Open-source low-code platform for building AI agent workflows and LLM applications using drag-and-drop interface, supporting multiple AI models, vector databases, and custom integrations for creating sophisticated conversational AI systems.
Build AI chatbots and workflows by dragging and dropping components — an open-source visual builder anyone can use.
Flowise is an open-source visual builder for creating LLM applications using LangChain and LlamaIndex components. You build applications by dragging and connecting nodes in a browser-based canvas — each node represents a LangChain or LlamaIndex component (models, chains, agents, tools, memory, vector stores), and connections define the data flow.
Flowise bridges the gap between code-based frameworks and no-code platforms. Under the hood, it's running actual LangChain/LlamaIndex code — the visual builder generates and executes real framework code, not a simplified approximation. This means you get the full power of these frameworks' integrations and abstractions with a visual development experience.
The platform supports a comprehensive set of components: chat models (OpenAI, Anthropic, Google, local models via Ollama), embeddings, vector stores (Pinecone, Weaviate, Qdrant, ChromaDB, Supabase), document loaders, text splitters, memory types, tools, agents, and chains. You can build everything from simple chatbots to complex RAG pipelines and tool-using agents.
Flowise chatflows (the visual workflows) can be deployed as API endpoints with a single click. The platform includes a built-in chat widget that can be embedded in websites, plus API access for integration with external applications. It supports streaming responses, conversation memory persistence, and file upload handling.
The platform also includes a marketplace for sharing and discovering community-built chatflows, providing starting templates for common use cases.
Flowise runs as a Node.js application deployable via npm, Docker, or one-click deployment on platforms like Railway, Render, and Replit.
Honest assessment: Flowise is the best option for developers who want visual LLM application development with real framework power underneath. It's not dumbed-down — you're building with actual LangChain components, which means you can create sophisticated applications. The tradeoff is that you need to understand LangChain/LlamaIndex concepts to use it effectively — it's a visual interface for these frameworks, not a replacement for understanding them. For developers who think visually and want faster iteration than writing code, Flowise significantly accelerates LLM application development.
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Flowise provides an excellent drag-and-drop interface for building LLM workflows based on LangChain components. Perfect for visual thinkers and rapid prototyping, though complex production deployments may outgrow the visual paradigm.
Browser-based canvas where you drag, drop, and connect LangChain/LlamaIndex components to build AI applications. Real-time preview shows component configurations and connection validation.
Use Case:
Building a RAG chatbot by visually connecting a PDF loader, text splitter, embedding model, Pinecone vector store, retrieval chain, and chat model — all without writing code.
Nodes for 100+ LangChain and LlamaIndex components: chat models, embeddings, vector stores, document loaders, memory, tools, agents, chains, and output parsers. New components are added with framework updates.
Use Case:
Creating a tool-using agent by connecting an OpenAI chat model to a ReAct agent node with web search, calculator, and custom API tool nodes.
Deploy any chatflow as a REST API endpoint with automatic documentation. Built-in chat widget generates embeddable HTML/JavaScript for website integration. Supports streaming, file uploads, and conversation persistence.
Use Case:
Deploying a customer support chatbot as an API that your React frontend calls, plus embedding the chat widget directly on your marketing site.
Multiple memory backends for persisting conversation history across sessions: in-memory, SQLite, PostgreSQL, Redis, MongoDB, and DynamoDB. Memory nodes connect to chains and agents for context-aware conversations.
Use Case:
Adding persistent conversation memory to a customer service chatbot so returning users don't have to repeat their issues.
Upload and manage documents through the UI, with automatic processing through configured text splitters and embedding models. View and manage vector store contents without external tools.
Use Case:
Uploading product documentation PDFs through the Flowise UI and watching them get processed, chunked, embedded, and stored in ChromaDB — ready for RAG queries.
Browse and import chatflow templates built by the community. Templates cover common patterns: document Q&A, conversational agents, data extraction, and multi-step workflows.
Use Case:
Starting with a community-built 'SQL Query Agent' template and customizing it with your database connection and specific query patterns.
Free
forever
$35.00/month
month
$65.00/month
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View Pricing Options →Building and iterating on RAG chatbots visually without writing boilerplate LangChain code
Prototyping LLM applications with non-engineering team members who can configure components visually
Deploying document Q&A systems quickly with built-in chat widgets and API endpoints
Teaching LangChain concepts visually — seeing how components connect clarifies framework abstractions
Flowise works with these platforms and services:
We believe in transparent reviews. Here's what Flowise doesn't handle well:
It helps significantly. Flowise visualizes LangChain/LlamaIndex components — understanding what a retriever, chain, or agent does makes the visual builder much more effective. You can start with marketplace templates without deep knowledge, but customization requires understanding the underlying frameworks. Flowise makes building faster, not conceptually simpler.
Both are visual LangChain builders. Flowise is Node.js-based, while Langflow is Python-based (important for deployment preferences). Flowise has a more mature chat widget and deployment features. Langflow has tighter LangChain Python integration and supports newer LangChain components faster. Both are open-source with active communities.
Flowise doesn't directly export chatflows as standalone Python/TypeScript code. Chatflows are stored as JSON configurations that Flowise interprets at runtime. If you outgrow the visual builder, you'd rebuild in code using the same LangChain components. The visual prototype serves as a blueprint for the code implementation.
Docker deployment on a cloud VM or container platform (AWS ECS, Google Cloud Run) is the most common production approach. Use PostgreSQL for persistent storage (chatflow configs, conversation memory). Set up proper authentication (Flowise supports basic auth and API key auth). For high-availability, run behind a load balancer with multiple instances.
Visual builder support for multi-agent conversations and handoffs.
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In 2026, Flowise added support for agent memory and state persistence, expanded its node library with 50+ new integrations including tool-calling agents, and improved the deployment experience with one-click cloud hosting options and API key management.
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