Visual LLM flow builder

Managed Flowise hosting

Flowise turns LangChain into a drag-and-drop canvas. Build RAG chatbots, SQL agents, and multi-step LLM flows — then embed them anywhere with a single script tag.

HostAgentes runs your Flowise instance on dedicated infrastructure. No prediction metering, no Node.js maintenance, no surprises. From $9.99/mo.

See pricing See popular flows

What is Flowise hosting?

Flowise is an open-source low-code platform for building LLM applications on a drag-and-drop canvas. It is built on LangChain and exposes its chains, agents, memory types, document loaders, and vector stores as visual nodes you connect with edges rather than code. The result is a running LLM pipeline — a chatbot, a RAG retriever, a function-calling agent — that also generates a REST API and an embeddable chat widget automatically.

Managed Flowise hosting means someone else runs the Node.js server, handles SSL certificates, applies upstream Flowise updates, and keeps your instance reachable around the clock. HostAgentes does exactly that on dedicated cloud resources, so you open a browser tab, see the canvas, and start building — without touching a terminal or a Docker compose file.

Plans start at $9.99/mo for a 1 vCPU instance with unlimited flows and predictions. The official Flowise Cloud meters predictions ($35/mo for 10,000 calls). HostAgentes charges for the underlying compute, not for how many times your chatbot answers a question.

Popular Flowise flows on HostAgentes

Flowise's node library covers a wide range of real-world LLM patterns. Here are six flows customers build most often.

Website chatbot with vector store grounding

Crawl your docs or marketing site, chunk the text, embed with OpenAI, store in Chroma, and serve a chatbot widget that cites its sources. The most common Flowise flow — live in under an hour.

Discord bot with OpenAI function calling

Connect a Discord webhook node to a function-calling agent. Users ask questions; the agent picks the right tool, executes it, and posts the reply — no custom Discord bot code required.

SQL agent that queries Postgres

Give Flowise read access to your database. A SQL agent node translates natural language questions into queries, runs them, and returns structured answers. Works with Postgres, MySQL, and SQLite.

Slack FAQ bot trained on Notion docs

Sync your Notion workspace into a vector store, then wire a Slack webhook to a retrieval QA chain. Team members ask in Slack; the bot answers from your actual documentation.

Video transcript summarizer

Feed YouTube transcripts or uploaded VTT files through a text splitter, summarize each chunk with a map-reduce chain, and output a structured briefing. Useful for research and media teams.

API docs conversational search

Ingest an OpenAPI spec or markdown reference into a retriever. Developers ask plain-English questions about endpoints; the flow returns exact parameter details with code examples.

A RAG flow in 14 lines of JSON

Flowise stores flows as JSON. Export, version, and redeploy them across instances. The flow on the right wires a PDF document loader through a text splitter, into OpenAI embeddings, into Chroma, then into a retrieval QA chain backed by GPT-4o mini.

You build this by dragging nodes onto the canvas — no code. The JSON is what Flowise saves behind the scenes. Import it into any Flowise instance with one click, including a fresh HostAgentes deployment.

Once saved, Flowise exposes a /api/v1/prediction/{flowId} endpoint and a chatbot widget iframe — both ready to embed without additional backend work.

rag-chatbot.flow.json 
{
  "nodes": [
    {
      "id": "pdfLoader",
      "type": "PDFLoader",
      "data": { "filePath": "./docs/handbook.pdf" }
    },
    {
      "id": "splitter",
      "type": "RecursiveCharacterTextSplitter",
      "data": { "chunkSize": 1000, "chunkOverlap": 100 }
    },
    {
      "id": "embeddings",
      "type": "OpenAIEmbeddings",
      "data": { "modelName": "text-embedding-3-small" }
    },
    {
      "id": "vectorStore",
      "type": "Chroma",
      "data": { "collectionName": "handbook" }
    },
    {
      "id": "retriever",
      "type": "VectorStoreRetriever",
      "data": { "topK": 4 }
    },
    {
      "id": "llm",
      "type": "ChatOpenAI",
      "data": { "modelName": "gpt-4o-mini", "temperature": 0 }
    },
    {
      "id": "chain",
      "type": "RetrievalQAChain",
      "data": { "returnSourceDocuments": true }
    }
  ],
  "edges": [
    { "source": "pdfLoader",   "target": "splitter"    },
    { "source": "splitter",    "target": "vectorStore"  },
    { "source": "embeddings",  "target": "vectorStore"  },
    { "source": "vectorStore", "target": "retriever"    },
    { "source": "retriever",   "target": "chain"        },
    { "source": "llm",         "target": "chain"        }
  ]
}

Built for the visual flow builder

These are the Flowise-specific capabilities that matter most when you move from prototype to production.

polyline

Drag-and-drop flow canvas

Wire LLM chains together visually. No YAML, no code files — connect a document loader to an embeddings node to a retriever in seconds. Flowise's canvas makes LangChain concepts tangible.

account_tree

100+ LangChain nodes

Every LangChain abstraction is a node: chains, agents, tools, memory buffers, document loaders, text splitters, output parsers. Build sophisticated pipelines without writing a single import.

chat_bubble

Embeddable chatbot widget

Every flow gets a ready-to-embed iframe and a one-line script tag. Drop a chatbot onto your marketing site, docs portal, or internal tool without backend work.

history

Flow versioning

Save named snapshots of any flow before you experiment. Roll back to a stable version in one click. Critical for teams shipping changes to live customer-facing chatbots.

model_training

Multi-LLM support

Route different nodes to different models. Use GPT-4o for reasoning, a cheaper model for summarization, and a local Ollama model for sensitive data. Your keys, our infrastructure.

code

Custom JS/Python escape hatch

When no built-in node covers your edge case, drop in a custom JavaScript or Python function node. Full expressiveness when the drag-and-drop canvas hits its limits.

Resource pricing, not prediction metering

You pay for a dedicated server, not for how often your chatbot answers a question. Every plan includes unlimited flows and unlimited predictions.

Basic

$9.99 /mo

Build and ship your first chatbot flows without touching a terminal.

  • 1 vCPU, 2 GB RAM
  • 20 GB storage
  • Unlimited flows
  • Unlimited predictions
  • All LangChain nodes
  • Embeddable chatbot widget
  • SSL + daily backups
  • Community support
Get started
Most popular

Pro

$29.99 /mo

For teams running production chatbots and RAG pipelines.

  • Everything in Basic
  • 2 vCPU, 4 GB RAM
  • 50 GB storage
  • Team management + roles
  • Integrated vector store
  • Flow versioning
  • Email support
  • Automatic updates
Get started

Max

$79.99 /mo

High-traffic chatbots and multi-tenant flow deployments.

  • Everything in Pro
  • 4 vCPU, 8 GB RAM
  • 100 GB storage
  • Multiple instances
  • Enterprise SSO
  • Priority support
  • Uptime SLA
Get started

7-day money-back guarantee. Cancel at any time.

The price you see is the price you pay. No overage fees, no renewal surprises.

From signup to first chatbot in 15 minutes

1

Choose a plan and deploy

Pick Basic, Pro, or Max. Your Flowise instance spins up on dedicated infrastructure in under 5 minutes — SSL included, subdomain assigned, no config files to touch.

2

Build your flow on the canvas

Open the drag-and-drop editor. Drag in a document loader, connect it to an embeddings node, route to a vector store, attach an LLM, and wire up a retrieval chain. Add your API keys in the credential manager.

3

Embed and ship

Click 'Embed' on any flow to get an iframe URL and a script tag. Paste either into your website, internal tool, or customer portal. The chatbot widget goes live immediately — no additional deploy step.

Why HostAgentes for Flowise?

Flowise runs fine on any Node.js server. HostAgentes makes sure you never have to think about that server again.

No prediction caps

Official Flowise Cloud charges $35/mo for 10,000 predictions and $65/mo for 50,000. If your chatbot gets busy, your bill spikes. HostAgentes charges for compute — your chatbot can answer a million questions and the price does not change.

Managed Node.js and LangChain updates

Flowise releases updates frequently. HostAgentes applies them automatically with zero-downtime restarts. You get new nodes and bug fixes without pulling a Docker image or running npm install.

Dedicated resources, not shared

A noisy neighbor cannot slow your chatbot down. Each HostAgentes plan allocates dedicated vCPU and RAM to your instance. Consistent response times are especially important for customer-facing chatbot widgets.

Integrated with Paperclip

If your Flowise flow needs to dispatch autonomous multi-step tasks, route them to Paperclip agents via a custom HTTP node. The two products are designed to work together — visual flows in Flowise, deep agent execution in Paperclip.

Frequently asked questions

How is Flowise different from Dify or Langflow?
All three are visual LLM builders, but they have distinct philosophies. Flowise is LangChain-native — every node maps directly to a LangChain or LlamaIndex abstraction, so developers familiar with those libraries feel at home immediately. Dify emphasizes a full app-builder experience with built-in user management and a prompt IDE. Langflow is closer to a pure graph editor and supports LangChain components but with a different visual metaphor. Flowise tends to be the fastest path from a LangChain tutorial to a deployed chatbot widget.
Can I embed a Flowise chatbot on my website?
Yes. Every Flowise flow automatically generates an embeddable chatbot. You get two options: a full-page iframe URL you can drop in a frame, or a one-line script tag that renders a floating chat bubble on any webpage. Both are configurable — you can set colors, the initial message, and whether the chatbot shows a file upload button. No backend code required on your site.
Does Flowise work with private or self-hosted LLMs?
Yes. Flowise supports any model that exposes an OpenAI-compatible API, which covers Ollama, LM Studio, vLLM, and most local model servers. Your HostAgentes Flowise instance can call a model running on your own hardware — useful for privacy-sensitive data that should not leave your network. You supply the endpoint and optional API key; Flowise handles the rest.
Can a Flowise flow call Paperclip agents as a node?
Yes. Flowise supports custom HTTP request nodes and custom tool nodes, which means you can call any REST endpoint — including the Paperclip agent API. Build the reasoning and retrieval layers in Flowise, then dispatch tasks to purpose-built Paperclip agents for execution. The two products are complementary: Flowise for chatbot UX and flow orchestration, Paperclip for autonomous multi-step agent tasks.
What's the difference between Flowise and LangChain?
LangChain is a Python and TypeScript library — you write code to define chains, agents, and tools. Flowise is a visual editor built on top of LangChain. It exposes LangChain's primitives as drag-and-drop nodes so you can assemble and test flows without writing application code. Under the hood, Flowise generates and executes the equivalent LangChain code. Developers who know LangChain will recognize every node immediately; non-developers can build useful flows without ever touching the source.

Build your first LLM flow today

Your Flowise instance live in under 5 minutes. Drag-and-drop canvas, chatbot widgets, unlimited predictions. From $9.99/mo.

Get started — $9.99/mo