Dify vs Flowise vs Langflow: which low-code LLM app builder should you use?

What are Dify, Flowise and Langflow?

All three let you build LLM applications by connecting blocks on a visual canvas: models, prompts, retrievers, tools and logic. They turn the result into a chat app or an API without writing a full backend.

Dify is the most platform-like. Beyond a workflow canvas, it includes a knowledge base for RAG, agent apps, model provider management, logs and annotation, and publishing of apps as web interfaces or APIs.

Flowise is a Node.js and TypeScript builder with roots in LangChain.js. It offers chatflows and agent flows, an embeddable chat widget and an API for each flow.

Langflow is a Python-based visual builder developed with backing from DataStax. Each component is Python code you can open and edit, and flows can be served as APIs or exported as JSON.

How do they compare side by side?

Why teams choose Dify

Dify covers the full lifecycle of an internal AI app. You connect model providers once, build a knowledge base from documents, design a workflow or agent, publish it and then review logs and user feedback, all in one interface.

That makes it attractive for companies where a small platform team supports many business users. Workspaces, roles and a consistent way to publish apps reduce the chaos of every team building its own bot.

Its knowledge base deserves attention on its own. You can tune chunking, choose embedding models, enable hybrid retrieval and reranking, and inspect which chunks were retrieved for a question, which makes debugging RAG much less of a guessing game.

The trade-offs are weight and licence. A self-hosted deployment runs several containers, including a database, cache, vector store and sandbox. The licence adds conditions to Apache 2.0, for example around multi-tenant SaaS use and branding, so read it before building a commercial service on it.

Why teams choose Flowise

Flowise is quick to start: install it with npm or run a single container, open the canvas and build a working chatbot in minutes. Its footprint is small enough for a modest VPS.

Each flow gets an API endpoint and an embeddable widget, which suits agencies and small businesses adding an AI assistant to a website or connecting a flow to an automation tool.

It offers less built-in governance than Dify, so larger teams may need to add their own processes for evaluation, prompt changes and access control.

Custom tools and nodes are written in JavaScript, so a team comfortable with Node can wrap internal APIs and reuse them across flows. Its agent flows support supervisor and worker patterns for simple multi-agent setups.

Flowise’s lineage in LangChain.js means many concepts, such as chains, retrievers and memory, map directly onto the framework, which makes it easier to move a flow into code later if you outgrow the canvas.

Why teams choose Langflow

Langflow appeals to Python developers who like visual prototyping but hate black boxes. You can open any component, read its code and change it, or write a new one as a Python class that appears on the canvas.

Flows can be run as API endpoints, and the project has added support for exposing flows as MCP tools, which connects visual flows to agent clients. Its permissive MIT licence keeps commercial options open.

Complex logic can become hard to maintain visually. Many teams prototype in Langflow, then move the stable parts into regular code when the flow grows large.

Because flows export as JSON and components are Python, Langflow fits reasonably well into a Git-based workflow. Keeping exported flows in the repository next to custom components makes reviews and rollbacks possible.

How much does it cost to run?

All three are free to self-host within their licence terms, and each has a managed cloud offering. Costs come from model tokens, embeddings, vector storage and the server.

Flowise and Langflow can run on a small server for a pilot. Dify’s multi-service stack needs more memory, but it replaces several tools you might otherwise run separately, such as a knowledge base and a logging dashboard.

Maintenance is the hidden line item. All three release often, and upgrades occasionally change node behaviour, so test flows after each upgrade.

Watch token usage per flow from the start. Visual builders make it easy to add an extra model call for classification or rewriting, and each one multiplies cost across every conversation.

Which should you choose?

• Company-wide internal AI apps with knowledge bases, publishing and logs: Dify.
• A chatbot or assistant embedded on a website, on a small server: Flowise.
• Python developers who want visual prototyping with editable code: Langflow.
• Building a commercial multi-tenant product on top: Langflow’s MIT licence is the least restrictive; review Dify’s and Flowise’s terms carefully.
• Workflow automation across business apps rather than AI apps: consider n8n instead.
• Complex agents that need strict control flow: code with a framework such as LangGraph may serve better.

How to choose in five steps

Step four usually decides it. Differences in retrieval settings and default prompts show up immediately when real documents and real questions go in, and they matter more than canvas aesthetics.

Keep the losing prototype for a few weeks. If the chosen tool hits a wall, you will already know what the alternative looks like with your data.

• Decide who builds: developers, a platform team, or business users.
• List the three apps you want in the first quarter and their data sources.
• Check licence terms against how you will use and distribute the result.
• Build the same RAG app in two tools with your real documents and compare answer quality.
• Test the path to production: authentication, API access, logging and upgrades.

Where low-code LLM builders break

RepoLoot’s catalog tags low-code AI builders and the projects built on them by licence and difficulty, which helps when you want working examples before choosing.

• Version control: visual flows stored in a database are harder to review than code; export flows and keep them in Git.
• Testing: few teams build evaluation sets for flows, so regressions go unnoticed.
• Secrets: API keys in shared canvases need proper credential management and access control.
• Scale: a flow that works for ten users may need queueing and worker setup for hundreds.
• Lock-in: flows do not transfer between these tools, so keep prompts and business logic documented.

Frequently asked questions

Is Dify open source?

Dify publishes its source under a licence based on Apache 2.0 with additional conditions, such as limits on certain multi-tenant SaaS uses and requirements around branding. You can self-host it, but read the licence file carefully before offering it commercially to customers.

Flowise or Langflow for a developer?

Pick by language. JavaScript and TypeScript teams will find Flowise’s custom nodes natural, while Python teams will prefer Langflow, where every component is editable Python. Both expose flows as APIs, so the rest of your stack can be anything.

Can these tools run with local models?

Yes. All three can connect to local models, commonly through Ollama or OpenAI-compatible servers, alongside commercial APIs. Local models keep data on your hardware, but check that the model you choose supports the tool calling or structured output your flow needs.

Are low-code LLM builders production-ready?

They run production workloads for many teams, especially internal tools. Treat them like any service: authentication, backups, monitoring, pinned versions and evaluation sets. For complex, business-critical logic, many teams move stable flows into code over time.