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FlowiseAI/Flowise

Flowise Is Archived: What the Visual Agent Builder Leaves Behind

Flowise lets you build AI agents and LLM workflows visually, dragging and dropping components for chatbots, RAG, and multi-agent systems instead of writing code.

55,570 stars25,093 forksTypeScriptLicense varies

At a glance

What is it?
Flowise, a visual builder for AI agents, has been archived. This review covers how it worked, how to run it, and what the repository archive means for teams choosing a tool now.
Who is it for?
Adopt Flowise only if you need a quick, self-hosted visual prototype and can accept the archived repository and verify current maintenance and support arrangements. Do not adopt it for a new production system.
Can I use it commercially?
Check first. The repository uses a licence we do not classify automatically, so read its LICENSE file before any commercial use.
Is it still maintained?
No. The owners have archived the repository on GitHub, so it is read-only and no longer receives changes.
What is it written in?
Mainly TypeScript, according to GitHub's language statistics.

Answers come from the project's GitHub data, last synced on November 13, 2026, and from our analysis. They are not legal advice.

From daily snapshots

Star history

  • FlowiseAI/Flowise55,570 ★ · +571
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Repository2026-07-282026-11-13
FlowiseAI/Flowise55k55.6k

571 Stars in the last 108 days, measured from the daily GitHub snapshot. Compare with other projects

Editorial analysis

The repository is archived

The first thing the README says is that Flowise has been archived. The banner links to a discussion titled Future of Flowise. The last release, [email protected], came on 2026-07-29, and the last push was on 2026-08-13. The GitHub repository is read-only while archived. Check the linked project discussion for any successor, fork or support arrangements. Any review of Flowise has to start with that fact, because the tool's usefulness now depends on whether a frozen codebase still solves your problem. For a visual agent builder, that is a serious question. LLM APIs change, node packages change, and a tool that cannot track those changes will rot quickly.

What Flowise Actually Did

Flowise was a visual builder for AI agents. The tagline is Build AI Agents, Visually. You connected nodes on a canvas to create agent flows. The repository is a monorepo with three modules. The server is a Node backend that serves API logic. The ui is a React frontend. The components module holds third-party node integrations. There is also an api-documentation module that generates swagger-ui API docs from Express. That structure tells you the core idea: the UI lets you drag nodes, the server executes the resulting graph, and the components package is the plugin system that lets different models and tools appear as draggable nodes.

The Mechanism: A Graph You Build, a Server That Runs It

The README does not describe the internal execution engine; the module layout shows only the pieces: a React UI, a Node server that serves API logic, and an api-documentation module that generates swagger-ui docs from Express. The components package is where integrations live. Each integration, whether it is a model provider or a tool, is a node that fits into the graph. That design is what made Flowise flexible.

Getting It Running: Commands That Still Work

The README gives clear commands. For a quick start, you need NodeJS version 20 or higher. Install globally with npm install -g flowise, then start with npx flowise start, and open http://localhost:3000. Docker is also supported. You can clone the repo, go into the docker folder, copy .env.example to .env, and run docker compose up -d. Or build the image yourself with docker build --no-cache -t flowise . and run it with docker run -d --name flowise -p 3000:3000 flowise. For developers, the setup uses pnpm. Clone, run pnpm install, then pnpm build, then pnpm start. The README even warns about a JavaScript heap out of memory error during build and suggests setting NODE_OPTIONS=--max-old-space-size=4096. That is a concrete detail that suggests the full build is heavy.

Limitations: Where Flowise Was the Wrong Tool

The biggest limitation is now the archive itself. But even before that, Flowise had constraints. The build process could run out of memory, which points to a large dependency tree. The components module means every integration is a separate node, and those nodes need maintenance. When an LLM provider changes its API, the node has to change too. With the project archived, no one is updating those nodes. Also, the README mentions environment variables for configuration, but it does not list them. You have to read the CONTRIBUTING.md file to find them. That is a documentation gap. For a team that needs a stable, long-lived system, a visual builder where the underlying nodes can break silently is risky. The wrong tool is any production deployment that expects updates.

Alternatives: What to Use Instead

The category has clear alternatives. LangFlow is a visual builder for LangChain pipelines, which is a similar approach: a graph UI that generates executable chains. Another alternative is to skip the visual layer entirely and write code with LangChain or LlamaIndex directly. That gives you full control and no dependency on a UI framework. The trade-off is that you lose the visual overview. If you need a visual tool and want active development, LangFlow is a real option. If you need longevity, code is safer. The archive makes Flowise a poor choice for new projects, but the alternative depends on whether you value the visual layer or the maintenance story.

Maintenance and License: What You Inherit

The license is Apache License Version 2.0, stated clearly in the README. That is permissive, so you can fork the code and modify it. That is the main path forward if you want to keep using Flowise: take the code, maintain your own fork, and update the component nodes yourself. The cost is significant. You would need to track changes in every LLM provider API and every tool integration. The monorepo has multiple packages, so the build and release process is not trivial. The README's heap error warning suggests the build is resource-hungry. There is no indication of a migration path from the archived version to anything else. The Future of Flowise discussion might contain that, but the README does not. So the maintenance cost is entirely on you.

The Verdict: A Frozen Tool for Prototyping Only

Flowise was a capable visual builder when it was alive. The architecture is sound: a React UI, a Node server, and a component plugin system. The commands are straightforward, and the Docker support makes deployment easy. But the archive changes the calculation. For a quick prototype that you throw away, Flowise still works if the dependencies install. For anything that needs to run for more than a few months, it is the wrong choice. You will be maintaining a fork or migrating later. The README's own banner points to a discussion, not a solution. That is the honest state of the project. If you want to build a visual agent demo this week, Flowise can do it. If you want to build a product, look elsewhere.

Editorial conclusion

Adopt Flowise only if you need a quick, self-hosted visual prototype and can accept the archived repository and verify current maintenance and support arrangements. Do not adopt it for a new production system. Verify first whether the archived codebase still installs cleanly on your Node version, and check the Future of Flowise discussion to see if a fork or successor is officially recommended. If you need ongoing maintenance, pick a maintained alternative.

Frequently asked questions

What is Flowise used for?

It is a visual builder for AI agents, described as building agents visually, with a Node backend serving API logic, a React frontend, a components layer of third party node integrations, and swagger-ui API documentation generated from express.

How do I install Flowise?

With Node.js 20.0.0 or newer, run `npm install -g flowise` and then `npx flowise start`, and open http://localhost:3000. For self-hosting there is also a compose route in the repository's docker folder and a local image build.

How do I install Flowise using Docker?

Clone the project, change into the `docker` folder at the root, copy `.env.example` to `.env`, run `docker compose up -d` and open http://localhost:3000, stopping the stack later with `docker compose stop`. To build the image instead, use `docker build --no-cache -t flowise .` and run it on port 3000.

How do I use the Flowise API?

The monorepo includes an api-documentation module that auto-generates swagger-ui API docs from express, and the server module is the Node backend serving API logic, so the endpoints are documented from the same code that serves them. The readme links the documentation site rather than listing endpoints itself.

Is Flowise AI free?

The source code in the repository is made available under the Apache License version 2.0, and the quick start, compose and image routes are all self-hosted. Flowise Cloud is offered as a separate hosted product, and the readme does not state its pricing.

Official sources

  1. Official documentation
  2. Official README
  3. Project repository
  4. Release notes
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