Dify vs Flowise: Choosing a Self-Hosted LLM App Builder for Production (2026)

Introduction

Most teams that build an internal chatbot, a document Q&A tool, or a small agent hit the same wall around week three. The prototype in a notebook works. Then someone asks for a prompt version history, a way for a non-engineer to edit the retrieval settings, an API key per consuming app, request logs that survive a restart, and a deployment that does not depend on one developer's laptop. At that point the question stops being "which model" and becomes "which application layer sits between the model and the product."

Dify and Flowise are the two open-source answers that come up most often in that conversation. Both give you a visual canvas, a hosted API for every app you build, a knowledge-base feature for retrieval, and a self-hosted deployment path. They differ in what they assume about your team. Flowise is a Node.js project built around LangChainJS components, and the canvas is a thin visual layer over code constructs an engineer already recognizes. Dify is a larger platform with a Python backend, a workflow engine, built-in dataset management, application-level observability, and a multi-service deployment.

The pain points in 2026 are mostly operational rather than model-related. Teams report agent flows that cannot be diffed in code review, retrieval settings changed in a UI with no audit trail, secrets pasted into node fields, and upgrade paths that break saved flows. A visual builder helps with iteration speed and hurts with reproducibility unless you plan for it.

This guide compares Dify and Flowise on architecture, retrieval, agent orchestration, API surface, and operations, then gives a runnable recipe that stands up Flowise with PostgreSQL and calls both products' HTTP APIs from one script. Nothing here is a measured benchmark. Where a number appears, it is a default value from the projects' documentation or a label you should verify against the release you install.

Architectural Breakdown & Core Mechanics

Flowise: a visual runner for LangChainJS graphs

Flowise runs as a single Node.js server with a React front end. Each node on the canvas wraps a component from LangChain in its JavaScript form: a chat model, an embedding model, a vector store, a retriever, a memory, a tool. Connecting nodes builds a graph; saving it stores the graph as JSON in the application database (SQLite by default, with PostgreSQL and MySQL supported through environment variables). When a request arrives at the prediction endpoint, the server instantiates the components from that JSON and runs the chain.

Two flow types matter. Chatflows are linear or lightly branching chains for Q&A and retrieval. Agentflows add multi-step orchestration with conditions, loops, and sub-agents. Because the components are LangChainJS classes, anything the library supports can usually be exposed as a node, and custom nodes are written in TypeScript. The cost is that your flow inherits the library's abstractions and its release churn.

Dify: a platform with its own workflow engine

Dify splits into several services: an API server (Python), a background worker for dataset indexing and async jobs, a web front end, a plugin daemon, a code-execution sandbox, and the data stores (PostgreSQL, Redis, and a vector database of your choice). The workflow engine is Dify's own, not a LangChain wrapper. Apps come in types: chat assistants, agents, text generators, and workflows (including a chatflow variant that adds conversation memory).

The knowledge-base feature is a first-class subsystem. You upload documents, pick a chunking strategy and an embedding model, choose between high-quality (vector) and economical (keyword) indexing, and tune retrieval with top-k, score thresholds, and optional reranking. Indexing runs in the worker, so large uploads do not block the API. This is the biggest practical difference: Dify manages the ingestion lifecycle for you, while Flowise gives you document loaders and vector store nodes that you arrange yourself.

Where state lives

Flowise keeps flow definitions, credentials (encrypted with a key you provide), chat history, and uploaded files in its database and storage path. Dify keeps app definitions, datasets, conversation logs, and annotations in PostgreSQL, with object storage for files and the vector database for embeddings. In both products, anything you cannot export as text is something you cannot code-review. Plan an export step, covered in the recipe below.

Comparative Benchmarks & Evaluation Matrix

This matrix compares design and operations. It has no latency or accuracy figures, because both depend on your model, documents, and hardware. Verify feature details against the exact version you deploy, since both projects ship frequently.

Who should not pick which: a two-person team that wants a single binary on a small VPS will find Dify's service count painful, and Flowise suits them better. A company that needs non-engineers to manage document collections and review answer quality will outgrow Flowise's loader-node approach faster than it expects. If you already write production code against LangChain, consider whether a visual layer helps at all, or whether an application framework like LlamaIndex in plain code gives you better testing and review.

For a code-first alternative, the catalog also lists LangChain as a tool, which fits teams that want every pipeline in version control.

Step-by-Step Implementation Recipe

The recipe below runs Flowise with PostgreSQL in Docker Compose, then provides a shell script that exports flows for version control and a client that calls either product's HTTP API. Environment variable names follow Flowise's documented configuration (DATABASE_TYPE, DATABASE_HOST, and so on); confirm them against the docs for your image tag, and pin that tag instead of using latest.

# docker-compose.yml  (pin image tags to versions you have tested)

services:

postgres:

image: postgres:16

restart: unless-stopped

environment:

POSTGRES_USER: flowise

POSTGRES_PASSWORD: ${PG_PASSWORD:?set PG_PASSWORD}

POSTGRES_DB: flowise

volumes:
- pgdata:/var/lib/postgresql/data

healthcheck:

test: ["CMD-SHELL", "pg_isready -U flowise"]

interval: 10s

timeout: 5s

retries: 5

flowise:

image: flowiseai/flowise:latest   # replace with a pinned tag

restart: unless-stopped

depends_on:

postgres:

condition: service_healthy

ports:
- "3000:3000"

environment:

PORT: 3000

DATABASE_TYPE: postgres

DATABASE_HOST: postgres

DATABASE_PORT: 5432

DATABASE_NAME: flowise

DATABASE_USER: flowise

DATABASE_PASSWORD: ${PG_PASSWORD:?set PG_PASSWORD}

SECRETKEY_PATH: /root/.flowise

APIKEY_PATH: /root/.flowise

volumes:
- flowise_data:/root/.flowise

command: /bin/sh -c "sleep 3; flowise start"

volumes:

pgdata:

flowise_data:

Start it, open http://localhost:3000, build a chatflow, and copy its ID from the URL. Then use this script. It exports every flow as JSON for Git, and it sends the same question to Flowise and to a Dify app so you can compare answers side by side on your own documents.

#!/usr/bin/env bash

# compare_builders.sh -- export Flowise flows and query Flowise and Dify with one question.

# Requires: curl, jq.  Set the variables below before running.

set -euo pipefail

FLOWISE_URL="${FLOWISE_URL:-http://localhost:3000}"

FLOWISE_API_KEY="${FLOWISE_API_KEY:-}"          # create under API Keys in the Flowise UI

FLOWISE_CHATFLOW_ID="${FLOWISE_CHATFLOW_ID:?set FLOWISE_CHATFLOW_ID}"

DIFY_URL="${DIFY_URL:-http://localhost/v1}"

DIFY_APP_KEY="${DIFY_APP_KEY:?set DIFY_APP_KEY}"  # app-level key from the Dify app's API Access page

QUESTION="${1:-What is our parental leave policy?}"

auth_header=()

if [ -n "$FLOWISE_API_KEY" ]; then

auth_header=(-H "Authorization: Bearer ${FLOWISE_API_KEY}")

fi

echo "== Exporting Flowise chatflows to ./flows =="

mkdir -p flows

curl -sS "${FLOWISE_URL}/api/v1/chatflows" "${auth_header[@]}" \

| jq -c '.[]' \

| while read -r flow; do

id=$(jq -r '.id' <<<"$flow")

name=$(jq -r '.name | gsub("[^A-Za-z0-9_-]"; "_")' <<<"$flow")

jq '.' <<<"$flow" > "flows/${name}-${id}.json"

echo "saved flows/${name}-${id}.json"

done

echo

echo "== Flowise answer =="

curl -sS -X POST "${FLOWISE_URL}/api/v1/prediction/${FLOWISE_CHATFLOW_ID}" \

"${auth_header[@]}" \

-H "Content-Type: application/json" \

-d "$(jq -n --arg q "$QUESTION" '{question: $q}')" \

| jq -r '.text'

echo

echo "== Dify answer =="

curl -sS -X POST "${DIFY_URL}/chat-messages" \

-H "Authorization: Bearer ${DIFY_APP_KEY}" \

-H "Content-Type: application/json" \

-d "$(jq -n --arg q "$QUESTION" '{inputs: {}, query: $q, response_mode: "blocking", user: "compare-script"}')" \

| jq -r '.answer'

Run it with FLOWISE_CHATFLOW_ID=... DIFY_APP_KEY=... bash compare_builders.sh "your question". Commit the flows/ directory so a changed retrieval setting shows up in a diff. For Dify, use the app DSL export from the app menu and commit that file the same way.

Practical notes from the pattern, not from a benchmark: use the same chunk size, embedding model, and top-k in both products before comparing answers, otherwise you are comparing configurations rather than platforms. Keep a fixed list of 20 to 50 real questions with expected source documents, and score retrieval (did the right chunk come back) separately from generation (did the model use it correctly). To run entirely on your own hardware, point either product at Ollama, and see the Ollama project page for serving details.

Strategic Catalog Integrations

The builders are only the application layer. These catalog entries cover the parts around them.

• Pipelines that begin as visual flows often need to move into code once they stabilize. The LangChain and CrewAI agentic rules give an editor-level ruleset for that migration, and the LangChain for LLM Application Development course explains the abstractions Flowise nodes wrap.
• If you want a polished chat front end on top of either builder's OpenAI-compatible or custom endpoint, Open WebUI is a common choice and can sit in front of local models.
• The deployment in this article is a Compose stack, so apply the Docker and Kubernetes DevOps rules for pinned tags, health checks, resource limits, and secret handling.
• Agent flows need a defined failure behavior. The resilient tool-use agent prompt is a reusable system prompt for retries and fallbacks, and the AI agent security evaluation checklist helps you review prompt injection and tool permissions before exposing an agent to users.
• To plan which processes to automate first, the AI workflow automation specialist prompt turns a process description into candidate workflow steps you can then build on the canvas.
• For a structured path through retrieval, evaluation, and deployment, the Full Stack LLM Bootcamp covers the application concerns that sit above either builder.

Frequently Asked Questions (FAQ)

Is Dify better than Flowise for RAG?

Dify has more built-in retrieval management: dataset uploads, chunking choices, an indexing worker, and retrieval tuning in the UI. Flowise gives you the same building blocks as nodes, which is more flexible if you want a specific loader or vector store and more work if you want governance. "Better" depends on whether your bottleneck is ingestion management or custom pipeline control.

Can Flowise and Dify run fully offline with local models?

Both can call a local server such as Ollama, so the model and embeddings can stay on your network. You still need to check that every node or plugin you use avoids outbound calls, since some tools, search nodes, and telemetry settings contact external services. Review the container's egress rules rather than assuming.

Is Dify really open source, and can I resell it?

Dify's repository is source-available under a license derived from Apache 2.0 with extra conditions, including restrictions on offering a multi-tenant service and on removing branding. Flowise uses Apache 2.0. If you plan to host it for customers or embed it in a product, have the license reviewed against your use before you commit.

Should I use Flowise or write LangChain code directly?

Use Flowise when speed of iteration and a shared visual surface matter more than code review. Switch to code when you need unit tests, typed interfaces, and pull-request review of every prompt change. Many teams prototype in the canvas, export the flow, and reimplement the stable parts in code.