Flowise is an open-source, low-code platform for building customized LLM flows and AI agents using a drag-and-drop visual editor. Built on top of LangChain.js, Flowise exposes LangChain's components — LLMs, memory modules, document loaders, vector stores, tools, and agents — as visual nodes that can be connected and configured without writing code.
Since its 2023 launch, Flowise has accumulated over 30,000 GitHub stars and built a community of thousands of developers who use it for prototyping, internal tools, and production deployments. Its approachable interface and LangChain foundation make it one of the fastest paths from "I want to build an AI chatbot" to "I have a working application."
Key Features#
Visual Node-Based Editor Flowise's canvas interface lets you build LLM applications by connecting pre-built component nodes. LLM nodes, memory nodes, retriever nodes, tool nodes, and agent nodes can be combined in any configuration. The visual representation makes the application architecture immediately understandable and shareable with non-technical team members.
Chatflow and Agentflow Modes Flowise supports two primary application types. Chatflows are conversation chains — linear or branching flows for chat-based applications. Agentflows implement a more sophisticated agent loop where an LLM plans and executes steps using tools, making multi-step decisions to complete tasks.
Built-in Vector Store Integrations Flowise integrates with 15+ vector databases including Pinecone, Chroma, Qdrant, Weaviate, Supabase Vector, and pgvector. You can upload documents, configure chunking, generate embeddings, and build RAG pipelines entirely through the visual interface.
Credential and API Management Flowise stores API credentials securely and manages them through a dedicated credentials panel. This means you configure OpenAI API keys, Pinecone credentials, and custom API endpoints once and reference them across all flows — safer than hard-coding credentials.
Embedded Chatbot Widget Every Flowise chatflow can be embedded on any website via a JavaScript snippet or iframe. This makes it practical to deploy internal knowledge base chatbots, customer-facing FAQ bots, and support assistants without custom front-end development.
Pricing#
Self-Hosted (Open Source): Free with no restrictions. Requires Node.js setup or Docker. Works on any VPS (common choice: $6–20/month DigitalOcean or Hetzner instance).
Flowise Cloud:
- Starter: Free — 1 chatflow, 100 predictions/month, limited storage
- Pro (~$35/month): Unlimited chatflows, 2,000 predictions/month, custom domains
- Enterprise: Custom pricing with SSO, SLA, dedicated support, on-premise deployment
The self-hosted path is the community default. Docker Compose deployment takes under 30 minutes with the official docs.
Who It's For#
Flowise is the right choice for:
- Developers who prefer visual prototyping before committing to code-first implementations
- Technical non-coders (data analysts, product managers) who understand LLM concepts and want to build with them
- Startups building AI MVPs where shipping a working demo quickly is more important than production architecture
- Agencies and consultants building AI-powered tools for clients across multiple industries
It is less suitable for teams building highly customized agent logic, for production systems with high reliability requirements, or for applications needing server-side streaming and complex state management.
Strengths#
Fastest visual RAG prototyping. From zero to a working RAG chatbot with Pinecone takes roughly 20 minutes with Flowise. This prototyping speed is a genuine competitive advantage for exploring what's possible before investing in custom development.
Large and active community. Flowise has a substantial YouTube tutorial ecosystem, active Discord community, and hundreds of community-contributed templates on GitHub. Finding help for common problems is easy.
LangChain parity. Because Flowise is built on LangChain.js, the components map directly to LangChain concepts. Teams that outgrow Flowise can migrate their understanding (if not their flows) to LangChain with minimal conceptual overhead.
Easy self-hosting. Docker Compose deployment is genuinely straightforward. The official documentation includes step-by-step guides for AWS, GCP, Azure, Railway, and Render deployments.
Limitations#
Node editor limitations for complex logic. Visual programming works well for linear and moderately branching flows. Highly complex conditional logic, dynamic loops, and sophisticated state management become awkward to express in node editors and may require dropping into custom code nodes.
JavaScript/Node.js only. Flowise runs on Node.js and its integrations are JavaScript-based. Teams with Python-first AI/ML infrastructure may find the ecosystem less compatible with their tooling choices.
Limited built-in monitoring. Out-of-the-box, Flowise provides basic analytics. Production observability — tracing individual execution paths, evaluating output quality, tracking latency by node — requires integration with external tools like LangSmith.
Related Resources#
Explore the full AI Agent Tools Directory for additional open-source and no-code platform options.
Compare related platforms: Dify profile for a more feature-rich open-source alternative, and LangChain profile for the code-first approach.
Comparisons: Flowise vs Dify: Open-Source LLM App Builder Comparison and Flowise Review: Building RAG Chatbots in 2026.
For tutorials, see Building Your First RAG Application with Flowise and AI Chatbot Examples: How Businesses Use Flowise in Production.