n8n vs Langflow: 2026 comparison

n8n and Langflow in brief

n8n and Langflow both let you build visual workflows involving AI agents. n8n emphasizes connections between applications, code and agents. Langflow emphasizes assembling models, tools and data sources into agents and AI workflows. Both support self-hosting and list web and API interfaces. Their differences include n8n's CLI, declared model coverage, Langflow's specified license and uneven pricing information. The useful choice depends on how you intend to compose your automation and how your team needs to operate it.

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

Langflow : A visual editor for connecting models, tools, and data sources to build and deploy AI agents and workflows.

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. n8n : 208.5K. Langflow : 156K. Edge: n8n.

Activity over a year

Commits over 52 weeks show whether the project moves fast, fixes bugs and keeps up with new models. n8n : 14,403 commits. Langflow : 4,979 commits. Edge: n8n.

Releases (6 months)

Regular releases signal a project that ships; very frequent releases can also mean frequent updates on your side. n8n : 17. Langflow : 26. Edge: Langflow.

Last commit

A repository with no commit for months may fall behind new APIs and security fixes. n8n : October 24, 2026. Langflow : October 22, 2026. Edge: n8n.

License

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

Self-hosting

Running the agent on your machine or server keeps your files and keys with you. n8n : yes. Langflow : 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. n8n : 6. Langflow : 5. Edge: n8n.

Interfaces

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

Risk level

Our risk level considers the access granted (terminal, files, network) and default human approval. n8n : moderate. Langflow : low. Edge: Langflow.

Community (forks)

Forks count developers who copied the project to adapt it: a sign of a living ecosystem. n8n : 61.2K. Langflow : 10.2K. Edge: n8n.

Track record

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

In charts

Our analysis

n8n describes visual workflows connecting applications, code and AI agents, with deployment in the cloud or on your own server. Langflow describes a visual editor for assembling models, tools and data sources into agents and AI workflows. n8n may therefore resonate with teams starting from applications and processes they need to connect. Langflow may feel more directly aligned with teams starting from the components of an AI system. This is a distinction in the descriptions, rather than proof of a rigid functional boundary. n8n uses TypeScript and Langflow uses Python, which may matter if your technical team plans to work with the code.

n8n was created on June 22, 2019 and has 208,491 GitHub stars and 61,222 forks. Langflow, created on February 8, 2023, has 155,979 stars and 10,240 forks. n8n records 14,403 commits over twelve months, compared with Langflow's 4,979. Langflow records 26 releases over six months, against n8n's 17. Their latest commits are dated October 24 and October 22, 2026, respectively. Scores of 85 and 93 out of 100 describe popularity, activity and framework, rather than quality. n8n lists Starter at €20 monthly, billed annually, with 2,500 monthly executions and agents included. No Langflow pricing is supplied for comparison.

Both records describe open source projects that support self-hosting. Langflow specifies an MIT license, while n8n's license is not supplied. Langflow therefore has a more explicit reuse framework in the available information. Its stated risk level is low, compared with medium for n8n, where missing license information is the listed reason. These classifications do not establish a ranking of technical security. Self-hosting also does not explain how information is handled when external models are involved. Neither record describes retention policies or human approval mechanisms. Those gaps remain relevant even if the deployment location and code licensing otherwise meet your project's requirements.

n8n lists web, CLI and API interfaces; Langflow lists web and API access. n8n names Claude, GPT, Gemini, DeepSeek, local models and OpenRouter. Langflow names Claude, GPT, Gemini, local models and multimodel support. These lists help you screen for compatibility with your intended setup, but they do not specify supported versions or the capabilities of each integration. A CLI may matter if your operations need command-line access. n8n's documented Starter allowance also provides an execution limit to compare with expected usage. Langflow's missing pricing prevents a complete cost comparison, including whether an otherwise similar deployment would fit the same budget.

Depending on your situation

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

Trying it out quickly

Here, n8n has the edge: it wins 3 of the 3 criteria that matter in this situation, namely interfaces (3 / 2), supported models (6 / 5), and popularity (github stars) (208.5K / 156K).

Production use

Neither stands out on the criteria for this situation (activity over a year (14,403 commits / 4,979 commits), last commit (October 24, 2026 / October 22, 2026), license (no declared license / MIT), and releases (6 months) (17 / 26)): decide with a trial.

Security and control

Here, Langflow has the edge: it wins 1 of the 2 criteria that matter in this situation, namely risk level (moderate / low) and self-hosting (yes / yes).

Which one to choose?

Choose n8n if…

• you value popularity (github stars) (208.5K)
• you value activity over a year (14,403 commits)
• you value last commit (October 24, 2026)
• you value supported models (6)
• you value interfaces (3)

n8n is a logical choice when your goal is to connect applications, code and agents, especially if CLI access matters alongside web and API interfaces. DeepSeek and OpenRouter are also explicitly listed. Its Starter plan gives you an initial budget reference, provided you account for the execution allowance and the requirement for annual billing in that offer.

Choose Langflow if…

• you value releases (6 months) (26)
• you value license (MIT)
• you value risk level (low)

Langflow fits teams that prefer to assemble models, tools and data sources visually, with web and API access. Its specified MIT license provides a clearer reuse framework in the supplied record. Python may also match your team's technical background. You would still need to establish costs, confirm particular integrations and determine how human review should operate for your workflows.

Strengths and limits of each

n8n

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

Langflow

• + Widely followed: 156K stars
• + Very active development
• + Frequent releases (30 in a year)

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 Sim, AutoGPT, Dify, Kestra, Trigger.dev, and Activepieces.

Related comparisons:

• n8n vs Make
• n8n vs Zapier
• Dify vs n8n
• n8n vs AutoGPT

Conclusion

n8n offers a CLI, an application-connection focus and a documented entry price. Langflow emphasizes assembling AI components and explicitly identifies its MIT license. Its higher score is not evidence of better output quality. Choose according to your architecture, access methods and reuse requirements. Langflow's missing prices and the absence of documented human approval mechanisms for both projects leave practical questions to resolve before deployment.