Running your own AI agent builder is no longer a privilege reserved for large teams. Today you can assemble a chatbot that answers from your company knowledge base, or an agent that pulls from several sources and returns a finished result, all with your mouse, without writing code from scratch. Flowise is exactly that kind of tool: a visual open-source builder that many people look for as an n8n alternative for tasks built around artificial intelligence.
In this article we explain what Flowise is, how it differs from n8n, and how to deploy it on your own VPS with Docker. The guide is written for a broad audience, so even without system administration experience you can follow the steps in order. It will be useful for small business owners, developers, marketers, and anyone who wants to keep their data and AI logic under their own control.
What Flowise Is and What It Does
Flowise is a visual builder for applications powered by large language models. It works on a drag-and-drop principle: on the canvas you drag ready-made blocks (a model, a prompt, a document loader, a vector store, a tool) and connect them into a single flow. Under the hood sits the popular LangChain library in its JavaScript version, so Flowise can be seen as a visual layer on top of it. The complex logic that teams used to write in code becomes visible at a glance as a clear diagram.
The project is open and distributed under the Apache 2.0 license. At the time of writing, the current release carries the number 3.1.2, and the repository has gathered around 52 thousand stars on GitHub, which points to a lively and active community. Inside, Flowise is made of three parts: a Node.js backend handles the logic and the API, a React interface draws that canvas, and a separate set of nodes connects third-party services and models.
A few basic terms that will come up later. An agent is a scenario where the model itself decides which tool to call for an answer. RAG (retrieval augmented generation) is an approach where the model answers by drawing on your uploaded documents on top of its general knowledge. A vector store is a special database that searches by meaning, matching fragments that are close in sense.
Flowise also has a cloud version, Flowise Cloud, billed by subscription, which takes the infrastructure worry off your hands. Self-hosting on your own server stays free in terms of software, and that is the path we cover in detail.
How Flowise Differs from n8n and When to Pick It
The phrase n8n alternative comes up often, so let us clear up something important right away. n8n and Flowise solve different tasks, even though both look like visual flow builders on the surface.
n8n is a general-purpose automation platform. Its strength lies in the number of ready integrations, counted in hundreds of connectors. It can read email, push data into spreadsheets, call third-party APIs, and send notifications to messengers. Artificial intelligence arrived in n8n as one capability layered on top of that automation.
Flowise is focused specifically on AI. It was built to assemble agents, chatbots, and RAG scenarios quickly, and it feels more confident in that niche. The result of a Flowise flow is usually exposed as an API endpoint, which another application then calls.
The difference also shows in maturity. n8n has been around longer, roughly seven years against three for Flowise, and its community is noticeably larger. In practice teams often run both tools together. Flowise acts as the brain that thinks with the help of a model, while n8n plays the role of the hands that distribute the result across services.
The takeaway is simple. If you need end-to-end automation of business processes with dozens of services wired in, n8n will be the closer fit. If the task centers on working with a language model, searching your own documents, or a conversational agent, it is more convenient to start with Flowise. For a classic automation scenario Serverspace offers a ready-made n8n hosting solution, while for the AI builder we will spin up Flowise ourselves below.
| Parameter | Flowise | n8n |
|---|---|---|
| Main purpose | AI agents, chatbots, RAG | Business process automation |
| Under the hood | Visual layer on top of LangChain.js | Flow builder with hundreds of connectors |
| AI agents | Fine-grained control of agent logic | Agent as a node inside a wider flow |
| Third-party integrations | Fewer, focused on AI components | Hundreds of ready connectors |
| Output format | API endpoint for an external app | Direct handoff to nodes: email, database, messengers |
| Learning curve | Low for AI tasks | A bit higher, with broader reach |
| License and cost | Apache 2.0, free when self-hosted | Open source, free self-hosting |
| When to choose | Conversational agents and document search | End-to-end automation across dozens of services |
What You Need to Get Started: Server and Software Requirements
Before installing, it helps to understand what hardware Flowise will live on and what software it needs.
The minimum requirements are modest. For getting acquainted and for small flows, a virtual server with one processor core and from 1 to 2 gigabytes of RAM is enough, plus from 10 to 20 gigabytes of disk. For a real workload it is better to budget more memory, and we explain why in the section on errors: on long scenarios Flowise can consume resources with a healthy appetite.
You will need three things on the software side. Node.js version 20 or newer if you plan to install through the package directly. Docker together with Docker Compose, which is the recommended and most predictable approach. A database to store flows and settings: by default Flowise uses the built-in SQLite, while for production use it is wiser to connect PostgreSQL.
A separate word on placement. If your audience is in Europe, keeping the server close to your users pays off: lower latency, faster response. This is where renting a VPS in the Amsterdam data center fits in. A European location also helps with GDPR and with keeping data inside the EU, which matters for any business that works with customer information. Per-minute billing lets you start with a minimal configuration for testing and scale resources up as the project grows.
Preparing the VPS: Installing Docker and Basic Setup
Let us assume the server is already rented and we have SSH access to it. From here we go step by step. The commands target a fresh Ubuntu, and for other distributions only the package names will change.
First we update the package list and the packages themselves so that we work on a current system:
sudo apt update && sudo apt upgrade -yThen we install Docker and the Docker Compose plugin. These commands add the official packages and the accompanying components:
sudo apt install -y docker-ce docker-ce-cli containerd.io docker-compose-pluginTo run docker without the sudo prefix, we add the current user to the docker group and apply the change:
sudo usermod -aG docker $USER
newgrp docker
Let us check that everything is in place. The commands will print the versions and confirm that the Docker daemon responds:
docker --version
docker compose version
If both commands returned version numbers, the base is ready. By the way, you can skip this step entirely. Serverspace offers a ready VPS with Docker from its one-click app catalog: the server comes up with Docker already installed, and you move straight to deploying Flowise.
Deploying Flowise with Docker Compose
Now for the interesting part. We will show two paths: a quick local launch to try things out, and a deployment with Docker Compose that is closer to production.
The shortest way to try Flowise works if Node.js is installed on the machine. It is enough to install the package globally and start it:
npm install -g flowise
npx flowise start
After that the interface opens in the browser at localhost on port 3000. This option is good for getting acquainted on a local computer, and for the server we use Docker.
Let us move to Docker Compose. First we clone the project repository and enter the right folder:
git clone https://github.com/FlowiseAI/Flowise.git
cd Flowise/docker
Inside there is a file with example environment variables. We copy it to the working .env file, which Docker picks up at startup:
cp .env.example .envThe .env file is worth opening to set at least a login and password for access, along with an encryption key. We will cover the variables in detail in the next section. After the edits we start the containers in the background:
docker compose up -dLet us check that the container came up and look at the logs if something went wrong:
docker compose ps
docker compose logs -f flowise
If all is well, Flowise becomes available at an address like http://server-address:3000. A login screen opens, where you enter the same login and password from the .env file. At this point the engine itself already works. To stop the application you use docker compose stop, and to update to a fresh version the containers are stopped, the new image is pulled, and they are brought back up.
At this stage we have a working Flowise reachable by IP address and port. Leaving it like this for public use is risky, so next we will close access behind a proper domain, encryption, and security settings.
Exposing It Safely: Nginx Reverse Proxy and SSL
Reaching the service by an IP address with a port is inconvenient and insecure. A pair made of a reverse proxy and an SSL certificate solves this. The reverse proxy accepts requests on the usual ports and passes them inward to port 3000, while the certificate turns on HTTPS encryption.
We install the Nginx web server and create a configuration for our application. In it we specify the domain name and the proxy rule. One important point: Flowise uses websockets for live message exchange, so the config includes headers to support them. It is also worth raising the timeouts, because model responses can be slow, and lifting the request body size limit if file uploads are planned. A sample settings block looks like this:
server {
listen 80;
server_name your-domain.io;
location / {
proxy_pass http://localhost:3000;
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
proxy_set_header Host $host;
proxy_read_timeout 300s;
client_max_body_size 50M;
}
}
After that we add an A record in the domain control panel pointing to the server IP address. Once the domain starts opening the application, we turn on HTTPS. A free certificate is convenient to issue through Let's Encrypt using the Certbot utility, which writes the needed lines into the Nginx configuration itself. Certbot also handles auto-renewal, for which a job is added to the scheduler.
A final security touch: direct access to port 3000 from outside is better closed with a firewall, so that all traffic goes only through the proxy. That way the application will not sit on the open internet on a bare port.
Setting Up Security and Key Environment Variables
Flowise is configured through variables in the .env file. Let us go over the ones that affect security and stability the most.
- Login and password. The variables for the user name and password turn on the authorization screen. Without them anyone who knows the address gains access to your flows, so setting them is mandatory.
- Encryption key. The FLOWISE_SECRETKEY_OVERWRITE parameter sets the key Flowise uses to encrypt stored credentials, for example access keys to models. For a production server it is wise to keep the value in a secure secret manager so that it does not sit openly in the file.
- Number of proxies. When Flowise sits behind a reverse proxy or a load balancer, the built-in rate limit can mistakenly count all requests as coming from a single address. The NUMBER_OF_PROXIES variable tells the application how many proxies stand in front of it, and the limits work correctly again.
- External dependencies for tools. If your custom functions inside Flowise use third-party npm packages, you list them in the TOOL_FUNCTION_EXTERNAL_DEP variable, otherwise they will be unavailable in the sandbox.
This set is enough to cover the basic risks. As the project grows, the list of variables can be extended, and the full list is in the Flowise documentation.
Common Errors When Launching Flowise and How to Avoid Them
At the start many people hit the same snags. We collected them in a symptom, cause, and fix format to make them easier to navigate. The full version of the table is moved to a separate HTML block, and below we go through the key points in words.
- The build fails with an out-of-memory error. When building from source yourself, a message about exit code 134 and heap out of memory appears. The cause is that the builder runs short of allocated Node.js memory. The fix: raise the NODE_OPTIONS limit to a value around 4096 megabytes and run the build again.
- The service eats too much RAM. On long scenarios and during bulk document uploads to the vector store, memory use grows and is not always released. This is a known story in the community. Scheduled worker restarts and a memory headroom on the server help, so cutting corners on RAM for a production workload is risky.
- The rate limit blocks all users. If Flowise sits behind a load balancer and the number-of-proxies variable is not set, the request limit starts counting everyone as one and blocks requests. It is cured by setting a correct NUMBER_OF_PROXIES value.
- Port 3000 is open to the internet. Leaving the application directly reachable on the port is unsafe. All external traffic should be routed through the reverse proxy, and the port itself closed with a firewall.
- The interface hangs on long conversations. Users have noted that after several messages in a row the response sometimes takes very long. Splitting complex scenarios into shorter ones and watching the context size help here.
Let us also be honest about the limitations overall. Flowise is strong for prototypes, internal tools, and small-business bots. For heavy production with high load and complex debugging the capabilities can fall short at times, and that is when it is worth looking toward code-first solutions.
| Symptom | Cause | Fix |
|---|---|---|
| Build fails, exit code 134 | The builder runs short of Node.js memory (heap out of memory) | Raise NODE_OPTIONS to around 4096 MB and rebuild |
| High RAM consumption | Memory is not released on long flows and bulk document uploads | Schedule worker restarts, keep memory headroom on the server |
| Rate limit blocks all users | The number-of-proxies variable is not set behind a load balancer | Set a correct NUMBER_OF_PROXIES value |
| Port 3000 open to the internet | Direct access to the application without a proxy | Close the port with a firewall, route all traffic through Nginx |
| Interface hangs on long conversations | An oversized context and heavy request chains | Split scenarios into shorter ones, watch the context size |
Where Flowise Shines: Typical Use Cases
To bring theory closer to practice, here are several typical scenarios that Flowise handles well.
- Support chatbot over a knowledge base. We upload documents, guides, and frequent questions into the vector store, and the bot answers customers by drawing on them. This takes the load off support for routine requests.
- Internal assistant over company documents. Employees ask questions about policies, contracts, and reports and get an answer with a link to the source, saving time on manual searching through folders.
- Prototype of an AI agent before development. Before ordering an expensive custom system, the agent logic is assembled in Flowise in an evening and the hypothesis is tested on real data.
- A site bot through an embeddable widget. Flowise ships a ready chat widget that drops onto a page with a couple of lines of code, which is handy for landing pages and online stores.
- Pairing with n8n for end-to-end automation. Flowise handles the smart part, while n8n picks up the result and distributes it across email, messengers, and CRM.
In all of these cases, hosting on your own server in a European data center gives control over the data and helps meet requirements for storing it within the region.
Conclusion: What We Built and What to Do Next
We went the whole way: we figured out what Flowise is and why people choose it as an n8n alternative for tasks built around artificial intelligence, prepared the server, installed Docker, deployed the application with Docker Compose, closed it behind a domain with encryption, and set up security. Now you have your own platform for assembling AI agents under full control.
What to do next. Open the interface, connect your first model through an access key, and build a simple flow, for example a chatbot over a single document. From there you can grow the diagram, add tools, and expose finished scenarios through the API.
Frequently Asked Questions
Is Flowise free?
The engine itself is open and free when self-hosted, and you only pay for the server and for model usage. There is also a cloud version on a subscription, which takes the infrastructure onto itself.
How does Flowise differ from LangFlow?
Both tools are visual and solve similar tasks. Flowise is built on the JavaScript version of LangChain, while LangFlow is written in Python. The choice often depends on which language is more comfortable for you to extend the logic in.
Can Flowise run without Docker?
Yes, through a package install with npm and a start command. For a local trial this is faster, while for a server Docker stays more reliable and predictable.
How much RAM do you really need?
For a test, one or two gigabytes will do, but for a real workload with RAG and long scenarios it is better to budget four gigabytes and more, guided by the behavior of your specific flows.
Is Flowise suitable for production?
For small and medium tasks, quite so. Under high load it is worth planning scaling, memory monitoring, and backups in advance, and moving part of the critical logic into code if needed.
