I get asked all the time: "What's the actual difference between LangChain and LangGraph?"
And honestly? The best way I've found to explain it is through food. Bear with me here this analogy is way better than it sounds.
LangChain Is Your Favorite Drive-Through
Picture this: you're hungry, you pull up to a drive through, order a burger and fries, grab your food, and you're on your way. The whole thing takes three minutes. That's LangChain.
It's fast, straightforward, and gets the job done. You tell it what you want, it processes your request, gives you an answer, and that's that. One and done.
In practice, this looks like:
Ask a question → get an answer
Feed in a document → get a summary
Provide some text → get a translation
Query your database → get relevant information
LangChain is perfect when you know exactly what you need and the path to get there is clear. It's like those days when you just want a quick meal without thinking too hard about it.
When LangChain is your go to:
You've got a straightforward task
The solution path is pretty obvious
One round of processing is enough
Speed and simplicity matter
Real-world example: You're building a customer support bot that answers FAQs. Someone asks "What's your return policy?" and you pull the relevant doc and answer. Clean, simple, done.
LangGraph Is Like Hitting Up a Buffet
Now imagine you're at a buffet instead. You walk in, scope out what's available, grab some appetizers, try a few things, realize you want more of the pasta, go back for seconds, decide to try the dessert section, maybe circle back for some salad... you're making decisions as you go based on what you've already tasted and how hungry you still are. That's LangGraph.
It's not about rushing through. It's about exploring, making decisions, circling back when needed, and knowing when you're actually done not just when you've completed one pass.
With LangGraph, your AI can:
Take a step, evaluate the result, then decide what to do next
Loop back to gather more information if the first attempt wasn't enough Choose different tools or approaches based on what it learns
Revise earlier decisions when new information comes in
Keep going until it's actually solved the problem (not just attempted it once)
When LangGraph makes sense:
The problem is complex or open ended
You don't know all the requirements upfront
The AI needs to think, check its work, and iterate
You want control over each decision point
Human oversight or approval is important
Real-world example: You're building a research assistant. Someone asks "Should we invest in this company?" The AI needs to search for financial data, analyze it, realize it needs more context about the industry, fetch that, compare competitors, maybe identify gaps in its research and fill them, then synthesize everything into a recommendation. That's not a one-shot deal—that's exploration and iteration.
The Real Difference in Plain English
LangChain: You order, you receive, you're done.
LangGraph: You explore, taste, decide what you need more of, go back, reassess, and finish when you're satisfied.
One is a straight line. The other is a journey with decision points.
Why This Analogy Actually Works
LangChain is about execution. You've already decided what to do; you just need it done.
LangGraph is about decision making. You're not sure what the best path is yet, so you need the flexibility to figure it out as you go.
It's the difference between:
Following a recipe (LangChain) vs. being a chef who tastes and adjusts (LangGraph)
Taking a direct flight (LangChain) vs. a road trip with stops (LangGraph)
Running a script (LangChain) vs. debugging and iterating (LangGraph)
Here's the Thing Nobody Tells You
LangGraph technically can do everything LangChain does. The LangChain team built it as an evolution of their original framework.
But and this is important that doesn't mean you should always use it.
Going to a buffet when you just want a burger is overkill. The extra options, the flexibility, the decision-making... it's just unnecessary complexity when you know exactly what you want.
Sometimes a drive through is perfect. Sometimes you need the buffet.
So Which One Do You Need?
Ask yourself this:
Does your task feel like:
"Do X, give me Y" → Go with LangChain Or does it feel like:
"Figure out what we need, explore the options, verify your thinking, adjust if necessary, and keep going until we have a solid answer" → That's LangGraph territory
The Bottom Line
I've been working with both frameworks for a while now, and here's what I've learned:
Start simple. If LangChain can handle your use case, use it. Don't overcomplicate things just because LangGraph has more features.
But when you hit that moment where you think "I wish the AI could just... decide to fetch more data" or "I need this to loop until it gets it right" that's when you know it's time for LangGraph.
Thanks
Sreeni Ramadorai
Top comments (0)