OpenAI responses API reference

So, OpenAI just dropped their new Responses API. If you're a developer building anything that feels like an AI agent, you're probably trying to figure out where it fits into your stack. Good news, you're in the right place.

This guide is a straightforward reference to help you understand what this new API is, how it stacks up against OpenAI's other tools, and whether there's a simpler way to get the same results without all the heavy lifting. Let's dig in.

What is the OpenAI Responses API?

The OpenAI Responses API is the company's newest and most advanced way to get responses from their models. Its main job is to make it easier to build stateful, multi-turn conversations where the AI can use tools and actually remember what you were just talking about.

Here's a simple way to think about it: if the Chat Completions API is like a calculator (great for single, one-off calculations), the Responses API is more like a full spreadsheet. It remembers your data and can run complex functions on it.

This new API rolls a few key features into one place that used to require a lot of manual coding:

• It remembers the conversation: The API can natively keep track of a conversation's context, so you don't have to keep stuffing the entire chat history into every single request.

• It has built-in tools: It ships with powerful tools like web search and file search right out of the box, letting the model pull in information that goes way beyond its training data.

• It brings everything together: It simplifies building complex AI agents by combining features from both the Chat Completions and Assistants APIs into a single, more direct interface.

As some folks in the developer community have noted, this is a move toward a more integrated, all-in-one service. While that makes a lot of development tasks easier, it's also a shift away from the simple, interchangeable endpoints that many open-source tools were built to use.

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Key features of the OpenAI Responses API

The real magic of the Responses API is in its integrated features, which handle tasks that used to be a massive headache to manage yourself. It's no longer just about getting a chunk of text back; it's about building an agent that can remember, learn, and take action.

Stateful conversation management

One of the biggest improvements is that the API is now "stateful," which is just a fancy way of saying it can remember your conversation. You don't have to manually pass the entire chat history back and forth anymore. The Responses API gives you two main ways to do this.

1. "previous_response_id": This is the easy route. You just pass the ID of the last response, and the API automatically links the new turn to the old one. It's perfect for creating simple, linear conversations without much hassle.

2. "conversation" object: If you're dealing with more complex stuff, like branching dialogues or long chats that you need to save and return to later, you can use the conversation object. It gives you a lot more control over how the chat history is managed and stored.

These are solid building blocks, but let's be real: building a production-ready system to manage conversation state for thousands of users across different platforms, like a Zendesk help desk and an internal Slack bot, is still a major engineering project. This is where a platform like eesel AI comes in handy, since it handles all that state management for you automatically. You get to focus on what your AI should say, not the plumbing needed to make it remember things.

Built-in tools

"Tools" are what give an AI model its superpowers, letting it break out of its knowledge bubble and interact with the world. The Responses API comes with some impressive ones built right in.

• Web Search: The model can browse the web for current information and give you answers with sourced citations. This is huge for any use case that needs up-to-the-minute info.

• File Search: You can give the model your own files, and it can perform a semantic search over them. It's great for building a Q&A bot that knows your company’s internal knowledge base inside and out.

• Code Interpreter: This tool gives the model a safe, sandboxed Python environment where it can write and run code. It's incredibly useful for analyzing data, solving tough math problems, or even generating charts on the fly.

Of course, getting your own specific knowledge into the system can still be a bit of a process. You have to upload files, manage vector stores, and write the API logic for any custom actions. With eesel AI, you can connect knowledge sources like Confluence or Google Docs in just a few clicks. Your AI agent gets instant access to your team's collective brain without you having to wrestle with APIs. You can even set up custom actions, like looking up an order in Shopify or triaging a support ticket, from a simple dashboard.

A screenshot from the OpenAI Responses API Reference showing how eesel AI connects to multiple business applications to build its knowledge base.

Structured outputs

The Responses API also makes it easier to get predictable, structured data back from the model. By using the "response_format" parameter, you can tell the model to return a response that fits a specific JSON schema you provide. This is perfect for things like automatically extracting a user's contact details from a support ticket or pulling product info from a customer question.

OpenAI Responses API vs. Chat Completions and Assistants

With the Responses API on the scene, developers now have three main tools from OpenAI. The company is already recommending the Responses API for new projects and has announced that the Assistants API will be retired in the first half of 2026. So, how do they compare?

The Chat Completions API is your workhorse for simple, stateless tasks. It's the fastest and gives you the most control, but you have to manage the conversation history yourself. It's great for one-off jobs like summarizing text, but building a full conversational agent with it means writing a lot of boilerplate code. It’s not going anywhere, so you can keep relying on it for those simpler use cases.

The Assistants API was, for a time, the go-to for agent-like behavior. However, it's notoriously slow and complex, making you juggle a bunch of different objects like Threads, Runs, and Steps. Since it’s officially on its way out, you should probably avoid starting any new projects with it.

That brings us to the Responses API. This is the new standard for any app that needs conversational memory or tools. It finds a great middle ground, offering the powerful stateful features of the Assistants API but with a much simpler, faster, and more flexible interface. If you're starting a new agent project today, this is where you should begin.

The developer's dilemma: Build on the API or use a platform?

When it’s time to build an AI agent for your business, you hit that classic "build vs. buy" fork in the road. Building directly on the OpenAI Responses API gives you total control, but it also saddles you with a lot of hidden work and long-term maintenance that can really slow you down.

The DIY path means more than just calling an endpoint. You'll have to:

• Manage conversation state for every single user, which gets tricky as you scale.

• Write the logic to handle tool calls, parse their outputs, and feed them back to the model correctly.

• Build out custom integrations to connect the AI to your existing tools, like your help desk or internal wikis.

• Create your own analytics and logging to figure out how the AI is doing and where it's tripping up.

• Deploy, monitor, and maintain all of this infrastructure yourself.

For most teams, this is a multi-month project that pulls engineers away from other work.

The platform path with eesel AI is a much faster alternative. Instead of starting from scratch, you get a ready-made platform that does all the heavy lifting.

• Go live in minutes, not months. You can connect your helpdesk (like Zendesk or Freshdesk) and knowledge sources with simple one-click integrations. eesel AI manages the entire backend, so you don’t need to write a single line of API code.

• Total control without the complexity. A powerful, no-code workflow engine lets you decide exactly which tickets get automated and what actions your AI can take, all from a user-friendly dashboard.

• Simulate with confidence. Before you unleash the AI on your customers, you can test it on thousands of your past support tickets. This gives you a surprisingly accurate forecast of your automation rate and shows you exactly how the AI will respond in the wild, a level of risk-free validation that's almost impossible to get when you're building it yourself.

An OpenAI Responses API Reference view of a simulation mode, showing predicted performance based on historical data.

Pricing: OpenAI API vs. a predictable platform

OpenAI's API pricing is token-based, which means you pay for what you use. While this is great for tinkering, costs can get unpredictable as your usage grows, especially when you start using advanced tools that chew through more tokens. You can check out the details on their official pricing page.

In contrast, eesel AI's pricing model is built for predictability. Plans are based on a flat monthly fee for a certain number of AI interactions, and there are no extra fees per resolution. This means your costs don't shoot through the roof just because you had a busy support month. You get all the power of OpenAI's best models without the surprise bills at the end of the month.

This OpenAI Responses API Reference includes an image of eesel AI's predictable, flat-fee pricing model.

Get started without the headache

The OpenAI Responses API is a seriously powerful tool for developers. It pulls the best features of OpenAI's previous APIs into one streamlined interface and is the clear path forward for building smart AI agents.

But building directly on the API is a big commitment that requires a lot of engineering hours and ongoing upkeep. For teams that want to move quickly and focus on shipping value, a platform is almost always the smarter choice.

eesel AI gives you all the capabilities of the Responses API and models like GPT-4o, but wrapped in a self-serve, fully customizable, and easy-to-use package. You can launch a powerful AI support agent in minutes, not months, and do it with the confidence that comes from thorough testing and predictable costs.

Ready to see how easy it can be? Try eesel AI for free and launch your first AI agent today.