OpenAI's AgentKit Just Killed Swarm

The Ghost of Swarm: Why a Dead Framework Is Trending

Swarm Hype Explodes again on X, and not because OpenAI secretly resurrected a dead framework. Scroll the Socials and you’ll see viral clips of “Swarm-style” agents doing code reviews, research sprints, and end‑to‑end workflows, each racking up thousands of likes and quote tweets from AI devs chasing the next productivity unlock.

The confusion comes from a simple mismatch: branding versus reality. Influencers keep name‑dropping OpenAI Swarm and teasing a mysterious “Swarm Update,” while the actual engine behind most of these demos is AgentKit, OpenAI’s new agent platform, not the 2024 experiment everyone remembers.

Swarm itself was a tiny, almost toy‑level orchestration framework OpenAI pushed in 2024. It showed how you could wire up multiple stateless agents in under 100 lines of Python using the old chat completions API, passing control from one Agent to another like a baton.

That minimalism made Swarm catnip for hackers and educators. You could skim a single file, understand the whole system, and fork an Example in minutes, but you also hit hard limits the second you tried to run anything serious on top of it.

Swarm shipped without production‑grade essentials. No built‑in memory, no structured tracing, no safety guardrails, and no opinionated patterns for retries, escalation, or human‑in‑the‑loop checkpoints—just bare prompt handoffs and some routing logic.

By March 2025, OpenAI quietly made that status official: Swarm was deprecated and shelved. Documentation and dev rel pointed people toward the newer agent SDK, a more robust foundation that would eventually sit underneath AgentKit.

So when creators talk about a 2025 Swarm Update, they are mostly talking about vibes, not a version bump. The phrase Swarm Framework stuck as shorthand for multi‑agent orchestration, even after the original codebase stopped evolving.

What actually changed the game in late 2025 was AgentKit’s arrival. OpenAI wrapped orchestration, safety, observability, and integrations into a single stack: visual builders, drop‑in chat UIs, and a connector registry with 200‑plus services.

Today’s hype cycle centers on that stack, not a zombie repo from 2024. Swarm is the ghost in the story—useful as a reference point—but the real plot now belongs to its far more powerful spiritual successor.

Meet AgentKit: OpenAI’s Real Multi-Agent Play

AgentKit arrived at DevDay on October 6, 2025 as OpenAI’s answer to a question Swarm never solved: how do you build multi-agent systems that don’t fall apart outside a demo? Where Swarm was a clever GitHub repo, AgentKit is a full-stack platform designed to live inside real products, with real users, and real SLAs.

Swarm stayed a prototype by design. It wired together stateless agents in under 100 lines of Python using the old chat completions API, but shipped with no memory layer, no tracing, and almost no safety. AgentKit flips that script with opinionated defaults for observability, policy, and scaling, so teams can move from hackathon to production without swapping frameworks mid-flight.

At the center sits the visual Agent Builder, a drag-and-drop canvas that looks more like Figma than a terminal. Developers chain planners, tools, retrievers, evaluators, and human checkpoints as nodes, then version, test, and promote those flows like any other software artifact.

Agent Builder also bakes in the ugly plumbing Swarm left to users. You define long-term memory stores, configure tool schemas, attach MCP servers, and wire guardrails at the graph level, so every agent in a workflow inherits the same safety and logging stack by default.

ChatKit turns those workflows into shippable UX. It gives teams an app-ready chat surface—web components, mobile SDKs, and design tokens—so the same agent graph can power internal consoles, customer-facing copilots, or embedded widgets without rebuilding the front end each time.

Underneath, ChatKit handles session state, user identity, and multi-tenant isolation. That matters when a single deployment might serve thousands of concurrent users and dozens of agent types, each with different permissions, tools, and data scopes.

The Connector Registry is where AgentKit pulls away from every Swarm-inspired clone. OpenAI ships more than 200 plug-and-play connectors for systems like Dropbox, Google Drive, Slack, Microsoft Teams, Salesforce, Jira, GitHub, and Snowflake, all managed from a central workspace.

Instead of hand-rolling OAuth flows and brittle API wrappers, teams flip connectors on, map roles and fields, and immediately expose those tools to specific agents. Policy controls and audit logs ride along, so security teams can actually sign off on multi-agent access to production data.

Taken together, Agent Builder, ChatKit, and the Connector Registry attack the fragmentation that’s defined agentic AI so far. AgentKit replaces a sprawl of bespoke scripts, UI shells, and integration glue with a single opinionated stack aimed at one job: turning multi-agent experiments into stable, supportable software.

From LEGOs to Logistics: Building with AgentKit

AgentKit’s Agent Builder looks less like an IDE and more like a no-code automation studio. Developers drag blocks for planners, tools, retrievers, and evaluators onto a canvas, wiring them together like Lego bricks. Under the hood it compiles into a full agent graph, but on the surface you’re rearranging colored nodes, not juggling async callbacks and brittle glue code.

Workflows that used to demand hundreds of lines of orchestration logic now fit on a single screen. You can route a user query to a planner, fan it out across several specialized agents, then aggregate their results into a final response. Every edge on the graph is explicit, which makes debugging multi-agent handoffs far less of a black box.

Human-in-the-loop guardrails live directly in this canvas. You drop in review checkpoints where a human must approve an action, sign off on a tool call, or override an agent’s decision. Instead of bolting on moderation at the API gateway, you model escalation paths visually: “if high-risk, pause and page legal.”

Power comes from the Connector Registry, which behaves like an app store for agent capabilities. OpenAI ships over 200 plug-and-play integrations, spanning: - Dropbox, Google Drive, and Box for file access - Slack, Teams, and email for communications - Salesforce, HubSpot, and assorted CRMs for customer data - GitHub, Jira, and CI tools for engineering workflows

You bind connectors to nodes in Agent Builder, so a “research” agent can pull PDFs from Dropbox while a “support” agent queries your CRM. OAuth, secrets rotation, and scopes stay centralized in the registry, not scattered across environment variables and bespoke scripts.

Once an agent graph works, ChatKit turns it into something users can touch. Developers embed ChatKit widgets into web apps, internal dashboards, or mobile clients, with full control over branding, roles, and permissions. A single ChatKit surface can route different intents to different agents behind the scenes, so “refund this invoice” quietly triggers finance automations while “summarize this deck” hits a knowledge worker agent.

For more technical detail, OpenAI’s own breakdown in Introducing AgentKit - OpenAI Official walks through these components and their production constraints.

Built for Battle: Enterprise-Grade Safety and Evals

Built for hobby projects, Swarm never had to worry about auditors or compliance teams. AgentKit does. OpenAI is pitching it as an enterprise-grade control plane for agents, with safety, observability, and optimization wired in from the first API call, not bolted on later.

Where Swarm shipped as “under 100 lines of code,” AgentKit ships with policy. Every request and tool call flows through guardrails that enforce organization-wide rules: which data an agent can touch, which connectors it can hit, and how aggressively it can act without human sign-off.

Data privacy moves from a GitHub example to a hard requirement. AgentKit bakes in PII masking, automatically redacting emails, phone numbers, account IDs, and other identifiers from traces and logs so teams can debug agents without leaking customer data into observability pipelines.

Jailbreak memes on X meet a much less forgiving runtime. AgentKit runs layered jailbreak detection on prompts, intermediate thoughts, and tool outputs, blocking prompt injection attempts, role hijacks, and data exfiltration patterns before they propagate through a multi-agent workflow.

Instead of developers screenshotting wild outputs, AgentKit leans on an integrated Evals framework. Teams can define eval sets, run them across agents and tools, and compare runs over time as they tweak prompts, routing logic, or models.

Crucially, those evals tie directly into production traces. Developers can: - Trace every step of an Agent across planners, retrievers, and tools - Attach scores from automated or human graders - Slice performance by customer segment, use case, or model version

That feedback loop sets up the next phase: reinforcement fine-tuning. In November 2025, OpenAI rolled out an RFT beta that lets teams optimize custom reasoning strategies and tool-use policies based on real-world traces, not synthetic benchmarks.

RFT doesn’t just nudge prompts. It trains models to choose better tools, sequence calls more efficiently, and skip unnecessary hops. Early internal tests show 04 Mini running up to 30% more token-efficient on complex, tool-heavy workflows when tuned with these agent-specific signals.

Stack all of that together and AgentKit stops looking like a dev toy. It looks like infrastructure.

From Hype to Reality: How Companies Are Winning Today

Hype cycles don’t pay invoices; shipping product does. AgentKit is already past the proof-of-concept stage, with real companies quietly wiring it into the parts of their business where latency, uptime, and dollars actually matter. The numbers coming out of early adopters read less like a lab demo and more like a playbook.

Payment processor Ramp is the clearest example. By rebuilding its internal engineering copilots on AgentKit’s multi-agent stack, Ramp reports cutting iteration cycles by roughly 70%. That means feature experiments, bug triage, and internal tooling updates move in days instead of weeks, because agents handle code review, regression checks, and documentation threads in parallel.

Under the hood, Ramp leans on specialized agents instead of one monolithic assistant. A planner agent breaks work into subtasks, a tooling agent hits CI/CD and observability APIs, and a documentation agent rewrites specs and changelogs. AgentKit’s Connector Registry glues this together, so each agent talks to the same code repos, ticketing systems, and logs without another brittle integration layer.

Coda is pushing just as hard on the customer side. Using AgentKit, the company automated roughly two-thirds of its inbound support tickets, routing only the gnarly edge cases to humans. Routine issues—billing confusion, workspace access, basic template questions—go through a triage agent, a retrieval agent that hits knowledge bases, and an escalation agent that flags anything uncertain.

Crucially, Coda keeps humans in the loop without drowning them. Agents draft responses, surface relevant docs, and propose resolutions; support reps approve, tweak, or override. Built-in evals and safety rails monitor accuracy, hallucination rates, and customer satisfaction scores so the system improves instead of silently drifting.

Taken together, those metrics—70% faster engineering cycles, 66% ticket automation—turn AgentKit from a flashy dev toy into something else: a repeatable, measurable toolkit for cutting operational drag at scale.

The Agentic AI Wave: This Is Bigger Than One Tool

AgentKit hype sits inside a much larger 2025 shift: AI is moving from single, do-everything models to networks of specialized agents that cooperate. Instead of one giant prompt trying to juggle planning, tool use, and verification, teams are spinning up planner agents, researcher agents, executor agents, and critics that negotiate over the best path forward.

Think ant colony, not supercomputer. Multiple agents swarm a problem from different angles, share partial progress, and converge on answers faster than a single monolithic system that has to reason end‑to‑end in one shot.

Researchers are formalizing this with “ant colony” style optimization over LLM swarms. A November 10 arXiv paper showed that multi-agent search, feedback, and voting loops can reach higher‑quality solutions with fewer failed runs, especially on complex reasoning and code tasks.

The catch: every extra agent adds computational overhead. Each handoff means more tokens, more context to juggle, more traces to store. Naive swarms balloon latency and cost, with dozens of sub-decisions and retries per user request.

Next‑gen models are starting to blunt that pain. GPT‑5’s multimodal stack can keep shared context across agents, route calls efficiently, and compress intermediate reasoning so swarms don’t just brute‑force their way through problems. OpenAI claims 04 Mini under RFT is already 30% more token‑efficient in multi‑step workflows.

Industry numbers back up why everyone is racing here. Internal benchmarks from trading firms show Jensen’s swarm sampling approach boosting quant rewards by 94%, while early adopters report 5–10x faster experimentation cycles when they move from single agents to coordinated teams.

Consultancies are codifying this into boardroom language. McKinsey calls agentic AI “the top frontier of tech,” projecting that AI‑driven workflows could jump from roughly 3% of enterprise processes today to 25% by the end of 2025 if multi‑agent systems mature as expected.

Ecosystems are fragmenting and accelerating at once. Anthropic’s MPC tooling exploded to 200+ community-built components in six months, while open‑source players like DeepSeek and Llama 3 chase more transparent, hackable stacks than OpenAI’s curated AgentKit.

Developers hungry for lower‑level control still mine the original Swarm ideas, with the OpenAI Swarm - GitHub Repository acting as a historical blueprint for lightweight orchestration even as AgentKit and its peers define the production future.

The Open-Source Arena: AgentKit vs. The World

AgentKit might be OpenAI’s shiny new engine, but it’s rolling into an arena that looks more like Kubernetes in 2016 than a cozy platform monopoly. Developers are already split into camps: stay inside OpenAI’s AgentKit walled garden, or bet on open, model-agnostic stacks that won’t age out with the next DevDay keynote.

Anthropic’s MPC ecosystem has become the default counterweight. Built around the Model Context Protocol, MPC turns tools, data sources, and whole backends into network-addressable capabilities that any compliant agent can call. Over 200 open tools launched in six months signal a very different philosophy from AgentKit’s curated Connector Registry and opinionated Agent Builder.

Where AgentKit promises batteries-included orchestration, MPC sells composability. You can wire Claude, GPT‑5, or a local Llama model into the same workflow as long as they speak MPC. That flexibility appeals to teams already burned by migrating off Swarm when OpenAI deprecated it in March 2025 and redirected everyone to the newer agent SDK stack.

Vendor lock-in is no longer an abstract worry; it’s a weekly X argument. Developers point out that AgentKit tightly couples: - Model choice - Orchestration logic - Telemetry and evals - Connectors and hosting

Switching away later means rebuilding not just prompts, but entire workflows, traces, and safety policies. MPC-first advocates counter that open protocols let you swap models or hosting providers without ripping out your agent logic.

Open-source challengers are sharpening that argument. Deepseek is pushing aggressively optimized, low-cost models that undercut GPT‑4.1 and GPT‑4.5 on price-per-token while staying “good enough” for many agentic workloads like code refactors, log triage, and document routing. For teams running thousands of concurrent agents, a 30–40% cost delta matters more than a few benchmark points.

Llama 3—often misspelled as Llama 3 in social threads but still the de facto open model brand—anchors a different strategy: self-hosted agents on your own GPUs or VPC. That path trades AgentKit’s polished safety stack and evals for full control over data residency, latency, and fine-tuning. Enterprises in finance and healthcare increasingly prototype on AgentKit, then harden on Llama-based stacks once requirements stabilize.

All of this pushes AgentKit into a familiar role: the fastest way to ship something real, not necessarily the final destination. In 2025’s agentic AI wave, the smart move for developers is designing for portability—treating AgentKit as one powerful endpoint in a wider, protocol-driven ecosystem rather than the only game in town.

The Developer's Dilemma: Pitfalls of the AI Gold Rush

Thirty-day AI war cycles sound exciting until you’re the one shipping into that blast radius. Agentic stacks now move from GitHub repo to “production” in a weekend, pushed by founders chasing screenshots for Socials and investors demanding Swarm Hype Explodes moments every month. Quality, testing, and basic observability often trail far behind the demo.

Multi-agent systems amplify every sharp edge. A single Agent hallucinating is bad; five agents handing off partial truths can quietly corrupt an entire workflow. Developers report more “it worked in staging” failures as agents misinterpret each other’s outputs, misroute tasks, or loop on the same subgoal until rate limits hit.

Subtle hallucinations become a structural problem, not a quirky model bug. Planning agents may invent tools that do not exist, fabricate API fields, or infer permissions that were never granted. In a swarm, those errors propagate: an executor trusts the planner, a retriever trusts the executor, and the final response looks polished but is wrong in ways that evade casual testing.

Debugging this mess is its own discipline. A non-trivial multi-agent workflow can emit thousands of trace events per request: tool calls, retries, planner revisions, sub-decisions, and cross-agent messages. Developers talk about scrolling through 5,000-line traces just to understand why a single support ticket escalated instead of resolving autonomously.

Latency also explodes. Each extra agent hop adds model latency, network round-trips, and tool overhead. Without ruthless pruning—fewer agents, capped planning depth, aggressive caching—teams see workflows that started at 3 seconds stretch to 30+ seconds, then time out entirely once real user traffic hits.

Scaling turns these annoyances into outages. Ten users hitting a multi-agent flow is charming; 10,000 concurrent sessions can trigger: - Sudden token-cost spikes - Tool API rate-limit storms - Queue backlogs that cascade across services

Opportunity remains enormous. Multi-agent systems are already 10x-ing some teams’ throughput, from code review pipelines to L2 support triage. But the gold rush mentality hides how much observability, evals, and ruthless simplification it takes to keep those agent swarms from collapsing under their own complexity.

The Future is a Swarm: What 2026 Looks Like

Swarm might be dead, but 2026 is shaping up to look more like its name than its code. AI agents are on track to become a universal UI layer, sitting above apps and APIs the way browsers sit above HTML. You won’t “open Figma” or “log into Jira” so much as tell a workspace agent what outcome you want and watch it orchestrate everything underneath.

Agent economies start to look real once those agents stop being one‑off copilots and start behaving like persistent services. Picture a repo where a swarm of specialized agents handles: - CI failures and flaky tests - Dependency upgrades and security patches - Regression triage and rollbacks all without a human touching the command line unless something truly novel breaks.

That’s not sci‑fi; it’s where the current curve points. Companies already wiring AgentKit into production workflows today are basically seeding 2026’s autonomous maintenance crews. Documentation like OpenAI Platform - Agents Documentation reads less like SDK notes and more like the spec sheet for a new operations layer.

Meta’s reported ~$70 billion CapEx binge is the loudest tell. You don’t pour that kind of cash into data centers just to run slightly better News Feed ranking. You do it to host planetary‑scale agent swarms that can power commerce, moderation, creation tools, and internal automation at a level where humans become exception handlers, not primary operators.

Fast‑forward a year and complex digital infrastructure starts to look like a multiplayer game run by AI teams. One agent cluster manages Kubernetes, another optimizes cloud spend in real time, another negotiates API contracts between services. Humans set policies, review dashboards, and step in when agents disagree or drift.

If 2025 was about proving multi‑agent systems work, 2026 is when they quietly take over the boring parts of running the internet.

Your Next Move: Stop Watching, Start Building

Stop scrolling X and doom-refreshing Swarm threads. Start a repo, open AgentKit, and wire up a tiny agent that does one painful task you repeat every day: triaging GitHub issues, generating PR review checklists, or summarizing incident reports. Ship a scrappy internal tool in a week, then harden it with logs, evals, and real users in week two.

AgentKit is not a nostalgia play or a Swarm Update in disguise. AgentKit is the engine: the agent builder, ChatKit, connector registry, and evals stack that turn a cool demo into a durable system. Swarm was a sketch; AgentKit is the production pipeline.

Treat agents as force multipliers, not job reapers. Teams wiring agents into existing workflows are already seeing 2–10x wins: support queues auto-resolved, CI noise filtered, sales follow-ups drafted before humans wake up. McKinsey-style forecasts aside, your own metrics—MTTR, cycle time, tickets per head—will tell you fast if the stack works.

Practical next moves for developers:

• Pick one workflow with clear metrics: SLAs, backlog size, lead time
• Use Agent Builder to chain a planner, a tool-calling executor, and a human approval step
• Plug into two or three data sources from the connector registry, not twenty
• Turn on evals and trace grading from day one

For tech leaders, the question is not “Swarm or not” but “agent layer or status quo.” Stand up a small tiger team with a 60–90 day mandate, a real budget, and a narrow KPI: cut one core process cost by 30%, or double throughput without new headcount. Bake vendor lock-in, privacy, and compliance reviews into the first sprint, not the last.

You do not need a moonshot. You need a working Agent in production. Build something small with AgentKit this month, share what breaks, and join the argument about what multi-agent systems should look like—before everyone else decides that future without you.

Frequently Asked Questions

What is OpenAI AgentKit?

AgentKit is OpenAI's production-ready toolkit launched in October 2025 for building, deploying, and managing multi-agent AI systems. It includes a visual builder, chat integration tools, and a connector registry.

Is AgentKit the new version of OpenAI Swarm?

No. Swarm was an experimental framework deprecated in March 2025. AgentKit is a completely new, more powerful, and enterprise-grade successor built for real-world production workflows, not an update to Swarm.

What are multi-agent AI systems?

Multi-agent systems involve multiple specialized AI agents collaborating to solve complex problems that a single agent cannot handle alone. They work together like a team, dividing tasks and sharing information.

Can I start using AgentKit today?

Yes, AgentKit was launched at OpenAI's DevDay on October 6, 2025, and is available for developers to build and deploy agentic workflows. Companies like Ramp and Coda are already using it in production.