LangChain vs LangGraph: what actually differs, and when to use which

LangChain and LangGraph come from the same team, share an ecosystem, and are constantly compared — usually by someone deciding what to build an agent on this quarter. The confusion is understandable: one grew out of the other, the documentation cross-links heavily, and both can technically build an “agent”.

Here is the practical difference, when each fits, and the part most comparisons skip: the work that remains after you pick one.

LangChain vs LangGraph: the short answer

LangChain is a component library: hundreds of integrations for models, vector stores, retrievers, and tools, plus composable chains for sequential LLM pipelines (prompt → model → parser → next step). It is at its best when data flows one way.

LangGraph is an orchestration framework for stateful agents: you define nodes and edges in a graph, and the framework manages state, branching, loops, persistence via checkpointers, and human-in-the-loop interrupts. It exists because real agents do not flow one way — they loop, branch, retry, and wait for people.

Rule of thumb: pipelines → LangChain; agents → LangGraph. They compose — LangGraph nodes routinely call LangChain components.

What each is best at

Dimension	LangChain	LangGraph
Mental model	Chains — directed pipelines	Graphs — nodes, edges, cycles, state
State	Mostly stateless between steps	First-class state object, checkpointed
Loops & branching	Awkward — chains want to move forward	Native — cycles and conditional edges
Human-in-the-loop	DIY	Interrupts and resumable runs built in
Multi-agent	Possible, manual	Supervisor and swarm patterns supported
Best for	RAG pipelines, extraction, simple assistants	Agents that decide, retry, and coordinate
Learning curve	Gentler start, abstractions run deep	Steeper start, more explicit control

When to use LangChain

Choose plain LangChain when the shape of your problem is a pipeline: retrieval-augmented answering, document extraction, summarization, classification. You get the integration catalog — genuinely the broadest in the ecosystem — without graph machinery you do not need. Be aware of the trade-off teams cite most: deep abstraction layers and a brisk pace of breaking changes that your codebase inherits.

When to use LangGraph

Choose LangGraph the moment your system needs to make decisions about its own flow: tool-using agents that loop until done, workflows that pause for human approval, supervisors delegating to specialists. Explicit graphs are easier to reason about than prompt-buried control flow, and checkpointed state gives you resumability. The cost: you are now maintaining orchestration code, and everything around it is still yours to build.

What neither gives you: the runtime

Pick either framework and the following list is still on your roadmap, because a framework runs inside your process — it is not a service:

• An API server with streaming (SSE) your product can consume
• Authentication, token scopes, and tenant isolation
• Session persistence and cross-session memory
• Tool security: per-agent scoping, forwarded end-user identity, confirmation gates
• An admin surface non-engineers can operate
• Audit logs, health checks, metrics, recovery behavior

That is months of platform engineering — the part that actually gates shipping. It is also exactly the layer SyntheticBrew ships as a product: a self-hosted Go runtime where agents are configuration, delegation and spawning have policies, tools carry your users’ real identity, and every step is streamed and audited. Your product integrates over REST + SSE in any language, and no framework abstractions enter your codebase.

If you are weighing the frameworks because an agent feature needs to ship, read our LangChain alternatives comparison — it covers when a framework is the right call and when a runtime gets you to production faster.

LangChain vs LangGraph FAQ