Table of Contents
Dify vs LangFlow vs Flowise: which OSS LLM-app builder fits your stack?
Drafted May 6, 2026 by Pondero Editorial.
How we built this comparison (read this first)
We have not run a 30-day production pilot on each of these three. This comparison is built from a vendor-documentation rubric and a careful read of each project’s LICENSE file. Where we describe how a tool behaves, we are paraphrasing official docs and the project repos as of early May 2026. Where we describe a code path or example, treat it as illustrative unless we explicitly mark it Tested. We will revisit this guide after a sandbox pilot of Dify (the lowest-cost install of the three) and update sections accordingly.
If you came here looking for a “we shipped 5 production agents on each platform” verdict, this is not that piece. We would rather be honest about scope than fabricate a pilot.
What “OSS LLM-app builder” actually means (and what it isn’t)
The category covers visual, low-code tools that let a small team assemble retrieval, agent, and tool-calling pipelines without writing a full application from scratch. They are a layer above raw frameworks like LangChain or LangGraph and a layer below hosted no-code agent platforms like Zapier Agents.
Two clarifications worth getting right up front:
- “OSS” is not a single license. Of the three, only LangFlow ships under a textbook permissive license (MIT). Dify and Flowise both have caveats that matter if you plan to host the tool for paying customers. We cover this in detail in the License posture section below.
- “App builder” is not the same as “agent framework.” If your team needs programmatic, code-first orchestration with full state machines and custom recovery logic, a framework like LangGraph is a better starting point. The three tools here are about getting a usable LLM application in front of users with less code, not about replacing a framework.
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 |
The scorecard is a starting point, not a winner. Read the deep-dives for each tool before mapping a profile to a column.
Dify deep-dive
Dify is the most “product-like” of the three. The console gives you Apps, Knowledge Bases, Tools, and Workflows as top-level objects, and the bundled chat / completion app templates feel like a finished product rather than a builder kit. The default self-host stack ships with a Postgres + Redis + Weaviate Compose file and an admin console at http://localhost/console, with API keys and per-app token controls baked in.
Where Dify earns its reputation is the Knowledge Base: ingestion, chunking, retrieval scoring, and citation-aware querying are first-class concepts in the UI and in the Knowledge Base API (docs.dify.ai/guides/knowledge-base). For a team whose first deliverable is “a chat-with-our-docs application,” this is the path of least resistance of the three.
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.
Illustrative example 1: Dify Docker self-host bring-up
# Illustrative, based on docs.dify.ai/getting-started/install-self-hosted/docker-compose
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://localhostIllustrative example 2: querying a Dify Knowledge Base via API
# Illustrative, based on docs.dify.ai/guides/knowledge-base; tokens redacted
curl -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}}'We have not executed these against a live Dify instance for this draft. The shapes match the public docs as of 2026-05-06.
LangFlow deep-dive
LangFlow is the most Python-native of the three. The official docs describe it as “an open-source, Python-based, customizable framework for building AI applications” with first-class agent and Model Context Protocol (MCP) support (docs.langflow.org). It is now backed by DataStax, which markets a hosted Langflow offering alongside the OSS project; if your team is already on DataStax / Astra DB, that integration is the cleanest hosted path.
The builder is a node graph where each node is a typed component (LLM, prompt, retriever, tool, custom Python). You can export and import flows as JSON, which is what makes LangFlow attractive to teams that want a visual layer for prototyping but a code-first deployment story afterwards.
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.”
Illustrative example 3: a 3-node LangFlow agent (exported JSON, abridged)
{
"description": "Illustrative 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 deep-dive
Flowise is the most JavaScript-native of the three. The repo is a TypeScript monorepo with a Node server, a React UI, and a components package; the docs list npm install -g flowise && npx flowise start as the headline install path (docs.flowiseai.com/getting-started). Internally, the components draw heavily from the LangChain JS ecosystem, so if your team already builds with LangChain in TypeScript, the mental model carries over.
Flowise’s strengths are the smallest install footprint of the three and a pragmatic component library that covers the common shapes (chains, agents, retrievers, vector stores, document loaders) without leaning on a Python runtime. Deployment to a single Node host is straightforward, and 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.
Illustrative example 4: Flowise install (textual)
# Illustrative, from docs.flowiseai.com/getting-started
# 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 -dWe have not executed this for the draft. The shapes match the public docs as of 2026-05-06.
License + commercial-use posture
This is the section you should read before any deployment decision. We pulled each LICENSE file directly from the repos.
Dify (langgenius/dify). GitHub reports the license as NOASSERTION. Reading the actual LICENSE file: it is a Modified Apache-2.0 with two practical restrictions. First, you may not use the Dify source to operate a multi-tenant environment without written authorization from LangGenius (one tenant equals one workspace with separated data and configurations). Second, you may not remove or modify the Dify logo or copyright information in the console or applications. Standard Apache-2.0 terms apply otherwise. The practical implication: hosting Dify for your own internal use, or for a single customer per workspace, is fine. Hosting Dify and reselling it as your own multi-tenant SaaS is not, without a commercial deal.
LangFlow (langflow-ai/langflow). Standard MIT license, copyright Langflow. Permissive use, modification, and distribution, subject to including the MIT notice. Of the three, this is the cleanest license for teams that want to redistribute, embed, or fork without a license conversation.
Flowise (FlowiseAI/Flowise). Dual-licensed. The Apache-2.0 license covers most content; specific paths under /packages/server/src/enterprise (including IdentityManager.ts) are under a separate commercial license, and third-party components retain their own licenses. The practical implication: you can self-host and deploy the OSS core without restriction, but the enterprise modules (the parts you would want for SSO, identity management, and multi-workspace governance) require a commercial agreement.
If your build relies on 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 the majority of internal deployments, but the multi-tenant clause (Dify) and the enterprise-module split (Flowise) are real and worth flagging 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 |
Illustrative example 5: decision matrix as CSV
team_profile,recommendation,rationale_short
"chat with our docs, product team",Dify,"first-class Knowledge Base + finished console"
"Python + LangChain shop",LangFlow,"native Python; JSON-exportable flows"
"TypeScript / Node team",Flowise,"smallest install; LangChain JS components"
"agency reselling to many clients",LangFlow,"MIT license; Dify multi-tenant clause forbids OSS-resale"
"regulated enterprise (SSO + audit)",none-of-the-OSS-cores,"plan for Dify Team / Flowise enterprise / Langflow hosted"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? The current LangFlow docs describe it as a Python-based framework that supports agents and MCP and works with any LLM or vector store. Earlier versions of the project leaned heavily on LangChain components; the current positioning emphasizes its independence and DataStax backing. Treat any “LangFlow is just a LangChain UI” claim as out of date until you confirm against the current docs.
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; Flowise also runs as a single Node process. For production, plan for a managed Postgres, a vector store sized to your corpus, and an LLM provider quota. The three Compose files do not replace a real production checklist.
Where this guide will change
We will update this guide after a Dify sandbox pilot, which the brief authors as the lowest-cost install of the three to actually run. When that happens, the Dify deep-dive section will switch from Illustrative framing to Tested with a date stamp, and we will add the real numbers (cold-start latency, retrieval recall on a known doc set, install time on a single VM). Until then, we would rather mark our scope honestly than imply a pilot we did not run.
If you are evaluating these tools for a specific procurement and need a deeper read, our best automation tools for ops leads round-up covers the adjacent hosted-platform decisions, and our n8n self-hosted vs Cloud calculus is a useful template for how we think about hosted vs self-managed trade-offs 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.