Flowise: Complete Platform Profile

Flowise is an open-source, drag-and-drop visual builder for LLM-powered applications. Built on top of LangChain.js and released in early 2023, it allows developers and non-developers alike to assemble AI workflows by connecting pre-built components on a canvas — without writing code. With more than 32,000 GitHub stars and a growing cloud offering, Flowise has become the leading visual builder for teams that want LangChain's power without LangChain's Python requirements.

The project fills an important gap in the AI tooling landscape. Not everyone who needs to build an AI application is a Python developer. Not every LangChain workflow justifies writing and maintaining custom code. Flowise makes it possible to prototype, deploy, and iterate on chatbots, RAG pipelines, and agent workflows through a browser-based interface, lowering the technical barrier significantly.

This profile examines what Flowise actually provides, where it excels, where it falls short, and how it compares to alternatives.

Browse the full AI agent profiles directory to compare Flowise with code-first alternatives.

Overview#

Flowise's interface is a node-based canvas where each node represents a LangChain.js component: an LLM, a vector store, a document loader, a memory module, a chain, or a tool. You connect nodes by dragging edges between their input and output ports. The resulting graph is the definition of your AI application.

When you hit "Save" and then access the generated chat API endpoint (or embed the built-in chat widget), Flowise runs your flow in real time. The application handles all the infrastructure: running the Node.js server, executing the LangChain.js flow, managing sessions, and serving the REST API.

Flowise is built in TypeScript and runs as a Node.js application. It can be self-hosted on any machine with Node.js available, deployed to a Docker container, run on Railway, Render, AWS, or GCP — or used via Flowise Cloud (the managed SaaS product). The self-hosted version is completely free; the cloud version adds managed infrastructure, team collaboration, and monitoring.

Core Features#

Visual Flow Canvas#

The drag-and-drop canvas is Flowise's defining feature. Every LangChain.js component is available as a node with clearly labeled input and output ports. You can build a complete RAG chatbot — document loader → text splitter → embeddings → vector store → retrieval chain → chat model — in minutes by placing and connecting nodes without writing a single line of code.

The canvas supports multiple simultaneous flows (called "Chatflows" for chatbots and "Agentflows" for agent-based workflows). Flows are saved as JSON and can be exported and imported for sharing or version control.

This approach to workflow building mirrors broader trends in visual programming. For context on how visual builders compare to code-first frameworks, see the Flowise vs LangFlow comparison.

Pre-Built Component Library#

Flowise ships with hundreds of pre-built nodes covering the full LangChain.js ecosystem:

LLMs and Chat Models: OpenAI, Anthropic, Google Gemini, Azure OpenAI, Groq, Mistral, local models via Ollama
Vector Stores: Pinecone, Weaviate, Chroma, Qdrant, Supabase, MongoDB Atlas, pgvector, and more
Document Loaders: PDF, Word, CSV, web scraper, Notion, S3, GitHub, YouTube transcripts
Memory: Buffer memory, summary memory, Zep, Redis, MongoDB
Tools: Calculator, web search, API caller, code interpreter

This library means most common AI application patterns can be assembled without custom code. For less common requirements, Flowise supports custom tools and custom nodes written in JavaScript.

Agentflows (Visual Agent Builder)#

Beyond simple chains, Flowise's Agentflow feature supports building multi-step agents visually. You can place an agent node (ReAct, OpenAI Functions, or Conversational Agent), connect tools to it, and configure its behavior — all through the UI. For multi-agent coordination, you can connect multiple agent nodes together, defining how they pass control and context between them.

This visual agent builder is one of Flowise's most significant capabilities and directly competes with the accessibility goals of n8n's AI nodes. For foundational concepts on how agent flows work, see the agent framework glossary entry.

Every Flowise chatflow generates an embeddable chat widget — a few lines of HTML or a React component — that can be placed on any website or application. The widget connects to the Flowise API endpoint and provides a fully functional chat interface. This makes Flowise particularly attractive for teams building chatbots for existing websites, support portals, or internal tools without frontend development work.

API Endpoint Generation#

Every flow automatically generates a REST API endpoint that accepts messages and returns responses. This means flows built in Flowise can be called from any application, language, or service — making Flowise the "AI backend" for applications built in languages other than JavaScript/TypeScript.

Document Storage and Upsert#

Flowise includes a document store feature for managing the documents in your RAG pipeline. You can upload files, configure chunking and embedding parameters, and "upsert" (index) them to your chosen vector store — all through the UI. This makes it possible to update a RAG pipeline's knowledge base without touching the flow definition.

Pricing and Plans#

Self-Hosted (Free): The open-source version is MIT licensed and completely free. You can run Flowise locally, on a VPS, or in Docker at no cost. Features are not restricted in the open-source version — the full node library, API endpoints, and chat widgets are available.

Flowise Cloud (Paid):

Starter: Approximately $35/month — managed hosting, 2 team members, custom domain
Pro: Approximately $65/month — more executions, 5 team members, priority support
Enterprise: Custom pricing — SSO, dedicated hosting, SLA, advanced security

The self-hosted option is financially attractive for technical teams comfortable with infrastructure management. The cloud option makes sense for teams that want to skip DevOps overhead or need the collaboration and monitoring features.

Strengths#

Fastest path from idea to working application. A functional RAG chatbot can be assembled in Flowise in under 30 minutes by someone with no AI coding experience. The visual interface eliminates the entire setup and boilerplate phase of development.

Accessible to non-developers. Product managers, content teams, and domain experts can build and modify Flowise applications without engineering support. This organizational accessibility is a genuine competitive advantage for enterprise deployments.

LangChain.js ecosystem. Flowise inherits all of LangChain.js's integrations, meaning the component library is as broad as LangChain's own. Teams that need a specific vector store, LLM, or data source will almost certainly find it as a native Flowise node.

Exportable flows as JSON. Flows are version-controllable JSON files, which means teams can use Git to track changes to their AI applications — an important consideration for serious deployments.

Self-hosting flexibility. Running Flowise on your own infrastructure gives complete data control, which is essential for applications handling sensitive data. Many enterprise use cases require that no data leaves the organization's cloud environment.

Limitations#

Limited programmatic control. Flowise's visual model works well for standard patterns but becomes awkward for workflows requiring complex conditional logic, dynamic tool selection based on runtime data, or non-standard coordination patterns. Code-first frameworks like LangChain or LangGraph handle these cases more cleanly.

JavaScript/TypeScript only. Flowise runs on LangChain.js, not the Python LangChain. Some components available in Python LangChain are not yet available in LangChain.js, and Python-specific tools (SciPy, NumPy-based processing, custom ML models) are not accessible.

Debugging complexity. When a flow produces incorrect output, identifying which node is responsible requires iterating through execution logs. The visual canvas does not provide step-by-step execution visibility in the way that a debugger does for code.

Scaling limitations. The Flowise server handles flow execution synchronously in many cases. High-concurrency production applications may need custom infrastructure or the cloud offering's managed scaling.

Ideal Use Cases#

Flowise is best suited for:

Customer support chatbots: RAG-based chatbots that answer questions from a product knowledge base, FAQ, or documentation site
Internal knowledge assistants: Company wiki, policy, or procedure query tools deployed as chat interfaces
Rapid prototyping: Testing different LLM, vector store, and retrieval combinations visually before committing to code
Non-technical teams: Marketing, sales, and operations teams that need AI tools without engineering support
Website chatbot integration: Embedding an AI assistant on an existing website using the Flowise widget in hours rather than weeks

For complex, highly customized agent workflows, see the how to build a research AI agent tutorial which covers a code-first approach that may be more appropriate.

Getting Started#

Local setup (self-hosted):

Install: npm install -g flowise
Start: npx flowise start
Open browser to localhost:3000
Create your first Chatflow using the visual canvas

Docker deployment:

Pull the image: docker pull flowiseai/flowise
Run with environment variables for your LLM API keys
Map port 3000 and optionally mount a volume for flow persistence

The official Flowise documentation includes detailed deployment guides for Railway, Render, AWS EC2, and GCP. For the cloud version, account creation on flowise.ai provides a hosted instance immediately.

How It Compares#

Flowise vs LangFlow: Both are visual LangChain flow builders, but they differ in implementation. Flowise is built on LangChain.js (TypeScript); LangFlow is built on LangChain Python. LangFlow has access to the fuller Python ecosystem. Flowise generally has a cleaner UI and better self-hosting documentation. The choice often comes down to whether your team is stronger in JavaScript or Python. See the detailed Flowise vs LangFlow comparison.

Flowise vs Dify: Dify is also a visual builder but with a stronger focus on production deployment features (versioning, A/B testing, annotation). Flowise is generally easier to self-host and has broader LangChain integration. Dify's cloud is more polished; Flowise's open-source experience is stronger.

Flowise vs n8n: n8n is a general workflow automation tool with AI nodes added. Flowise is purpose-built for LLM applications. If your use case is "connect AI to 400 other apps," n8n is more appropriate. If your use case is "build a sophisticated AI chatbot," Flowise is better suited.

Flowise vs code-first frameworks: If your team writes Python or TypeScript and your workflow has complex logic, LangChain or LangGraph will serve you better. Flowise is not a replacement for code-first development — it is an alternative for use cases where the visual model is sufficient. See the AutoGen directory entry for a representative code-first alternative.

Bottom Line#

Flowise is the most mature, well-maintained visual builder for LLM applications and earns its 32,000-star GitHub status. For teams that cannot or do not want to write code, it provides remarkable capability. For use cases that fit the RAG chatbot pattern — the majority of AI assistant applications — Flowise may be all you need. Its limitations (reduced programmatic control, JavaScript-only ecosystem, debugging difficulty) are real but not show-stoppers for appropriate use cases. Teams should choose Flowise when speed of development and accessibility to non-technical users outweigh the need for low-level control.

Best for: Non-technical teams, rapid prototypers, and production chatbot deployments where LangChain.js's integration coverage meets the requirements.