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Flowise 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 source52k stars
Flowise

Visual drag-and-drop builder for AI agents and LLM workflows โ€” no code required

Open source12k stars
IronClaw

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

Category
Flowise
IronClaw
Tagline
Visual drag-and-drop builder for AI agents and LLM workflows โ€” no code required
Defense-in-depth Rust agent with enterprise-grade security and TEE enclaves
Deployment
Self-hosted / Flowise Cloud
Self-Hosted
Pricing
Open source and free to self-host. Flowise Cloud starts at $35/month.
Usually affordable for individuals or small teams, with some recurring model or hosting costs.
Channels
Web, api, Slack, Teams
Telegram, Slack, Web
Open source
Yes
Yes
Privacy
Self-hosted deployment keeps data on your infrastructure.
Good privacy posture for most teams, especially when self-hosted or carefully configured.
Flowise pros
  • Visual builder โ€” no code required.
  • Largest ecosystem of integrations of any open-source agent builder.
  • 51K+ GitHub stars, active community.
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.
Flowise cons
  • Visual workflows hard to debug at scale.
  • Less flexible than code-first frameworks.
  • Self-hosting requires some DevOps 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
Flowise gotchas
  • Complex flows can hit LLM rate limits silently.
  • Docker deployment recommended over npm for stability.
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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