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📖 The AI Tool Bible

Dify vs LangGraph

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

 
Dify
Agents
LangGraph
Agents
TaglineOpen-source LLMOps platform for building agentic workflows, RAG pipelines, and AI applicationsStateful, graph-based agent orchestration from LangChain.
CategoryAgentsAgents
PricingFreemium· Cloud: Sandbox free (200 message credits, 1 user, 5 apps) / Professional $59/mo (~$49/mo annually, 5k credits, 3 users, 50 apps) / Team $159/mo (~$133/mo annually, 10k credits, 50 users, 200 apps). Community Edition free and self-hostable under an Apache-2.0-derivative license. Enterprise pricing on request (SSO, commercial licensing, professional support).Freemium· Developer: $0 / seat per month · Plus: $39 / seat per month · Enterprise: Custom pricing
ModelModel-agnostic: OpenAI (GPT-4o, GPT-4.1), Anthropic Claude, Google Gemini, Mistral, Cohere, Ollama, and any OpenAI-compatible endpointBYO (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
Websitedify.aiwww.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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