Best AI Agent SDKs Compared (2026): LangGraph, CrewAI, OpenAI, Anthropic, and Google ADK

Gartner reported a 1,445% surge in multi-agent system inquiries from Q1 2024 to Q2 2025. By June 2026, six SDKs dominate production agent deployments: LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK, Google ADK, and Microsoft Semantic Kernel. Each takes a different approach to the same problem: how to build agents that work reliably in production.

This guide compares all six on architecture, benchmarks, token efficiency, and gateway compatibility. If you are choosing an SDK for a new project or evaluating a migration, this is the comparison you need.

The six SDKs at a glance

LangGraph: the production state machine

LangGraph models agents as nodes in a directed graph with explicit edges. You define exactly how control flows between steps, where errors route, and when humans intervene. This is the opposite of "let the LLM figure it out." You tell the graph what happens next.

Why teams pick it:

Klarna runs a customer service agent on LangGraph handling 85 million users. The deployment reduced average resolution time by 80%, a figure Klarna's own engineering team published. LinkedIn, Uber, and Replit also run LangGraph in production.

The key advantage is the interrupt() primitive, stable since LangGraph v1.0. It pauses execution at any node, persists the full state to a checkpoint store, and resumes after human approval or external input. This is critical for regulated workflows: every decision is auditable, every state transition is logged.

Token efficiency: Benchmarks from AI Dev Day India show LangGraph uses 30 to 40% fewer tokens than CrewAI on medium complexity tasks. The reason: LangGraph routes between nodes using code (if/else, match statements), while CrewAI uses LLM calls to decide task handovers. On a standard research and summarize workflow looped 100 times, CrewAI spent $4.10 in prompt tokens on orchestration alone. LangGraph spent near zero on routing decisions.

Latency: Under load testing, LangGraph adds 120ms of orchestration overhead per node. CrewAI spikes to 450ms per task transition due to LLM-driven delegation.

Observability: Native integration with LangSmith provides full state transition traces per node. Every checkpoint is inspectable, replayable, and searchable. For teams that need audit trails, this is the most mature observability story in the market.

When to use it: Your workflow has conditional paths, retry logic, or human approval gates. You need checkpointing that survives restarts. You are building for production and need monitoring, persistence, and streaming. You already use LangChain for other parts of your stack.

from langgraph.graph import StateGraph, END
 
graph = StateGraph(AgentState)
graph.add_node("research", research_agent)
graph.add_node("analyze", analysis_agent)
graph.add_node("human_review", human_review_node)
graph.add_edge("research", "analyze")
graph.add_conditional_edges("analyze", route_by_confidence)
graph.add_edge("human_review", END)

CrewAI: the rapid prototyping framework

CrewAI models agents as a team of specialists. Each agent gets a role, goal, backstory, and a set of tools. Tasks describe what needs to be done and who does it. The Crew object handles execution order. A working two-agent pipeline takes about 25 lines of code with minimal documentation.

Why teams pick it:

CrewAI has 52,000 GitHub stars and claims 100,000 certified developers. NVIDIA announced a "CrewAI Factory" partnership for GPU-optimized agent deployments. The framework also supports Flows, a newer abstraction for event-driven agent pipelines that gives more control than the default sequential/hierarchical process modes.

Where it shines: If your use case maps to a team of humans (researcher, analyst, writer), CrewAI models it naturally. The learning curve is the lowest of any framework in this comparison. Non-engineers can read the agent definitions and understand the architecture.

Where it struggles: Precise control flow. Sequential and hierarchical process modes cover most workflows, but conditional routing ("if step 3 fails, retry step 1 with different parameters") requires workarounds. Token cost is higher: benchmarks show 4,500 tokens per run on tasks where LangGraph uses under 2,000.

When to use it: You need a working prototype today. Your task decomposes into specialist roles. Your team includes non-engineers who need to understand the agent architecture. You value code readability over fine-grained control.

from crewai import Agent, Task, Crew
 
researcher = Agent(
    role="Senior Research Analyst",
    goal="Find the most relevant data",
    tools=[search_tool, scrape_tool]
)
 
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process=Process.sequential
)
result = crew.kickoff()

Claude Agent SDK: the coding specialist

The Claude Agent SDK gives you the same tools, agent loop, and context management that power Claude Code, programmable in Python and TypeScript. The SDK ships built-in tools for reading files, running shell commands, editing code, searching the web, and pattern matching across codebases. No tool implementation required on your side.

Built-in tool catalog:

Version: v0.2.104 as of June 17, 2026. The TypeScript SDK bundles a native Claude Code binary, so you do not need a separate Claude Code installation.

Benchmark performance: Claude Code with Opus 4.8 scores 88.6% on SWE-bench Verified and 69.2% on SWE-bench Pro. With Fable 5, those numbers jump to 95.0% and 80.3%, though Fable 5 is export-suspended as of June 12, 2026.

Subagents and hooks: The SDK supports subagent spawning for parallel task execution and a hook system (PreToolUse, PostToolUse, Stop, SessionStart, UserPrompt) for injecting custom logic at every stage of the agent loop.

When to use it: You are building coding agents. You want out-of-the-box file I/O, shell access, and code editing without implementing tool handlers. You are comfortable with Anthropic as your primary provider, or you route through a gateway for multi-provider flexibility.

OpenAI Agents SDK: the sandbox runtime

The OpenAI Agents SDK (v0.15.1) takes a different approach from the framework-centric options. Instead of orchestrating multiple tools in your process, it spins up sandboxed environments where agents work autonomously with persistent state.

Key features in v0.14 and v0.15:

The v0.14 release introduced Sandbox Agents, a major new surface centered on SandboxAgent, Manifest, and SandboxRunConfig. Agents work inside persistent isolated workspaces with files, directories, Git repos, mounts, snapshots, and resume support. Sandbox backends include local (Unix), containerized (Docker), and hosted options for E2B, Modal, Cloudflare, Vercel, and Daytona.

The v0.15 release improved model refusal handling, surfacing refusals as explicit ModelRefusalError instead of empty text or retry loops.

Sandbox memory: Agents can reuse lessons from prior runs through progressive disclosure and multi-turn memory grouping, with configurable isolation boundaries and persistent storage backends including S3, R2, GCS, and Azure Blob Storage.

When to use it: You want agents that execute in isolated sandboxes, especially for code execution and untrusted workloads. You need persistent workspace state across agent runs. Your infrastructure is OpenAI-native and you want the tightest integration with GPT-5.5 and Codex.

Google ADK 2.0: the graph-based orchestrator

Google ADK hit General Availability on May 19, 2026 with a v2.0 release that transitions the framework from a hierarchical agent executor to a graph-based execution engine. Agents, tools, and functions are evaluated as individual nodes within a workflow graph. This is the same architectural shift LangGraph made years ago, now available with native Gemini integration.

Core features:

Graph-based workflows for deterministic agent execution. Dynamic workflows with code-based logic for iterative loops and complex decision branching. Collaborative workflows with coordinator agents and multiple subagents. Native support for the Agent2Agent (A2A) protocol for cross-framework agent communication.

Language support: Python, TypeScript, Go, Java, and Kotlin. The broadest language coverage of any SDK in this comparison.

Model flexibility: ADK works with Gemini natively and supports other providers through adapters. For multi-provider routing, connect ADK to a gateway like Requesty through the LiteLLM adapter or direct OpenAI-compatible endpoint configuration.

When to use it: You need the broadest language support. Your agents run on Google Cloud and you want native Vertex AI integration. You are building cross-framework agent systems using the A2A protocol. You want graph-based control similar to LangGraph with tighter Google ecosystem ties.

Microsoft Semantic Kernel: the enterprise connector

Semantic Kernel is Microsoft's SDK for building AI agents in enterprise environments, particularly .NET shops already invested in Azure. It uses a plugin/planner architecture where AI capabilities are added as plugins and the kernel plans execution steps.

Why it matters for enterprises: Deep integration with Azure OpenAI Service, Microsoft 365, and Entra ID. If your organization runs on the Microsoft stack, Semantic Kernel offers the most natural integration path.

When to use it: You are building in C# or Java. Your infrastructure is Azure-centric. You need enterprise identity management (Entra ID) and compliance controls baked into the agent framework.

Head to head comparison

How an AI gateway connects every SDK

Each SDK makes LLM calls. If you use multiple SDKs across your organization (which most teams do by June 2026), you end up with fragmented cost tracking, no unified failover, and no way to compare model performance across agent types.

An AI gateway like Requesty solves this by sitting between every SDK and every provider:

Unified cost tracking: Every LLM call from every SDK routes through one endpoint. You see per-team, per-SDK, per-model cost breakdowns in a single dashboard. No more reconciling bills from three different providers.

Automatic failover: If Anthropic goes down mid-run, Requesty fails over to the next model in your fallback chain in under 50ms. Your agent loop does not crash.

Latency-based routing: Requesty's latency routing measures real-time provider performance on a rolling window and sends each request to the fastest available model. For agents making hundreds of LLM calls per task, the cumulative time savings are substantial.

Smart routing by task type: Code generation goes to Opus 4.8. Simple classification goes to Gemini 3.5 Flash. Requesty's Smart Routing dispatches by request complexity, cutting costs by 50% or more while maintaining quality.

The integration is the same for every SDK: change your base URL to router.requesty.ai/v1 and use your Requesty API key. Your existing code works unchanged.

# LangGraph with Requesty
from langchain_openai import ChatOpenAI
 
llm = ChatOpenAI(
    base_url="https://router.requesty.ai/v1",
    api_key="your-requesty-key",
    model="anthropic/claude-opus-4-8"
)
 
# CrewAI with Requesty
from crewai import LLM
 
llm = LLM(
    model="openai/gpt-5.5",
    base_url="https://router.requesty.ai/v1",
    api_key="your-requesty-key"
)

Decision matrix: which SDK for which job

The bottom line

The agent SDK landscape in 2026 has matured past the "pick one framework" era. Most production teams use two or three: a vendor SDK for its native tools (Claude Agent SDK for coding, OpenAI Agents SDK for sandboxed execution) and a framework (LangGraph or CrewAI) for multi-agent orchestration.

The unifying layer is your AI gateway. Route every SDK through Requesty, and you get unified cost tracking, automatic failover, and the freedom to swap models across any SDK without rewriting agent code. One endpoint, 400+ models, every SDK connected.