dify vs langflow: a platform for teams versus a Python tool for developers

Two active builders with different centers of gravity

This comparison is between two healthy projects, not an active one and a dying one. Dify's last push was September 11, 2026 with a 1.17.0 release in August 2026, and Langflow's last push was also September 11, 2026 with v1.11.5 in August 2026. Both give you a visual canvas for AI workflows, RAG pipelines and agents, so the surface looks similar at a glance. The differences are underneath. Dify is a TypeScript platform: a self-contained deployment with a dashboard, model management, monitoring and a cloud offering, aimed at teams that want everything in one collaborative workspace. Langflow is a Python package: install it with uv, run it, and treat the flow as something you can also export or serve directly. One is a product to run; the other is a library with a visual front end. That distinction drives most of the comparisons below.

What each one puts on the canvas

Dify's README lists seven headline features: a workflow canvas, comprehensive model support across hundreds of proprietary and open-source LLMs from dozens of providers plus OpenAI API-compatible models, a prompt IDE, a RAG pipeline with out-of-box text extraction from PDFs, PPTs and other common formats, agent capabilities built on LLM function calling or ReAct with 50-plus built-in tools such as Google Search, DALL E, Stable Diffusion and WolframAlpha, LLMOps for monitoring logs and performance, and Backend-as-a-Service APIs on every offering. Langflow's README leads with a visual builder, source code access so any component can be customized in Python, an interactive playground with step-by-step control, multi-agent orchestration with conversation management and retrieval, deployment as an API or JSON export, deployment as an MCP server, observability through LangSmith, LangFuse and other integrations, and enterprise security and scalability claims. The practical contrast: Dify gives you more out of the box as a managed workspace, Langflow gives you a shorter path from component to running Python service.

Getting each one running

The install paths split along language lines. Dify runs through Docker Compose: copy the .env.example file into the docker folder, run docker compose up -d, open localhost/install and initialize. The README lists minimum requirements of 2 CPU cores and 4 GiB RAM and expects Docker Compose 2.24.0 or later. Langflow installs as a Python package with uv: uv pip install langflow -U, then uv run langflow run, and the app appears at 127.0.0.1:7860. It requires Python 3.10 through 3.14 and the README recommends uv as the package manager, though a Docker image and a Windows and macOS desktop app that bundles all dependencies are also documented. For a developer already living in Python, Langflow is one command away. For a team running many services in Docker, Dify's Compose file matches the environment you already have. The two readings of effort are different, and neither is wrong: Dify assumes a server stack, Langflow assumes a Python environment.

Turning a flow into a product

Both projects answer the same question, what happens after the flow works, with different mechanisms. Dify's answer is a product layer: every offering comes with APIs so business logic can call the workflows, the LLMOps features monitor logs and performance over time so prompts, datasets and models can improve from production data, and observability integrations cover Opik, Langfuse and Arize Phoenix. There is also Dify Cloud for a zero-setup start and an enterprise edition reached through sales. Langflow's answer is closer to the code: deploy a flow as an API, export it as JSON for Python apps, or deploy it as an MCP server so the flow becomes a tool that any MCP client can call. The MCP path is a distinctive feature for teams building with MCP clients. Both can get a flow to production; Dify leans on its platform and managed services, Langflow leans on standard Python and MCP tool surfaces that fit into an existing application stack.

Operations: what maintenance looks like

Operating Dify means operating a multi-service Docker deployment. The adoption analysis of the project says the recommended setup is Docker Compose with several services, which you commit to maintaining, and the analysis flags that teams unable to do that should look elsewhere. Operating Langflow means running a Python application: the analysis of Langflow notes the requirements of Python 3.10 to 3.14 and uv, and points to the Docker deployment guide for configuration options in production. Both projects released three versions in August 2026, so both expect you to track updates. One operational difference is the component library: Langflow's analysis says to confirm that the specific LLM and vector database integrations you need are present in the current component library, since you are assembling flows from components. Dify's equivalent check is model providers: confirm your target providers are in its supported list before relying on them.

Where each falls short

Dify's limits come from being a platform. The adoption analysis says teams needing deep customization of the underlying orchestration logic will find the platform's boundaries, because you operate inside the canvas and cannot rewrite the engine, and a multi-service Docker deployment is a permanent operational commitment. Its licence is also an open question: the repository does not clearly state a licence and GitHub classifies it as custom, so the analysis advises checking the licence terms against your use case. Langflow's limits come from being a tool. The analysis says developers who need fine-grained control over every component's internals will be constrained, and teams that prefer a code-first workflow with no GUI should skip the visual layer entirely. Langflow's MIT licence is a point in its favour, and its environment constraints, Python 3.10 to 3.14 with uv, are small but real. Neither project is weak overall; each simply serves a specific team shape well and a different team shape poorly.

Licence, maintenance and the choice

On licence the two differ clearly: Langflow is MIT, Dify carries a custom licence that the repository does not clearly state and GitHub cannot classify, so a legal review for Dify is mandatory before adoption. On maintenance both are healthy and both were pushed on September 11, 2026, with August release cadence on both sides. For concrete situations: choose Dify if you are a team that wants a self-hosted visual platform covering workflows, RAG, agents, models and monitoring in one workspace, can maintain the Docker Compose stack, and has cleared the licence terms, and verify your model providers are in the supported list before building. Choose Langflow if you are a Python developer who wants to paint flows and ship them as APIs or MCP servers, and verify that Python 3.10 to 3.14 with uv fits your environment and that the integrations you need exist in the component library.

Bottom line

Choose Dify when the buyer is a team that wants a complete self-hosted platform with workflows, RAG, agents, model management and APIs, and can maintain Docker Compose, and choose Langflow when the buyer is a Python developer who wants a visual builder that deploys flows as APIs or MCP servers on an MIT licence. Verify first: for Dify, the licence terms and your model providers; for Langflow, the Python and uv setup and the presence of your needed LLM and vector database integrations in the component library.

Sources

1. langgenius/dify repository
2. langgenius/dify README
3. langflow-ai/langflow repository
4. langflow-ai/langflow README