LangGraph vs Dify

An orchestration primitive vs an application platform

LangGraph and Dify solve the same broad problem — building agent applications — at two very different layers. LangGraph is a low-level orchestration library: you model an agent as a graph of nodes and edges over a shared, persisted state, and you write that graph in Python or TypeScript. Dify is a platform: a visual app builder and workflow canvas with a native RAG pipeline and multi-provider model management, designed so teams can ship retrieval and agent apps without wiring the plumbing themselves.

The core decision is therefore code-first agent orchestration versus a visual AI-app platform. LangGraph gives you maximum control over how an agent moves through its steps and how its state persists, at the cost of writing and owning that code. Dify gives you speed and a batteries-included environment — RAG, model management, a canvas — at the cost of building inside a platform rather than against a primitive. Neither is a subset of the other; they sit at opposite ends of the control-versus-speed axis.

Code-first control vs visual iteration

LangGraph is code-first by design. Nodes, edges, conditional branches, loops, and human-in-the-loop interrupts are all expressed in code, which is exactly what engineering teams that need to control and debug agent control flow step by step want. The trade is that there is no drag-and-drop surface: everything is programmed, and the learning curve is steeper because you think in graphs and shared state.

Dify is visual-first. Its app builder and workflow canvas let teams assemble prompts, tools, retrieval, and agent steps without writing orchestration code, which shortens the path from idea to a working app and opens the work to non-pure-engineering teams. The trade is that you iterate inside the platform's model of an app rather than owning the control loop in code, so highly custom control flow is harder to express than it is in a hand-written graph.

State, persistence, and RAG

LangGraph is built around a typed, shared state object. With a durable checkpointer, it can persist graph state and continue from recorded checkpoints after an interrupt or failure. That does not guarantee exactly-once external side effects: nodes and tools need safe retry and idempotency boundaries. Retrieval is build-your-own; LangGraph orchestrates the RAG components you wire in rather than shipping a complete product pipeline.

Dify inverts that emphasis. It is stateful at the platform level and ships a native RAG pipeline with managed vector-store integrations, so retrieval apps work out of the box without assembling the retrieval plumbing. If native RAG and managed model access are the fastest path to your app, Dify provides them directly; if durable, code-level control of agent state is the priority, LangGraph provides that natively.

Deployment, licensing, and lock-in

Both can run on your own infrastructure — LangGraph as a self-hosted library and Dify as a self-hostable platform (or its managed cloud) — so data control is achievable on either. The licensing differs: LangGraph is MIT-licensed open source, while Dify is source-available under the Dify Open Source License (Apache 2.0 plus restrictions). For teams that require fully permissive open-source terms, that distinction matters.

Lock-in tracks the layer choice. LangGraph, being a code-first library with model-agnostic adapters, carries the lower lock-in: your control flow lives in your code. Dify, as a platform, concentrates more of your app inside its builder and conventions, which is the natural cost of the speed it provides. Weigh the lower lock-in and code ownership of LangGraph against the faster delivery and built-in RAG of Dify.

Who should choose LangGraph

LangGraph is the right call for engineering teams that want code-first, explicit control over agent control flow and state: long-horizon agents with loops and branches, human-in-the-loop approval steps, and workflows where resuming from a checkpoint without redoing work is a requirement. Its MIT license, model-agnostic adapters, and library form factor keep lock-in low and the control loop in your own codebase. Choose it when owning the orchestration in code matters more than shipping fast on a canvas.

Who should choose Dify

Dify is the right call when speed and a batteries-included environment matter more than low-level control. Teams building RAG applications, wanting a visual canvas for prompts and agent steps, or needing to deliver AI apps without a pure-engineering staff get the most from its native RAG pipeline, managed multi-vendor models, and self-hostable deployment for data control. It also ships the product surface LangGraph does not: a hosted chat UI and embed widget, document upload, and a model gateway, so a customer- or team-facing AI app is a day-one feature rather than weeks of front-end work. Choose it when fast visual iteration and out-of-the-box retrieval outweigh the code ownership and permissive licensing that LangGraph offers.

When to use both together

The two are not strict substitutes — they occupy different layers, and a common production pattern uses both. Dify owns the user-facing chat product (UI, RAG, model gateway, team workspace) and calls out over HTTP to a heavier LangGraph agent that owns the stateful control flow behind it. Before forcing one tool to do both jobs, consider that split: Dify for the product surface, LangGraph for the durable, branchy agent backend.

A useful gut check: many "we need an agent framework" decisions are really "we need an AI product platform" decisions in disguise, and Dify ships faster than a Python library plus custom UI. Equally, many "we need a low-code AI platform" decisions are stateful-agent problems wearing a canvas costume, where LangGraph stays sane exactly where a visual builder hits ceilings. Pick by the shape of the agent and who is building it, not by the brand.