Dify vs Langflow

Last reviewed Aug 26, 2026 · 636 words

On stars this is a coin flip — 153,524 for Dify, 153,686 for Langflow — and both pitch a drag-and-drop canvas for building LLM apps, so people treat them as interchangeable. They are not. Dify is an application platform: accounts, knowledge bases, logs, a place your finished app lives. Langflow is a flow editor: a canvas where you wire components, then call the result from your own code. Choose by where the application will actually live, not by the screenshots, which look nearly identical.

What "platform" buys you with Dify

Dify (Apache-2.0, started 2023) ships the whole operational wrapper: a built-in RAG engine with dataset management, agent and tool support, prompt versioning, per-request logs and annotations, and multi-user workspaces with roles. You publish an app and hand a colleague a URL; nobody writes glue code. That wrapper is also why the catalogue lists it at 4 GB minimum RAM and "heavy multi-container stack" — the standard compose file runs an API service, a worker, the web frontend, Postgres, Redis, a vector store, and a code sandbox. Budget real disk, a backup plan, and note that some enterprise features (SSO and friends) sit behind a paid tier even though the core is open.

What "editor" buys you with Langflow

Langflow (MIT, Python) starts as one container and 2 GB of RAM. Every node on the canvas is Python you can open, edit, or replace with a custom component class, which makes it the better tool when the LLM logic is a piece of a larger codebase rather than the product itself. Flows export as JSON and are exposed as REST endpoints, so the typical pattern is: sketch on the canvas, then call the flow from your app. The catalogue's warning about frequent breaking changes is earned — pin your version and read release notes before upgrading, because flows built on one release do not always load cleanly on the next.

Two caveats before you expose either one

Both tools execute code and hold API keys, so neither belongs on the open internet. Langflow deserves extra caution: a 2025 remote-code-execution flaw in an unauthenticated code-validation endpoint (CVE-2025-3248) was exploited in the wild before most installs patched. Treat it as what it is — a service that runs arbitrary Python — and keep it behind a VPN or an authenticating proxy. Dify's larger stack fails more gracefully but gives you 6-plus containers to patch instead of 1.

Observability is built into one and bolted onto the other

Dify records every request, token count, and latency figure in its own UI. With Langflow you assemble that yourself, typically by pointing traces at Langfuse or a similar tracing stack. If you're accountable for an LLM feature in production, that difference is worth more than any canvas feature; the rest of the tooling around it is covered in the self-hosted AI stack writeup.

Decision table

What I'd do

Prototype in Langflow when the deliverable is code; deploy Dify when the deliverable is an app with users. If I had to keep exactly one on a team server, I'd keep Dify — the RAG and observability layers are the parts you'd otherwise rebuild badly — and I'd give Flowise a look first if the JavaScript ecosystem is home. Solo Python developers should default the other way: Langflow, pinned to a known-good version.