From OpenAI Swarm to AgentKit: A Walkthrough of Agentic AI

A single tweet or GitHub repo can set off a wave of hype in AI. Recently, there's been a lot of buzz about OpenAI's "Swarm," an old framework for building AI agents. Many people thought Swarm was making a comeback with major improvements. But that's not quite what's happening.

The real story isn't about Swarm coming back. It's about a bigger shift in how we build AI systems. Instead of using one giant AI model for everything, developers are now building teams of smaller, specialized agents that work together. Think of it like a company where different departments handle different tasks, rather than one person trying to do everything.

This article explains what actually happened to OpenAI's Swarm and why it was shut down. More importantly, it covers what replaced it: OpenAI's AgentKit. You'll see how AgentKit works, what tools it provides like the Agent Builder and Connector Registry, and how companies are already using it to get real results.

We'll also look at the bigger picture. The "agentic AI" trend is about more than just one tool. You'll learn about the research behind these systems, how different companies are competing in this space, and what it means for you as a developer.

A look back: the promise and limitations of OpenAI Swarm

To understand where things are headed, it helps to know where they started. The recent excitement around "Swarm" comes from a real project that OpenAI built and then shut down. Understanding what happened with Swarm makes it easier to appreciate what's happening now.

What was Swarm? An experimental prototype

OpenAI's Swarm framework isn't new. It came out in 2024 as an experimental tool meant for learning and research. The idea was simple: give developers an easy way to experiment with multiple AI agents talking to each other.

Swarm was incredibly simple by design. With less than 100 lines of Python code, you could connect several agents that would use OpenAI's Chat Completions API to communicate and pass work between each other. It was basically a sandbox where developers could play around with multi-agent concepts.

The core concept: simple, stateless agent orchestration

Swarm took inspiration from nature. Think about how ants work in a colony. Each ant does something simple, but when you put them all together, they accomplish complex tasks. Swarm tried to do the same thing with AI agents.

You could set up different agents with different jobs. A Planner Agent might break down a big request into smaller tasks. An Executor Agent could handle one of those tasks, like writing code or pulling data from a database. A Reviewer Agent could check the work before moving on. Swarm gave you the basic framework to connect these agents, but that simplicity turned out to be a problem.

Why Swarm fell short: the missing pieces for production

Swarm was fine for experiments, but it wasn't built for real-world apps. It was missing some critical features that you absolutely need for building reliable, secure systems.

No built-in memory: Swarm agents couldn't remember anything from earlier in a conversation. This made it really hard to handle complex workflows where you need to keep track of what happened before.

No tracing or debugging: When something broke, figuring out what went wrong was a nightmare. There was no way to see how information moved between agents or what decisions they made along the way.

Lack of safety guardrails: Swarm didn't have basic security features that modern AI needs. It couldn't mask sensitive information like credit card numbers, detect when someone was trying to trick the model, or validate what agents were doing.

OpenAI shut down Swarm in March 2025 because of these limitations. They pointed developers to something better: the OpenAI Agents SDK. That was the first real step toward building production-ready agent systems.

The next evolution: introducing OpenAI AgentKit

Shutting down Swarm wasn't the end of the story. The ideas were too good to throw away. On October 6, 2025, at their DevDay conference, OpenAI announced the real successor: AgentKit. This isn't just an updated version of Swarm. It's a complete toolkit built from scratch to help you create, deploy, and manage multi-agent systems that can actually run in production. AgentKit is what people should be excited about.

A production-ready successor to Swarm

AgentKit has everything Swarm didn't. It's powerful, scales well, and includes all the features you need for building enterprise apps. It keeps the core idea of agents working together but adds proper tools for visual development, easy integration with other services, and real security features.

Deconstructing AgentKit: the three core components

AgentKit isn't just one tool. It's three separate parts that work together, each handling a different part of building agents.

Agent Builder: visual workflow creation

The Agent Builder is the main feature of AgentKit. It's a drag-and-drop interface that turns agent orchestration into something visual, like building a flowchart. Instead of writing a bunch of code to manage how agents talk to each other, you just connect nodes on a canvas.

Agent Nodes: These let you create specialized agents. You give each one a name, instructions, and pick which model it should use (like GPT-4.1 or GPT-4o mini).

Tool Nodes: These give your agents access to tools. They might search files, browse the web, or run custom functions you write.

Logic Nodes: These add conditional logic to your workflows. You can use if/else statements, loops, and even require user approval at certain points.

Core Nodes: Basic building blocks for starting and ending workflows, figuring out what users want, and other fundamental operations.

The visual approach makes multi-agent systems way more accessible. You can design, test, and fix workflows much faster than writing everything in code.

ChatKit: seamless app integration

After you've built a workflow in Agent Builder, ChatKit helps you put it into your own apps. It's a set of tools for building chat interfaces and widgets. For example, you could build a weather widget powered by multiple agents. A user types in where they're going, one agent figures out the airport code, another gets the weather data, and a third formats everything nicely. ChatKit makes it easy to drop this kind of experience into your product.

Connector Registry: a universe of tools

An agent is only useful if it can actually do things. The Connector Registry is a library of over 200 ready-to-use connections to popular services. This changes everything. Your agents can now directly interact with:

Productivity Tools: Dropbox, Microsoft Teams, Slack

CRMs and Sales Tools: Salesforce, HubSpot

Developer Tools: GitHub, Jira

Databases and more

These connections give your agents the ability to actually take action across all the software your business uses.

Built for the real world: safety, guardrails, and evaluation

Unlike Swarm, AgentKit comes with enterprise-level security built in. It has safety features that run automatically to make sure your agents behave properly.

PII Masking: Automatically finds and hides sensitive information like names, addresses, and credit card numbers before sending anything to the model.

Jailbreak Detection: Watches for tricky prompts designed to make the AI do things it shouldn't.

Built-in Evals: Gives you tools to trace and measure how well your agents perform. You can see every decision they make, track how many tokens they use, and find bottlenecks to optimize.

AgentKit in action: real companies, real results

Companies are already using AgentKit and seeing real benefits.

Klarna: The payment company built a support agent with AgentKit that now handles two-thirds of their customer service tickets. That's a huge efficiency gain.

Ramp: The finance platform added agent workflows to their development process and cut their product iteration time by 70%.

These aren't just tech demos. AgentKit is the first agent toolkit that actually works at scale and delivers measurable business value.

The bigger picture: understanding the multi-agent AI boom

AgentKit's launch is a big deal, but it's part of something even bigger. The whole AI industry is shifting toward multi-agent systems. We're moving away from trying to build one super-powerful AI that does everything, and toward teams of specialized AIs that work together.

Beyond a single tool: the rise of agentic teams

The core idea is pretty straightforward. For complicated tasks, one AI model, no matter how good, isn't always the best solution. It's better to build a team of agents where each one is really good at one thing, and they collaborate to get the job done.

Here's an example of how this might work for a marketing campaign:

A Manager Agent gets the big-picture goal: "Launch a campaign for our new product." It breaks that down and gives tasks to specialist agents:

• An Analyst Agent gathers data about who you're trying to reach
• A Copywriter Agent writes the ad copy
• A Designer Agent creates the visuals
• A Planner Agent schedules all the social media posts
• A Checker Agent reviews everything to make sure it's consistent and high quality

This approach where agents work together and pass things off to each other works better than trying to make one model do everything. It's more reliable, more efficient, and easier to scale.

Academic foundations: swarm intelligence meets LLMs

There's real research backing this up. A November 2025 paper on arXiv called "LLM-Powered Swarms: A New Frontier or a Conceptual Stretch?" looked at how swarm intelligence principles (like how ants optimize their paths) could apply to large language models. The research showed that multiple agents working together can find good solutions way faster than a single agent working alone.

The main problem they found was compute cost. Running multiple models at once uses a lot of computing power. That's where the next generation of AI models comes in.

Tackling the compute challenge with smarter models

Instead of just throwing more compute at the problem, newer models are getting more efficient. OpenAI's upcoming GPT-5 is rumored to have a multi-model architecture that will make agent systems more powerful while using tokens more efficiently. We're already seeing this trend with GPT-4o mini, which is 30% more efficient than the previous version. This makes running multiple agents cheaper and faster.

The competitive landscape: open source vs. walled gardens

OpenAI isn't the only company working on this. The multi-agent trend has created a competitive market with lots of options.

OpenAI's AgentKit is powerful and well-integrated, but it's more of a closed ecosystem. It's easy to use and works great with OpenAI's models, but you're somewhat locked into their platform.

Anthropic's MPC Ecosystem and other open-source frameworks give you more flexibility. In just six months, developers built over 200 tools using these open standards. This gives you more control and helps you avoid getting locked into one vendor.

This competition is good. It's pushing everyone to innovate faster and giving developers more choices.

What this means for you: a developer's guide to the agentic future

So what does all this mean for you as a developer? The implications are big. There are huge opportunities here, but also some challenges you need to know about.

The opportunity: massive upside and 10x productivity

The biggest takeaway is the potential for a huge productivity boost. Agents could become your best developer tool, handling boring tasks and acting like super-efficient teammates. Agents can already do things like:

• Write boilerplate code
• Automate code reviews
• Generate unit tests
• Research topics and summarize documentation
• Manage deployment workflows

When agents handle this stuff, you can focus on the creative, high-level work like architecture and problem-solving. The idea of 10x productivity isn't hype. It's actually becoming real.

The challenges: navigating hype, pitfalls, and technical hurdles

There are some downsides and problems to watch out for.

The "30-Day AI War Cycle": Things are moving incredibly fast. Companies are shipping new features constantly, which sometimes means things aren't as polished or well-documented as they should be.

Vendor Lock-in vs. Open Standards: You need to make some strategic choices. Do you go with an integrated system like AgentKit because it's easy to use? Or do you stick with open standards like MCP (Multi-agent Communication Protocol) for more flexibility long-term? People are having heated debates about this on X and other platforms.

The Latency Problem: Multi-agent systems involve a lot of steps. Agents hand off work to each other, retry failed tasks, make sub-decisions, and call tools. Each step adds a bit of delay. Figuring out how to optimize these complex workflows so your users don't notice the lag is a real engineering challenge.

The dawn of the agent economy

Looking ahead, we're not just building apps with agents. We're building an agent economy. In the future, swarms of autonomous agents won't just build software but also maintain it, debug it, and upgrade it with minimal human help. They'll become like persistent digital workers in a new kind of economy, working with each other to get things done.

Final thoughts

The recent buzz around OpenAI's Swarm was based on a misunderstanding, but it pointed to something important. The era of single, do-everything AI models is ending. We're moving toward collaborative, multi-agent systems. This isn't just hype. It's a fundamental change in how we build software, and it's already delivering real value.

AgentKit isn't Swarm coming back. It's a much more powerful, production-ready upgrade that's driving this wave of agentic AI. With its visual builder, extensive integrations, and enterprise security features, it gives developers an unprecedented toolkit for building the next generation of AI applications.

For developers, this is a critical moment. The tools are maturing, the use cases are clear, and the potential for a massive productivity jump is real. Here's what you should do: start experimenting now. Try out AgentKit, check out the open-source alternatives, and think about how you could build your own teams of AI agents. The question isn't whether agents will change how you build software. It's how you'll use them to supercharge what you can do and build things you couldn't before.