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Agno vs Dify

Side-by-side comparison of two agent options that often come up together when people are choosing between self-hosted frameworks, managed assistants, and extensible AI tooling.

Open source40k stars
Agno

High-performance multi-agent framework — build, run and manage teams of AI agents at scale

Open source139k stars
Dify

Production-ready platform for building and deploying agentic workflows with a visual interface

Category
Agno
Dify
Tagline
High-performance multi-agent framework — build, run and manage teams of AI agents at scale
Production-ready platform for building and deploying agentic workflows with a visual interface
Deployment
Self-hosted / Agno Cloud
Self-hosted / Managed cloud (Dify Cloud)
Pricing
Open source and free to self-host. Agno Cloud available for managed deployments.
Open source and self-hostable for free. Dify Cloud starts at $59/month for teams.
Channels
Web, api, CLI
Web, api, Slack, Teams
Open source
Yes
Yes
Privacy
Self-hosted deployments keep data on your infrastructure.
Self-hosted deployment keeps data on your infrastructure. Dify Cloud sends data to Dify servers.
Agno pros
  • Extremely fast — benchmarks show 3x LangGraph speed.
  • Native multi-agent team support built-in.
  • Strong memory architecture with multiple storage backends.
Dify pros
  • Visual workflow builder lowers the barrier to building agentic apps.
  • Production-ready with observability, versioning, and team collaboration.
  • Supports RAG pipelines, tool calling, and multi-agent orchestration.
Agno cons
  • Developer-focused — no visual builder.
  • Ecosystem smaller than LangChain/LangGraph.
  • Requires Python knowledge.
Dify cons
  • Heavier infrastructure than lightweight agent frameworks.
  • Best suited for app builders, not researchers or coding agents.
  • Managed cloud tier can get expensive at scale.
Agno gotchas
  • Formerly called Phidata — old docs may use old name.
  • Start with single agent before multi-agent orchestration.
Dify gotchas
  • Designed for building agent-powered apps, not for personal AI assistant use cases.
  • Self-hosting requires Docker and some ops knowledge.

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