Graph API vs Functional API

LangGraph has two APIs: the Graph API (StateGraph) and the Functional API (@entrypoint + @task). Both compile to the same runtime. The choice is about debugging visibility, testability, and team collaboration — not capability. This article helps you pick the right one and avoid the failure modes that catch engineers off guard.

Quick Reference

→Graph API = declarative: StateGraph + TypedDict state + add_node + add_edge. Full visual debugging in LangGraph Studio.
→Functional API = imperative: @entrypoint + @task + plain Python. Less boilerplate, no state schema required.
→Both compile to the same Pregel runtime — checkpointing, streaming, human-in-the-loop, and Platform deployment work identically.
→Graph API wins on: LangGraph Studio visualization, cycles, conditional edges, Send() for dynamic parallelism, explicit shared state.
→Functional API wins on: less boilerplate, pure-function testing, entrypoint.final for decoupled checkpoints, previous for short-term memory.
→Combine freely: call @entrypoint from a StateGraph node, or invoke a compiled graph from inside @task.
→Default to Functional API for linear pipelines. Graduate to Graph API when you need cycles, 3+ routing branches, or visual debugging.

Do You Actually Need to Choose?

Most engineers ask which API to use before they need to. Both APIs are fully interoperable — you can call @entrypoint from inside a StateGraph node, and you can invoke a compiled graph from inside @task. For a new workflow, start with the Functional API. You can always call it from a Graph API orchestrator later without rewriting it. The real question is not 'which one forever' but 'which one for this specific workflow, right now.'

Functional API: simple decorated functions. StateGraph: explicit graph definition.

Same runtime, different ergonomics

Both APIs compile to the same Pregel execution engine. Checkpointing, streaming, human-in-the-loop interrupts, and LangGraph Platform deployment work identically regardless of which API you use. The differences are in code style, debugging visibility, and how you manage state — not in what the runtime can do.

The Real Tradeoffs

The APIs are not equivalent in practice. Shared runtime means identical execution guarantees — it does not mean identical engineering experience. Here are the seven dimensions that actually matter when choosing.

Decision Table: Which API for Your Use Case

Use case	API	Reason
RAG pipeline (fetch → rank → generate)	Functional API	Linear. No cycles. Simple if/else branching at most. @entrypoint + 3 @tasks is 20 lines.
Multi-agent supervisor routing between specialized agents	Graph API	Conditional edges make routing explicit and visualizable. Each agent is a node with its own tool loop.
Approval workflow with human review	Either — Functional is simpler	interrupt() works identically in both. Functional API saves boilerplate for a linear approve/reject flow.
Agent think-act-observe loop (ReAct)	Graph API	Cycles are first-class in Graph API. A while loop in Functional API loses per-iteration checkpointing.
Data processing pipeline with 3 sequential transforms	Functional API	Linear pipelines are the Functional API's home territory. Less setup, easier to test.
5+ agent system with complex routing and shared state	Graph API	Shared TypedDict state with reducers, conditional routing, and Studio visualization are all worth the boilerplate here.
Existing codebase you want to add HITL to	Functional API	Wrap existing functions in @task and the @entrypoint. Minimal refactoring required.
New greenfield agent, team size > 3	Graph API	Graph structure becomes team documentation. New engineers understand routing by reading the graph, not reverse-engineering function bodies.

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