Flowise¶

What it is¶

Flowise is an open-source visual builder for LLM applications. Built on top of LangChain, it provides a drag-and-drop interface to create complex chains, agents, and RAG pipelines.

What problem it solves¶

It lowers the barrier to entry for building LLM applications by providing a no-code/low-code interface. It enables rapid prototyping and allows non-developers or technical product managers to iterate on prompt engineering and workflow logic visually.

Where it fits in the stack¶

Orchestration / Builder Layer. It sits above your LLM providers and vector databases, serving as the "brain" and interface for your AI applications.

Typical use cases¶

Customer Support Bots: Building RAG-powered bots that answer questions based on company documentation.
Workflow Prototyping: Quickly testing different LangChain components (retrievers, agents, tools) before committing to code.
Internal Tools: Creating specialized assistants for data extraction, summarization, or translation that team members can use via a simple UI.
Agentic Workflows: Designing agents with access to custom tools (APIs, calculators, search).

Strengths¶

Visual Programming: Makes complex LangChain logic intuitive and easy to reason about.
Self-Hostable: Can be deployed easily via Docker, ensuring data privacy for local homelab use.
Extensibility: Supports custom tools and JavaScript snippets for complex logic.
Integrated API: Automatically generates REST endpoints for every chatflow you build.

Limitations¶

LangChain Coupling: If a feature isn't in LangChain (or hasn't been integrated into Flowise yet), it can be difficult to implement.
Version Management: Managing changes and rollbacks to visual flows can be more challenging than versioning code.
Resource Usage: Running the Flowise server and its UI adds overhead compared to a lightweight script.

When to use it¶

When you want to build and iterate on LLM applications visually.
When you need to provide a GUI for team members to interact with AI workflows.
For rapid prototyping of RAG and agentic systems.

When not to use it¶

When you need maximum programmatic flexibility or want to use frameworks like LlamaIndex or DSPy.
For simple scripts where the overhead of a visual builder is unnecessary.

Getting started¶

1. Installation and Startup¶

The easiest way to run Flowise is via Docker:

docker run -d --name flowise -p 3000:3000 flowiseai/flowise

Alternatively, use npx:

npx flowise start

2. Building Your First Flow¶

Open http://localhost:3000 in your browser.
Click "Add New" to create a chatflow.
Drag components from the sidebar (e.g., "OpenAI Chat Model", "Recursive Character Text Splitter", "In-Memory Vector Store").
Connect the components and click "Save".

API examples¶

Triggering a Prediction via REST API¶

Every chatflow can be triggered via a POST request.

curl -X POST "http://localhost:3000/api/v1/prediction/<CHATFLOW_ID>" \
     -H "Content-Type: application/json" \
     -d '{
            "question": "What are the benefits of using a visual builder?",
            "overrideConfig": {
                "temperature": 0.5
            }
         }'

Passing External Variables¶

You can pass variables into your flows (e.g., a user ID or session context) that can be used within prompt templates.

curl -X POST "http://localhost:3000/api/v1/prediction/<CHATFLOW_ID>" \
     -H "Content-Type: application/json" \
     -d '{
            "question": "Summarize my last orders",
            "overrideConfig": {
                "vars": {
                    "user_id": "12345"
                }
            }
         }'

Sources / references¶

Contribution Metadata¶

Last reviewed: 2026-06-01
Confidence: high