Dify vs LangGraph

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Dify Agents	LangGraph Agents
Tagline	Open-source LLMOps platform for building agentic workflows, RAG pipelines, and AI applications	Stateful, graph-based agent orchestration from LangChain.
Category	Agents	Agents
Pricing	Freemium· Professional: $590 · Team: $1590 · Sandbox: Free · Enterprise: Custom · Community: Free	Freemium· Developer: $0 / seat · Plus: $39 / seat · Enterprise: Custom pricing
Model	Model-agnostic: OpenAI (GPT-4o, GPT-4.1), Anthropic Claude, Google Gemini, Mistral, Cohere, Ollama, and any OpenAI-compatible endpoint	BYO (Claude / GPT / open)
Editorial score	—	8.8 / 10
Use cases	RAG chatbot over internal documentsCustomer support automationMulti-step agent workflowsInternal copilots and assistantsDocument Q&A and summarisationPrompt orchestration and A/B testingEmbeddable chat widgets on marketing sitesMCP tool publishing for Claude and other clientsKnowledge base search APIsWorkflow automation with LLM decision nodes	stateful agentshuman-in-loopproduction
Pros	Genuinely open-source (Apache-2.0-derivative) with a mature self-hosted Docker/Kubernetes deployment path Visual workflow builder covers branching, tool use, agents, and human-in-the-loop without dropping to code Model-agnostic: swap OpenAI, Anthropic, Gemini, Mistral, Ollama, or any OpenAI-compatible endpoint per node Built-in RAG pipeline handles chunking, embeddings, reranking, and multiple vector stores out of the box Publishes any app as a hosted UI, embed, REST API, or MCP tool with logs and analytics attached Large plugin/marketplace ecosystem and one of the most active LLM-framework communities on GitHub Cloud tier has a real free sandbox so you can prototype before choosing self-host vs SaaS	Reliable, debuggable agent graphs Built-in persistence + HITL Production-grade Tight LangSmith integration
Cons	Self-hosting the full stack (API, worker, sandbox, vector DB, Redis, Postgres) is heavier than it looks and version upgrades occasionally break workflows Cloud message-credit limits are tight on the Professional tier and overages push teams to Team or self-host quickly Workflow debugging is weaker than code-first frameworks: complex agent loops can be hard to trace step-by-step Commercial use of the Community Edition has licence caveats (multi-tenant SaaS resale, logo/branding) that need reading before shipping Evaluation and offline testing tooling is thinner than dedicated eval platforms like Langfuse or Braintrust	Steeper learning curve than CrewAI Verbose to set up
Website	dify.ai	www.langchain.com

Pick Dify if

✅ Genuinely open-source (Apache-2.0-derivative) with a mature self-hosted Docker/Kubernetes deployment path
✅ Visual workflow builder covers branching, tool use, agents, and human-in-the-loop without dropping to code
✅ Model-agnostic: swap OpenAI, Anthropic, Gemini, Mistral, Ollama, or any OpenAI-compatible endpoint per node
✅ Built-in RAG pipeline handles chunking, embeddings, reranking, and multiple vector stores out of the box

Pick LangGraph if

✅ Reliable, debuggable agent graphs
✅ Built-in persistence + HITL
✅ Production-grade
✅ Tight LangSmith integration

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