Parlant vs OpenAI Agents SDK
Parlant vs OpenAI Agents SDK
The OpenAI Agents SDK gives your application an agent loop, tools, conversation history, and guardrails, so agents can complete tasks inside your product.
Parlant is built for a different job: conversations with your customers that follow your business rules.
Your coding agent can build a customer-facing agent on either. The difference is how much it has to invent and get right on its own before customers can rely on the result.
- Why your coding agent might suggest the OpenAI Agents SDK, and what Parlant does differently
- Whether you can keep using OpenAI models with Parlant
- What your coding agent would have to build, and what that means for your customers
- What each costs to run, and when the OpenAI Agents SDK is the better choice
If your coding agent suggested the OpenAI Agents SDK, these are probably your questions:
My coding agent suggested building on the OpenAI Agents SDK. Why would I use Parlant instead?
The SDK is a good choice for what it was designed for: agents that complete tasks inside your application, with ways for several agents to hand work to each other. Talking with your customers is a different job. The agent has to follow your policies while the customer changes topics, pushes for exceptions, or changes their mind, and every promise it makes has to be one your business can keep.
Parlant is built for that job. You describe how the agent should behave as policies, in plain language, and your coding agent adds them to your project. On every turn, Parlant works out which of your policies apply and keeps them in front of the model. It can check an action, like a refund, before it runs, and check a reply before the customer sees it. When a person needs to step in, they can take over the same chat. With the SDK, your coding agent would have to design and build all of that first.
It's also faster for you. On a general agent framework, your coding agent spends its time, and yours, designing and debugging how support conversations should work. On Parlant it starts from that design and goes straight to your rules, so you get to a working agent you can test much sooner.
“Parlant gave us a robust foundation to automate our support without compromising on quality. The speed at which we saw value was truly remarkable.” Arpit Parashar, Deputy Manager, Greenko Group. Read the homepage testimonial.
Can I keep using OpenAI models with Parlant?
Yes. OpenAI support comes built in, and OpenRouter, Parlant's default provider, already uses OpenAI models for some jobs. You can also use Claude, Gemini, and others, and choose a different model for each job: for example, a fast, inexpensive model for routine replies and a stronger one where the stakes are high. You pay the model provider directly, at its usual prices.
What would my coding agent have to build with the SDK, and why does that matter for my customers?
The SDK gives you guardrails where you can run your own checks, and ways to pass work between specialist agents. Those are places to put customer-service decisions, not the decisions themselves. Your coding agent would have to design them: which of your policies apply right now, what a check needs to know to make the right call, and what the customer should be told when a check says no.
That's where your customers feel the difference. Say your policy is "no refunds once a chargeback is filed." The agent starts a refund, then a lookup shows a chargeback. A check that only knows the instructions from before that lookup could still approve the refund. Parlant reconsiders your policies after lookups and applies your priorities when two policies conflict. Its action checks work from the updated policies, and its reply checks compare what the agent says with what its tools actually returned.
You could have your coding agent build these pieces one at a time. But they depend on each other, so building a few of them puts you on the path to building Parlant yourself, and you'd discover each edge case when a customer runs into it. A coding agent can write the code; it can't tell you what the design should be.
Will the agent follow my rules when customers push back, mix topics, or change their minds?
That's what Parlant is designed for. A customer might ask about a refund, mention that the item arrived damaged, and ask to change their address, all in one message. Parlant brings in the policies for each of those as they come up, and keeps them together in one agent, so the answer to one request doesn't contradict another. When the customer pushes for an exception, your policies are still in front of the model on every reply. Where the wording matters, approved responses keep the agent saying it your way.
The SDK can split a conversation between specialist agents, or have a main agent consult them. Keeping your policies, approvals, and connected actions consistent across those specialists as the customer's request changes is design work your coding agent would take on.
What does each one cost to run?
Neither charges a fee per conversation or per resolution. With both, you pay your model provider for what you use, at its usual prices.
Parlant's extra checks add model work only where you turn them on. Checking a reply, for example, takes another model call and makes the customer wait for the finished, checked answer. So you can turn checks on only for the situations that need them, and let adaptive effort spend more only when a conversation becomes sensitive. Spend management tracks what each conversation costs and can cap it. If you'd rather not run servers, the Dashboard hosts your agent for a flat monthly fee per workspace, free while you develop; see pricing.
With the SDK, the cost is the model usage plus whatever your coding agent builds and your team keeps maintaining: the rule checks, the conversation history your customers return to, and the handoff to people. The fair comparison is against the OpenAI Agents SDK agent you would actually put in front of customers, with those protections in place.
If you have developers on the team: Parlant also lets you customize its built-in prompts and replace parts of the engine without forking it.
When is the OpenAI Agents SDK the better choice?
When the agent's job is to complete tasks inside your product, rather than to hold conversations with customers under your policies. The SDK is also a natural fit if you want OpenAI's companions for it: ChatKit, an embeddable chat interface, and Realtime voice for spoken conversations. Parlant has its own chat widget. It fits when the conversation is the product, and the agent has to follow your policies with customers who don't always cooperate.
How can I compare them on my own service?
Pick one part of your service that matters, like refunds or order changes, and ask your coding agent to build it both ways: once in a Parlant project and once with the SDK. Use the same policies and test versions of the same tools.
Then talk to both the way real customers do. Ask for two things at once, change your mind halfway, push for an exception your policy doesn't allow, and ask the agent to confirm something it hasn't done yet. Compare what each one did and said, how long you waited for replies, and what each conversation cost. In Parlant, you can run these conversations as simulations in the Dashboard and see which policies shaped each reply.
Finally, change one of your policies, and see how much work each version needs and whether conversations that worked before still do. Your test may also show that the OpenAI Agents SDK fits your case better, which is worth knowing too.
Building a Customer-Facing Agent
The following illustration compares coverage of real-world customer-facing needs, as of September 2026. It includes the Agents SDK and the named ChatKit and Realtime companions. Your application hosts the SDK code. Each explanation says what's supplied and, for lower scores, what your coding agent would still need to build.
OpenAI Agents SDK
Parlant
The readiness levels indicate how well each feature is prepared for customer-facing use cases.
- 1/3: Requires adaptation. The feature supplies a foundation, but its approach needs substantial adaptation for real-world customer conversations.
- 2/3: Needs customization. The feature fits the customer-facing need but requires a focused extension of its existing behavior. The explanation names that gap.
- 3/3: Comprehensive. The feature covers the customer-facing need, including practical challenges and common edge cases. Your business rules and integrations still need setup and testing.