OpenAI’s Agent Builder (aka “AgentKit”) just landed, and the hot take everywhere is “RIP n8n.” After studying two in-depth creator tests and rebuilding their comparisons, the real story is simpler: one is built for conversations, the other for automation. Pick the right tool for the job.
The Big Picture
Both videos I’m drawing on—by Nate Herk and Nick Puru—built like-for-like flows and scored the platforms across similar categories. Their shared conclusion: OpenAI Agent Builder excels at getting polished, chat-centric experiences live quickly, while n8n remains the stronger choice for event-driven, back-office automation at scale.
If you remember nothing else, remember this framing:
- Agent Builder = conversation-first (chat agents, embedded widgets, frictionless access to OpenAI models).
- n8n = automation-first (triggers, schedules, 500+ native integrations, sub-workflows, and full control).
Ease of Use: From “What does this do?” to “It just works”
Both creators start with a simple test: build an agent that searches the web and answers a question.
- In Agent Builder, you drop an Agent node, add Web Search as a tool, and you’re done—no API keys, no plumbing. The canvas is intentionally sparse: about a dozen primitives, clearly labeled, low intimidation factor. As Nick puts it, it “just works” for a first-time builder.
- In n8n, the same result is absolutely possible but initially noisier: lots of categories, many nodes, and you’ll likely need to choose a search provider, add credentials, and connect a chat model. Beginners feel that learning curve up front.
Verdict: If you need to prototype a chatty assistant today with minimal setup, Agent Builder is the friendliest on-ramp. If you’re comfortable wrangling connections and want depth later, n8n’s early complexity pays dividends.
Triggers & Automation: Where n8n Runs Away With It
This category reveals why the “RIP n8n” narrative misses the mark.
- Agent Builder workflows begin with a Start that expects a message—human text or an API call. Today there’s no built-in schedule, webhook, or app-event trigger you can just drop on the canvas. You can call an agent programmatically (SDK/API) or wire it behind another system, but the product itself remains chat-first, not “listen to Gmail, CRM, Slack, S3, or a database and react.”
- n8n ships with real triggers for real systems: Gmail “new message,” Slack “reaction added,” Google Drive changes, webhooks, CRON/schedules, and more—plus generic HTTP endpoints. That lets you build agents that run in the background and scale with business activity (lead capture, ticket updates, invoices, etc.).
Verdict: For background, event-driven automations, n8n wins decisively. Agent Builder is excellent once a conversation starts—but it isn’t designed to start itself in response to external events.
Tools & Integrations: Native Depth vs. MCP Routes
Tools define what your agent can actually do.
- Agent Builder includes built-ins like Web Search, File Search, Guardrails, and MCP servers—including connectors to Google/Gmail/Calendar/Drive and Outlook. That’s enough for a surprising amount of practical work, and you can extend via MCP (Model Context Protocol). Both creators stress that MCP makes Agent Builder more useful than it first appears. Nick also highlights a route to 500+ apps via an MCP aggregation layer he calls out (his example: “Rube”), which effectively fans Agent Builder out into a broad integration surface.
- n8n offers 500+ native integrations right on the tin, plus the all-purpose HTTP Request node when a vendor has an API but no packaged node. Critically, sub-workflows (callable flows) let you compose an “orchestrator” agent that delegates to specialist agents—clean, reusable architecture for complex systems. Nate demonstrates exactly this modular pattern.
Verdict: If you value native breadth and composable orchestration, n8n is still the powerhouse. If you’re committed to OpenAI’s ecosystem and willing to leverage MCP to reach your apps, Agent Builder becomes far more capable than its minimalist palette suggests.
Model Support: One Garden vs. a Model Bazaar
- Agent Builder gives you all of OpenAI’s models, including reasoning variants, with dead-simple access (no keys to juggle). But you’re locked to OpenAI; there’s no first-class Claude, Gemini, Llama, etc. If OpenAI is your go-to—and for many workloads, it is—this is a feature, not a bug.
- n8n lets you choose Anthropic, Google, Azure OpenAI, Cohere, or even route through OpenRouter to access hundreds of models—or run local models when you self-host. That freedom matters when you want the “best tool for the task” and vendor redundancy. Both reviewers emphasize the advantage.
Verdict: If you need multi-model strategies, swap-outs, or on-prem LLMs, n8n is the clear choice. If you’re standardizing on OpenAI anyway, Agent Builder is optimized for that path.
UI & Embedding: Agent Builder’s Secret Weapon
This is where OpenAI’s approach shines.
- Agent Builder + ChatKit: You get a polished, brandable chat interface out of the box, a Widget Studio to generate interactive widgets (forms, selectors, visualizations), and easy embeds. For customer-facing or internal apps that need to look professional without writing front-end code, this is gold. Both videos call this out as a major value add. (Nate even highlights a “widget” output mode that lets the agent drive UI components.)
- n8n does have a basic chat message UI (for testing or minimal embeds), but it’s utilitarian. If you want a beautiful interface, you’ll usually build a custom front end (React/Vue/etc.) and treat n8n as the brain. That’s fine for teams with in-house devs—but it is more work.
Verdict: Agent Builder wins for UI speed-to-value. If your deliverable is a branded assistant or internal tool users will click on tomorrow morning, it’s hard to beat.
Deployment, Data Control, and Compliance
- Agent Builder runs in OpenAI’s cloud. That’s convenient—no servers, no ops, and updates flow automatically—but it also means OpenAI controls the environment. For orgs already standardized on OpenAI, this is acceptable; for strict data residency or on-prem mandates, it’s often a blocker.
- n8n is source-available and flexible: use n8n Cloud for convenience, or self-host for full control, even pairing with local models so sensitive data never leaves your network. Both creators frame this as a defining advantage for enterprises and agencies.
Verdict: If compliance/ownership is a must, n8n is designed to meet you where you are. If convenience is king, Agent Builder’s managed experience is extremely attractive.
Observability & Evaluation
A subtle—but important—difference appears when you debug.
- Agent Builder includes evaluations, traces, and prompt optimization. You can inspect prompts, timing, and actions. However, as Nate notes, following data through nodes is less intuitive right now—you’ll rely on logs and evaluation views rather than a left-in/right-out visual at each step.
- n8n makes data flow visual by default: each node shows inputs, configuration, and outputs; executions list; and green “pass” paths make it obvious what ran. For ops teams who live in pipelines, this style of step-by-step transparency is reassuring.
Verdict: If you’re running production automations and need to chase a record through a graph, n8n feels more familiar. Agent Builder’s eval tooling is strong for LLM quality; its pipeline introspection is catching up.
Pricing, Ecosystem, and Maturity
- Agent Builder is brand-new and evolving quickly. Templates, widgets, and evaluations are promising, but the ecosystem (courses, templates, community recipes) is still growing. The two reviewers expect rapid iteration, given OpenAI’s velocity.
- n8n is battle-tested with years of community content and thousands of templates/tutorials. If you want a how-to for almost any integration pattern, it probably exists—another reason agencies gravitate to it.
Verdict: For a ready-made library of patterns, n8n has the head start. Agent Builder’s ecosystem will likely grow—especially if MCP becomes the lingua franca for tools.
So… Which Should You Choose?
Choose OpenAI Agent Builder if you:
- Need a customer-facing or internal chat experience that looks professional this week.
- Live comfortably inside OpenAI’s model garden.
- Want low-friction access to web search, file search, guardrails, and polished embeds without front-end work.
- Are fine triggering agents via messages/API rather than app-event triggers.
Choose n8n if you:
- Need event-driven, background automations (Gmail, Slack, CRM, webhooks, CRON).
- Want 500+ native integrations, HTTP-to-anything, and sub-workflows for modular agent orchestration.
- Require multi-model flexibility or on-prem deployment with local LLMs.
- Care deeply about observability, data control, and compliance.
A Mindset That Outlasts the Tools
Both Nate and Nick close on the same, very practical advice: stop obsessing over which tool and get excellent at finding the work where AI creates compounding value—lead response, ticket triage, repetitive back-office tasks, workflow glue that grows as volume grows. Become tool-agnostic and outcomes-centric; the stack will keep evolving, but the ROI math and process discovery skills do not go out of style.
Final Take
OpenAI Agent Builder did not “kill” n8n—and likely wasn’t meant to. It lowered the bar to shipping conversational agents with polished UI and evaluation tools. Meanwhile n8n remains the leading automation engine for teams who need triggers, breadth of integration, modular architecture, and deployment control.
In other words:
- Use Agent Builder when the primary interface is a conversation.
- Use n8n when the primary behavior is automation.
If your roadmap includes both (and most roadmaps will), you’ll probably end up integrating them: let n8n listen to the world, and call Agent Builder when you want a best-in-class conversational moment—or vice versa. That’s not a rivalry; it’s a division of labor.
Sources
- Nate Herk, “I Tested OpenAI’s AgentKit Against n8n: What You Need to Know.” (YouTube). Comparison across ease of use, triggers, tools, models, UI, deployment; includes scoring and evaluation/observability notes. YouTube
- Nick Puru, “OpenAI Agent Builder vs n8n: I Tested Both… Here’s What Nobody’s Telling You.” (YouTube). Side-by-side build, “conversation-first vs automation-first” framing, MCP route to 500+ apps, and UI/embedding analysis. YouTube
Note: Dates, features, and UI details above reflect what both creators documented in the week following Agent Builder’s public launch. As with any young product, expect rapid changes. For the most current specifics, check the videos and their update notes.
