Rivet vs Flowise
Detailed side-by-side comparison to help you choose the right tool
Rivet
🟡Low CodeAI Development Platforms
Rivet: Visual IDE for building, testing, and debugging AI agent workflows using a node-graph interface by Ironclad.
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FreeFlowise
🟡Low CodeAutomation & Workflows
Open-source no-code AI workflow builder and visual LLM application platform with drag-and-drop interface. Build chatbots, RAG systems, and AI agents using LangChain components, supporting 100+ integrations.
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FreeFeature Comparison
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💡 Our Take
Choose Rivet if you want a desktop-first IDE with YAML-based graphs that integrate into Git workflows and a TypeScript SDK for embedding in your own application. Choose Flowise if you prefer a browser-based visual builder with a built-in web runtime and faster setup for prototyping LangChain-based flows without a dedicated deployment pipeline.
Rivet - Pros & Cons
Pros
- ✓Completely free and open-source under MIT license with no seat-based pricing
- ✓YAML-based graph files enable standard Git version control and code review workflows
- ✓Production-validated by Ironclad, Attentive, and Bento — not just a prototyping tool
- ✓Real-time remote debugger shows live execution inside your deployed application
- ✓Desktop-first architecture keeps prompts and API keys on your local machine, not a vendor cloud
- ✓Public integrations with ecosystem partners like AssemblyAI for audio transcription
Cons
- ✗Desktop app requirement excludes browser-only or Chromebook development environments
- ✗Smaller community and plugin library than code-first frameworks like LangChain
- ✗Visual graphs can become unwieldy when agent workflows grow past dozens of nodes
- ✗Production integration requires engineering effort with the TypeScript SDK
- ✗No built-in hosted deployment — teams must run the executor in their own infrastructure
Flowise - Pros & Cons
Pros
- ✓Visual builder backed by real LangChain/LlamaIndex code — full framework power without writing boilerplate, with 35,000+ GitHub stars validating community trust
- ✓Comprehensive component library covering 100+ LLMs, embeddings, and vector databases including OpenAI, Anthropic, Google, Ollama, Pinecone, Weaviate, Qdrant, ChromaDB, and Supabase
- ✓One-click API deployment with built-in chat widget for website embedding plus TypeScript and Python SDKs — fast path from prototype to deployment
- ✓Open-source and self-hostable with simple Node.js deployment via npm install -g flowise, Docker, or one-click cloud platforms like Railway, Render, and Replit
- ✓Enterprise-ready with horizontal scaling via message queues and workers, on-prem and cloud deployment options, plus full execution traces supporting Prometheus and OpenTelemetry
- ✓Active community marketplace with pre-built chatflows for common use cases (RAG, agents, customer support) and Human-in-the-Loop (HITL) workflow support
Cons
- ✗Requires understanding LangChain/LlamaIndex concepts — the visual interface doesn't abstract away framework complexity
- ✗Complex workflows with many conditional branches become visually cluttered and hard to manage on the canvas
- ✗Debugging node connection issues can be frustrating — error messages from the underlying framework are passed through without simplification
- ✗Custom component development requires TypeScript knowledge and understanding of Flowise's component architecture
- ✗Cannot export chatflows as standalone Python/TypeScript code — applications remain coupled to the Flowise runtime
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