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Agno vs IronClaw

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 source12k stars
IronClaw

Defense-in-depth Rust agent with enterprise-grade security and TEE enclaves

Category
Agno
IronClaw
Tagline
High-performance multi-agent framework — build, run and manage teams of AI agents at scale
Defense-in-depth Rust agent with enterprise-grade security and TEE enclaves
Deployment
Self-hosted / Agno Cloud
Self-Hosted
Pricing
Open source and free to self-host. Agno Cloud available for managed deployments.
Usually affordable for individuals or small teams, with some recurring model or hosting costs.
Channels
Web, api, CLI
Telegram, Slack, Web
Open source
Yes
Yes
Privacy
Self-hosted deployments keep data on your infrastructure.
Good privacy posture for most teams, especially when self-hosted or carefully configured.
Agno pros
  • Extremely fast — benchmarks show 3x LangGraph speed.
  • Native multi-agent team support built-in.
  • Strong memory architecture with multiple storage backends.
IronClaw pros
  • Open source with transparent code and flexible deployment options.
  • Security posture is excellent for sensitive workflows.
  • Strong privacy story for users who care where data runs.
Agno cons
  • Developer-focused — no visual builder.
  • Ecosystem smaller than LangChain/LangGraph.
  • Requires Python knowledge.
IronClaw cons
  • Smaller channel support (Telegram, Slack, web only)
  • Rust ecosystem less mature for agent tooling
  • Setup complexity higher due to security hardening requirements
Agno gotchas
  • Formerly called Phidata — old docs may use old name.
  • Start with single agent before multi-agent orchestration.
IronClaw gotchas
  • You should expect ongoing hosting, uptime, and secret-management work if you deploy it for real users.
  • Recurring subscription or model spend can matter more than the headline feature list.

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