Choosing an AI Agent SDK in 2026: LangGraph vs CrewAI vs Claude vs OpenAI vs Google

The SDK explosion: why choice matters now

Eighteen months ago, there were three ways to run multi-agent workflows: write your own orchestration (ropy and dangerous), use LangChain (flexible but opinionated), or pick a niche framework and pray it didn't break your assumptions.

Now every major AI company ships its own SDK. Anthropic (Claude Agent SDK), OpenAI (Agents SDK), Google (ADK 2.0), Microsoft (Semantic Kernel). Meanwhile LangGraph (the specialized sub-framework that split from LangChain) has become production-grade for large teams, and CrewAI has exploded for quick prototyping. Six viable options when a year ago you had one.

This matters because the framework you pick determines whether your agents are cheap and fast, or whether they'll bloat with orchestration overhead and token costs that grow faster than your workload. A 100-loop research task costs $4.10 in orchestration tokens on CrewAI but near zero on LangGraph. A single task transition takes 450ms on CrewAI versus 120ms on LangGraph. That's not a minor difference — it's the gap between 'ships Friday' and 'we need three more cloud instances.'

“The 1,445% surge in multi-agent inquiries from Q1 2024 to Q2 2025 signals this is no longer experimental. Teams are shipping agentic workflows to production. And they're discovering that the wrong SDK choice cascades into latency, cost, and reliability problems.”

Six SDKs, six tradeoffs: the decision matrix

LangGraph — Production workflows with audit trails

LangGraph models agents as nodes in a directed graph with explicit edge transitions. The output: production-grade state management, checkpointing, and human-in-the-loop interrupts that let you pause and inspect an agent mid-execution.

Real example: Klarna (85M users) runs agentic workflows through LangGraph. Why? Because they can checkpoint every step, audit every decision, and jump back in if something drifts. Token efficiency is the other advantage — 30 to 40% fewer tokens than CrewAI on medium-complexity tasks, because edge transitions happen in code, not through LLM-driven delegation.

Pick LangGraph if: you're shipping to production, you need conditional logic, you care about cost, or your compliance team wants to see the audit trail.

CrewAI — Rapid prototyping for the impatient

CrewAI is the opposite stance. A working two-agent pipeline takes about 25 lines of code. No graph definition, no state machine, no checkpointing — you define agents as role-based team members and CrewAI orchestrates the conversation.

The learning curve is so shallow that non-engineers can build multi-agent systems. The tradeoff: you lose fine-grained control, and token costs spiral because every task transition runs through an LLM to decide what comes next. That 450ms-per-transition overhead adds up fast if you're chaining thirty steps together. But if you're building a prototype to test whether the idea works, you need proof in a weekend, not a quarter.

Pick CrewAI if: you're prototyping, you're not sure yet what your workflow is, or you're building a one-off agentic system and don't care about recurring cost.

Claude Agent SDK — Coding tasks and file manipulation

Anthropic ships this as part of Claude Code: agents that inherit built-in file I/O, bash execution, code editing, web search, and regex operations. The design assumption is clear — these agents are meant to build, ship, and automate development work.

SWE-bench performance: Claude Opus achieves 88.6% pass rate on verified benchmarks, meaning it can solve real engineering problems without human intervention. Slack, GitHub-focused workflows, and automation systems are the native use case.

Pick Claude Agent SDK if: you're building coding agents, automating file systems, or running development-focused agentic workflows.

OpenAI Agents SDK — Sandboxed execution and untrusted workloads

OpenAI's SDK centers on the SandboxAgent environment: persistent state, snapshots, and resume capabilities. The backend abstraction matters — you can run workloads locally, in Docker, on E2B, Modal, or any cloud deployment you choose.

This is the right choice if you need to execute untrusted code safely. The isolation layer isn't a theoretical nice-to-have; it's load-bearing. You can snapshot an agent mid-execution, inspect the state, and resume — a capability that matters when you're orchestrating third-party models or running code from external sources.

Pick OpenAI Agents SDK if: you're running untrusted code, you need multi-cloud flexibility, or isolation and snapshots are non-negotiable.

Google ADK 2.0 — Multi-language infrastructure at scale

Google released ADK 2.0 in May 2026, moving away from older patterns to a graph-based execution model similar to LangGraph. The differentiator: five language support (Python, TypeScript, Go, Java, Kotlin) and native integration with Vertex AI and Gemini.

A2A protocol support (agent-to-agent communication) is interesting if you're running multiple frameworks and need them to talk to each other without translation layers. The broad language support appeals to infrastructure teams running polyglot systems.

Pick Google ADK 2.0 if: your team is multi-language, you're on Google Cloud, or you need A2A protocol support.

Microsoft Semantic Kernel — Enterprise .NET and Azure

Semantic Kernel is the enterprise play: deep integration with Azure OpenAI, Microsoft 365, and Entra ID. The plugin/planner architecture is built around large organizations that need compliance, identity management, and audit trails baked in.

If your company runs .NET and Azure, Semantic Kernel is the path of least resistance. If you're outside that ecosystem, there's little reason to pick it.

Pick Semantic Kernel if: you're an enterprise using .NET, Azure, and Microsoft 365.

Performance benchmarks: the cost of the wrong choice

Here's why this decision isn't academic. On a 100-loop research task:

CrewAI: $4.10 in orchestration tokens alone. LangGraph: near zero. Task transitions on CrewAI spike to 450ms per step. LangGraph: 120ms. At scale, this becomes a constraint — if your agentic workflow runs a thousand tasks a day, you're looking at the difference between a $100/month bill and a $4,000/month bill. And the difference between a system that feels responsive and one that feels sluggish.

This isn't an argument against CrewAI — it's perfect for what it's designed for. It's an argument for making the decision deliberately, not by accident.

The emerging pattern: SDK + gateway

One more pattern to watch: production teams are combining vendor-specific SDKs (Claude, OpenAI, Google) with orchestration frameworks (LangGraph, CrewAI) and routing them through AI gateways like Requesty. The gateway sits between your code and the model, providing unified cost tracking, automatic failover between providers, and model-agnostic routing without code changes.

Why? Because it lets you stay model-agnostic. You can build against Claude today, switch to OpenAI's faster model next month, and Gemini the month after — all without rewriting your agents. The gateway also handles failover: if one provider is degraded, traffic switches automatically.

The decision framework

Here's the question framework that gets you to an answer:

1. Is this a prototype or production? (Prototype → CrewAI, Production → LangGraph)

2. Does the workflow require code execution? (Yes → Claude Agent SDK, No → Continue)

3. Do you need to run untrusted code safely? (Yes → OpenAI Agents SDK, No → Continue)

4. What's your team's language and infrastructure? (Multi-language/Google Cloud → Google ADK 2.0, .NET/Azure → Semantic Kernel, No strong constraint → LangGraph)

5. Do you need model-agnostic routing? (Yes → Any SDK + gateway, No → Pick based on your primary model)

Following this tree should get you to an answer in five minutes. If you're still unsure, LangGraph is the safe default — it's production-grade, cost-efficient, and has no hard opinions about which model you use.

“The wrong SDK choice cascades. The right one moves the problem from 'we need to rebuild this' to 'we just need to ship faster.'”

What's next: the direction of agent SDKs

Watch for three trends: (1) Convergence on graph-based execution — LangGraph and Google ADK both moved here, and CrewAI will eventually follow. Explicit graphs are harder to reason about than role-based teams, but they're faster and cheaper. (2) Gateway adoption — unified cost tracking and failover will become table stakes, and vendor lock-in will become a more costly mistake. (3) Integration of safety and compliance — as agents move to production, checkpointing and audit trails won't be nice-to-haves, they'll be required.

The exciting part is that no one SDK is winning yet. If you build carefully, you can stay portable. The next eighteen months will probably see consolidation, but right now, building agentic systems means choosing deliberately, not defaulting.