n8n vs OpenAI AgentKit: What We Learned in Our Deep Dive

Today, October 6, 2025, OpenAI has officially entered the agent-building arena with the launch of AgentKit. As a major force in the AI ecosystem, OpenAI's entry with a new visual workflow builder is a significant event. But for developers already working with powerful existing tools, the critical question is: how does this brand-new platform stack up against the competition?

This article provides a developer-focused first look at AgentKit, comparing its architecture, developer experience, and UI capabilities against established frameworks. We'll examine its strengths and weaknesses relative to the open-source automation powerhouse n8n, and introduce Inkeep, a comprehensive agent platform that aims to provide the best of both worlds.

OpenAI's AgentKit: The Polished Chat Experience

As a brand-new offering, AgentKit's primary strength lies in its tight integration with ChatKit, an open-source library for creating sleek, modern chat interfaces.

Where AgentKit Shines: The User Interface (UI)

• Rich UI Rendering: AgentKit excels at creating visually appealing chat experiences. Its Widget Builder allows developers to generate custom UI components using natural language, which can then be easily dropped into a workflow. The platform automatically structures the agent's output to match the widget, streamlining the process of building interactive, user-friendly applications.
• Seamless Chat Integration: ChatKit provides a polished, out-of-the-box solution for deploying agents. This focus on the front-end experience makes AgentKit an attractive option for projects where the user-facing chat interface is the top priority.

Architectural and Developer Experience Challenges

Despite its UI prowess, AgentKit's underlying architecture presents several hurdles that can increase development overhead.

1. Rigid, Sequential Routing

• AgentKit's core weakness is its lack of autonomous routing. Therefore 'agents' cannot intelligently select from multiple tools or sub-agents based on on their descriptions. Instead, developers must manually insert an if/else node to manage decision-making. This adds an extra, often cumbersome, step. In contrast, frameworks like n8n and Inkeep allow a parent agent to autonomously choose the correct tool from a connected set based on their descriptions.

2. Bloated Workflows

• Agent flows in AgentKit are strictly sequential. And as we have alluded to in our previous blogs, sequential flows are a core feature for workflows — not AI Agents. A tool's response cannot be passed back to the agent that called it; it must be routed to a new, separate agent. This creates fragmented and unnecessarily complex workflows.

Example: A Simple Weather Agent

• In AgentKit, this requires six different nodes: an agent to extract the location, a tool to convert location to coordinates, a second agent just to parse the coordinates from the tool's text output, another tool to get the forecast, and so on.

• In Inkeep, for example, this is accomplished with just two nodes. A single agent can extract the location, call a tool to get coordinates, receive the result, and then intelligently decide to use a second tool to get the weather forecast.

3. Tool Management Overheads

• Each MCP tool server can only connect to one tool at a time. To expose multiple tools to an agent, a developer must create a separate node for each one and wire them all through a complex if/else node, complete with manually defined routing conditions.

4. Ecosystem & Vendor Lock-In

• AgentKit is a closed-source platform that exclusively supports OpenAI models. This limits flexibility and prevents developers from leveraging models from providers like Anthropic, Google, etc.

5. Limited Code to UI Interoperability

• While you can export a visual workflow to a TypeScript SDK, this is a one-way street. You cannot import code to create a visual workflow. Furthermore, this export feature breaks as soon as you add any MCP servers to your graph.

n8n: The Open-Source Automation Powerhouse

n8n stands in stark contrast to AgentKit, prioritizing flexibility, customizability, and developer freedom. It does much better in terms of creating autonomous, agentic workflows.

Key Advantages of n8n:

• More Autonomous Agentic Workflows: Unlike AgentKit, n8n agents can connect directly to multiple tools and sub-agents, intelligently routing tasks based on their descriptions without needing manual if/else logic.
• Open-Source and Model-Agnostic: As an open-source platform, n8n offers complete transparency and customizability. It integrates with hundreds of models, giving developers the freedom to choose the best LLM for their specific needs.
• Powerful Automation: At its core, n8n is a robust workflow automation tool with a vast library of integrations, making it ideal for complex back-end processes.

Limitations:

While powerful on the back-end, n8n's front-end chat UI is less developed than AgentKit's. It lacks the rich, customizable UI components and the seamless widget integration that make AgentKit's and Inkeep's chat experience so polished.

Additionally, while the autonomy of its agents are a big step up from AgentKit's, agents in n8n still can not autonomously send messages to their subagents. Rather, n8n subagents must predefine what structured input from the parent agent they expect using n8n's fromAI() syntax in its prompt.

This step means that to create truly autonomous agents with n8n, you have to introduce some code which adds additional overhead to the builder experience.

Inkeep: The Best of Both Worlds and Beyond

Inkeep emerges as a powerful synthesis, combining the UI polish of AgentKit with the back-end flexibility of n8n, and more. It excels at both creating rich chat experiences and building complex, truly autonomous agentic workflows.

Where Inkeep Stands Out:

1. Effortless, Truly No-Code Routing

• Inkeep abstracts away the complexity of agent routing. You can connect multiple tools and sub-agents directly to a parent agent, and the parent will autonomously delegate tasks using natural language. No if/else code blocks or complex expressions like $input.output_parsed.classification == "flight_info" are required.

2. Superior Developer Experience:

• Simplified Tool Management: Define your MCP servers once and then freely connect them across your workflows. Moreover, the UI provides at-a-glance clarity on which models each agent is using and which tools each MCP server carries.
• Dynamic Context Injection: Inkeep allows you to make API calls or fetch data and inject it directly as context into your workflow's first agent. In AgentKit, this would require wrapping the API call in a custom tool server, adding unnecessary steps.
• Lightweight Frontend Integration: Integrating an Inkeep agent into your front end is radically simpler than with AgentKit. Instead of setting up a dedicated back-end endpoint to manage client secrets, you simply pass the agent URL and an API key into Inkeep's open-source UI component library.

3. Customizable and Powerful Chat UI:

• Inkeep's open-source component library is far more customizable than n8n's and enables the kind of rich UI rendering that AgentKit is known for. It gives developers fine-grained control, allowing you to store structured LLM outputs in data components from any node and reference them later for precise UI rendering.

Feature Comparison at a Glance

Conclusion

Choose AgentKit if your sole priority is a polished, OpenAI-powered chat UI and you are willing to navigate a rigid agent builder experience for this new tool.

Choose n8n if you need a powerful, open-source automation engine with maximum model flexibility and are less concerned with having a rich, out-of-the-box chat interface.

Choose Inkeep if you are looking for a comprehensive solution that doesn't force you to compromise. It delivers a superior developer experience, powerful autonomous agent capabilities, and a highly customizable, rich UI, making it the ideal framework for building sophisticated, production-ready AI agents without the overhead.