Python, TypeScript, Java, Go, Kotlin: Which One Is Best to Build an AI Agent?

Python is the best programming language for AI agents in most projects. It has the biggest shelf of ready-made tools and gets you to a first working version fastest. TypeScript can beat it inside web products. Java, Kotlin and Go win in narrower cases.

Choose badly and you’ll feel it months on, when hiring drags or the agent has to talk to your older company software. And most teams are choosing for the first time. Just 31% of developers use agents so far, per the Stack Overflow 2025 Developer Survey. That leaves little experience out there to borrow from.

Python, TypeScript, Java, Go and Kotlin each get a straight look in the sections ahead, good points and bad. Near the end, there’s an easy way to match one to your team.

What Should You Look For in a Language for Agents?

Look at what tools already exist for it and how easy hiring will be. Then ask how well it fits your software and how it holds up under real customers. Raw speed matters less than you’d think.

An agent is a program you hand a goal to. Given a goal, it splits the work into steps and handles each one itself, checking with a chat model on what to try next. Take a customer who wants to know where their order is. The agent finds it and writes back.

The chat model handles the thinking, which means your language seldom holds the agent back. Most of the waiting is the model writing its reply. Where the language does matter is build speed. It also shapes who can maintain the agent down the road and how well it connects to everything else your business runs.

Tools matter first. A few languages already come with ready-made kits for the dull jobs, like remembering a conversation or reaching into your files. Hiring comes next, because an agent needs care for years. Then comes fit. A company that writes all its software in one language will probably need extra people to handle a different one.

Does Python Still Deserve the Top Spot for AI Agents?

Yes, for most teams. Python has the biggest shelf of agent tools and the most people who can work with it. Its main weakness shows up when thousands of people use the agent at once.

Python’s share among developers rose 7 points in one year, based on the same Stack Overflow survey. Data work and smart-software projects drive much of that. New agent tools tend to land in Python first, so teams using it rarely sit waiting for support.

Python also reads almost like plain notes. Even someone who doesn’t code, like a product manager, can often follow what a Python agent is doing. That’s handy for a small team trying out ideas. If a first working version is due in a few weeks, Python is the usual starting point.

Python gets harder to manage as things grow. Once a big team edits the same agent every week, Python code can turn slow and messy. Lots of customers chatting at once also call for extra care. When heavy traffic is likely, teams often leave the thinking part in Python and give the crowd-handling to another language.

Before picking anything, a good AI development company will ask how many people will use the agent, since that answer can change the whole plan.

Does TypeScript Make Sense for Agents in Web Products?

TypeScript is usually the best fit when the agent sits inside a website or web app. It builds on JavaScript, the language behind most websites, and adds a proofreader that flags many slips before the program runs.

Per GitHub’s yearly report, TypeScript passed Python and JavaScript in August 2025 to become the most used language on the platform. About 2.6 million people contributed in it that month. The same GitHub Octoverse report says Python still leads for AI and data work. So both are healthy choices, just for different jobs.

Picture a help chat box inside your online store. The people who built the store already write TypeScript. If they also build the agent, there’s no handoff between two teams and no second set of tools to learn. One group owns the whole thing.

Google has released a TypeScript version of its agent kit, so the major tools no longer belong to Python alone. Where TypeScript lags is number crunching and model training, since fewer helper tools exist for that work. Agents that lean on that kind of work still do better in Python.

Are Big Companies Better Off Building Agents in Java?

Yes, when Java already runs your systems. Your own developers can build the agent, and it goes through the same security reviews as everything else you ship. It costs more typing, and early experiments move slower.

Many large companies run their daily work on Java. Switching an entire team to Python just for one agent can cause more trouble than it solves. Java kits such as Spring AI and LangChain4j exist so those teams can add agents without leaving their home language.

Java’s strength is its long track record. Slow, careful change is a plus when an agent handles payments or customer records. A company that already writes Java also has security people who know how to review it.

Java is slower to work in. The code takes longer to write, so testing ten ideas in one week is a stretch compared with Python. One route is to try the idea in Python and rebuild the winner in Java. When the agent must connect to systems you already run, generative AI development services can help you plan that move.

Where Does Go Fit in Agent Building?

Go fits agents that serve a lot of people at once. Go programs start fast and juggle many jobs at the same time without needing much hardware, which keeps running costs down.

Google’s own write-up on its agent kit says a Go program is ready the moment it starts. It adds that Go suits agents doing many things at once. You can read that in the Google Cloud developer blog.

Go does the heavy lifting around an agent and leaves the thinking to other tools. Imagine ten thousand customers chatting with agents all at once. Go handles that load comfortably.

It has fewer agent kits than Python, though. Hiring can take longer as well, because fewer developers know Go. One setup uses Python for the decisions and Go for carrying the traffic.

Should You Build Agents in Kotlin?

If your product is already written in Kotlin, it’s worth a serious look, Android apps above all. Its community is smaller, so expect fewer guides and fewer answers when you get stuck.

Kotlin is a close relative of Java. It runs on the same engine, and plenty of Android apps are written in it. JetBrains, the company that created Kotlin, built an agent kit called Koog. It works for both Kotlin and Java teams, and it can run agents on Android phones and iPhones too. Google’s agent kit added a Kotlin version as well.

The pull is simple. The appeal is simple reuse. A team with a Kotlin phone app and Kotlin servers can build the agent alongside them, without learning anything new.

The weak spot is community size. When something breaks at midnight, there are fewer answers online than for Python. A team that doesn’t already write Kotlin will find an easier start elsewhere.

How Do These Five Languages Stack Up?

Python leads on tools and on getting a first version out fast. TypeScript is the strongest inside web product. Java and Kotlin make sense where a company already runs on them, and Go earns its place under heavy traffic.

Which Language Fits Your Team Best?

The first thing to look at is the language your developers use daily. Then think about where the agent will run and how big the crowd using it will be. The answers usually point to one language fast.

No one on your team has built an agent before? Go with Python. Guides and developers are plentiful, and moving to something else later is always possible. For an agent inside a web product your team already runs, TypeScript makes the most sense. And a company whose core systems run on Java, with a strict security team, usually does best staying there.

Expecting thousands of people at once, with a tight running budget? Look at Go. If your phone app is written in Kotlin and you want the agent to share that code, Kotlin is worth considering.

Many real projects use two languages. Python often handles the thinking while Java, Go or TypeScript handles the part that touches customers. That works fine as long as someone owns the link between them.

Google’s agent kit now supports Python, Go, TypeScript, Java and Kotlin, which tells you the big vendors expect teams to mix and match.

Why Work With iTechnolabs?

iTechnolabs is certified to ISO 27001:2013 and ISO 9001:2015. Behind that sit 500+ delivered apps and a team of 300+ developers.

The first covers information security, which matters once an agent starts handling customer records. The second covers quality management in how projects run.

Custom software for companies at different stages is what we do, so building around systems you already have is nothing new to us. Tell us the goal and we’ll weigh each language against your budget and your team’s skills with you.

Conclusion

For a brand-new project where speed to a working agent matters most, Python is the usual place to begin. An agent that belongs in a web product usually fits TypeScript better. Companies built on Java or Kotlin usually do best sticking with them. Go is worth a close look if heavy traffic and low running costs worry you most.

No single language wins every time. Python leads because it has the most tools, the most people and the shortest path to a first version. Even so, GitHub’s report shows TypeScript growing fast, and Google backing five languages shows how mixed the field has become.

Picking a language your team can’t maintain, or one that clashes with software you already depend on, does more damage than any ranking can repair. Strong Java developers will ship a better Java agent than a shaky Python one. And if a second language turns out to be needed, a good plan leaves room to add it.

Begin small and try the agent on real tasks for a few weeks. If it holds up, invest in the bigger build. Working in that order protects your budget and leaves you free to change course.

Frequently Asked Questions

What is the best programming language for AI agents?

For most teams, Python is the best programming language for AI agents. New tools usually arrive in Python before anywhere else. Developers who know it are easy to find, and a first working version comes together quickly. TypeScript can edge ahead inside web products, and Java inside large company systems.

Python or TypeScript: Which Suits an AI Agent Better?

It mostly depends on where the agent will live. Python is better when the agent needs data work or you want the most tools. If the agent sits in a website or web app, TypeScript usually fits better, because a single team can then own the whole build.

Does Java Make Sense for an AI Agent?

Yes. Java has kits like Spring AI and LangChain4j built for agents. It suits companies whose main systems already run on Java. The agent can use the same people, the same security checks and the same servers. Quick experiments take a bit longer than in Python, though.

Is Go a Smart Pick for Building Agents?

Go shines under a heavy load, with many users chatting with agents at the same time and a goal of low running costs. It starts fast and handles many jobs together. Go has fewer agent kits than Python, so some teams keep Python for the decisions and use Go around it.

Is Kotlin Worth Using for an AI Agent?

Kotlin fits best when your product is already built in it, Android apps especially. JetBrains makes a kit called Koog that works for Kotlin and Java teams and can run on phones. Teams new to Kotlin will have an easier start with Python or TypeScript.

Is It Okay to Mix Languages in One Agent?

Yes, and mixing is common. Python often handles the decision-making while Java, Go or TypeScript looks after the part that talks to customers. That lets each half use the language that fits it. The arrangement works well when one person is responsible for how the two halves connect.

Will the Programming Language Slow Down an Agent’s Replies?

Barely, if at all. The chat model spends most of the time writing its reply, and your code adds very little on top. Language choice matters more for build speed and hiring, and for how well the agent fits the software you already run.