.
LangGraph: Control for Complex, Stateful Agents
LangGraph, from the LangChain team, models an agent as a graph. Each node is a step (a model call, a tool, a check), edges decide what runs next, and a typed state object travels through the graph.
Why teams choose it:
- Explicit control over every transition, which makes behaviour predictable and testable.
- Checkpointing, so a long-running agent can pause, survive a restart and resume.
- Interrupts for human approval at any node, which is essential for agents that change data or spend money.
- Works with any model provider, in Python and JavaScript.
Watch out for: more concepts to learn, more code for simple cases, and abstractions that can hide what is sent to the model if you are not careful. Pair it with tracing, for example LangSmith or Langfuse, from day one.
CrewAI: Role-Based Multi-Agent Teams
CrewAI describes a system as a crew: agents with a role, a goal and a backstory, assigned tasks and run in a process such as sequential or hierarchical. Flows add event-driven orchestration for more structured production use.
Why teams choose it:
- Very quick to get a multi-agent prototype working, such as a researcher, a writer and a reviewer.
- The role metaphor is easy to explain to non-technical stakeholders.
- Model-agnostic and has a growing set of built-in tools.
Watch out for: multiple agents talking to each other multiply token cost and latency, and free-form collaboration is harder to make reliable than a fixed flow. Many problems that look like they need a crew run better as one agent with a clear sequence of steps.
OpenAI Agents SDK: Small and Readable
The OpenAI Agents SDK is a lightweight library with a handful of primitives: agents (a model with instructions and tools), handoffs (one agent passing control to another), guardrails (checks on input and output) and sessions. Tracing is built in.
Why teams choose it:
- Minimal abstractions, so the code reads like the problem.
- Handoffs make triage patterns easy, for example a front-desk agent routing to billing or technical support.
- Built-in tracing that works out of the box on the OpenAI platform.
Watch out for: it is designed around OpenAI models first, and complex branching or long-running workflows need more of your own code than LangGraph.
Claude Agent SDK: Agents That Work Like Claude Code
Anthropic’s Claude Agent SDK exposes the same agent loop that powers Claude Code. It comes with tools for reading and editing files, running commands and searching, plus support for subagents, permissions and MCP servers. It suits agents that operate on documents, code or a workspace rather than only calling business APIs. Its natural fit is Claude models, and connecting your own systems through MCP is a first-class pattern.
One Support Agent, Built Four Ways
Take a common business case: an agent that reads an incoming support email, looks up the customer and their recent orders, checks the refund policy and drafts a reply for a person to approve.
- In LangGraph, you draw it as a graph: classify the email, fetch customer data, retrieve the policy, draft the reply, then an interrupt node that waits for approval before sending. If the server restarts while waiting, the checkpoint lets the run resume where it stopped.
- In CrewAI, you might define a researcher agent that gathers customer and policy details and a writer agent that drafts the reply, run in sequence. It is quick to set up, but two agents mean more model calls than the task strictly needs.
- With the OpenAI Agents SDK, you define one agent with three tools (customer lookup, order lookup, policy search) and an output guardrail that blocks replies promising refunds above a limit. A triage agent can hand off billing questions to a specialist agent.
- With plain tool calling, you write a loop of perhaps a hundred lines in your existing backend, store the draft in your database and use your normal admin screen for approval.
For this use case, plain tool calling or the OpenAI Agents SDK is usually enough. LangGraph becomes the better choice when the same agent grows into a process with many branches, waits for several approvals or runs for hours.
Observability, Evaluation and Cost
Whatever framework you choose, three things decide whether an agent survives production.
- Tracing: record every run, including each model call, tool call, input and output. LangSmith pairs naturally with LangGraph, the OpenAI Agents SDK has tracing built in, and open-source tools such as Langfuse work with all of them.
- Evaluation: keep a test set of real tasks with known good outcomes and re-run it after every prompt, model or tool change. Frameworks do not provide this for you; it is project work.
- Token cost: multi-agent designs multiply model calls. A crew of three agents discussing a task can cost several times more than one agent following a fixed sequence. Measure cost per completed task, not per call.
Framework choice also affects maintenance. Agent libraries still release breaking changes often, so pin versions, keep your business logic in plain functions the framework calls, and you can swap frameworks later without rewriting everything.
Language and Ecosystem Support
Most teams pick the framework that fits their existing stack. LangGraph has mature Python and JavaScript versions and the largest ecosystem of integrations through LangChain. CrewAI and the OpenAI Agents SDK are Python-first, with the OpenAI SDK also available for TypeScript. The Claude Agent SDK is available in TypeScript and Python. If your product backend is Laravel or another stack without a native SDK, run the agent as a small Python or Node.js service, or call the model API with tool calling directly from your existing code.
When You Do Not Need a Framework at All
Every major model API supports tool calling directly. For a focused agent with two to five tools, a short loop in your own code is often the best choice:
- Fewer dependencies, which matters because agent frameworks still change quickly.
- Full visibility of every prompt and token, which makes debugging and cost control easier.
- Easier to fit into an existing Laravel, Django or Node.js application and its tests.
Our rule of thumb: start with plain tool calling. Add a framework when you need checkpointing, human approval mid-run, parallel branches or several cooperating agents, and pick the one whose model matches the shape of the problem.
How to Choose: A Decision Checklist
- Is it one agent with a few tools? Plain tool calling or the OpenAI Agents SDK.
- Does it run for minutes or hours, pause for approval or need to resume after failure? LangGraph.
- Does it genuinely need several specialised roles? Prototype in CrewAI, then decide whether a fixed flow would be more reliable.
- Does it work mostly with files, code or documents in a workspace? Claude Agent SDK.
- Must you switch model providers freely? LangGraph, CrewAI or your own loop.
Whichever you choose, the framework is a small part of success. Evaluation sets, guardrails, monitoring and clean tool design decide whether an agent works in production. That is the focus of our AI agent development work, and it is also the biggest factor in what an AI agent costs.
FAQ
Is LangGraph better than CrewAI?
Neither is better in general. LangGraph gives more control and suits complex production workflows that need checkpoints and human approval. CrewAI is faster for prototyping multi-agent ideas. Many teams prototype in CrewAI and build the production version in LangGraph or plain code.
Can I use the OpenAI Agents SDK with Claude or other models?
It supports other providers through adapters, but it is designed around OpenAI models and its tracing works best on the OpenAI platform. If you plan to mix providers heavily, a model-agnostic framework or your own loop is simpler.
Do I need a framework to build an AI agent?
No. Tool calling is built into the OpenAI, Anthropic and Google model APIs. A focused agent with a few tools is often clearer and easier to test as your own code. Frameworks earn their place when you need persistent state, approvals or multi-agent coordination.
Which agent framework is best for production?
LangGraph is the most common choice for complex production agents because of its explicit control flow and checkpointing. For simpler agents, the OpenAI Agents SDK, the Claude Agent SDK or plain tool calling are all production-ready when paired with evaluation and monitoring.
Where does MCP fit with these frameworks?
MCP is a standard way to expose tools and data to any agent. LangGraph, CrewAI, the OpenAI Agents SDK and the Claude Agent SDK can all use tools from MCP servers, so building your integrations as an MCP server keeps them reusable whichever framework you choose.