OpenAI Agent Builder
My experience developing Agentic workflows with the latest low/no code Agentic tool from OpenAI
This week, on the 6th of October 25, OpenAI released AgentKit, a collection of tooling designed to make it easy to build and work with Agentic systems. There are 3 main components within this AgentKit ecosystem
Agent Builder
Connector Registry
ChatKit
This article focuses on the first part of the AgentKit ecosystem, which is the agent builder.
Agent Builder is a no/low code tool that provides a visual canvas for composing logic with drag-and-drop nodes, connecting tools, and configuring custom guardrails. It supports preview runs, inline eval configuration, and full versioning—ideal for fast iteration.
In a market that has increasingly seen the likes of Langflow, n8n, crewAI and other low/no code tools, agent builder is OpenAI’s version of this low/no code agent building SaaS. I spent some time digging into this platform and built a couple of agents with increasing levels of complexity.
Agent 1 - A simple web search agent
I built the first agent by connecting the start node, the agent node and the end node. The agent instructions were simple - use the necessary tools and craft a response. The tool that I added was OpenAI’s web search tool and the model that I used was GPT-5. You can see in the screenshots below how the setup looks like. This took me under 5 minutes, and the agent did a good job of answering the question on the weather in New York. Nice!
Agent 2 - Stock Market Analysis and Plotting
The second agent I built with the agent builder was to build a flow in which one agent would help me at doing market research about various company stocks. And the other agent would excel at generating plots. The overall high level design of nodes looks like the screenshot below
In the stock finder agent, I added the web search tool so that the agent could look up stock information from the internet. I added a structured response from my first agent in the following way. This is so that the later steps can reference these json fields and make deterministic decisions. (Not everything in an Agentic system needs to be non-deterministic.) The If/Else node will be utilizing this structured response to route the flow of execution further.
I then used the set state node to set a new variable named stocks_str to this structured output. You’ll see why this was a key step a bit later. I then added the If/Else node to check if the stock_name as a field was found in the output of the first agent. This would ensure that the plotting agent wouldn’t be triggered unless the stock data was found and was available for us to use.
Further, I added the second agent which is our plotting agent, and gave it access to Code Interpreter tool (which allows an agent to write it’s own python code, more here). To give it the context of the structured json, I added the following prompt which also contains the state.stocks_str that we had defined earlier. The passed context must be a string and hence the implementation.
You can see the final output in a 3 minute video below. The agentic workflow successfully found the stock price of Amazon for the last 6 months, and gave me the code to plot it using python. I wish it’d have rendered the code directly in-line but in my testing, I couldn’t get the plot to render on my screen. The widget output format didn’t work for me, even after I added a custom widget with graphs. I did however copy-paste the code in google colab and got a nice plot as you can see below.
My observations
The handling of conversion history is seamless. The agent remembers what the user chatted about and follow-up questions are answered without any development effort from the user.
MCP support is okay. The connectors from OpenAI at launch are very few and there’s a lot of work required to get them set up properly. MCP at launch does not support OAuth and the multiple users have reported connectors generally not working, which is something I assume will eventually be fixed in the future. However, this definitely does limit the kind of projects that I can build at launch. I was planning on building a 3rd more complex agent, but the general errors in the platform made it impossible to do so
Tools like these are more common than ever, and I’d argue that if your current workflow is living happily in n8n or similar, I see no reason to switch at the moment. The idea behind this one again is just OpenAI entering the game of low/no code agent builder SaaS. At this time, this platform does not have any strategic advantage except the name of OpenAI to back it up.
It’d be interesting to see how this integrates with chatkit and connector registry. I’ll be exploring that next and see what I can build with this. In the meantime, thank you so much for reading and I hope you have a wonderful day!