LangChain vs LangGraph: Which Should You Choose?
Teams building LLM applications in Python or TypeScript quickly meet both of these — LangChain, the original integration framework, and LangGraph, its sibling built for stateful agent orchestration. They come from the same company and interoperate, so this isn't a rivalry; it's a question of how much control flow complexity your application actually has, and where you want that complexity to live.
LangChain
LangChain wins for linear pipelines and integration breadth. If your application is a sequence — take input, retrieve context, call a model, parse output — its chain abstractions and enormous catalog of pre-built integrations for vector stores, document loaders, and model providers get you moving fast. RAG pipelines, summarization flows, and extraction tasks fit its shape naturally. For prototypes and straightforward production pipelines where the flow is a line rather than a loop, LangChain's off-the-shelf components typically beat hand-rolling the same glue code.
LangGraph
LangGraph wins the moment your application needs cycles, branching, or durable state. Agents that loop — call a tool, evaluate the result, decide what to do next — are awkward to express in chains but natural as a graph. You get explicit state that persists across steps, checkpointing so long-running work survives restarts, human-in-the-loop interrupts for approval gates, and sane patterns for multi-agent coordination. For production agent systems where you need to see, test, and control every transition, LangGraph's explicitness is the point.
The architectural difference: pipeline versus state machine
LangChain models computation as composition — components piped together, data flowing one direction from input to output. That's a great fit for a large class of LLM apps, and its real gift is the integration layer: hundreds of loaders, stores, and providers behind consistent interfaces.
LangGraph models computation as a state machine: nodes that do work, edges that route between them — conditionally, and crucially, backwards. A graph can loop until a condition is met, branch on a model's decision, pause for human input, and resume from a checkpoint after a crash. If you've ever tried to build an agent loop with retries and approval steps inside a linear chain abstraction, you've felt exactly the gap LangGraph exists to fill. The two aren't competitors so much as answers to differently-shaped problems — and LangGraph freely uses LangChain's integrations inside its nodes.
Learning curve and team implications
LangChain is faster to start and easier to staff for: its patterns resemble ordinary pipeline code, tutorials are abundant, and a competent Python or TypeScript developer is productive within days. The criticism it attracts — layers of abstraction that obscure what's actually sent to the model — is fair for complex apps, and debugging deep chains can frustrate teams who'd rather see the raw prompts.
LangGraph asks for more upfront thinking: you design state schemas and explicit transitions before writing much logic. That's a real cost for simple apps and a bargain for complex ones, because the structure you're forced to define is exactly what makes the system debuggable and testable later. One honest caveat that applies to both: for genuinely simple use cases — one model call, a bit of formatting — direct SDK calls with no framework at all are often the cleanest choice, and I steer clients there regularly.
Migration and lock-in considerations
Because LangGraph builds on LangChain's component ecosystem, migration between them is incremental rather than a rewrite: a chain can become a node in a graph, and teams commonly start with chains, hit the complexity wall, and lift the workflow into a graph while keeping their retrievers, prompts, and integrations intact. That's the smoothest upgrade path in this ecosystem.
The lock-in to weigh is conceptual as much as technical. Both frameworks shape how your team thinks about LLM applications, and unwinding either from a mature codebase is tedious — the abstractions thread through everything. Mitigate it the boring way: keep prompts in your own files, keep business logic out of framework callbacks, and treat the framework as orchestration rather than architecture. Done that way, even a framework exit later is a bounded refactor instead of a rewrite.
Decision walkthrough by scenario
Startup shipping a RAG-powered feature — search docs, answer with citations: LangChain covers this shape well, and you'll ship faster using its retrieval components than assembling your own. Product whose core is an autonomous agent — multi-step research, tool-using assistants, workflows with approval gates: start in LangGraph from day one; retrofitting durable state into a chain-based app is more painful than learning graphs up front.
Enterprise needing auditable, resumable long-running workflows: LangGraph's checkpointing and explicit transitions map directly to those requirements. Existing LangChain codebase straining under branching logic and retry hacks: migrate the tangled workflow to a graph and leave the simple chains alone — partial adoption is fully supported. And if your app is one or two model calls with light glue: skip both frameworks and call the SDK directly; add orchestration when the control flow earns it.
Decision checklist
- → Does your application's control flow contain loops or conditional branches?
- → Do you need workflows to pause for human approval and resume later?
- → Must long-running tasks survive process restarts via checkpointing?
- → Is your app a linear pipeline that LangChain's components already cover?
- → Would direct SDK calls with no framework be simpler for your use case?
- → Does your team have the appetite for designing explicit state up front?
- → Are you already on LangChain and hitting the complexity wall?
Frequently asked questions
Is LangGraph replacing LangChain?
No — they're complementary tools from the same company, and LangGraph uses LangChain's integrations internally. LangChain remains the integration layer and the fit for linear pipelines like RAG; LangGraph is the orchestration layer for stateful, cyclic agent workflows. The vendor's own guidance points agent builders toward LangGraph, but that reflects the problem shape, not a deprecation of LangChain.
Can I migrate from LangChain to LangGraph incrementally?
Yes, and this is the designed path. Existing chains, retrievers, and integrations slot into LangGraph nodes without rewriting, so you can lift just the complex workflow — the one accumulating retry hacks and branching logic — into a graph while simpler pipelines stay as chains. Most teams migrate one workflow at a time rather than converting a whole codebase, and the two coexist cleanly in production.
Do I need LangChain or LangGraph at all to build an AI agent?
Not necessarily. A basic agent is a loop: call a model, execute the tool it requests, feed back the result, repeat — and the provider SDKs support this directly with modest code. Frameworks earn their place when you need durable state, checkpointing, human-in-the-loop gates, or multi-agent coordination. A sensible rule: start with the SDK, and adopt LangGraph when your hand-rolled loop starts growing state management you don't want to own.