Dify vs Langflow 2026: The Two 150K-Star Giants Compared Honestly

Updated 2026-09-30 · 8 min read · ★★★★☆ 4.5/5 editorial · Best for: developers comparing the two leading open-source visual LLM app builders before committing

Dify and Langflow are the two giants of open-source LLM app building, and the numbers make the rivalry explicit: langgenius/dify at 157,580 stars vs langflow-ai/langflow at 155,390 — a gap of barely 2,000 stars, both pushed daily. Pick the wrong one and you will feel it for months: migrated workflows, retrained teams, re-architected infrastructure.

Here is the honest verdict up front: choose Dify if you are building a product or a team practice around LLM apps — you need the RAG pipeline, the agent framework, LLMOps, and the Cloud-to-self-hosted path. Choose Langflow if you are a developer who wants the best visual agent builder with the smallest footprint — MIT license, Python package install, no platform to learn beyond the canvas. They overlap heavily, but they are not the same tool, and this comparison shows exactly where they diverge.

This comparison is based on both projects' official documentation and GitHub READMEs (star counts verified September 30, 2026), plus public user feedback — not hands-on testing. We earn no commission from Dify, Langflow, or any open-source tool listed here. Nobody paid for these rankings. Links to our CustomGPT.ai review may lead to pages containing affiliate links, which are disclosed there. For the standalone assessments, see our Dify review and the wider field in Dify alternatives.

At a glance

1. Onboarding and ease of use: Langflow wins

Langflow's quickstart is uv pip install langflow -U followed by uv run langflow run — one Python package, and the builder opens at localhost:7860. There is also Langflow Desktop for Windows and macOS that bundles every dependency, so non-Python users can skip environment management entirely. The mental model is one thing: components on a canvas, wired into flows, tested in the playground.

Dify's quickstart is also Docker-simple (docker compose up -d), but what you land in is a platform: workspaces, datasets, prompt IDE, model provider configuration, app types (chatbot, agent, workflow, text generator). That richness is the product — and it is also a learning curve. A developer can be productive in Langflow in an afternoon; Dify takes days to understand as a system.

Winner: Langflow. Less to learn, faster to first working agent.

2. Workflow and agent capability: Dify wins on breadth, Langflow on focus

Both offer visual workflow building, but they emphasize different things. Dify's canvas sits inside a platform that also gives you versioned prompts, dataset management, annotation-driven improvement, and agents that run code in sandboxes and install their own tools. Langflow's canvas is the product: richer component-level control, Python access to customize any component's source, multi-agent orchestration with conversation management, and the ability to deploy any flow as an API endpoint or an MCP server.

For pure "build a sophisticated agent flow" work, Langflow's builder is arguably the sharper instrument. For "build agents inside a managed lifecycle with evaluation and iteration," Dify's surrounding platform wins. If your workflow needs are really about connecting SaaS services with AI sprinkled in, neither is the best answer — that is n8n's territory (see Dify alternatives).

Winner: Dify for agent programs at team scale; Langflow for individual developers building flows.

3. RAG capability: Dify wins clearly

This is the least close dimension. Dify ships a dedicated RAG pipeline: document ingestion, text extraction from PDFs and PPTs, chunking strategies, retrieval tuning, and citation-grounded answers — as a first-class module, not a bag of components. Langflow handles RAG through components in its flow library (document loaders, splitters, vector store nodes), which is flexible but means you assemble the pipeline yourself and own its quality.

If retrieval quality on real documents is your core risk, Dify's purpose-built pipeline is the safer bet — or go specialist with RAGFlow (covered in Dify alternatives).

Winner: Dify.

4. Self-hosting complexity: Langflow wins

Dify's Docker Compose stack is a multi-service deployment: API server, web frontend, worker queues, vector database, and more. The README's 2-core/4GB minimum gets the dashboard running; production needs real capacity planning, backups, and upgrade discipline across 170 releases of a fast-moving project.

Langflow is a Python application with a frontend. You can pip install it, run it in one Docker container, or use the Desktop app. There is less to break, less to update, less to monitor. For a solo developer or a small team without DevOps, the operational difference is decisive.

Winner: Langflow, by a wide margin for small teams.

5. Ecosystem and community: effectively tied, different shapes

By raw numbers they are twins: ~157k vs ~155k stars, both with 10k+ forks, active Discords, and daily development. The shapes differ. Dify's ecosystem includes a plugin marketplace, MCP server support, 30+ README translations signaling a huge international community, and commercial gravity from its Cloud and Enterprise tiers — companies paying for a product fund its development. Langflow's ecosystem leans developer: deep Python customizability, framework-agnostic API/MCP exports, and observability integrations with the LangSmith/LangFuse stack data teams already use.

One cautionary note for both: stars are a lagging indicator. The archived Flowise (54,890 stars, dead) proves that momentum matters more than totals — check commit recency and funding models, not just the star chart. Both Dify and Langflow pass that test today.

Winner: tie. Dify has commercial momentum; Langflow has developer-tooling momentum.

6. Business model and license: Langflow wins for the permissive-license crowd

This dimension has a clear, factual split. Langflow is MIT — do nearly anything, including commercial embedding, with minimal obligations. Dify uses the Dify Open Source License, described in its own README as "based on Apache 2.0 with additional conditions." For internal tools and SaaS built on Dify, this rarely bites; for redistributing Dify itself or embedding it in a product you sell, get the LICENSE file read by someone qualified.

On business model: Dify has an official hosted Cloud and an Enterprise tier (SSO, RBAC, SLAs), which means a funded team with revenue keeping the project alive — and a vendor whose roadmap you partially depend on. Langflow has no official hosted tier; you deploy it yourself wherever you want, which means total independence and total operational responsibility.

Winner: Langflow on license permissiveness; Dify on "someone is paid to keep this alive."

7. Who should choose which

Choose Dify if: - You are a team shipping LLM apps as products or internal platforms - RAG quality, prompt management, and LLMOps matter to your workflow - You want to prototype on Cloud and move to self-hosted without rebuilding - You need the marketplace, BaaS APIs, and multi-app management

Choose Langflow if: - You are a developer who wants the best visual agent builder, period - MIT licensing matters for how you will use or embed the software - You want minimum operational overhead (pip install and go) - Your flows need to become APIs or MCP servers inside a larger system

Choose neither if: you just need a chatbot on your docs with zero infrastructure — read our CustomGPT.ai review instead. Or if your problem is automation across SaaS tools, look at n8n in our Dify alternatives guide.

The bottom line

Dify is the platform; Langflow is the instrument. Platforms win when the work is ongoing, collaborative, and production-shaped — teams, evaluation loops, hosted-to-self-hosted migrations. Instruments win when a skilled individual needs maximum leverage with minimum ceremony. Both are excellent, both are free to self-host, and both will still be here next year — which is more than the late Flowise could say. Pick by your team shape and your tolerance for operating infrastructure, not by the star count.

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