Short answer: there is no single "best" AI agent platform — there's the right one for your model, your language, and how much you want to own. Pick by three questions, in order: (1) which model are you already paying for, (2) what language does your team ship in, and (3) do you want to self-host or buy a managed runtime. Everything below is organized around those three, so you can stop reading the moment your row matches.

Here's the fast version, by scenario:

The at-a-glance table above has the license and hosted-runtime column for each. The rest of this piece is the reasoning, one platform at a time.

The one deprecation to know before you commit#

The most important 2026 fact for anyone choosing a platform this quarter: OpenAI announced on June 3, 2026 that it is winding down AgentKit's visual Agent Builder and the Evals platform. Evals goes read-only on October 31, 2026; both shut down on November 30, 2026. OpenAI is steering builders to the code-first Agents SDK instead. ChatKit — the embeddable chat UI — is unaffected.

What it means: if you were about to standardize a team on OpenAI's drag-and-drop agent canvas, don't. Build on the Agents SDK, which is open source (MIT) and, usefully, provider-agnostic — it supports OpenAI's Responses API plus 100+ other models, so it's not a one-way door back to OpenAI billing. This is also a reminder of the whole category's half-life: a visual builder launched in late 2025 is being retired barely a year later. Own the framework; rent the runtime.

The code-first frameworks, ranked by what they're actually for#

OpenAI Agents SDK — the default if OpenAI is already your model. Instructions, tools, guardrails, and multi-agent handoffs in a small, MIT-licensed package. No platform fee; you pay per token. Best when you want production agents fast and don't need an opinionated state machine.

Claude Agent SDK — this is Claude Code's engine as a library (it was renamed from "Claude Code SDK" in late 2025). It exposes the same agent loop, built-in tools, subagents, and permission system that run the coding agent. Reach for it when your agent needs deep, unattended access to a filesystem, a shell, or a codebase. If you're weighing it against a graph framework, we broke that choice down in Claude Agent SDK vs LangGraph.

LangGraph — the pick when control matters more than convenience. It models agents as explicit stateful graphs, which is exactly what you want for cyclical, long-running, human-in-the-loop workflows where you need to inspect and resume state. Core is MIT; LangGraph Platform is the hosted runtime (free Developer tier, paid Plus and Enterprise). The trade-off is a steeper learning curve than the "just give it tools" SDKs. Our loop vs graph vs handoffs decision guide is the fastest way to know whether you actually need the graph.

CrewAI — role-based multi-agent "crews" in Python, MIT core, free with no usage caps on the open framework. Best for quickly prototyping agents that collaborate (a researcher hands to a writer hands to a critic). Commercial tiers (Enterprise/AMP managed, Factory self-hosted) add orchestration when you outgrow the framework.

Microsoft Agent Framework — the unified successor to Semantic Kernel and AutoGen (both moved to maintenance mode in late 2025). It reached 1.0 GA on April 3, 2026, is MIT-licensed for .NET and Python, and its hosted harness reached GA around August 2026. This is the enterprise-grade choice for teams already living in .NET and Azure who need production SLAs, telemetry, and type safety.

Google ADK — Google's open, code-first framework, available across Python, TypeScript, Go, Java, and Kotlin, with Vertex AI Agent Engine as the managed runtime (rebranded the "Gemini Enterprise Agent Platform" at Cloud Next 2026). The right call if your compute and data already live in Google Cloud and you want a Gemini-native path.

Vercel AI SDK — a provider-agnostic TypeScript SDK for AI app UIs: streaming, structured outputs, tool calls, and, since AI SDK 7 (June 25, 2026), production agent features like approvals, durability, and telemetry. Best for web and frontend teams shipping the actual product surface — chat, copilots, agent UIs — with one-line provider switching.

When to skip code entirely#

If you (or the person who'll maintain this) don't write code, a visual builder is the honest answer: n8n when the agent is one step inside a larger operations automation (webhook → database → Slack), Flowise for fast "chat with your data" and RAG prototypes, Langflow for visual multi-agent flows with a Python export hatch when you outgrow the canvas. All three are self-hostable and each offers a managed cloud. We compared them head-to-head in n8n vs Flowise vs Langflow.

The decision, in one paragraph#

Don't shop for a winner; match three columns. Model you already pay for narrows you to a first-party SDK (OpenAI, Claude, Gemini/ADK) or a deliberately provider-agnostic one (Vercel AI SDK, OpenAI Agents SDK). Language decides the rest: TypeScript-heavy product teams lean Vercel AI SDK; Python data teams lean CrewAI or LangGraph; .NET/Azure shops take Microsoft Agent Framework. Ownership is the reversible bit: start on the open-source framework (they nearly all have a permissive core), and only rent a managed runtime — LangGraph Platform, Vertex AI Agent Engine, CrewAI Enterprise — when deploying and observing agents, not building them, becomes your bottleneck.

If you want the layer beneath the platform choice — models, memory, tools, evals, and the rest of the stack a solo team has to assemble — we laid out all of it in the 12 decisions behind a founder's agent stack, and if your agent is a coding agent specifically, start from the best AI coding tools in 2026.

One last discipline, because the AgentKit wind-down proves it: this category rewrites itself every few months. Choose the platform that's cheapest to leave.