The State of
Agent
Frameworks

The Agents SDK is OpenAI's production successor to the experimental Swarm. Its core abstraction is the handoff: agents transfer control to each other explicitly, carrying conversation context through the transition. The Apr 2026 update added native sandbox execution — agents now run in controlled computer environments with built-in support for Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, and Vercel.

from agents import Agent, Runner

triage = Agent(
    name="Triage",
    instructions="Route to billing or tech",
    handoffs=[billing_agent, tech_agent],
)
result = Runner.run_sync(triage, "Refund my order")
print(result.final_output)

The SDK that powers Claude Code, exposed as a library. The design philosophy: giving a model real access to a real computer is a shorter path to capable agents than reinventing tool-calling on top of a chat API. Anthropic's own research shows the orchestrator+subagent pattern outperforming single-agent benchmarks by up to 90% when sub-agents work in parallel.

from claude_agent_sdk import query, ClaudeAgentOptions

async for message in query(
    prompt="Review this codebase: security, perf, tests",
    options=ClaudeAgentOptions(
        allowedTools=["Read","Glob","Grep","Task"],
        agents={
            "security-reviewer": {"prompt": "You audit auth"},
            "perf-reviewer": {"prompt": "You profile"},
        },
    ),
):
    print(message)

LangGraph is the runtime LangChain pivoted to after openly saying "use LangGraph for agents, not LangChain." Production-tested at scale: Klarna runs LangGraph for support across 85 million users, cutting resolution time by 80%. In a controlled 2,000-task benchmark across four frameworks, LangGraph was fastest on latency across all five test tasks.

from langgraph.graph import StateGraph, MessagesState, START, END

def classify(state): ...
def research(state): ...

g = StateGraph(MessagesState)
g.add_node("classify", classify)
g.add_node("research", research)
g.add_conditional_edges("classify", route_by_intent)
g.add_edge("research", END)
agent = g.compile(checkpointer=memory)

CrewAI models multi-agent collaboration as a team of role-playing agents — each with role, backstory, goal, and tools. The 2025 enterprise breakout was real: $18M Series A, 100,000+ agent executions per day, 150+ enterprise customers. Distinctive feature: a hierarchical process mode that auto-generates a manager agent overseeing task delegation.

from crewai import Agent, Task, Crew

researcher = Agent(role="Senior Researcher",
    goal="Surface industry trends",
    backstory="PhD in market analysis...")
writer = Agent(role="Tech Writer", ...)

crew = Crew(agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process=Process.hierarchical)
crew.kickoff()

ADK introduces an event-driven runtime that orchestrates agents, tools, and persistent state into cohesive applications. The Runner asks the Execution Logic to process; each LLM invocation, tool call, or callback yields events back. This bidirectional model enables observability impossible with synchronous APIs. ADK 2.0 added a graph-based Workflow Runtime for deterministic flows with routing, fan-out/fan-in, loops, retry, HITL, and nested workflows.

from google.adk import Agent, Workflow

researcher = Agent(name="researcher",
    model="gemini-flash-latest",
    tools=[google_search])
writer = Agent(name="writer", ...)

root = Workflow(name="pipeline",
    edges=[("START", researcher, writer)])
# adk run path/to/agent  or  adk web

Microsoft sunset two parallel projects (Semantic Kernel and AutoGen) into one unified framework, released as 1.0 in April 2026. The framework provides AutoGen's simple agent abstractions plus Semantic Kernel's enterprise features — session-based state, type safety, middleware, telemetry — and adds graph-based workflows for explicit multi-agent execution paths.

// .NET — Microsoft Agent Framework
var agent = new ChatAgent(
    name: "compliance",
    description: "Reviews documents for GDPR",
    chatClient: azureOpenAI.AsChatClient("gpt-4"),
    tools: [reviewerTool]);

var workflow = WorkflowBuilder
    .StartWith(intakeAgent)
    .Then(compliance)
    .Then(approval)
    .Build();