There’s a new wave of developer tools popping up every day. If you’re anything like me, you’ve probably lost count of the number of times you’ve read “Just plug in your LLM and boom — you’ve built an AI agent.”

But the truth is, there’s a big difference between slapping a chatbot next to an LLM prompt and designing an AI agent that can actually do useful work in the real world.

When you start building beyond simple question-answering — especially when you want your AI Agent to use tools, make decisions, or follow a multi-step workflow — you’ll quickly bump into two patterns that everyone’s talking about: the ReAct agent and LangGraph.

So which one should you pick for your next project?

Let’s break it down, dev-to-dev.

The ReAct pattern stands for Reason + Act. It’s an elegant idea: instead of the LLM spitting out a one-shot answer, it thinks out loud in steps:

• Reason: The LLM explains what it wants to do next.
• Act: It calls a tool (a Python function, API, whatever).
• Observe: It sees the tool’s output and reasons again.
• Loop: Repeat until it’s ready to give a final answer.

This is what the create_react_agent in LangChain does. You don’t write any fancy graphs or routing. You just give your LLM some tools (functions) and a good system prompt that says, “Hey buddy, here’s what you can use — figure it out!”

Honestly, it’s pretty magical when you see it work the first time.

The ReAct agent pattern is brilliant when:

• You want to test an idea quickly.
• You have a few tools — maybe a calculator, a web search, a database lookup.
• You trust your LLM to pick the right tool and not hallucinate too badly.
• You don’t need to control the exact order in which tools are used.

Basically, if you can trust your LLM to think for itself — ReAct agents are your friend.

Now, imagine you want your AI Agent to do something more complicated.

Let’s say:

• You need a Supervisor agent that breaks a task into parts.
• You have Sub-agents, each with their own specialty.
• You need to guarantee the workflow follows exactly the order you want.
• You want to add conditions: If this fails, try that. If the user says X, skip step Y.

Good luck doing all that with just a ReAct prompt.

This is where LangGraph comes in.

LangGraph lets you define an explicit graph — a map of how your AI Agent should work:

• Nodes: Each step — an LLM call, a function, a tool, or even another sub-agent.
• Edges: The paths — what happens after each step.
• State: A shared memory that you update at each node.

The best part? The graph enforces the flow. Even if your LLM tries to go rogue and skip ahead, the graph won’t allow it. Think of it as your AI Agent’s “manager” — politely but firmly keeping it on track.

This is the question every developer asks at some point: Can’t I just tell my ReAct agent to follow steps 1 → 2 → 3?

Short answer: Yes, you can.

Long answer: Should you? Probably not for anything complex.

You can get clever with prompts. You can say:

“First, check if the user wants a refund. If yes, ask for the order number. Then verify the order. Then call the return tool. Then send the confirmation.”

If your LLM is well-behaved, it’ll follow that flow. Until it doesn’t.

LLMs are brilliant, but they’re also unpredictable. The more complicated your workflow, the more brittle it becomes if you rely only on prompt instructions. One weird edge case and the LLM might skip a step, forget a tool, or do something twice. Debugging that is about as fun as debugging a race condition at 3 AM.

This is the single biggest takeaway:

• ReAct agent: The LLM decides what to do next, based on the prompt and the conversation history.
• LangGraph: You decide what happens next, with code and explicit routing.

If your workflow must happen in a particular way, and you want to sleep well at night, use LangGraph.

One of my favorite things is combining both. Who says you have to choose one?

You can:

• Use LangGraph as the skeleton: Supervisor → Sub-agent → Tool → Next step.
• Inside a node, use a ReAct loop for that step’s flexible tool use.

Think of it like this:

• LangGraph: The strict project manager.
• ReAct: The junior dev who’s good at figuring things out on the fly.

Best of both worlds.

Let’s say you’re building a customer support AI Agent for an e-commerce store.

Simple ReAct version:

• The AI talks to the user, picks a tool (check order status, cancel order, get shipping info).
• It loops: reason → act → answer.

Perfect for common questions.

LangGraph version:

• The AI must first verify the user.
• Then check order eligibility.
• Then trigger refund or escalation.
• Must log each step for compliance.
• If anything fails, it should fallback and ask for more info.

That’s a workflow — it needs guarantees. A ReAct agent could do this, but you’d be crossing your fingers every time it runs. LangGraph makes it robust.

As developers, we like determinism. We like tests that pass. We like things that work the same way every time.

LangGraph gives you that superpower for AI Agents:

• You can test each node.
• You can see the state at each step.
• You can add logging and debugging.
• You can retry or reroute when something fails.

It’s everything you wish you had when your ReAct agent suddenly forgets step 2 of your 5-step refund flow.

Here’s the big takeaway — bookmark this for your next project 👇

And here’s your cheat sheet for choosing the right tool:

At the end of the day, both the ReAct agent and LangGraph are brilliant patterns for building modern AI Agents. They just solve different problems.

If you’re just starting out — spin up a ReAct agent, give it some tools, and have fun watching it think step by step. But when you’re ready to scale up and you need your AI Agent to follow the plan every single time — bring in LangGraph. Your future self will thank you.

Happy building. And may your agents never hallucinate the wrong API call at 2 AM.

Want me to help you write up a ReAct agent and a LangGraph version for the same task? Just ping me — I’ll bring the coffee. ☕🚀