What is Flowise, and how is it used to build AI applications?

Flowise is an open-source visual platform built on LangChain. Developers and product teams can use it to design AI pipelines, retrieval-augmented generation (RAG) workflows and agents without writing all the orchestration code themselves.

What are the benefits of building agents with a visual interface?

A visual workflow can help teams test ideas and tools. Configure model behavior, connect databases and test chains without writing Python or JavaScript scripts for every step.

How can I add Flowise chatbots and agents to a website or app?

After you save a workflow, Flowise provides a JavaScript widget and a REST API endpoint that you can integrate into a website, support system or mobile app.

Does Flowise support Persian and custom models?

Flowise orchestrates the workflow logic. Persian-language questions, document analysis and conversations depend on the connected model supporting Persian.

Why host Flowise on Deplomo instead of running it locally?

Run Flowise in an isolated container with persistent storage for chat history. Connect the model providers your account can access, and size the service within your zone package limits.

Deplomo

AI workflows and agents

Build AI agents with Flowise without writing code

Deploy Flowise on Deplomo, a visual platform based on LangChain for designing AI workflows, RAG systems and agents.

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Flowise features on Deplomo

Visual workflow editor, persistent storage and connections to supported AI providers.

Build agents visually without code

Design multi-step AI workflows, processing chains and agents with a drag-and-drop interface.

Deploy Flowise on the cloud platform

Deploy Flowise from a template and configure CPU, RAM and storage for your workload.

Connect to vector databases and knowledge bases

Connect vector databases to build retrieval systems and retrieval-augmented generation (RAG) workflows.

Connect to models and external APIs

Connect to commercial and open-source models such as OpenAI, Claude and Ollama, along with webhooks and custom tools.

Flowise FAQs

Answers to common questions about building agents, connecting knowledge bases and deploying Flowise:

Flowise guide: build AI agents with a visual workflow editor

Building AI systems can involve coding with frameworks such as LangChain, managing memory pipelines and debugging prompts. Flowise provides a visual, modular interface for creating AI workflows, chatbots connected to documents and automated agents. Availability and service security depend on your deployment and configuration.

What is Flowise, and how does its visual interface work with LangChain?

Flowise provides a visual interface for LangChain workflows. Instead of writing code to define chains, tools, memory and language models, connect blocks on a canvas.

To build semantic search and retrieval-augmented generation (RAG) workflows, add a vector database to the canvas. For example, connect the qdrant in Flowise to index documents and answer questions based on their content. To connect business processes and webhooks to AI workflows, use an automation tool such as Install n8n alongside Flowise.

Flowise running locally compared with cloud deployment

Running Flowise on a local system or unmanaged server can make persistence, process monitoring and access to external language model providers your responsibility.

Flowise runs in an isolated container and can use persistent storage. Connections to providers such as OpenAI and Anthropic require the relevant API credentials and remain subject to each provider’s access rules and availability.

Compare Flowise on Deplomo with manual coding and traditional server setup

The table compares different ways to use Flowise:

Feature	Flowise on Deplomo	Manual coding with LangChain	Running locally on a personal computer
Design and prototyping	Build an agent visually without writing code	Write code and debug libraries	May work for initial tests but require additional work for production use
Service availability and reliability	Runs in a cloud container with persistent storage	Requires you to maintain and monitor the server	Stops when the local machine is shut down
Embed the widget and use its output	Generate a JavaScript widget and API for your website	Requires separate backend and frontend development for chat	May require additional setup to share publicly
External model provider access	Use your API credentials and verify provider access	May require proxy configuration in code	Depends on local network and provider access

Flowise’s visual workflow editor and a managed cloud environment can help teams build AI products without managing every infrastructure component themselves.

Build an AI agent with Flowise on Deplomo

Follow these steps to set up Flowise and build an agent:

Deploy Flowise from the marketplace When international service creation is available, select Flowise from the Deplomo marketplace and follow the template setup.
Choose resources Choose RAM and CPU resources for your workflows.
Open the visual editor Use the configured domain to open the Flowise dashboard.
Design a workflow and agent Connect a language model, upload text documents and embed the chatbot on your site.

Deploy Flowise on Deplomo

Flowise lets teams use AI models without spending all their time writing infrastructure code. Deploy it on Deplomo and configure it for your workflow.

Set up a Flowise workspace and design an assistant for your organization.

Build AI agents with Flowise

Automated setup, custom domains and a fixed monthly package.

Get started with Flowise