dify vs n8n: an LLM application platform versus workflow automation with AI
Dify and n8n both let you build AI workflows on a visual canvas, but the products are different sizes. Dify is an LLM application platform centered on models, RAG and agents. n8n is a workflow automation platform with native AI capabilities, connecting to 1500-plus integrations and existing business systems. Both are TypeScript, both were pushed on September 11, 2026, and both use licences that need checking, so the choice turns on whether AI is the product or one step inside a larger automation.
At a glance
| Project | langgenius/dify | n8n-io/n8n |
|---|---|---|
| Licence | Custom licenceCustom licence: read the LICENSE file | Custom licenceCustom licence: read the LICENSE file |
| Maintenance | Commits in the last dayLast push September 29, 2026 | Commits in the last six monthsLast push September 25, 2026 |
| Language | TypeScript | TypeScript |
| GitHub stars | 157,512 | 205,953 |
| Read more | Our analysisGitHub | Our analysisGitHub |
Which one to choose
Choose dify if the deliverable is an LLM application: a chat app, an agent or a RAG pipeline where model management, prompts, retrieval and evaluations are the main work, and you want a platform purpose-built for that.
Choose n8n if you are automating business workflows that happen to include AI: connecting models to your existing systems and 1500-plus integrations, mixing visual nodes with JavaScript and Python code, and adding human approvals, role-based access and audit trails.
An AI platform and an automation platform that speaks AI
The two projects overlap on a visual canvas but live in different product categories. Dify describes itself as an open-source LLM application development platform: its README centers on AI workflow, RAG pipeline, agent capabilities, model management and observability, and its position is that you go from prototype to production without leaving the platform. n8n describes itself as a fair-code platform to build and deploy AI agents and workflows, and its README frames AI as one capability inside a larger automation product: 1500-plus integrations, 9000-plus workflow templates, custom code with JavaScript, Python and npm packages, human approvals and enterprise features such as role-based access and audit trails. Both are actively maintained and were pushed on September 11, 2026. The practical question is whether your project is an AI application with some automation around it, which points to Dify, or an automation workload with AI steps inside it, which points to n8n.
What each one gives you to build with
Dify's building blocks are AI-specific. The README lists a workflow canvas, model support covering 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 formats, agents based 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, and Backend-as-a-Service APIs on every offering. n8n's building blocks are generic automation nodes with an AI layer. Its README highlights model flexibility without lock-in across OpenAI, Anthropic, Google and open-source models, multi-step AI workflows with logic, tool use and human approvals, custom code in JavaScript, Python and npm packages, and full observability. The count difference is telling: n8n's 1500-plus integrations aim at every system you already run, while Dify's built-in tools and model providers aim at AI operations specifically. Teams that mostly need AI should not pay for integration breadth they will not use, and teams that mostly need automation should not be limited by an AI platform's narrower connector set.
Getting each one running
Deployment scales differ. Dify runs through Docker Compose with multiple services: copy the .env.example file into the docker folder, run docker compose up -d, open localhost/install and initialize. The README states minimums of 2 CPU cores and 4 GiB RAM and expects Docker Compose 2.24.0 or later. n8n runs as a single container: an install script, or manual Docker with a named volume, a published port 5678 and the image docker.n8n.io/n8nio/n8n, then the editor at localhost:5678. The data volume matters because n8n stores its workflows, credentials and execution history there, so the analysis of the project flags testing the Docker deployment on your target infrastructure with attention to port 5678 and the data volume. If you already operate Docker Compose stacks, Dify fits the pattern with more moving parts; if you want the smallest possible footprint, a single n8n container is lighter. Neither install is hard, but the operational surface after install differs by an order of magnitude.
How each handles models and tools
Dify's model story is a managed marketplace. The platform integrates providers and self-hosted solutions, keeps a supported-provider list, and treats model access as a platform service: you manage keys and models centrally, and the prompt IDE lets you compare model performance in one interface. Tools are part of the platform too, with built-in tools and custom tools for agents. n8n's model story is connection rather than management. The README emphasizes model flexibility and no lock-in, connecting to OpenAI, Anthropic, Google or open-source models and switching providers without changing your architecture, and it points to an AI and LangChain guide for the wiring. n8n does not position itself as a model manager; it positions itself as the place where models meet your other systems. If your team wants one place to manage models, prompts and evaluation, Dify's platform fits. If you already have model access sorted and need models wired into workflows across your infrastructure, n8n's integration-first approach fits better.
Operations and governance
Both projects carry production storylines, with different emphases. Dify's operations are LLMOps-shaped: monitor and analyze application logs and performance over time, improve prompts, datasets and models from production data and annotations, with observability integrations including Opik, Langfuse and Arize Phoenix, and APIs on every offering so business logic can call the workflows. n8n's operations are enterprise-shaped: the README lists role-based access, audit trails, support for sensitive data, self-hosting or secure cloud deployment, and full observability, alongside its cloud option at app.n8n.cloud. Both can be self-hosted with Docker. For governance, n8n's README documents audit trails and role-based access explicitly, while Dify's README excerpt mentions enterprise features only through the enterprise contact path. If your compliance story needs documented role-based access and audit trails today, verify them in the current docs on both sides rather than assuming parity from a README summary.
Where each falls short
Dify's weaknesses are platform-shaped. The adoption analysis says teams that need deep customization of the underlying orchestration logic will hit the platform's boundaries, that the multi-service Docker Compose deployment is an ongoing commitment, and that the licence is an open item because the repository does not clearly state one. Its integration set is AI-focused, so connecting to long-tail business systems means checking whether a connector exists or building one. n8n's weakness is licensing for commercial use. The project is fair-code under the Sustainable Use License, which the analysis describes as prohibiting certain commercial uses without an enterprise licence, so a team that needs a permissive open-source licence cannot use n8n without evaluating the enterprise terms and cost. The analysis also advises confirming the AI features meet your model and tool requirements, because the AI layer is newer than the core automation engine. One project restricts you inside a platform; the other restricts you at the licence level.
Licence, maintenance and the choice
Maintenance is not a differentiator: both were pushed on September 11, 2026, with recent releases in late August 2026, and neither is archived. Licences need attention on both sides but for different reasons. Dify's repository does not clearly state a licence and GitHub classifies it as custom, so verify the terms against your use case. n8n is explicitly fair-code under the Sustainable Use License with an enterprise licence for additional features, so commercial deployments must evaluate those terms and their cost. For concrete situations: choose Dify when the product is an AI application, meaning chat, agents or RAG where model management, prompt work and retrieval dominate, and verify the licence terms, model providers and built-in RAG coverage of your document types first. Choose n8n when you are automating workflows that include AI across many systems, and verify the Sustainable Use License against your commercial use, the AI features against your model and tool needs, and the Docker deployment on your infrastructure with port 5678 and the data volume.
Bottom line
Choose Dify when the deliverable is an LLM application, with model management, prompts, RAG and agents as the core work, and choose n8n when AI is one step inside broader workflow automation across many systems. Verify first: for Dify, the licence terms, your model providers and document type coverage in RAG; for n8n, the Sustainable Use License against your commercial use, the AI features against your model and tool requirements, and the single-container Docker setup with its port and data volume in your infrastructure.