Dify vs LangFlow: Choosing an LLM App Builder

Dify vs LangFlow comes down to one question: are you shipping a production LLM application, or are you prototyping an agent pipeline? Dify is an application platform with hosting, API keys, user management, and a built-in RAG pipeline baked in. LangFlow is a visual builder for LangChain-style flows that exports to code you run yourself. Pick Dify when you want a finished product fast. Pick LangFlow when you want to design a flow visually and then own the runtime.

This article walks through both tools in enough depth that you can set either one up today, understand where they diverge, and decide which one fits your stack. No benchmarks, no made-up numbers, just what each tool actually does and how to use it.

What Dify Actually Is

Dify markets itself as an "LLM app development platform," and that's accurate. It's not just a flow builder, it's closer to a lightweight SaaS-in-a-box for AI apps. When you spin up Dify (self-hosted via Docker or through their cloud offering), you get:

• A visual workflow canvas for chaining LLM calls, tools, and conditionals
• A built-in knowledge base / RAG system with document ingestion, chunking, and retrieval settings
• Auto-generated REST APIs for every app you build
• A hosted chat widget you can embed on a website
• User-facing apps (chatbot, text generator, agent) with their own settings UI
• Team workspaces, API key management, and usage logging

The mental model: you build an "app" in Dify (a chatbot, an agent, a workflow, a text completion tool), configure its prompt, model, and any knowledge bases or tools it needs, and then Dify hosts it. You get an API endpoint and a shareable web app out of the box. There is no separate "export to Python" step, because Dify itself is the runtime.

Setting Up Dify Locally

git clone https://github.com/langgenius/dify.git
cd dify/docker
cp .env.example .env
docker compose up -d

That brings up Dify's full stack: the web console, the API service, a vector database, Redis, and Postgres. Once the containers are healthy, visit http://localhost/install to create your admin account, then log in and you land on the app dashboard.

From there, creating a RAG-backed chatbot looks like this:

1. Go to Knowledge and create a new knowledge base
2. Upload documents (PDF, Markdown, plain text, or connect a URL crawler)
3. Configure chunking (fixed-length or parent-child chunking) and pick an embedding model
4. Go to Studio and create a new "Chatbot" app
5. Attach the knowledge base under the app's context settings
6. Pick your LLM (Dify supports OpenAI-compatible endpoints, Anthropic, local models via Ollama, and others through its model provider settings)
7. Publish, and you immediately get a chat UI, an embed snippet, and a REST API

The REST API is the part most engineers care about. Every Dify app exposes an endpoint you call like this:

curl -X POST 'http://localhost/v1/chat-messages' \
  -H 'Authorization: Bearer app-xxxxxxxx' \
  -H 'Content-Type: application/json' \
  -d '{
    "inputs": {},
    "query": "What does our refund policy say about digital goods?",
    "response_mode": "streaming",
    "user": "user-123"
  }'

That's a production-ready endpoint with retrieval, prompt templating, and conversation history handled server-side. You didn't write any backend code.

What LangFlow Actually Is

LangFlow is a visual editor for building LLM-powered flows, originally built on top of LangChain's abstractions (chains, agents, tools, memory) and now broadened to support a wider set of components including LangGraph-style agent loops. The canvas is drag-and-drop: you place nodes like "Prompt," "OpenAI Model," "Chroma Vector Store," "Text Splitter," or "Agent," and wire their inputs and outputs together.

Where Dify treats the "app" as the unit (a chatbot, an agent, a completion tool with a fixed shape), LangFlow treats the "flow" as the unit: any directed graph of components you can imagine, and you decide what wraps around it. LangFlow's real strength is prototyping and iteration speed for anyone already comfortable with LangChain's component model, because every node in the canvas maps to an actual class you could otherwise instantiate in Python.

Setting Up LangFlow Locally

python -m venv langflow-env
source langflow-env/bin/activate
pip install langflow
langflow run

This starts a local server (default http://localhost:7860) with the visual canvas. You can also run it via Docker:

docker run -p 7860:7860 langflowai/langflow:latest

Building a RAG flow in LangFlow means dragging out and connecting these components yourself:

1. A File or URL loader component to bring in source documents
2. A Text Splitter component (recursive character splitter, token splitter, etc.) with chunk size and overlap fields
3. An Embedding Model component (OpenAI, HuggingFace, Ollama, etc.)
4. A Vector Store component (Chroma, Pgvector, Astra DB, and others are supported)
5. A Retriever node feeding into a Prompt node
6. A Chat Model node wired to the prompt
7. A Chat Output node to expose the result

Every one of those is a separate box on the canvas with its own configuration panel. This is more manual than Dify's "attach a knowledge base" toggle, but it also means you can see and change every step of the pipeline, swap components, or insert custom Python nodes mid-flow.

Once a flow works, LangFlow exposes it as an API too:

curl -X POST 'http://localhost:7860/api/v1/run/<flow-id>' \
  -H 'Content-Type: application/json' \
  -d '{
    "input_value": "Summarize the attached contract in three bullet points",
    "output_type": "chat",
    "input_type": "chat"
  }'

You can also export the flow as JSON and load it programmatically with the langflow Python package, or, since LangFlow flows map onto LangChain/LangGraph objects, pull the underlying logic into a standalone Python script if you want to leave the visual tool behind entirely.

The Core Difference: Platform vs Canvas

This is the part people miss when they compare the two on feature lists alone.

Dify is opinionated about what an "app" looks like. It ships with conversation memory, user session handling, a moderation layer, annotation/feedback collection on responses, and a plugin marketplace for tools (web search, code execution, image generation) that plug straight into an app without you wiring anything. If your use case fits one of Dify's app types (chatbot, agent, workflow, text generator), you get a huge amount of infrastructure for free.

LangFlow is unopinionated about the end product. It doesn't assume you want a chatbot; it assumes you want to build a graph of LLM operations and decide for yourself how it's consumed. That's why LangFlow feels more natural to teams who are going to eventually take the flow logic and rebuild it in code, whether in LangChain, LangGraph, or a custom orchestrator. LangFlow is a design surface more than it is a hosting platform.

A concrete way to feel the difference: try building a multi-agent system where one agent does research and hands off to a second agent that writes a report.

In Dify, you'd use its Workflow app type with an "Agent" node type, or chain two agent nodes with conditional routing, staying inside Dify's node vocabulary and letting Dify manage state between steps.

In LangFlow, you'd likely reach for its agent components built on LangGraph primitives, wiring explicit state objects and handoff logic yourself, node by node, with full visibility into every message passed between agents.

Neither is "better" here. Dify gets you to a working handoff faster. LangFlow gets you a handoff whose internals you fully understand and can port to raw code without translation.

RAG Pipelines: Built-in vs Assembled

Since retrieval-augmented generation is the most common thing people build with either tool, it's worth comparing the RAG experience directly.

Dify's knowledge base is a managed feature. You upload documents, pick a chunking strategy from a dropdown, choose an embedding model, and Dify handles indexing, storage, and retrieval settings (top-k, score threshold, reranking) through a settings panel. Multiple apps can share the same knowledge base. There's also a hybrid search option (keyword plus vector) exposed as a toggle, not something you have to assemble.

# Dify: RAG is a knowledge-base attachment, not a pipeline you build
# 1. Knowledge > Create Knowledge Base > upload docs
# 2. Studio > New App > Chatbot > Context > attach knowledge base
# 3. Done — retrieval happens automatically on every query

LangFlow's RAG is a pipeline you assemble from primitives: loader, splitter, embedder, vector store, retriever. This is more work, but it's also more flexible. If you need a custom chunking function, a reranking step from a specific provider, or a retriever that filters by metadata before scoring, you add or swap a node rather than hoping a settings panel exposes that knob.

# LangFlow: RAG is a graph you wire yourself
# Loader -> Splitter -> Embedder -> VectorStore -> Retriever -> Prompt -> ChatModel -> Output
# Every arrow is a connection you draw and every box has its own config

If your RAG needs are standard (chunk a PDF, embed it, retrieve top-k, answer), Dify gets you there with almost no configuration. If your RAG needs are non-standard (custom preprocessing, multiple retrievers merged, a reranker from a provider Dify doesn't support out of the box), LangFlow's assembled approach has more headroom.

Deployment and Ownership

Dify is designed to be the thing running in production. Its Docker Compose stack includes everything needed to serve traffic: API gateway, worker queue, vector database, object storage for uploaded files. When you publish an app, that app is live at a stable API endpoint immediately. Dify Cloud exists if you don't want to self-host at all.

LangFlow is designed to be the thing you use to author a flow, which then either runs inside LangFlow's own lightweight server or gets exported and embedded into your own application. Teams that already have a backend (a FastAPI service, a Node server) often use LangFlow purely as a design tool: build and test the flow visually, then call it from their existing service via the LangFlow API, or extract the flow definition and reconstruct it with LangChain/LangGraph code checked into their own repo.

This matters for who should own the running system long-term. If your team doesn't want to maintain LLM orchestration code and is fine with Dify's app shapes, let Dify own runtime and ops. If your team wants LLM logic to live in your own codebase, with your own tests, your own CI, and your own deploy pipeline, LangFlow's export path (or hand-porting the flow to code) fits better because you're not depending on a third platform to serve production traffic.

Extensibility and Custom Code

Both tools let you drop into custom code, but the shape differs.

Dify has a plugin system: you write a plugin (Python, following Dify's plugin SDK) that shows up as a new node type or tool inside the app builder. This is the right move when you want a reusable capability across many apps, like a custom API integration or a specialized retrieval method, but it's a more structured process than adding a script.

# Dify plugin skeleton (simplified)
from dify_plugin import Plugin, Tool

class MyCustomTool(Tool):
    def _invoke(self, tool_parameters: dict):
        # your logic here
        query = tool_parameters.get("query")
        result = call_internal_api(query)
        return self.create_text_message(result)

LangFlow lets you write a Custom Component directly on the canvas: a Python class with inputs and outputs that becomes a node you can drag anywhere, no packaging or plugin registration step required for local use.

from langflow.custom import Component
from langflow.io import MessageTextInput, Output
from langflow.schema import Data

class MyCustomComponent(Component):
    display_name = "My Custom Node"
    inputs = [MessageTextInput(name="query", display_name="Query")]
    outputs = [Output(display_name="Result", name="result", method="run")]

    def run(self) -> Data:
        query = self.query
        return Data(value=call_internal_api(query))

For fast, one-off custom logic, LangFlow's inline component editing is quicker to iterate on. For a capability you want reused and versioned across a team's many apps, Dify's plugin model is more structured and closer to a real package.

When to Pick Dify

Pick Dify if:

• You need a working chatbot, agent, or internal tool live behind an API this week
• Non-engineers on your team (support, ops, content) need to tweak prompts or knowledge bases without touching code
• You want built-in user management, conversation logs, and feedback collection without building them
• Your RAG needs are close to standard (document upload, chunk, embed, retrieve)
• You're fine with Dify being the long-term runtime, not just a design tool

When to Pick LangFlow

Pick LangFlow if:

• You (or your team) already think in LangChain/LangGraph terms and want a visual layer over that
• You want to prototype an agent architecture visually, then port the logic into your own codebase
• Your pipeline has non-standard steps: custom retrievers, multi-step preprocessing, unusual tool chains
• You want full visibility into every step of a flow rather than a managed feature behind a settings panel
• Your backend already exists and you just need LangFlow as a design and testing surface, not a production host

Running Them Side by Side

Nothing stops you from using both. A common pattern: prototype agent logic in LangFlow because the component-level visibility makes debugging chain-of-thought issues easier, then once the shape is right, either call the LangFlow-hosted flow from a lightweight wrapper, or rebuild the finalized logic as a Dify workflow so non-engineers can maintain prompts and knowledge bases going forward without needing access to the LangFlow canvas or the underlying code.

If you're evaluating both today, the fastest gut-check is to build the exact same small RAG chatbot in each: upload three documents, ask five questions against them, and time how long it takes you to get a working API endpoint. Dify will almost always win on setup speed. Then ask a harder question: swap the retriever for a custom reranking step. LangFlow will almost always win on flexibility. That trade-off is the whole comparison in miniature.

FAQ

Is Dify open source? Yes, Dify's core is open source and self-hostable via Docker Compose. There's also a hosted cloud version if you don't want to run the infrastructure yourself.

Is LangFlow open source? Yes, LangFlow is open source and installable via pip or Docker. It's maintained under the broader LangChain ecosystem.

Can I export a LangFlow flow to plain Python code? LangFlow flows map to LangChain and LangGraph components, so you can reconstruct the equivalent logic in a standalone Python script using those same underlying classes. LangFlow doesn't do a one-click "export to .py" for every flow shape, but because every node corresponds to a real class, porting the logic out is a direct translation rather than a rewrite.

Can Dify apps be embedded in an existing website? Yes. Published Dify apps include an embeddable chat widget snippet and a REST API, so you can either drop in the widget or call the API from your own frontend.

Do both tools support local/self-hosted LLMs? Yes. Both support OpenAI-compatible endpoints, which covers most local model servers (Ollama and similar), alongside hosted providers like Anthropic and OpenAI.

Which one is easier for a non-technical team member to use? Dify, generally. Its app-centric UI (prompt editor, knowledge base uploader, settings panels) is built for people who won't touch code. LangFlow's canvas is still visual, but understanding what a "Text Splitter" or "Retriever" node does assumes some familiarity with how LLM pipelines work.

Can I run RAG with reranking in both tools? Dify exposes reranking as a configuration option in the knowledge base retrieval settings if your chosen model provider supports it. LangFlow requires you to add a reranker as an explicit node in the flow, which gives you more control over which reranking provider and parameters you use.

Do I have to choose one permanently? No. Many teams prototype in LangFlow for the visibility into each step, then formalize the finished design as a Dify app for day-to-day operation by non-engineers. Treat the choice as "which tool for this stage of the project," not a permanent platform bet.