Comparison · Updated 2026-06-18
LangGraph vs Dify
Two open-source projects, both used in production, both compared constantly -- and they do not really compete on the same layer. LangGraph is a Python library for stateful, graph-shaped agent execution: nodes, edges, checkpoints, persistence. Dify is a full self-hostable AI product platform: a workflow canvas, RAG pipeline, model gateway, chat UI, and team workspace shipped together. Picking the wrong one is expensive: a chat product built on raw LangGraph is months of UI work; a stateful agent forced through Dify's canvas hits ceilings the moment the workflow gets weird. This is the honest side-by-side.
LangGraph
State-graph agent framework from the LangChain team. Nodes, edges, persistent state, time-travel debugging -- explicit control flow for stateful agents.
See alternatives →Dify
Self-hostable AI product platform. Workflow canvas, RAG pipeline, model gateway, chat UI, and a team workspace shipped together -- batteries-included for AI apps.
See alternatives →The short answer
- Winner for code-first stateful agents: LangGraph. Branches, retries, persistence, and resumable runs are first-class.
- Winner for AI products with RAG and chat UI: Dify. Document upload, retrieval, and a usable chat interface are day-one features.
- Winner for learning curve: Dify -- non-engineers ship apps in an afternoon.
- Winner for production debuggability: LangGraph -- time-travel and replay are real.
- Best for: LangGraph for engineering-owned agent backends; Dify for product/ops-owned AI applications.
Snapshot comparison
Before the section-by-section breakdown, the one-screen version.
| Dimension | LangGraph | Dify |
|---|---|---|
| Primary shape | Code-first Python library | Self-hosted product platform |
| Audience | Engineers | Product, ops, builders |
| License | MIT | Open source (custom licence) |
| Maintainer | LangChain Inc. | LangGenius |
| Model coverage | Any (LangChain providers) | Any via built-in model gateway |
| Built-in RAG | Assemble yourself | Document upload + chunking + retrieval |
| Built-in chat UI | None | Hosted chat + embed widget |
| State management | Explicit, persistable, time-travel | Implicit (workflow runs) |
| Branching & retries | First-class graph edges | Workflow canvas branches |
| Resumable runs | Checkpoints survive crashes | Limited |
| Human-in-the-loop | Native interrupt + resume | Manual approval steps |
| Learning curve | Steeper -- think in graphs | Drag-and-drop canvas |
| Hosted option | LangGraph Platform (paid) | Dify Cloud (paid tiers) |
| Best for | Stateful, branchy, long-running agents | RAG chatbots, internal AI products |
Two different mental models
The right tool depends on which of these reads like your problem.
LangGraph thinks "state machine with LLM nodes". You define a graph in Python: nodes are functions (often LLM calls), edges are transitions, and state is a typed object passed between nodes. Branching is conditional edges. Retries are loops. Pausing for human approval is a checkpoint. The mental model is closer to a workflow engine than to a chatbot platform.
Dify thinks "AI product platform". You log into a self-hosted dashboard, drag blocks onto a canvas, attach documents to a knowledge base, pick a model, and publish a chat app. The mental model is closer to Retool or n8n than to a Python library. The platform is the product; the workflow canvas is one feature among many.
If your problem is "ship an internal chatbot over our policy docs that the support team can edit prompts in", that is Dify shaped. If your problem is "a stateful agent that retries failed tool calls, waits for human approval, and replays on demand", that is LangGraph shaped.
Use cases -- when each one wins
LangGraph fits when
- Plan-and-execute agents. Planner proposes steps; executor runs them; planner adjusts based on the result.
- Long-running workflows. Agents that pause for hours or days waiting for human approval, then resume from saved state.
- Retries and self-correction. Loops that re-prompt with error context until a tool call succeeds.
- Auditable production agents. Workflows where every transition needs to be inspectable, replayable, and time-travel debuggable.
- Engineering-owned backends. The agent lives inside a larger Python service alongside business logic.
Dify fits when
- Internal RAG chatbots. Upload PDFs and policies, get a working Q&A bot for support, HR, or sales enablement.
- Customer-facing chat apps. Embed a chat widget on a website without writing the UI.
- Non-engineer prompt iteration. Product or ops staff edit prompts and flows in a canvas without shipping code.
- Model gateway needs. One place to manage API keys, swap providers, and track usage across many apps.
- AI prototypes that should look like products. Where time-to-demo matters more than backend control.
Learning curve
Dify is friendlier in the first afternoon. Spin up Docker, log in, drag blocks, attach a knowledge base, publish a chat app. A non-engineer can ship a working internal bot the same day. The mental model is "build an app on a canvas" -- familiar to anyone who has used Zapier, n8n, or Bubble.
LangGraph is steeper in the first week, gentler at month three. You have to think in terms of state schemas, nodes, edges, and conditional routing before you ship anything. The reward arrives when production starts behaving unpredictably: LangGraph gives you the tools to see exactly what happened, replay it, and patch the transition. Dify gives you logs and a canvas to re-inspect.
Practical rule: if the people building the AI experience are product, ops, or solo builders, Dify clicks instantly. If they are software engineers shipping agents into a service, LangGraph clicks once they accept the up-front graph thinking. Pick by team shape, not by feature checklist.
Pricing comparison
Both projects are open source and free to self-host. The real bill is model inference and, optionally, a hosted tier.
| Cost line | LangGraph | Dify |
|---|---|---|
| Framework / platform licence | Free (MIT) | Free self-host (custom licence) |
| Self-hosting infrastructure | Any Python host | Docker stack (app + DB + vector store) |
| Model inference | Pay-per-token (any provider) | Pay-per-token (any provider via gateway) |
| Hosted runtime | LangGraph Platform: usage-based | Dify Cloud: tiered subscription |
| Observability | LangSmith (paid, generous free tier) | Built-in usage + logs |
| Vector store | BYO (Pinecone, Weaviate, pgvector) | Bundled (Weaviate or external) |
| Hidden costs | State storage at scale | Self-host ops (DB, vector, upgrades) |
The pattern: licence cost is zero for both. Model inference dominates. Dify adds platform infrastructure (database, vector store, web app) which is real ops work but rounding error vs the inference bill at any non-trivial usage. LangGraph adds no platform overhead -- it is a library inside your existing service.
Final verdict
These two are not really substitutes -- they are competing for the same decision. The right call comes down to two questions: who is building the AI experience, and what shape is the agent underneath?
- Engineering team shipping a stateful agent backend: LangGraph wins. The graph model exists for exactly this shape, and persistence keeps long workflows safe.
- Product / ops team shipping a RAG chat product: Dify wins. Knowledge base, chat UI, and model gateway are day-one features instead of weeks of plumbing.
- Both at once: Dify on the front for the chat product, LangGraph behind an HTTP endpoint for the heavy agent. This is a common pattern and worth considering before forcing one tool to do both jobs.
Meta-recommendation: a lot of "we need an agent framework" decisions are actually "we need an AI product platform" decisions in disguise -- and Dify ships faster than any Python library plus custom UI. A lot of "we need a low-code AI platform" decisions are actually stateful agent problems wearing a canvas costume -- and LangGraph will stay sane where Dify hits ceilings. The wider landscape is in the AI Agent Frameworks pillar; the deeper shortlists are best LangGraph alternatives and best Dify alternatives.
Next reads
FAQ
- LangGraph vs Dify -- which one should I pick?
- If your team is engineers shipping a stateful, branchy agent into a production codebase, pick LangGraph. Explicit state, graphs, and checkpoints are first-class. If your team is product or ops staff building an AI app on top of a visual canvas with RAG and a chat UI included, pick Dify. They occupy different layers: LangGraph is a Python library; Dify is a full self-hostable AI product platform.
- Is Dify just a UI for LangGraph?
- No. Dify is its own platform with a built-in workflow canvas, RAG pipeline, model gateway, and chat UI. LangGraph is a code-first library you import into a Python service. There is overlap on "build an agent workflow", but the audiences and surface areas are different -- engineers vs builders, code vs canvas.
- Is LangGraph harder to learn than Dify?
- Yes, on day one. LangGraph requires thinking in state schemas, nodes, and edges; Dify requires dragging blocks on a canvas. A non-engineer can ship a Dify app in an afternoon. An engineer can ship a more reliable LangGraph agent in a week. Different learning curves for different audiences.
- How do token costs compare?
- Similar per call -- both call the same underlying models. LangGraph has the edge for long workflows because per-node state slices keep prompts small. Dify adds a model gateway and RAG infrastructure cost on top of inference; for a small team running modest traffic, that overhead is often dwarfed by the inference bill anyway.
- Can Dify replace LangGraph in production?
- For chat-shaped AI products with RAG, document upload, and a workflow canvas -- yes, comfortably. For deeply custom stateful agents (multi-day workflows, human-in-the-loop branches, replay debugging) -- no. Dify is a product platform; LangGraph is a control-flow library. The honest answer depends on the shape of the agent, not the brand.
- Are LangGraph and Dify open source?
- Yes. LangGraph is MIT-licensed and maintained by LangChain Inc. Dify is open source under a custom licence (free to self-host for most use cases; some restrictions on multi-tenant SaaS resale). Both are embeddable; read the Dify licence if you plan to resell it as a managed service.
- Which one wins for RAG workloads?
- Dify -- out of the box. It ships with document upload, chunking, retrieval, and a chat UI on day one. LangGraph can do RAG, but you assemble the retrieval pipeline yourself. For "internal Q&A bot over our docs", Dify is faster to ship; for "RAG inside a larger stateful workflow", LangGraph wins.
- Can I use LangGraph and Dify together?
- Yes -- Dify can call out to external APIs, so a complex LangGraph workflow can sit behind an HTTP endpoint that a Dify app invokes. This is a common pattern: Dify owns the user-facing chat product, LangGraph owns the heavy stateful agent behind it.