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Flowise vs TinyClaw

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 source?? stars
TinyClaw

Personal autonomous AI companion with plugin-based extensibility and episodic memory

Category
Flowise
TinyClaw
Tagline
Visual drag-and-drop builder for AI agents and LLM workflows โ€” no code required
Personal autonomous AI companion with plugin-based extensibility and episodic memory
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
Discord, Telegram, WhatsApp, 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.
TinyClaw pros
  • Open source with transparent code and flexible deployment options.
  • 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.
TinyClaw cons
  • Setup leans technical and will slow down non-operators.
  • Security posture is weak for high-trust or regulated workflows.
Flowise gotchas
  • Complex flows can hit LLM rate limits silently.
  • Docker deployment recommended over npm for stability.
TinyClaw 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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