Dify vs Langflow: App Platform or Flow Builder

Dify vs Langflow is a choice between two visual builders that both self-host and both want to be your open source LLM app builder. They disagree about what you're building.

• Dify is an application platform. Its documentation describes a platform for building agents, agentic workflows, and chatbots that draw on your own data, then publishing them as web apps or through APIs.
• Langflow is a developer-oriented flow builder. Its documentation calls it an open-source, Python-based, customizable framework, with a visual editor on top.

The question underneath: do you want a finished application surface out of the box, or a canvas that hands you flows and code to wire into your own stack? Everything below is checked against each vendor's own documentation and repository as of October 2026.

Quick comparison

What each one is for

Dify's premise is that the AI application is the product. When you publish an app, Dify's publishing docs say it creates a web app and an API endpoint from the latest configuration. You can also embed the web app as a chat widget or iframe, or expose the app as an MCP server for tools like Claude Desktop and Cursor. Monitoring is in the same product: the docs list a dashboard, logs, and an annotation system.

Langflow's premise is that the flow is the product, and you decide where it runs. Its introduction says the primary purpose is to create and serve flows, built by connecting component nodes on a canvas and tested in a Playground. From there you can call the Langflow API from your own code, or deploy a Langflow server to serve flows over the public internet.

That's why people compare them and then pick differently. Dify takes decisions off your plate. Langflow leaves them with you, along with the control that comes with them.

Dify vs Langflow on building

Dify gives you a vocabulary of app types. Per its key concepts page, the two main types are Workflow, for single-turn tasks, and Chatflow, which fires at every turn of a conversation and keeps conversation variables. The docs list Chatbot, Agent, and Text Generator as three more basic app types, which run on the same workflow engine with simpler interfaces. A workflow can start from a user input or from a trigger, on a schedule or in response to an external event. Apps export to a YAML file in Dify's own DSL, which makes them portable between Dify instances.

Langflow gives you components and the source behind them. Everything is a component node, and the README says you can customize any component using Python. That's the sharpest difference in practice. If a node doesn't do what you need, you open it and change it. In Dify the equivalent move is to write a plugin, or call your own API from a tool.

Langflow also changes how it ships. Its 1.12 release post says a plain install now includes only a curated set of providers, and opt-in providers are installed separately. That suits developers who want a small environment. It's one more thing to know if you expected everything to be in the box.

Knowledge and RAG

Both do retrieval over your own data. They differ in how much they manage for you. If the term is new, what is RAG covers the idea; this section is only about who handles which part.

Dify treats it as a feature area. Its Knowledge docs describe knowledge bases you create, manage, and test: you can view and edit documents and chunks, simulate user queries to test retrieval, add metadata for filtered search, and change the embedding model or retrieval strategy later. You can also connect an external knowledge base through an API.

Langflow treats it as components you wire. Its vector data docs describe embedding model components, vector store components that read and write to vector databases, and knowledge bases with built-in support for several embedding models. As of Langflow 1.12, most vector store bundles are not in the default installation, so you install the provider you want. That's more assembly and more freedom to choose the store.

If you want to manage document sets and test retrieval from a dashboard, Dify is nearer. If you want to choose and swap each part of the pipeline in a visual graph, Langflow is.

MCP and integrations

Both speak the Model Context Protocol, from different angles.

Langflow's MCP server docs say it works as both an MCP server and a client. As a server it exposes your flows as tools that MCP clients can call, over streamable HTTP with SSE as a fallback. As a client, flows can use tools from other MCP servers. If your goal is to turn flows into tools for another agent, this is a first-class path.

Dify can publish an app as an MCP server too, per its publishing docs. On the integration side, its integrations docs list model providers, tools, data sources, triggers, and agent strategies, installable from a marketplace, from GitHub, or from a local upload. The tool category includes MCP servers, published workflows, and OpenAPI services. Dify's integration model is a plugin catalogue you install into a workspace. Langflow's is components plus code.

Licence: not the same kind of open

This is where the two diverge and where people get it wrong.

Langflow's repository LICENSE is the MIT licence. Permissive, no extra conditions.

Dify's LICENSE is a modified Apache 2.0 with two added conditions. Dify's own docs describe it as open source, but the added conditions are real, so read them before you assume plain Apache 2.0:

1. Multi-tenant service. Unless Dify authorizes it in writing, you may not use its source code to operate a multi-tenant environment. The licence defines one tenant as one workspace.
2. Logo and copyright. If you use Dify's frontend, you may not remove or modify the logo or copyright information in the Dify console or applications. The licence says this doesn't apply to uses that don't involve its frontend, and defines the frontend as the web/ directory or the "web" Docker image.

For an internal team running one workspace and leaving the branding alone, that's rarely an obstacle. For an agency running separate workspaces for clients, or a company rebranding the console, it is. Dify's alternatives guide goes into the rest of the field.

Self-hosting and running it

Both can run on your own infrastructure, with different on-ramps.

Dify self-hosts with Docker Compose. Its deployment docs list a minimum of 2 CPU cores and 4 GiB of RAM, and Docker Compose 2.24.0 or later. Dify Cloud is the managed alternative.

Langflow gives you more routes. Its installation docs list Langflow Desktop (recommended), Docker, the Python package, and install from source. The README asks for Python 3.10 to 3.14 and recommends uv for the package. One catch: the docs say some features aren't available in Desktop, including the Shareable Playground and Voice Mode. Langflow's site also points to a hosted cloud account and says it's the same Langflow whether you run the open-source version or the cloud.

Self-hosting either one means you own upgrades, backups, and uptime. If nobody on your team will, compare the managed options instead.

Who owns Langflow

Check this before you commit, since it changes what "maintained" means. Langflow's own site and docs, as read in October 2026, don't say who owns it. The primary source is IBM. In February 2025, IBM announced it would acquire DataStax, which makes Langflow, and said it would continue to support the open-source Langflow community. The repository, release notes, and docs are the live evidence of whether that holds. Langflow's blog shows 1.12 released on September 1, 2026, with a desktop build later that month.

Dify is made by LangGenius, whose name appears in the licence, and its repository is under that organization.

The step before either

Both tools assume you'll design the thing: choose the retrieval approach, write the prompts, wire the nodes, and decide where it runs. They save you the runtime, not the design.

Sometimes the useful setup already exists. Someone shared an agent or workflow that does your job, and what's blocking you is running it, not designing it. Taku is built for that case. It's an AI-native desktop workspace where you mirror an AI setup someone already got working, run it, remix it into your own, and keep it. It isn't an app builder or a flow framework and doesn't compete with Dify or Langflow, which are stronger when you're building and serving something new. Taku is in Beta, and the Mac app is available now. The free app library shows what mirroring looks like.

Which to pick

1. Want a user-facing app and API without building either? Dify.
2. Managing knowledge bases and reviewing logs is the main work? Dify.
3. Python developers who want to edit components? Langflow.
4. Want your flows to act as tools for other agents over MCP? Langflow is the more direct path, though Dify can publish MCP servers too.
5. Running separate client workspaces or rebranding the console? Read Dify's licence conditions first; Langflow's MIT licence has none.
6. Moving off Flowise? See Flowise alternatives for how Langflow and Dify map onto it.

If both would work, build the same small flow in each. An afternoon will tell you more than any comparison.

FAQ

Is Langflow or Dify better?

Neither wins outright. Dify suits teams that want a platform that ships the app surface, knowledge management, and monitoring. Langflow suits developers who want a visual builder with Python underneath. Pick by what you want to own.

Is Dify open source?

Dify's docs call it open source, but its LICENSE is Apache 2.0 with two additional conditions: a multi-tenant restriction (one tenant is one workspace) and a requirement to keep the logo and copyright in its frontend. It isn't plain Apache 2.0.

Is Langflow open source?

Yes. The repository's LICENSE file is the MIT licence, and the docs describe Langflow as open source.

Langflow vs Dify: which is easier to self-host?

Both are manageable. Dify uses Docker Compose. Langflow offers Desktop, Docker, and a Python package. Some Langflow features, such as the Shareable Playground, aren't in Desktop.

Can I use both?

Yes, and the seam is an API. Each exposes what you build over an API, and each supports MCP, so one can call the other as a tool.

Which is better for a chatbot over my documents?

Dify gives you managed knowledge bases and a published web app in one place. Langflow can do the same job, but you assemble the vector store and the front end yourself.