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Dify vs LangGraph

DifyLangGraph

Bottom line: Dify for teams building LLM apps and agents quickly; LangGraph for engineering teams building production agents.

DifyDify

Open-source platform for building production-ready LLM apps and agents.

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LangGraphLangGraph

Graph-based orchestration for stateful, controllable LLM agents.

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Votes00
PricingFreemiumFreemium
CategoryAgent FrameworksAgent Frameworks
Tags
llmopsopen-sourceai-agentsragworkflow
agentsorchestrationllm-frameworkopen-sourceworkflow
Best for
  • Teams building LLM apps and agents quickly
  • Organizations with data-residency needs
  • Developers who want an open-source, self-hostable stack
  • Engineering teams building production agents
  • Developers needing controllable, resumable workflows
  • Teams already invested in LangChain
Pros
  • Genuinely open-source and self-hostable for strong data control
  • All-in-one: workflow, RAG, agents, and prompt IDE in one workspace
  • Low-code visual canvas lowers the barrier to building
  • Broad model and provider support, including self-hosted models
  • Large, active community and a marketplace ecosystem
  • Explicit, debuggable control over agent state and flow
  • MIT-licensed core, free to self-host
  • Model-agnostic, not tied to one LLM vendor
  • Strong support for cycles, checkpointing, and human-in-the-loop
  • Backed by the widely used LangChain ecosystem
Cons
  • License is not fully permissive; multi-tenant resale and branding removal are prohibited
  • Real cost is dominated by separate LLM token spend, not the platform fee
  • Message-credit model on paid tiers can feel limiting at scale
  • Self-hosting adds ops and maintenance burden
  • Free tier's one-time credits are essentially a demo allowance
  • Steeper learning curve than high-level agent builders
  • Requires comfort with graph and state concepts
  • Paid platform and observability are separate products
  • Documentation and APIs have evolved quickly
  • Can be overkill for simple single-shot LLM tasks

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