Choosing between n8n and OpenAI AgentKit is about matching your use case to each platform’s strengths and costs. AgentKit bundles ChatKit and Evals under standard API usage with no separate platform fee, while n8n charges per workflow execution and supports self hosted options. This guide compares architecture, features, pricing, and real world fit so you can decide with confidence.
Quick Verdict Table
| Best For | n8n | OpenAI AgentKit |
|---|---|---|
| Solo builder | Flexible workflows and easy self hosting | Fast agent prototypes with built in UI and evals |
| Small team | Predictable execution pricing and broad connectors | Polished chat experiences with guardrails and tracing |
| Enterprise | Self hosted control and concurrency limits | Centralized connector governance and integrated evals |
| Budget buyer | One execution counts once regardless of steps | Low token rates on smaller interactions and Batch tier |
| Power user | Multi model routing and MCP triggers | Agents SDK parity and visual Agent Builder |
N8N vs OpenAI AgentKit Quick Answer
Short answer: choose AgentKit for AI centric, user facing agents with built in evals and governance, and pick n8n for integration heavy, deterministic automations with self hosted control.
AgentKit brings an integrated stack that includes ChatKit for embeddable chat and Evals for step level grading, all under standard API pricing according to the AgentKit launch. n8n focuses on workflow automation with execution based pricing, where the Starter plan lists 2,500 executions for 20 euros per month on n8n pricing. If you want a cost anchor for conversational agents, GPT 4o has indicative rates that start at about 2 dollars and 50 cents per million input tokens in the Holori guide.
Quick context before we compare features
AgentKit ships embeddable chat UIs through ChatKit and a visual Agent Builder, while n8n has expanded its AI orchestration with multi agent patterns and an MCP Server Trigger described on the n8n AI page. Reports note ChatKit and Evals are generally available, while the canvas and connector registry were rolling out in beta at launch as covered by VKTR news.
N8N vs OpenAI AgentKit Features Compared
AgentKit optimizes for agent reasoning, UI embedding, and evaluation, while n8n optimizes for broad integrations, reliable automation, and model flexibility. AgentKit’s guardrails and Evals are part of the core workflow, and ChatKit accelerates front end delivery as pointed out in the MarkTechPost report. n8n’s 2025 focus adds multi agent orchestration and MCP triggers across its larger automation canvas on n8n AI.
Feature Comparison Table
| Feature | n8n | OpenAI AgentKit | Winner |
|---|---|---|---|
| End user UI | Requires custom app or third party UI | ChatKit embeddable chat included | AgentKit |
| Evals and guardrails | Operational logs and replays | Built in Evals and safety controls | AgentKit |
| Integrations and connectors | Large library plus HTTP and code nodes | Built in tools and a governed Connector Registry | n8n for breadth |
| Model flexibility | Multi provider and local models | Optimized for OpenAI runtime | n8n |
| Hosting options | Cloud and self hosted | Provider hosted only | n8n |
| Orchestration focus | Deterministic automation with AI steps | Agent reasoning with visual canvas | Tie by use case |
| Concurrency control | Clear plan limits and queue scaling | Managed by platform, fewer knobs | n8n |
| Developer workflow | Step level replay and debugging | Visual builder plus Agents SDK parity | Tie by preference |
Outside the table, two details often decide quickly. First, ChatKit can cut front end work for user facing agents, as noted in the Superprompt guide. Second, n8n’s visual and code nodes make long, multi system automations easier to operate and debug across steps, described on n8n features.
Pricing and Value
AgentKit costs are token based with tier choices that trade price for latency, while n8n is execution based where a 200 step run still counts once. OpenAI tiers like Batch, Flex, Standard, and Priority are summarized in the BytePlus guide, and a simple overview of current API numbers appears in the MuneebDev pricing. n8n’s cloud plans and concurrency caps are posted on n8n pricing.
Pricing and Value Table
| Plan or Cost basis | n8n | OpenAI AgentKit | Notes |
|---|---|---|---|
| Billing unit | Executions per month | Tokens per million | One workflow run counts once in n8n |
| Entry cloud plan | Starter at 20 euros per month | No separate platform fee | AgentKit usage is under standard API pricing |
| Mid cloud plan | Pro at 50 euros per month | Tiers for cost and latency | Choose Batch or Flex for lower cost where latency allows |
| Self hosted tier | Business with 40,000 executions | Provider hosted only | Self hosted shifts infra and ops to your team |
| Model costs | Not applicable to tool itself | GPT 4o and others | Token rates vary by model and tier |
| Concurrency | 5 to 200 plus by plan | Managed by OpenAI | n8n offers explicit concurrency settings |
If you plan a user facing support agent with modest tokens per session, Standard tier can be cost effective, and you can test GPT 4o Mini for cheaper paths using the numbers in the Holori guide. If you run long workflows with heavy API IO and occasional AI calls, n8n’s per execution billing often wins on predictability on n8n pricing.
Which Should You Choose
If your goal is to ship a polished agent that talks to customers, choose AgentKit for the built in chat UI, evaluations, and centralized governance. If your main need is to run many systems tasks on a schedule or in response to events with clear operational control, choose n8n for its execution model and self hosted option.
Use Case Fit Table
| Job to be done | Recommended pick | Why |
|---|---|---|
| Customer support chat | OpenAI AgentKit | ChatKit UI, integrated Evals, and guardrails speed delivery |
| Multi system nightly sync | n8n | Execution based pricing and easy branching across APIs |
| Internal analytics assistant | OpenAI AgentKit | Fast to prototype with visual canvas and step level traces |
| High control back office automation | n8n | Self hosted option and explicit concurrency controls |
| Multi model experimentation | n8n | Route across providers and local models without lock in |
A few extras may tilt your decision. AgentKit’s Connector Registry provides centralized control of data and tool access across OpenAI properties as noted by VKTR news. AgentKit also supports traceable, versioned workflows with built in grading, which can reduce the time you spend proving quality according to the DataNorth note. On the other side, n8n’s AI orchestration features, including MCP integration, make it a strong backbone for automation first teams on n8n AI.
Why it matters: picking the right foundation affects time to first result, operational costs, and long term flexibility. In 2025, the most common winning pattern is a hybrid. Use AgentKit for the agent experience and reasoning, and let n8n run the surrounding business workflows and data plumbing. That split aligns each tool to what it does best and keeps both costs and risk in check.
Ready to map this choice to your roadmap and get a build plan you can ship this quarter? Let us help you pick the right stack and cost model for your use case.