OpenAI realtime tool calls: A complete overview

Conversational AI is getting pretty wild. We're moving beyond the clunky chatbots of yesterday and into a world with voice agents that can actually hold a conversation in real-time. But what makes them truly useful isn't just that they can talk, it's that they can do things. That’s where OpenAI Realtime Tool Calls come into the picture. This is the tech that lets a voice agent perform actions and pull in live data mid-sentence, turning a simple chat into something genuinely helpful.

This post will walk you through what this technology is, how it works, and where it really shines. We'll also get real about the challenges of trying to build with it from scratch. While OpenAI's raw API is powerful, trying to tame it is a major engineering project. As you'll see, there are much simpler ways to get all the power without the headaches.

What are OpenAI Realtime Tool Calls?

So, what's the big deal with these tool calls? Simply put, they're a feature in OpenAI's Realtime API that lets a voice AI connect to external tools during a live conversation. This is a big jump from the function calling you might be familiar with from text-based models. The key difference is speed. Realtime tool calls happen with incredibly low latency, which is essential for voice chats where even a tiny pause can feel awkward and break the flow.

Think of it this way: it’s like giving your voice assistant the ability to not just listen and talk, but to also open up another app to find an answer for you, all while you're still talking.

This is what turns a voice agent from a neat party trick into a real workhorse. It’s the magic that lets them check your order status, book an appointment, or pull up your account details on the fly. For things like customer support, sales, or even just a personal assistant, this capability is non-negotiable.

How OpenAI Realtime Tool Calls work

Unlike a standard API call where you send a request and get a response, the Realtime API keeps an open line, using something like a WebSocket or WebRTC. This allows for a continuous, back-and-forth conversation between your app and the OpenAI model.

The official docs point to two main ways to connect: WebRTC for browser-based apps and WebSocket for things running on a server. Whichever you use, the process for a tool call during a live chat follows a few key steps.

Let's walk through what happens when you ask your voice agent a question:

1. Setting the stage: Your app connects to the Realtime API and tells it which "tools" or functions the AI is allowed to use. This could be anything from "lookup_order_status" to "check_product_inventory".

2. The user speaks: You start talking. Your app streams your voice directly to the API in little chunks.

3. The AI gets an idea: As the AI listens, it decides if it needs to use one of its tools to answer you. If you ask, "Hey, where's my latest order?" the model recognizes it needs to trigger the order lookup tool.

4. The API sends a signal: The API sends an event back to your app that basically says, "I need you to run a function." This message includes the function's name and any arguments, like "name: "lookup_order"" and "arguments: {"order_id": "12345"}".

5. Your app does the work: Your backend code catches this signal and runs the function. It might ping your Shopify database or internal API to get the order status. Let's say it finds out the order has "shipped."

6. Sending the results back: Your app then packages that "shipped" status into a message and sends it back to the Realtime API, letting the model know what it found.

7. The final answer: Armed with this new info, the model generates a natural-sounding audio response and streams it back to you. You'll hear something like, "I've just checked, and your order #12345 has shipped!"

This whole loop happens in the blink of an eye, creating a smooth conversational experience that feels surprisingly natural.

Key use cases and benefits of OpenAI Realtime Tool Calls

Realtime tool calls are what allow voice agents to solve actual problems. Here are a few places where this tech is already making a difference.

Customer support automation

This is probably the biggest one. An AI agent can handle a ton of common support questions instantly, any time of day.

• Order management: An agent can check order statuses, find tracking numbers, or start a return by calling a company's backend systems, whether that's Shopify, Magento, or something custom.

• Account inquiries: Customers can ask about their balance or recent transactions, and the agent can securely pull that data from a CRM or customer database.

• Ticket management: By connecting to a helpdesk like Zendesk or Freshdesk, an agent can create, update, or escalate support tickets right from the call.

Interactive personal assistants

Beyond support desks, voice agents with tool-calling skills can be genuinely useful personal assistants.

• Scheduling: They can book appointments or check your availability by hooking into services like Google Calendar.

• Communication: An agent could draft and send an email for you or post a message to a Slack channel, all from a quick voice command.

Internal IT & HR support

Companies are also using this to automate their internal helpdesks, freeing up IT and HR folks from repetitive questions.

• IT helpdesk: An employee could ask a voice bot, "What's the status of my IT ticket?" The agent can then call the Jira or ServiceNow API to give an immediate update.

• HR questions: A new hire could ask about company policies, and the agent could pull answers straight from an internal knowledge base in Confluence or Google Docs.

The payoff for getting this right is pretty obvious: conversations flow without those awkward, robotic pauses; voice agents become active problem-solvers; and customers and employees get answers right away, without sitting on hold.

Challenges of building directly with OpenAI Realtime Tool Calls

While the OpenAI Realtime API is an incredible piece of tech, trying to build a production-ready voice agent on top of it is a whole different beast. It's not a weekend project, and it comes with a bunch of engineering hurdles that can trip up even skilled teams.

Complicated initial setup

Right from the start, you're not just hitting a simple REST API. You have to manage persistent WebSocket or WebRTC connections, juggle dozens of different server and client events, and write a lot of resilient code just to handle the back-and-forth. This requires specialized real-time engineering skills that aren't always easy to find. You're basically building a mini infrastructure project just to get to square one.

Difficult context management

The Realtime API has a hard 15-minute limit on sessions. If a conversation goes longer, or if you want the agent to remember a user from a previous call, you're on your own. You'll have to build a system from scratch to save, summarize, and reload conversation history. That's a lot of extra work and another place where bugs can creep in.

Lack of a testing environment

This might be the biggest risk of all. The raw API gives you no way to safely test your agent before you point it at your customers. You just have to build it, deploy it, and cross your fingers. There’s no way to know your potential automation rate, estimate your costs, or find out where the agent is likely to stumble. It’s a pretty high-stakes guessing game.

In contrast, a platform like eesel AI was designed specifically to fix this. It has a powerful simulation mode that lets you test your agent on thousands of your own past support conversations. You can see exactly how it would have handled real-world situations, get accurate forecasts on resolution rates, and tweak its behavior before it ever talks to a live customer.

Manual and rigid workflows

With the raw API, every tool call, every escalation path, and every bit of logic has to be hard-coded by a developer. Want to change the agent's tone or add a new tool? That means another development cycle. This makes the whole system rigid and locks out the non-technical people, like support managers, who actually know what the agent should be doing.

A managed platform like eesel AI completely changes the game with a fully customizable workflow engine and a simple UI. Your support team can set up rules, customize the AI's personality, and connect new tools without writing any code. It gives you the power of the API with the flexibility your business actually needs.

OpenAI Realtime Tool Calls pricing

Cost is obviously a huge factor when you're looking at voice agents. OpenAI's pricing for its real-time models is based on how many "tokens" are used for both the audio coming in and the audio going out. Because everything is broken down into these tokens, it can be tough to predict what a single conversation will actually cost.

Here are the current rates for the main speech-to-speech models:

Model	Input (per 1M tokens)	Cached Input (per 1M tokens)	Output (per 1M tokens)
"gpt-realtime"	$32.00	$0.40	$64.00
"gpt-realtime-mini"	$10.00	$0.30	$20.00

While OpenAI gives you a big discount for "cached" input tokens (parts of the audio it has already processed), your costs will still bounce around depending on how long people talk and how chatty the AI is. This token-based model can lead to some unpredictable bills, which makes budgeting a challenge.

This is another area where a platform approach can make life easier. For example, eesel AI offers transparent, predictable pricing based on a set number of AI interactions per month. You know exactly what you’re paying, with no surprise charges based on tokens or resolutions.

The simpler, faster alternative to building with OpenAI Realtime Tool Calls

The OpenAI Realtime API is an amazing piece of foundational tech. But as we've seen, building a business-ready voice agent involves so much more than just the core AI. You need connection management, testing tools, context handling, a scalable way to call functions, and an interface that your team can actually use.

This is where a managed platform comes in. Instead of spending months and a small fortune on an engineering team to build all that infrastructure from the ground up, you can use a solution that's already done the heavy lifting.

eesel AI is a platform that handles all this complexity behind the scenes. Our AI Agent uses powerful models like OpenAI's but wraps them in a self-serve platform built for customer support and ITSM. You get all the power of real-time tool calls without any of the engineering overhead.

With a platform like eesel AI, you can:

• Go live in minutes: Use one-click integrations with help desks like Zendesk, Freshdesk, and Intercom to get up and running right away.

• Have total control: Use a visual, no-code workflow builder to define exactly what your AI does, from its personality to the tools it can access.

• Roll out with confidence: Simulate your agent's performance on thousands of your past support tickets to know exactly what to expect before you flip the switch.

Putting it all together

So, what's the takeaway? OpenAI Realtime Tool Calls are a huge step forward for conversational AI, making it possible to create voice agents that can do more than just talk.

However, the DIY approach of building directly on the API is a long, expensive, and risky road. For most businesses, it just isn't a practical choice.

If you want to deploy a reliable and effective voice agent without having to hire a whole new engineering team, a platform like eesel AI is the fastest and safest way to get there. You get all the benefits of the cutting-edge tech, without any of the headaches.

Ready to build a powerful AI voice agent without the engineering marathon? Sign up for eesel AI for free and see how you can automate your frontline support in minutes.