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Flowise

Visual low-code builder for AI agents, RAG apps, chatbots, agentic workflows, multi-agent systems, LangChain-based components, API-serving flows, and self-hosted deployments through npm, Docker, and cloud platforms.

by FlowiseAI·added 2026-06-18·
CLI
HarnessCLI
Review first — review before installing

Open the source and read safety notes before installing.

Safety notes

  • Flowise can turn visual flows into callable chatbots, agents, RAG pipelines, and API endpoints. Review each flow's tools, credentials, webhooks, and external actions before production use.
  • Self-hosted deployments should protect the UI, credentials, flow exports, logs, and API endpoints with authentication, network controls, secret management, and backups.
  • RAG flows may ingest private documents, chunk content, create embeddings, and query external vector databases; scope datasets and retention before sharing flows.
  • Agentic workflows and multi-agent systems can call external services repeatedly; set quotas, rate limits, and approval gates for write actions.
  • The repository license file says most code is Apache-2.0, while enterprise directories and files with explicit copyright notices use a commercial license; verify license boundaries before redistribution.

Privacy notes

  • Prompts, uploaded documents, chunks, embeddings, vector metadata, tool inputs, tool outputs, model responses, credentials, flow definitions, logs, and exported chatflows may contain private data.
  • Model providers, embedding providers, vector stores, document loaders, search APIs, workflow integrations, and hosted Flowise Cloud may receive data depending on flow configuration.
  • Do not commit `.env` files, provider keys, vector database secrets, webhook URLs, exported credentials, generated logs, or private flow templates.
  • Before publishing a chatbot or API endpoint, review whether prompts, source documents, dataset IDs, and tool outputs are exposed to users or downstream systems.

Prerequisites

  • Node.js 20.0.0 or newer for npm installation.
  • Docker Compose if using the documented Docker self-host path.
  • Provider credentials for selected LLMs, embedding models, vector stores, document loaders, tools, or workflow integrations.
  • Persistent database/storage configuration, auth settings, and environment variables before exposing a shared Flowise instance.
  • A review plan for which users can create chatflows, agentflows, tools, webhooks, credentials, and public API endpoints.

Schema details

Install type
cli
Troubleshooting
No
Source repository stats
Scope
Source repo
Collection metadata
Estimated setup
20 minutes
Difficulty
intermediate
Tool listing metadata
Pricing
freemium
Disclosure
editorial
Application category
DeveloperApplication
Operating system
Web
Full copyable content
npm install -g flowise
npx flowise start

About this resource

Overview

Flowise is a visual, low-code builder for AI agents, RAG applications, chatbots, agentic workflows, and multi-agent systems. It provides a web UI where teams compose model nodes, tools, memory, retrievers, vector stores, prompts, and API-facing flows without writing every integration from scratch.

This entry fills a clear directory gap. Langflow and Dify are already present as adjacent visual builders, but Flowise is a separate high-search project with strong demand around "Flowise AI agent builder", "Flowise RAG", "chatflow", "agentflow", and self-hosted LangChain-style visual workflows.

Install

Install the published npm package:

npm install -g flowise
npx flowise start

The README also documents Docker Compose and Docker image paths:

docker compose up -d

For source development, the monorepo uses pnpm, with server, ui, components, and api-documentation modules.

Core Capabilities

Area Flowise Coverage
Visual builder Drag-and-connect flow authoring for LLM apps, chatbots, RAG, and agents
Agent workflows Agentic workflows, agentflow patterns, multi-agent systems, tools, memory, and orchestration nodes
RAG Document loaders, chunking, embeddings, retrievers, vector stores, reranking, and chat-with-data patterns
Runtime Node backend, React frontend, component integrations, and Swagger/OpenAPI-style API documentation
Deployment npm package, Docker Compose, Docker image, source development, Flowise Cloud, and self-hosting docs for major cloud platforms
Integrations LangChain-style components, model providers, vector databases, workflow automation, APIs, and webhook-oriented deployment patterns

Use Cases

  • Prototype RAG chatbots over internal docs or knowledge bases.
  • Build visual agent workflows that call tools and route steps.
  • Publish a chatflow or agentflow behind an API endpoint.
  • Compare visual agent builders such as Flowise, Langflow, Dify, and LobeHub.
  • Let non-specialist teams inspect the graph behind a chatbot or RAG workflow.
  • Self-host an AI chatbot builder with provider credentials kept in your own infrastructure.

Source Review

Verified on 2026-06-18:

  • GitHub reports FlowiseAI/Flowise as an active repository with 53,000+ stars, 24,000+ forks, and latest release flowise@3.1.2.
  • The repository description says "Build AI Agents, Visually"; topics include low-code, no-code, LangChain, RAG, chatbot, workflow automation, agentic AI, agentic workflow, agents, and multi-agent systems.
  • The README documents Node.js >=20.0.0, npm install -g flowise, npx flowise start, Docker Compose, Docker image usage, source development with pnpm, environment variables, documentation, self-hosted deployment options, and Flowise Cloud.
  • The npm registry resolves package flowise version 3.1.2 with the flowise CLI binary.
  • The repository license file says enterprise directory content and files with explicit copyright notices use a commercial license, while content outside those restricted areas is available under Apache-2.0.

Safety and Privacy

Flowise makes it easy to expose a flow as a chatbot or API endpoint. That is useful, but it also means credentials, prompts, tools, retrievers, and document sources need review before a flow is shared. Treat exported flows like configuration code: inspect them for secrets, endpoints, prompts, dataset names, and write-capable tools.

For RAG and agent workflows, test retrieval quality, rate limits, and tool side effects before trusting output. A visually clear graph is not a security boundary; users still need auth, logs, secret storage, and production review.

Duplicate Check

Checked current content/tools/, content/mcp/, content/agents/, content/skills/, guides, collections, open pull requests, and repository-wide content for FlowiseAI/Flowise, Flowise, flowise, Flowise AI agent builder, Flowise RAG, Flowise chatflow, Flowise agentflow, visual AI agents, low-code AI agent builder, LangChain visual builder, and multi-agent workflow builder. Existing entries cover adjacent builders such as Langflow, Dify, LobeHub, Open WebUI, and RAGFlow, but no dedicated Flowise tools entry, exact source URL duplicate, target file, or open duplicate PR was found.

#flowise#ai-agents#agentic-workflow#multi-agent#rag#low-code#no-code#langchain#chatbot#self-hosted

Source citations

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How it compares

Flowise side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

FieldFlowise

Visual low-code builder for AI agents, RAG apps, chatbots, agentic workflows, multi-agent systems, LangChain-based components, API-serving flows, and self-hosted deployments through npm, Docker, and cloud platforms.

Open dossier 		AnythingLLM

Local-first AI application for private chat, document RAG, workspace agents, MCP-compatible tools, model routing, memories, scheduled tasks, multimodal workflows, multi-user Docker deployments, and self-hosted agent automation.

Open dossier 		Dify

Production-ready LLM app and agentic workflow platform with visual workflows, RAG pipelines, agent capabilities, model management, observability, prompt IDE, APIs, Dify Cloud, and self-hosted Docker Compose deployment.

Open dossier 		Open WebUI

Self-hosted AI platform and web UI for Ollama, OpenAI-compatible APIs, RAG, Python function tools, model builder workflows, artifacts, web search, vector databases, enterprise auth, observability, plugins, and MCP-adjacent OpenAPI integrations.

Open dossier
Trust
Install riskReview firstReview firstReview firstReview first
Notes Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓
Categorytoolstoolstoolstools
Sourcesource-backedsource-backedsource-backedsource-backed
AuthorFlowiseAIMintplex LabsLangGeniusOpen WebUI
Added2026-06-182026-06-182026-06-182026-06-18
Platforms
CLI
CLI
CLI
CLI
Source repo————
Safety notes✓Flowise can turn visual flows into callable chatbots, agents, RAG pipelines, and API endpoints. Review each flow's tools, credentials, webhooks, and external actions before production use. Self-hosted deployments should protect the UI, credentials, flow exports, logs, and API endpoints with authentication, network controls, secret management, and backups. RAG flows may ingest private documents, chunk content, create embeddings, and query external vector databases; scope datasets and retention before sharing flows. Agentic workflows and multi-agent systems can call external services repeatedly; set quotas, rate limits, and approval gates for write actions. The repository license file says most code is Apache-2.0, while enterprise directories and files with explicit copyright notices use a commercial license; verify license boundaries before redistribution.✓AnythingLLM can run agents, scheduled tasks, MCP-compatible tools, browser-like workspace actions, developer APIs, and external model calls; scope tools and credentials before enabling them for users. The upstream Docker guide includes examples that add the SYS_ADMIN capability to the container. Review whether that capability is acceptable for the host before copying production run commands. Multi-user Docker deployments need normal production controls: authentication, TLS, network isolation, secret management, persistent-volume ownership, backups, and upgrade planning. Agent tools, custom agents, model routing, memories, and scheduled tasks can change behavior over time; use least privilege, logging, review gates, and rollback plans for write-capable workflows. Localhost services such as Ollama, Chroma, LocalAI, or LM Studio may need Docker host routing adjustments; avoid exposing local provider ports wider than intended.✓Dify can orchestrate workflows, RAG pipelines, agents, tools, APIs, model providers, and production application endpoints; review tool permissions and user-triggered actions before exposing apps. Self-hosted deployments need normal production controls: authentication, TLS, network isolation, secret management, backups, database maintenance, object storage policy, and upgrade planning. Agent and workflow nodes can call external tools, model providers, HTTP APIs, search tools, and custom integrations; apply least privilege and approval gates for write actions. Enterprise, marketplace, cloud, and modified-license terms should be reviewed before using Dify as a multi-tenant service or white-labeled frontend. Prompt IDE changes, workflow edits, model-provider changes, and dataset updates can alter production behavior; use versioning, staged releases, and rollback paths.✓Open WebUI can run Python function-calling tools, RAG ingestion, web search, web browsing, image generation, plugins, and model/provider integrations; review each capability before enabling it for untrusted users. Docker examples expose web ports and persistent volumes. Mount persistent data, set admin/auth controls, and avoid treating demo defaults as production hardening. Python function tools and plugin pipelines can execute application logic and access configured services. Restrict tool creation and plugin installation to trusted administrators. RAG and web browsing can ingest local documents, URLs, cloud files, and extracted text; test indexing quality and permissions before exposing private corpora to users. Open WebUI uses a custom Open WebUI License with branding restrictions and enterprise-license exceptions. Verify license terms before redistribution, white-labeling, or commercial deployment.
Privacy notes✓Prompts, uploaded documents, chunks, embeddings, vector metadata, tool inputs, tool outputs, model responses, credentials, flow definitions, logs, and exported chatflows may contain private data. Model providers, embedding providers, vector stores, document loaders, search APIs, workflow integrations, and hosted Flowise Cloud may receive data depending on flow configuration. Do not commit `.env` files, provider keys, vector database secrets, webhook URLs, exported credentials, generated logs, or private flow templates. Before publishing a chatbot or API endpoint, review whether prompts, source documents, dataset IDs, and tool outputs are exposed to users or downstream systems.✓Uploaded documents, parsed chunks, embeddings, workspace memories, prompts, chat history, agent state, scheduled task inputs, MCP payloads, provider responses, logs, and API calls may contain sensitive data. The README documents anonymous telemetry and an opt-out through DISABLE_TELEMETRY=true or the in-app privacy setting; review this before using regulated or confidential data. Even with telemetry disabled, outbound calls may still go to configured LLMs, embedding models, vector databases, external tools, cdn.anythingllm.com, GitHub, or GitHubusercontent depending on the deployment. Keep provider keys, JWT secrets, workspace invite links, storage paths, private documents, and generated citations out of public prompts, screenshots, issues, and examples.✓Prompts, uploaded documents, knowledge-base chunks, embeddings, workflow variables, tool arguments, tool results, API requests, model responses, logs, annotations, and observability data may contain sensitive user or business data. Do not store API keys, database credentials, private documents, customer records, regulated data, or internal URLs in examples, public apps, logs, screenshots, or shared prompts. Review data paths for every model provider, embedding provider, reranker, tool, observability integration, storage backend, and Dify Cloud or self-hosted deployment component. RAG and knowledge-base features need deletion, retention, access control, source freshness, and permission filtering policies before ingesting private corpora.✓Chats, prompts, uploaded files, document chunks, embeddings, vector metadata, web search results, browser-fetched pages, Python tool inputs, plugin outputs, voice/video data, logs, metrics, and traces may contain private data. Configured model providers, vector databases, document extraction engines, web search providers, image providers, object storage, Redis, auth providers, and observability backends may receive user data. Keep provider keys, OAuth/LDAP/SSO secrets, database URLs, object storage keys, plugin credentials, uploaded files, RAG indexes, and OpenTelemetry exports out of public repos and screenshots. Define retention, deletion, tenant separation, group permissions, export policy, and audit review before using Open WebUI as a shared internal workspace.
Prerequisites
  • Node.js 20.0.0 or newer for npm installation.
  • Docker Compose if using the documented Docker self-host path.
  • Provider credentials for selected LLMs, embedding models, vector stores, document loaders, tools, or workflow integrations.
  • Persistent database/storage configuration, auth settings, and environment variables before exposing a shared Flowise instance.
  • Docker for the documented self-hosted path, or the desktop application for a local workstation install.
  • At least the upstream minimum host resources, with disk sized for documents, embeddings, vector storage, models, logs, and backups.
  • A local or remote LLM provider, embedding provider, and optional speech or image models for the workflows the workspace will run.
  • A storage, backup, retention, and access-control plan before ingesting private documents or opening a multi-user Docker instance.
  • Docker and Docker Compose for the documented self-hosted quick start, or a Dify Cloud workspace.
  • At least the upstream minimum CPU and memory resources for local deployment.
  • Model provider credentials for the LLMs, embedding models, rerankers, or API-compatible routes the application will use.
  • Storage, database, vector, observability, network, and backup planning before hosting real user data.
  • Python 3.11 or 3.12 for pip installation, or Docker/Kubernetes for container deployment.
  • Ollama, OpenAI-compatible endpoint, OpenAI API key, or another configured model provider.
  • Persistent storage for the application database and uploaded/RAG content; Docker users must mount `/app/backend/data` to avoid data loss.
  • Optional vector database, document extraction, web search, image generation, speech, enterprise auth, object storage, Redis, or observability services depending on enabled features.
Install
npm install -g flowise
docker pull mintplexlabs/anythingllm
docker compose up -d
pip install open-webui
Config————
Citations
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