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Flowise vs Majordomo

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

Closed sourceN/A stars
Majordomo

Mobile-first managed OpenClaw hosting with iOS app and residential proxy

Category
Flowise
Majordomo
Tagline
Visual drag-and-drop builder for AI agents and LLM workflows โ€” no code required
Mobile-first managed OpenClaw hosting with iOS app and residential proxy
Deployment
Self-hosted / Flowise Cloud
Managed Cloud
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
iMessage
Open source
Yes
No
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.
Majordomo pros
  • Strong privacy story for users who care where data runs.
  • Can handle meaningful autonomous work instead of acting only as a reactive chatbot.
Flowise cons
  • Visual workflows hard to debug at scale.
  • Less flexible than code-first frameworks.
  • Self-hosting requires some DevOps knowledge.
Majordomo cons
  • Closed-source offering, so portability and vendor transparency are limited.
  • Security posture is weak for high-trust or regulated workflows.
  • Channel coverage is narrow, so distribution options are constrained.
Flowise gotchas
  • Complex flows can hit LLM rate limits silently.
  • Docker deployment recommended over npm for stability.
Majordomo gotchas
  • This is an add-on, not a full standalone assistant, so you will usually pair it with another agent.
  • Recurring subscription or model spend can matter more than the headline feature list.

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