Parlant vs Mastra
Parlant vs Mastra
Mastra is a TypeScript agent framework for building agents, memory, tools, and custom flows.
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 Mastra
- What your coding agent would build from scratch, and what that means for your customers
- What happens when customers mix topics or change their minds
- What it costs to run, and when Mastra is the better choice
If your coding agent, an engineer, or an article suggested Mastra, these are probably your questions:
My coding agent says it can build our support agent on Mastra. Why use Parlant instead?
It can. Mastra gives your coding agent good building blocks: agents, memory, and tools, plus ways to add custom logic around what the model reads and writes. What it doesn't give you is a design for support conversations. Your coding agent has to work that out 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. A person can take over the same chat when needed. 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 Mastra, 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.
- Remembering what the customer confirmed or turned down in a long conversation.
Mastra makes it easy to add custom checks, and your coding agent can write each one. The hard part is knowing what to ask it to build, because these pieces depend on each other. In Parlant, long-conversation summaries keep confirmations and refusals, action checks use the rules for the current request, and reply checks look for evidence that an action actually happened. Each depends on the others getting their part right. Parlant has already found those edge cases and tested them together, 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?
Only if your coding agent designs it to. 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." The replacement depends on the warranty decision and on the new address. If the agent handles those topics separately, it can place a replacement the warranty doesn't cover, or send it to the old address.
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. When your rules change, you describe the change and Parlant works out when the new rule applies, instead of your coding agent rewiring the conversation.
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 is Parlant against the Mastra 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, written in Python. 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 Mastra the better choice?
When your product is built in TypeScript and the AI features live inside it, Mastra fits naturally. It's built for TypeScript teams creating custom agent flows, and it's a good choice when the job isn't mainly a conversation with your customers.
Parlant's SDK is Python. Your coding agent writes the code either way, but adding a Python service to an all-TypeScript product is a real cost for whoever runs it. Your app talks to the Parlant agent through the React chat widget or the conversation API, so the rest of your product stays in TypeScript. Weigh that extra service against the customer-facing design you'd otherwise have your coding agent build and maintain.
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 Mastra.
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. Your test can also show that staying with Mastra makes more sense.
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.
Mastra
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.