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Dify vs AutoGen

DifyAutoGen

Bottom line: Dify for teams building LLM apps and agents quickly; AutoGen for teams already running AutoGen in production.

DifyDify

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

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AutoGenAutoGen

Microsoft's multi-agent conversation framework (now in maintenance mode).

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Votes00
PricingFreemiumFree
CategoryAgent FrameworksAgent Frameworks
Tags
llmopsopen-sourceai-agentsragworkflow
agentsmulti-agentopen-sourcemicrosoftorchestration
Best for
  • Teams building LLM apps and agents quickly
  • Organizations with data-residency needs
  • Developers who want an open-source, self-hostable stack
  • Teams already running AutoGen in production
  • Researchers studying multi-agent systems
  • Developers prototyping agent collaboration
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
  • Pioneered accessible multi-agent conversation patterns
  • Free and open source
  • Backed by Microsoft Research with strong documentation
  • 0.4 architecture is asynchronous and more observable
  • Works with many model providers
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
  • In maintenance mode as of 2026, no new feature focus
  • Microsoft steers new projects to the Agent Framework
  • Multiple version lines (0.2 vs 0.4/0.7) cause confusion
  • Multi-agent loops can be hard to control and cost-predict
  • Less enterprise tooling than the successor framework

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