Flowise

Flowise is an open-source TypeScript-based no-code builder for LLM applications. You assemble chatbots, RAG pipelines, and multi-agent workflows on a visual canvas, then embed them via a chat widget or call them through an auto-generated API. It's self-hostable via Docker with a straightforward setup and has a managed cloud tier starting at $35/month.

Flowise gets something right that many visual LLM builders get wrong: it's actually clean. Open the canvas and it doesn't overwhelm you. The components are organized, the node panels are readable, and the embedded chat widget works the first time. That sounds like table stakes, but in the no-code AI tooling space, it genuinely isn't.

I've watched teams spin up Flowise for internal document search tools, customer support bots, and sales automation pipelines, and in every case the time from "first login" to "something working" was under an hour. That's the real value proposition here: low barrier to a working system.

What Flowise is

Flowise is an open-source TypeScript application for building LLM-powered tools visually. The GitHub repo at github.com/FlowiseAI/Flowise has roughly 38,000 stars. The codebase is entirely TypeScript: a Node.js Express backend and a React frontend. No Python runtime anywhere. That matters for deployment simplicity, especially in organizations where Python adds a DevOps conversation.

The project started in 2023, developed quickly, and has stayed actively maintained. Unlike some competitors that acquired their way into more engineering resources, Flowise has grown through community adoption. The result is a tool that feels like it was built by people who actually use it, with sensible defaults and a feature set driven by real user requests rather than investor narratives.

Two core application types exist in Flowise: Chatflows and Agentflows. Chatflows are straightforward: an LLM with optional retrieval, tools, and memory, wrapped in a chat interface. Agentflows are the newer, more capable format introduced in Agentflow v2, supporting multiple agents with tool access running in sequential or parallel configurations.

The canvas and component library

The Flowise canvas is where you spend most of your time. Components live in a searchable left panel. You drag one onto the canvas, and a configuration panel opens on the right. Wire outputs to inputs by drawing connections between orange (output) and purple (input) handles.

Flowise's component library is its own: it doesn't wholesale import LangChain's component ecosystem the way Langflow does. That means the library is smaller, but it also means quality is more consistent. Every component in Flowise is maintained by the Flowise team rather than being an auto-generated wrapper around a LangChain abstraction.

The categories available:

• LLMs and chat models: OpenAI, Anthropic, Google Vertex AI, Mistral, Ollama (local), Groq, Together AI, Amazon Bedrock, Azure OpenAI, Replicate
• Vector stores: Pinecone, Chroma, Weaviate, Qdrant, FAISS, PGVector, Supabase, Milvus, and several others
• Document loaders: PDF, CSV, JSON, HTML, DOCX, YouTube transcripts, web scraping, Notion, Confluence, S3
• Text splitters: recursive character, token-based, markdown, HTML, code-specific
• Tools: SerpAPI, Brave Search, Calculator, custom function nodes, HTTP request node
• Memory: buffer window, summary, vector store-backed, Redis, Zep, Motorhead
• Agents: OpenAI function agent, ReAct agent, conversational agent, tool agent

For 90% of real-world LLM app use cases, this library is sufficient. The gap compared to Langflow appears when you need a niche LangChain integration that Flowise hasn't built a native component for.

RAG in Flowise

Setting up a retrieval-augmented generation pipeline in Flowise is the workflow I've walked the most people through, because it's the most common real-world use case. The pattern:

1. Document loader (PDF, web scrape, Notion page)
2. Text splitter (chunk size and overlap configured in the node)
3. Embedding model (OpenAI, HuggingFace, or local via Ollama)
4. Vector store (Chroma locally, Pinecone or Qdrant for production)
5. Retriever (query the vector store, configured to return the top-K chunks)
6. Chat model (the LLM that generates the answer)
7. Conversational retrieval chain (ties it together with conversation history)

This takes about 15 minutes to set up and produces a working document Q&A chatbot. The built-in test chat is available immediately in the editor. You can adjust chunk size and top-K and retest without restarting anything.

The honest limitation: Flowise doesn't give you retrieval quality evaluation tooling the way Dify does. You can manually test queries and see which chunks are returned, but there's no structured evaluation workflow or retrieval scoring. For teams that need to systematically measure retrieval quality across a test set, you'll need to build that outside Flowise.

Agentflow v2 and multi-agent support

Flowise added multi-agent support through Agentflow v2, which gives you a separate canvas type where you can define multiple agent nodes and connect them with explicit routing. Agents in this mode can have their own tool access, system prompts, and LLM configurations.

The common patterns this supports:

• A supervisor agent that decides which specialist to call
• Parallel agents running independent subtasks with results merged afterward
• Sequential handoffs where each agent's output becomes the next agent's input

This is more capable than the single-agent chatflow, and it handles a lot of real-world delegation scenarios. It's not as expressive as frameworks like CrewAI or AutoGen where agents have explicit roles, goals, and can engage in complex multi-turn conversations with each other. But for straightforward routing and delegation patterns, Agentflow v2 works without you writing any code.

The embedded chat widget

One feature where Flowise consistently earns praise: the embedded widget. Every published chatflow gets a script tag you can drop into any HTML page. It renders a floating chat button, opens a chat panel, and routes conversations to your Flowise flow.

<script type="module">
  import Chatbot from 'https://cdn.jsdelivr.net/npm/flowise-embed/dist/web.js';
  Chatbot.init({
    chatflowid: 'your-flow-id',
    apiHost: 'https://your-flowise-instance.com',
  });
</script>

That's it. No backend code. No frontend framework. Just add the script tag to your site. Customization options let you set the bot name, initial message, theme colors, and placeholder text through JavaScript config keys.

This is a big deal for non-engineers who want to deploy a chatbot to their website. It's also useful for engineers who want to ship quickly without building a chat UI from scratch.

Getting started

For local development:

npm install -g flowise
npx flowise start

Open http://localhost:3000. Create a new chatflow, drag in a Chat Model node and a Conversational Chain node, connect them, configure your API key in the model node, and hit the test chat. You have a working chatbot.

For persistent self-hosting with Docker:

docker pull flowiseai/flowise
docker run -d -p 3000:3000 \
  -v ~/.flowise:/root/.flowise \
  flowiseai/flowise

The SQLite database is stored in ~/.flowise by default. For production, configure DATABASE_PATH, use a PostgreSQL instance, and set up proper environment variables for FLOWISE_USERNAME and FLOWISE_PASSWORD to enable authentication.

Pricing

Self-hosted is free. Apache 2.0 with no usage restrictions.

Flowise Cloud starts at $35/month for the Starter plan. This gives you a managed Flowise instance with automatic updates and backups. For teams that don't want to manage their own infrastructure, it's reasonable pricing. The Starter plan has limits on the number of flows and storage that most small teams won't hit.

Enterprise pricing is custom. For organizations that need dedicated infrastructure, SLA guarantees, or volume pricing on API calls, they negotiate directly.

Flowise vs the alternatives

Flowise vs Langflow

This is the comparison most people make. Flowise is TypeScript, Langflow is Python. Flowise has a cleaner UI; Langflow has a larger component library. Flowise has a polished embedded widget; Langflow has a code export feature. For teams starting fresh, Flowise's simpler self-hosting story (no Python dependency) and cleaner interface make it the easier starting point. For teams already using LangChain who want visual debugging, Langflow is more natural.

Flowise vs Dify

Dify is a more complete LLMOps platform with built-in RAG quality testing, prompt versioning, annotation workflows, user feedback collection, and a more polished published application layer. Flowise is a leaner tool focused on flow building and chat widget deployment. If you need a full-lifecycle platform for managing AI applications in production, Dify is ahead. If you need something simpler and more focused, Flowise is easier to set up and maintain.

Flowise vs code-based frameworks

Flowise and LangChain or LangGraph are not substitutes. Flowise is for building applications visually. LangChain and LangGraph are for building agent logic in code. Some teams use both: Flowise for rapid prototyping and stakeholder demos, code-based frameworks for production hardening. The flow definitions don't transfer between them, but the conceptual design often does.

Limitations to know before you commit

The component library, while solid, has gaps. If you need an integration with a specific vector store or document source that Flowise doesn't have a native component for, you'll write a custom function node. That works, but it's a step up in complexity from dragging pre-built components.

The analytics dashboard is basic. It shows executions, token counts, and error rates, but nothing sophisticated. For production monitoring, you'll integrate an external tool (Datadog, Grafana, or a dedicated LLM observability platform) by capturing logs from the Flowise container.

Large and complex flows are hard to manage visually. Twenty nodes is comfortable. Forty nodes is a maze. Flowise has no grouping, no node annotations, and no mini-map. The canvas becomes a cognitive overhead problem at scale.

Finally, while Flowise is MIT-licensed for self-hosting, any tools or integrations that require third-party services (vector databases, LLM APIs) have their own costs that add up quickly. The platform is free; the infrastructure it calls is not.

Who should use Flowise

Flowise is a strong choice for teams that want a self-hosted, no-code LLM application builder with a clean TypeScript stack and a reliable embedded chat widget. Product teams, marketing teams, and operations teams who need AI-powered chatbots and document search tools without backend development dependencies will get genuine value from it.

Engineering teams that prefer staying close to code, or teams that need extensive multi-agent coordination logic, will hit limits faster. At that point, LangChain, LangGraph, or CrewAI offer more control in exchange for more complexity.

The embedded chat widget and clean Docker self-host story are Flowise's strongest differentiators. If those match what you need, it's worth the 20 minutes it takes to spin up a local instance and build your first flow.

Key features

• Visual drag-and-drop canvas for LLM workflows with 100+ native nodes
• Agentflow v2: multi-agent visual builder with sequential and parallel execution
• Built-in document loaders, text splitters, and vector store connectors for RAG
• REST API and webhook endpoints auto-generated for every published flow
• Embedded chat widget with a single script tag
• Variable system for parameterizing flows without editing nodes
• Analytic dashboard for tracking flow executions, token usage, and errors