Dify vs n8n: 2026 comparison

Dify and n8n in brief

Dify and n8n both provide visual workflows involving AI and can be self hosted. Their starting points differ. Dify is presented as a workspace for building AI applications, agents and workflows that incorporate document collections. n8n connects applications, code and AI agents, either in the cloud or on your own server. Both show recent development activity and substantial GitHub followings. The useful distinction is therefore the role AI plays in your automation, together with the deployment and usage framework you need to establish.

Dify : Dify provides a visual workspace for building AI applications, agents and workflows connected to document retrieval pipelines.

n8n : n8n builds visual workflows connecting applications, custom code and AI agents, with cloud and self-hosted deployment options.

Both meet the same need, which makes the comparison useful. We compare them on 11 verifiable criteria, using the same data as their profiles, collected from official sites and GitHub.

Comparison table

Criterion by criterion

For each criterion: why it matters, each one's value and who has the edge. A criterion without data on both sides is not decided.

Popularity (GitHub stars)

Stars measure developer interest; a widely followed project has more tutorials, integrations and answers. Dify : 159.4K. n8n : 208.5K. Edge: n8n.

Activity over a year

Commits over 52 weeks show whether the project moves fast, fixes bugs and keeps up with new models. Dify : 9,551 commits. n8n : 14,403 commits. Edge: n8n.

Releases (6 months)

Regular releases signal a project that ships; very frequent releases can also mean frequent updates on your side. Dify : 9. n8n : 17. Edge: n8n.

Last commit

A repository with no commit for months may fall behind new APIs and security fixes. Dify : October 24, 2026. n8n : October 24, 2026. A tie on this point.

License

A clear open-source license (MIT, Apache…) allows commercial use and modification; without one, usage rights are unclear. Dify : no declared license. n8n : no declared license. A tie on this point.

Self-hosting

Running the agent on your machine or server keeps your files and keys with you. Dify : yes. n8n : yes. A tie on this point.

Supported models

An agent that accepts several model families lets you pick the best value, or a local model. Dify : 9. n8n : 6. Edge: Dify.

Interfaces

Terminal, code editor, web app or chat: more interfaces mean more ways to fit it into your work. Dify : 2. n8n : 3. Edge: n8n.

Risk level

Our risk level considers the access granted (terminal, files, network) and default human approval. Dify : moderate. n8n : moderate. A tie on this point.

Community (forks)

Forks count developers who copied the project to adapt it: a sign of a living ecosystem. Dify : 25.2K. n8n : 61.2K. Edge: n8n.

Track record

An older project has had time to fix early flaws; a recent one often brings the latest ideas. Dify : April 2023. n8n : June 2019. Edge: n8n.

In charts

Our analysis

Dify is maintained by LangGenius, was created on April 12, 2023, and is described as an open source project written in Python. Its visual workspace brings together AI applications, agents and workflows incorporating document collections. That focus may suit projects organized around an AI application and its knowledge sources. n8n was created on June 22, 2019, uses TypeScript and is also described as open source. Its stated approach starts with connecting applications, code and AI agents. This difference can guide your initial choice: building an AI application with Dify, or coordinating several connected components with n8n, depending on the process you need.

As of October 24, 2026, Dify has 159,425 GitHub stars and 25,193 forks, compared with n8n’s 208,491 stars and 61,222 forks. The records list 9,551 versus 14,403 commits over twelve months, and nine versus seventeen releases over six months. Both show a latest commit dated that day. These measures describe popularity and development activity, not code quality or support effectiveness. Their scores of 83 and 85 concern popularity, activity and framework. Dify’s price is unspecified. n8n lists Starter at EUR 20 per month, billed annually, with 2,500 monthly executions and agents included. That allowance should be assessed against your own expected workload.

Both projects support self hosting, allowing you to consider deployment on your own infrastructure. That alone does not explain which data may be sent to the models you select. Dify lists Claude, GPT, Gemini, DeepSeek, Qwen, Mistral, Llama, local models and multiple model support. n8n lists Claude, GPT, Gemini, DeepSeek, local models and OpenRouter. Neither record supplies a license, despite describing the projects as open source. Both receive an intermediate risk level because license information is missing. This is a gap in the documented usage framework rather than evidence of a technical vulnerability, and it leaves the applicable conditions unresolved in these records.

Dify provides web and API interfaces, while n8n adds a command line interface to web and API access. That extra interface may matter if command line operations form part of your routine. Both can be self hosted, and n8n’s description also explicitly mentions cloud use. Human validation is unspecified for each project, so the records do not establish an approval mechanism you can rely on. They also provide no detailed agent access information. To assess everyday fit, begin with the process you intend to automate, then clarify permissions, validation steps and deployment conditions before assuming that the listed capabilities cover those operational requirements.

Depending on your situation

The right choice depends on use. For a few common situations, here are the criteria that matter and who wins (Dify value / n8n value).

Trying it out quickly

Here, n8n has the edge: it wins 2 of the 3 criteria that matter in this situation, namely interfaces (2 / 3), supported models (9 / 6), and popularity (github stars) (159.4K / 208.5K).

Production use

Here, n8n has the edge: it wins 2 of the 4 criteria that matter in this situation, namely activity over a year (9,551 commits / 14,403 commits), last commit (October 24, 2026 / October 24, 2026), license (no declared license / no declared license), and releases (6 months) (9 / 17).

Security and control

Neither stands out on the criteria for this situation (risk level (moderate / moderate) and self-hosting (yes / yes)): decide with a trial.

Which one to choose?

Choose Dify if…

• you value supported models (9)

Dify is a logical fit when your starting point is an AI application, an agent or a workflow built around document collections. Its visual interface, web and API access, and listed model options align with that focus. It also supports a self hosting plan, subject to clarifying the missing license information, pricing and any human validation steps your process requires.

Choose n8n if…

• you value popularity (github stars) (208.5K)
• you value activity over a year (14,403 commits)
• you value releases (6 months) (17)
• you value interfaces (3)
• you value community (forks) (61.2K)

n8n fits projects that connect applications, code and AI agents through visual workflows. Consider it if you need command line access or want to evaluate cloud deployment alongside your own server. Its larger GitHub activity figures describe project momentum. For the Starter offer, compare the stated 2,500 monthly executions with your expected workload rather than relying on popularity alone.

Strengths and limits of each

Dify

• + Widely followed: 159.4K stars
• + Very active development
• + Frequent releases (23 in a year)
• − No licence declared

n8n

• + Widely followed: 208.5K stars
• + Very active development
• + Frequent releases (17 in a year)
• − No licence declared

The best test is still yours: take three real tasks from your day and give them to both for a week. Our “Which AI agent?” quiz can also guide you.

What about the others?

If neither suits you, look at Langflow, Sim, AutoGPT, Kestra, Trigger.dev, and Activepieces.

Related comparisons:

• n8n vs Make
• n8n vs Zapier
• n8n vs AutoGPT
• AutoGPT vs Dify
• n8n vs Langflow
• Dify vs OpenClaw

Conclusion

Dify fits an approach where an AI application and its document sources form the center of the project. n8n suits automation that connects applications, code and agents. Both are active and self hostable, while popularity does not determine suitability. Before choosing, clarify the unspecified license, required validation steps and intended deployment. Dify’s missing price information should remain an unknown; it does not support calling the project free or assuming it will cost less than n8n.