LangChain vs LangGraph: Which Does Your AI Project Need?

If you're scoping an AI feature, you'll hear both names quickly. They're from the same team and they're often used together, but they solve different problems, and picking the wrong one shows up later as either wasted complexity or a system you can't control. Here's the distinction in plain terms.

The one-line difference

LangChain is for a sequence: take input, retrieve some context, call a model, return an answer. LangGraph is for a process: multiple steps that loop, branch, wait for a human, and hold state across the whole run. A chain goes forward; a graph can go back.

When LangChain is enough

• A chatbot that answers from your documents (retrieval, then a model call).
• A single-shot task: summarize this, classify that, extract these fields.
• A tool the model can call once to fetch data mid-answer.
• Anything where the flow is essentially linear and finishes in one pass.

When you need LangGraph

• A procedure with several steps that must happen in order, with state carried between them.
• Human-in-the-loop approval, where the system pauses and waits for a person before continuing.
• Branching logic: if the evidence is thin, loop back and gather more before proceeding.
• Multiple agents with different jobs coordinating on one task.

A real example

We built the AI core of a workplace-procedures platform where practitioners are walked through a 12-step investigation, with a person approving each consequential step. That is not a chain. It has to hold the state of the investigation, pause for human approval, branch on what the evidence shows, and resume, which is exactly what LangGraph exists for. A plain chain would have forced us to rebuild that control flow by hand, badly.

How to decide

Start with the simplest thing that could work. If your feature is 'answer questions from our data', that's retrieval and a model call, and you don't need a graph. If a person has to review or approve steps, or the process loops and holds state, reach for a graph before you find yourself reinventing one. When in doubt, scope it with someone who has shipped both to production rather than just demoed them.