Parlant vs LangGraph
Parlant vs LangGraph
LangGraph is a general agent framework for building agents and automated processes as graphs of steps.
Parlant is built for one 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 use Parlant when your coding agent could build on LangGraph
- What your coding agent would build from scratch, and what that means for your customers
- What happens when customers mix topics, and when your rules change
- What it costs to run, and when LangGraph is the better choice
If your coding agent, an engineer, or an article suggested LangGraph, these are probably your questions:
My coding agent says it can build our support agent on LangGraph. Why use Parlant instead?
It can, because LangGraph is generic enough to build almost anything. That's also the catch. LangGraph gives your coding agent building blocks. It doesn't know how a support conversation should work, so your coding agent has to design that part itself: which rules apply right now, what to check before acting, and what the agent may say.
Parlant already contains that design. You describe your business rules in plain language, and Parlant brings the right ones into each conversation as it develops, then checks the agent's actions and replies against them. Your coding agent spends its time on your service, not on the machinery underneath it.
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 allowed our team to focus on solving the core business problem instead of spending time on low-level orchestration.” Sarthak Dalabehera, Principal Engineer, Slice Bank. Read the homepage testimonial.
“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.
What would my coding agent have to build from scratch, and why should I care?
On LangGraph, your coding agent would build the parts that decide whether customers can trust the agent. For example:
- Bringing in the right rules when a conversation moves between topics, and keeping earlier ones in play.
- Checking an action before it runs, so the agent doesn't cancel an order or issue a refund the customer never confirmed.
- Checking a reply before it's sent, so the agent doesn't tell a customer a refund went through when it didn't.
- Keeping critical replies in your approved wording.
- Letting a person take over the same chat, and handing it back without losing anything.
- Remembering what the customer agreed to in a long conversation.
Your coding agent can write each of these. The hard part is that they depend on each other: a reply check is only as good as what it knows about the actions that ran, and an action check is only as good as the rules it was given. Getting them to work together means discovering edge cases one customer complaint at a time. Parlant has already done that work, and tested it, so you start from an agent that behaves well and shape it from there.
Will a hand-built agent follow my rules when customers mix topics or change their minds?
Customers don't ask one thing at a time. Take: "I need to replace my headphones, but first check if the warranty covers it, and also change my delivery address."
A common way to build this on LangGraph is to split the agent into specialists, one for returns, one for warranties, one for delivery, with a supervisor routing between them. Each specialist only sees the rules for its own topic. So the returns specialist can place a replacement the warranty doesn't cover, or send it to the old address. Every topic was recognized correctly, and the customer still got the wrong result.
Parlant brings the relevant rules, information, and tools into one agent's context, so connected requests are handled together, including when the customer changes their mind halfway.
What happens when our policies change?
In Parlant, a policy is a plain-language rule. When the business changes, you describe the change to your coding agent, it updates or adds the policy, and Parlant works out when it applies. You don't redraw the conversation's paths.
In a hand-built agent, where a new rule goes depends on how the agent was built. A rule that crosses topics may have to be added in several places, and every change needs retesting across the conversations it touches. That's manageable for a handful of rules and gets harder as your service grows. Scaling complexity shows how Parlant keeps a growing rule set manageable.
What does it cost to run, and what do I give up?
Both send their work to model providers you choose and pay directly. Parlant's extra checks add model work only where you turn them on. A reply check, for example, costs more and makes the customer wait for the checked answer. You can save it for the situations that matter, and use adaptive effort to spend more only when a conversation becomes sensitive. Spend management tracks what each conversation costs.
The fair comparison isn't Parlant against a bare LangGraph agent. It's Parlant against the LangGraph agent you'd actually put in front of customers, with the protections your coding agent would have to add.
What you give up is building everything your own way: you adopt Parlant's design for customer conversations. You can still choose the model for each job, and developers can customize prompts or replace parts of the engine when a requirement calls for it.
When is LangGraph the better choice?
When the job isn't a conversation with your customers. LangGraph is a strong fit for automated work that runs in steps, pauses for approval, and recovers after a failure, such as processing documents or running an internal back-office process. It also makes sense if your team already runs other agents on LangGraph and LangSmith.
You don't always have to choose. A Parlant tool is a function in your project, so your customer-facing agent can start a process your team built elsewhere, including on LangGraph.
How do I compare them on my own service?
Pick one part of your service that's genuinely tricky: a refund with exceptions, a request that touches several topics, or an action that sometimes fails and has to be explained honestly. Give your coding agent the same brief twice, once for a Parlant test project and once for LangGraph.
Then run the same customer conversations against both, including different phrasings, follow-up questions, and a customer who changes their mind. Did each agent do the right thing? Did it say only what's true? How long did customers wait, and what did it cost?
Finally, change one rule and run the conversations again. Count how much work the change took and whether anything that worked before broke. When something goes wrong in Parlant, its diagnostics show which rules and checks led to the reply. Parlant earns its place if it gives your customers better answers, makes changes easier, or both.
Building a Customer-Facing Agent
The illustration below compares what each one supplies for an agent that talks to real customers. The legend after it explains the readiness levels.
LangGraph
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.