Table of Contents
Dify vs LangFlow vs Flowise: which OSS LLM-app builder fits your stack?
Drafted May 6, 2026 by Pondero Editorial.
The deciding axis here is not visual-builder polish. It is the license clause that decides whether you can put the tool in front of paying customers. On that axis LangFlow wins outright: it is plain MIT, the only one of the three you can embed, fork, or resell without a vendor conversation. Dify's LICENSE forbids running it as a multi-tenant SaaS without written authorization. Flowise is Apache-2.0 for the core but ships SSO, identity, and workspace governance under a separate commercial license. Pick on UX and you will discover the license problem in legal review, after you have built on it.
That ordering also flips on use case, and the flip is sharp: if your first deliverable is a chat-with-our-docs product and the deployment is single-tenant or internal, Dify's first-class Knowledge Base saves enough build time to absorb its license narrowness. Below: the architecture that produces these differences, the exact LICENSE clauses, and the five team profiles where the call changes.
These are visual, low-code tools. A small team assembles retrieval, agent, and tool-calling pipelines without writing a full application from scratch. They sit a layer above raw frameworks like LangChain or LangGraph, and a layer below hosted no-code agent platforms like Zapier Agents. If your team needs programmatic, code-first orchestration with full state machines and custom recovery logic, a framework like LangGraph is the better starting point. These three are about getting a usable LLM application in front of users with less code.
For neighbouring categories, see our Make vs n8n comparison for general workflow automation and the best AI automation tools roundup for the broader ops landscape.
Three-way feature scorecard
| Dimension | Dify | LangFlow | Flowise |
|---|---|---|---|
| Primary language | Python + TypeScript | Python | TypeScript / Node.js |
| Visual builder UX | Polished, opinionated | Drag-and-drop graph | Drag-and-drop graph |
| RAG / Knowledge Base | First-class, built in | Component-based | Component-based |
| Agent + tool calling | Yes (built-in agent) | Yes (LangChain components) | Yes (LangChain JS components) |
| Hosted option | Dify Cloud (Sandbox / Pro / Team) | DataStax-hosted offering | Flowise Cloud + self-host |
| Self-host | Docker Compose, K8s | Docker, Python install | npm (npx flowise start), Docker |
| License | Modified Apache-2.0 with restrictions | MIT | Apache-2.0 + commercial enterprise modules |
| Built on | Custom Python core | Visual layer over a component graph | LangChain JS components |
| Best fit | Product teams shipping a full LLM app | Python teams who like LangChain ergonomics | JS / Node teams or LangChain JS users |
Dify, up close
Dify is the most product-like of the three because it is not built on a component-graph engine. The console exposes Apps, Knowledge Bases, Tools, and Workflows as first-class database objects, not as nodes you wire by hand. That architecture is why the bundled chat and completion templates feel finished: the retrieval pipeline is a managed service inside Dify, not a graph you assemble. The default self-host stack ships a Postgres, Redis, and Weaviate Compose file plus an admin console at http://localhost/console, with API keys and per-app token controls baked in. That same product-not-toolkit architecture is what the multi-tenant license clause exists to protect: Dify is shaped to be resold, so the vendor reserves the right to be the one reselling it.
The Knowledge Base is where that architecture pays off. Ingestion, chunking, retrieval scoring, and citation-aware querying are managed concepts in the UI and in the Knowledge Base API (docs.dify.ai/guides/knowledge-base). In LangFlow and Flowise you assemble the same pipeline from a loader node, a splitter node, an embedder, and a vector-store node, and you own the wiring when chunking needs tuning. Dify owns it. For a chat-with-our-docs deliverable that is the shortest path of the three, and it is the use case that justifies accepting the license narrowness.
Dify Cloud's headline pricing tiers as of May 2026 are Sandbox (free, 200 message credits, 1 team member, up to 5 apps), Professional ($59 per workspace per month, 5,000 credits, 3 members, up to 50 apps), and Team ($159 per workspace per month, 10,000 credits, 50 members, up to 200 apps), with annual billing offering roughly a 17% discount per the published pricing page (dify.ai/pricing).
Where Dify stops being the right answer: if your team wants to host Dify and resell it as a multi-tenant SaaS to your own customers, the license forbids that without explicit authorization (see License posture, below). If your differentiation depends on a custom UI that does not show Dify branding, you also need a commercial conversation with LangGenius first.
git clone https://github.com/langgenius/dify
cd dify/docker
cp .env.example .env
# OPENAI_API_KEY=<OPENAI_API_KEY> at minimum
docker compose up -d
# Console available at http://localhostcurl -X POST 'http://localhost/v1/datasets/<DATASET_ID>/retrieve' \
-H 'Authorization: Bearer <API_KEY>' \
-H 'Content-Type: application/json' \
-d '{"query": "what are the banned phrases in our style guide?", "retrieval_model": {"search_method": "hybrid_search", "top_k": 4}}'These shapes match the public Dify docs as of 2026-05-06. Confirm the .env keys against your install before you run them.
LangFlow, up close
LangFlow is the most Python-native of the three. The official docs call it "an open-source, Python-based, customizable framework for building AI applications" with first-class agent and Model Context Protocol (MCP) support (docs.langflow.org). DataStax backs it now and markets a hosted Langflow offering alongside the OSS project. Already on DataStax or Astra DB? That integration is the cleanest hosted path you have.
The builder is a node graph. Each node is a typed component: LLM, prompt, retriever, tool, custom Python. Flows export and import as JSON. That is the feature that makes LangFlow attractive to teams that want a visual layer for prototyping but a code-first deployment story afterward.
Where LangFlow stops being the right answer: if no one on your team writes Python, the friction of installing a Python runtime and managing virtualenvs is real, and Flowise will feel more natural. If your application is a packaged product where the visual builder is a feature for end users, LangFlow's UX leans more "developer tool" than "embedded product surface."
{
"description": "LangFlow 3-node agent: ChatInput -> Agent -> ChatOutput",
"data": {
"nodes": [
{"id": "ChatInput-1", "type": "ChatInput", "data": {"display_name": "Chat Input"}},
{"id": "Agent-1", "type": "Agent", "data": {"display_name": "Agent", "tools": ["WebSearch"], "model": "anthropic/claude-sonnet-4"}},
{"id": "ChatOutput-1", "type": "ChatOutput", "data": {"display_name": "Chat Output"}}
],
"edges": [
{"source": "ChatInput-1", "target": "Agent-1"},
{"source": "Agent-1", "target": "ChatOutput-1"}
]
}
}This is an abridged shape that matches the structure of LangFlow flow exports. It will not import as-is; treat it as a sketch of what the JSON looks like, not a working file.
Flowise, up close
Flowise is the most JavaScript-native of the three. The repo is a TypeScript monorepo: Node server, React UI, components package. The docs list npm install -g flowise && npx flowise start as the headline install path (docs.flowiseai.com/getting-started). The components draw heavily from the LangChain JS ecosystem, so a team already building with LangChain in TypeScript carries the mental model straight over.
Two strengths stand out. Smallest install footprint of the three, and a pragmatic component library covering the common shapes (chains, agents, retrievers, vector stores, document loaders) with no Python runtime. Deployment to a single Node host is straightforward. The Docker Compose path is a one-config bring-up.
Where Flowise stops being the right answer: if your stack is Python-only, you take on a Node runtime to use Flowise. If you need the enterprise governance modules (single sign-on, identity manager, workspace multi-tenancy), those modules are under a separate commercial license, which means "Flowise is Apache-2.0" is true in spirit but not for every code path you would want in a regulated deployment.
# Option A: npm
npm install -g flowise
npx flowise start
# UI at http://localhost:3000
# Option B: Docker Compose
cd docker
cp .env.example .env
docker compose up -dThese match the public Flowise docs as of 2026-05-06. Both paths are the documented headline installs.
License + commercial-use posture
Read this before any deployment decision. We pulled each LICENSE file straight from the repos.
Dify (langgenius/dify). GitHub reports the license as NOASSERTION because the LICENSE file is a Modified Apache-2.0, not an OSI-recognized identifier, so GitHub's license detector refuses to classify it. The two added restrictions are what matter. One: you may not use the Dify source to operate a multi-tenant environment without written authorization from LangGenius, where the license defines one tenant as one workspace with separated data and configurations. The clause is keyed to the workspace boundary specifically because that boundary is Dify's resale unit. Two: you may not remove or modify the Dify logo or copyright information in the console or applications. Standard Apache-2.0 otherwise. Hosting Dify for internal use, or one workspace per customer where you are not selling the platform itself, is fine. Hosting one Dify instance that serves many billing customers from separated workspaces is the exact pattern the clause forbids without a commercial deal.
LangFlow (langflow-ai/langflow). Standard MIT, copyright Langflow. The whole codebase, not a core with carve-outs. There is no branding clause, no multi-tenant clause, no enterprise directory under a second license. This is why it is the only one of the three an agency can fork, white-label, and resell without a vendor conversation.
Flowise (FlowiseAI/Flowise). Dual-licensed by directory, not by feature flag. Apache-2.0 covers most paths; /packages/server/src/enterprise (including IdentityManager.ts) is under a separate commercial license, and third-party components keep their own licenses. The split is mechanically important: SSO, identity management, and multi-workspace governance are not gated behind a runtime check you could legally toggle, they are source files you do not have an open license to run in production at all. "Flowise is Apache-2.0" is true for the core and false for exactly the governance code a regulated deployment needs.
Need a clean OSS posture for procurement or open-core resale? LangFlow is the least-friction choice on license terms. Dify and Flowise are both fine for most internal deployments. The multi-tenant clause on Dify and the enterprise-module split on Flowise are real, and they belong in any procurement memo.
Decision matrix: 5 typical team profiles
| Team profile | Picks | Why |
|---|---|---|
| Product team, "chat with our docs" is the goal | Dify | First-class Knowledge Base + finished console |
| Python team already on LangChain / LangGraph | LangFlow | Native Python components, JSON-exportable flows |
| TypeScript / Node team, LangChain JS users | Flowise | Same component model in their language |
| Agency reselling LLM apps to many clients | LangFlow (or Dify w/ commercial deal) | Dify multi-tenant clause forbids OSS-resale |
| Regulated enterprise needing SSO + audit | None of the OSS cores alone | Plan for Dify Team / Flowise enterprise modules / DataStax-hosted Langflow |
The procurement question that actually decides this is not "which UX" but "what is the deployment shape." This is the order to ask it in.
1. Do you resell access to many billing customers from one hosted instance?
yes -> Dify is out (multi-tenant clause). LangFlow (MIT) or a Dify commercial deal.
no -> continue.
2. Do you need to remove vendor branding from the shipped UI?
yes -> Dify is out (branding clause). LangFlow, or Flowise core.
no -> continue.
3. Do you need SSO / identity / multi-workspace governance in production?
yes -> Flowise core is out for that path (enterprise dir is commercial-licensed).
Plan for Dify Team, Flowise Enterprise, or DataStax-hosted Langflow.
no -> continue.
4. Is the first deliverable chat-with-our-docs, single-tenant or internal?
yes -> Dify. The managed Knowledge Base is the shortest path and the
license restrictions above do not bind this shape.
no -> match language: Python team -> LangFlow; Node/TS team -> Flowise core.FAQ
Is Dify open source? Dify's LICENSE is a Modified Apache-2.0 with multi-tenant and branding restrictions. GitHub displays the license as NOASSERTION. It is open source for most internal uses; it is not OSI-approved and is not usable as-is to build a competing multi-tenant SaaS.
Is LangFlow built on LangChain? Not anymore in the way the name suggests. Early LangFlow was a visual layer over LangChain components. The current docs position it as a standalone Python framework with native agent and MCP support that works with any LLM or vector store, under DataStax backing. The "LangFlow is just a LangChain UI" claim is out of date; the dependency is no longer load-bearing the way it was pre-DataStax.
Is Flowise built on LangChain JS? The Flowise components ecosystem draws from LangChain JS, and the docs reference LangChain in community guides. If you already work in LangChain JS, the mental model maps directly.
Which one has the best RAG support? Dify, by a margin, if "best" means "least-effort path from documents to a working retrieval-aware app." LangFlow and Flowise both support RAG via components, but Dify's Knowledge Base is a first-class object, not a component you wire up.
Can we self-host all three on a single VM? Yes for development. Each ships a Docker Compose path, and Flowise also runs as a single Node process. Production is different. Plan for a managed Postgres, a vector store sized to your corpus, and an LLM provider quota. The three Compose files are not a production checklist.
The call, and when it flips
Default to LangFlow. It is the only one of the three that imposes no license conversation on resale, embedding, branding, or forking, and the MIT terms hold for the whole codebase rather than a core with carve-outs. That single property dominates for any team that is not certain its deployment will stay internal forever.
The call flips to Dify when the first deliverable is chat-with-our-docs and the deployment is single-tenant or internal. The managed Knowledge Base removes the loader/splitter/embedder/vector-store wiring you own in the other two, and Dify's resale and branding clauses do not bind a shape that never resells the platform. The call flips to Flowise when the team is Node-only and the install footprint matters, accepting that the enterprise governance directory is commercial-licensed if SSO ever becomes a requirement.
None of the three OSS cores alone clears a regulated SSO-plus-audit bar. That is not a tie, it is a different purchase: Dify Team, Flowise Enterprise, or DataStax-hosted Langflow, priced and contracted separately.
For the adjacent hosted-platform decisions, see our best automation tools for ops leads round-up, and our n8n self-hosted vs Cloud calculus for how we weigh hosted against self-managed in this category.
Sources: Dify repo and LICENSE (github.com/langgenius/dify); Dify docs (docs.dify.ai); Dify pricing (dify.ai/pricing); LangFlow repo and LICENSE (github.com/langflow-ai/langflow); LangFlow docs (docs.langflow.org); Flowise repo and LICENSE.md (github.com/FlowiseAI/Flowise); Flowise docs (docs.flowiseai.com). Repo star counts and freshness windows referenced from Pondero internal research HIL-390 (research date 2026-05-02). Star counts are point-in-time and not restated as a fixed claim in the body.