Capability · Comparison
Dify vs Langflow
Dify and Langflow both let you design LLM applications visually, but they lean in different directions. Dify is an end-to-end LLMOps platform — workflows, datasets, agents, eval, and logs as a single product. Langflow (from DataStax) is a drag-and-drop front end for LangChain/LangGraph, designed for developers who want visual composition with code-level escape hatches.
Side-by-side
| Criterion | Dify | Langflow |
|---|---|---|
| Core model | Opinionated LLMOps platform | Visual LangChain IDE |
| Primary user | Product + ops + non-dev | Developers |
| Code export | API + JSON workflow export | Python code export from canvas |
| Built-in eval | Yes | Limited — via LangSmith |
| Dataset management | First-class | Basic |
| Corporate backing | LangGenius | DataStax (Astra DB) |
| License | Dify Open Source License | MIT |
| RAG native | Yes, end-to-end | Compose via nodes + Astra DB integration |
Verdict
Pick Dify if your team needs an opinionated platform that covers RAG, agents, workflows, and eval with minimal plumbing — and if non-developers will use it. Pick Langflow if your team lives in LangChain, wants visual orchestration, and needs to export production Python code. Langflow is also the natural choice if you're already on DataStax Astra DB. For dev-heavy teams that want the full LangChain ecosystem and MIT licensing, Langflow; for ops-heavy teams that want everything-in-one, Dify.
When to choose each
Choose Dify if…
- You want datasets, RAG, agents, and eval in one opinionated product.
- Non-developers will create or edit flows.
- You value an integrated ops UX over flexibility.
- You want to ship fast with minimal integration work.
Choose Langflow if…
- Your team builds on LangChain / LangGraph directly.
- You want to export working Python code from the canvas.
- MIT licensing without brand restrictions matters.
- You're on DataStax Astra or want that integration.
Frequently asked questions
Can Langflow deploy to production?
Yes — either as a hosted service (DataStax Astra offers Langflow deployment) or via exported Python code that runs on your own infra.
Does Dify support LangChain?
Not natively — Dify uses its own abstractions. If your team is committed to LangChain primitives, Langflow is the more natural fit.
Which is better for RAG?
Dify for a turnkey RAG pipeline with dataset and chunking baked in. Langflow if you want full control over the retrieval graph and embed custom nodes.
Sources
- Dify — GitHub — accessed 2026-04-20
- Langflow — GitHub — accessed 2026-04-20