AI Agent SDK Landscape December 2025: The Ultimate Comparison

AI Agent SDK Landscape December 2025: The Ultimate Comparison

As of: December 2025 — This comprehensive guide compares all leading Agent SDKs: Claude Agent SDK, OpenAI Agents SDK, Google ADK, LangGraph, Vercel AI SDK, CrewAI, AutoGen, and more.

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TL;DR

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Introduction: The Agent Revolution of 2025

The year 2025 marks the breakthrough of AI Agents from experimental technology to production-ready infrastructure. With the introduction of the Model Context Protocol (MCP) as an open standard and its donation to the Linux Foundation in December 2025, we now have a common foundation for agent development.

Key Developments in 2025:

• March 2025: OpenAI officially adopts MCP
• April 2025: Google launches ADK with A2A protocol
• September 2025: Anthropic releases Claude Agent SDK
• October 2025: OpenAI launches AgentKit
• December 2025: MCP is donated to Linux Foundation (Agentic AI Foundation)

This guide helps developers choose the right framework for their specific requirements.

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1. Claude Agent SDK (Anthropic)

Overview

The Claude Agent SDK gives developers access to the same tools, agent loops, and context management systems that power Claude Code itself. It was released in September 2025 alongside Claude Sonnet 4.5 and Claude Code 2.0.

GitHub Stars: ~15k+ | Languages: Python, TypeScript

Core Features

• Built-in Tools: File operations, Bash commands, code editing out-of-the-box
• Subagents: Native support for agent hierarchies
• Permission Framework: Granular control over agent actions
• MCP Native: Direct integration with Model Context Protocol

Code Example (Python)

from claude_agent_sdk import Agent, Tool
from claude_agent_sdk.tools import BashTool, FileReadTool, FileWriteTool

# Agent mit Built-in Tools erstellen
agent = Agent(
    name="code-assistant",
    model="claude-sonnet-4-5",
    tools=[
        BashTool(),
        FileReadTool(),
        FileWriteTool()
    ],
    system_prompt="""Du bist ein erfahrener Software-Entwickler.
    Analysiere Code, finde Bugs und implementiere Lösungen."""
)

# Agent ausführen
result = await agent.run(
    "Analysiere die Datei src/api.py und finde potenzielle Security-Issues."
)

print(result.output)

Code Example (TypeScript)

import { Agent, BashTool, FileTools } from '@anthropic/agent-sdk';

const agent = new Agent({
  name: 'typescript-assistant',
  model: 'claude-sonnet-4-5',
  tools: [
    new BashTool(),
    ...FileTools.all()
  ]
});

const result = await agent.run({
  task: 'Refaktoriere die React-Komponenten in src/components/'
});

Subagents for Complex Workflows

from claude_agent_sdk import Agent, Subagent

# Hauptagent mit Subagents
main_agent = Agent(
    name="project-manager",
    subagents=[
        Subagent(
            name="researcher",
            task="Recherchiere technische Anforderungen",
            tools=[WebSearchTool(), FileReadTool()]
        ),
        Subagent(
            name="implementer",
            task="Implementiere die Lösung",
            tools=[BashTool(), FileWriteTool()]
        ),
        Subagent(
            name="tester",
            task="Schreibe und führe Tests aus",
            tools=[BashTool(), FileReadTool()]
        )
    ]
)

result = await main_agent.run(
    "Implementiere eine REST-API für User-Management"
)

When to Choose Claude Agent SDK?

• Long-running, autonomous agents
• Production-grade infrastructure required
• Strong permission control needed
• Native MCP integration desired
• Already in the Anthropic ecosystem

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2. OpenAI Agents SDK

Overview

The OpenAI Agents SDK offers native integration with the OpenAI ecosystem and introduced Hosted MCP in March 2025 — the ability to use MCP servers directly in the cloud without local installation.

GitHub Stars: ~25k+ | Languages: Python, TypeScript

Core Features

• Hosted MCP Tools: Cloud-hosted MCP servers
• Function Calling: Native tool integration
• Handoffs: Agent-to-agent delegation
• Built-in Tracing: Debugging and evaluation

Code Example with Hosted MCP

from agents import Agent, Runner, HostedMCPTool

# Agent mit Hosted MCP Tool
agent = Agent(
    name="GitHub Assistant",
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "github",
                "server_url": "https://gitmcp.io/openai/codex",
                "require_approval": "never"
            }
        )
    ]
)

result = await Runner.run(agent, "Liste alle offenen PRs im Repository")
print(result.final_output)

Local MCP Server Integration

from agents import Agent, Runner
from agents.mcp import MCPServerStdio

async def main():
    # Lokalen MCP-Server starten
    async with MCPServerStdio(
        name="Filesystem",
        params={
            "command": "npx",
            "args": ["-y", "@modelcontextprotocol/server-filesystem", "./docs"]
        }
    ) as fs_server:

        agent = Agent(
            name="Doc Assistant",
            instructions="Du hilfst bei der Dokumentations-Verwaltung.",
            mcp_servers=[fs_server]
        )

        result = await Runner.run(
            agent,
            "Finde alle Markdown-Dateien und erstelle eine Übersicht"
        )
        print(result.final_output)

asyncio.run(main())

Function Tools with Decorator

from agents import Agent, function_tool

@function_tool
def calculate_metrics(revenue: float, costs: float) -> dict:
    """Berechnet Business-Metriken aus Umsatz und Kosten."""
    profit = revenue - costs
    margin = (profit / revenue) * 100 if revenue > 0 else 0
    return {
        "profit": profit,
        "margin": round(margin, 2),
        "roi": round((profit / costs) * 100, 2) if costs > 0 else 0
    }

agent = Agent(
    name="Financial Analyst",
    tools=[calculate_metrics],
    model="gpt-4o"
)

result = await Runner.run(
    agent,
    "Berechne die Metriken für Q4: Umsatz 1.5M, Kosten 800k"
)

When to Choose OpenAI Agents SDK?

• Already in the OpenAI ecosystem
• Hosted MCP without infrastructure management desired
• Rapid prototypes with 1-2 step workflows
• Teams with limited agent experience

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3. Google ADK (Agent Development Kit)

Overview

Google ADK is Google's response to the agent revolution. It offers deep integration with Vertex AI and Gemini models, supporting both MCP and Google's own A2A (Agent-to-Agent) protocol.

Languages: Python, TypeScript, Go, Java | Status: GA on Vertex AI

Core Features

• Multi-Agent Orchestration: Native support for agent hierarchies
• Vertex AI Integration: Seamless deployment on GCP
• A2A Protocol: Google's agent interoperability standard
• Session Management: Persistent conversations

Code Example (Python)

from google.adk.agents import Agent
from google.adk.tools import google_search

# Einfacher Research-Agent
research_agent = Agent(
    name="topic_research_agent",
    model="gemini-2.5-flash",
    description="Agent für Themen-Recherche",
    instruction="""Du bist ein Content-Stratege und Researcher.
    Analysiere Trends und liefere actionable Insights.""",
    tools=[google_search]
)

# Session erstellen und Query ausführen
session = await session_service.create_session(
    app_name=research_agent.name,
    user_id="user-123"
)

async for event in research_agent.run(
    session=session,
    message="Recherchiere die Top AI-Trends für Q1 2026"
):
    print(event)

Multi-Agent Workflow with ADK

from google.adk.agents import Agent, SequentialAgent
from google.adk.tools import code_execution, google_search

# Spezialisierte Agents definieren
planner = Agent(
    name="planner",
    model="gemini-2.5-pro",
    instruction="Erstelle detaillierte Projektpläne.",
    tools=[google_search]
)

coder = Agent(
    name="coder",
    model="gemini-2.5-flash",
    instruction="Implementiere Code basierend auf Spezifikationen.",
    tools=[code_execution]
)

reviewer = Agent(
    name="reviewer",
    model="gemini-2.5-pro",
    instruction="Reviewe Code auf Qualität und Security.",
    tools=[code_execution]
)

# Sequential Pipeline
pipeline = SequentialAgent(
    name="dev_pipeline",
    agents=[planner, coder, reviewer]
)

result = await pipeline.run(
    "Entwickle eine REST-API für Inventory Management"
)

TypeScript Example

import { Agent } from '@google/adk';

const agent = new Agent({
  name: 'typescript-agent',
  model: 'gemini-2.5-flash',
  instruction: 'Du bist ein hilfreicher Assistent.',
  tools: [/* tools */]
});

const response = await agent.chat({
  message: 'Erkläre mir TypeScript Generics',
  sessionId: 'session-123'
});

When to Choose Google ADK?

• Enterprise on GCP/Vertex AI
• Multi-agent orchestration required
• A2A protocol for agent interoperability
• Gemini models preferred

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4. LangGraph (LangChain)

Overview

LangGraph is the production-ready framework for stateful, multi-step agent systems. It extends LangChain with graph-based workflow orchestration and explicit state management.

GitHub Stars: ~35k+ (LangChain Ecosystem) | Languages: Python, TypeScript

Core Features

• Graph-based Orchestration: Explicit nodes and edges
• State Management: Persistent, checkpointed state
• Durable Execution: Agents survive crashes
• Human-in-the-Loop: Interrupts for human intervention
• LangSmith Integration: Observability and debugging

Code Example: Simple Graph

from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from typing import TypedDict, Annotated
import operator

class AgentState(TypedDict):
    messages: Annotated[list, operator.add]
    next_step: str

def research_node(state: AgentState) -> AgentState:
    """Recherche-Schritt"""
    llm = ChatOpenAI(model="gpt-4o")
    response = llm.invoke(state["messages"])
    return {
        "messages": [response],
        "next_step": "analyze"
    }

def analyze_node(state: AgentState) -> AgentState:
    """Analyse-Schritt"""
    llm = ChatOpenAI(model="gpt-4o")
    response = llm.invoke(
        state["messages"] + [{"role": "user", "content": "Analysiere die Ergebnisse"}]
    )
    return {
        "messages": [response],
        "next_step": "end"
    }

def router(state: AgentState) -> str:
    """Routing-Logik"""
    if state["next_step"] == "end":
        return END
    return state["next_step"]

# Graph erstellen
workflow = StateGraph(AgentState)

# Nodes hinzufügen
workflow.add_node("research", research_node)
workflow.add_node("analyze", analyze_node)

# Edges definieren
workflow.set_entry_point("research")
workflow.add_conditional_edges("research", router, {
    "analyze": "analyze",
    END: END
})
workflow.add_conditional_edges("analyze", router, {
    END: END
})

# Kompilieren und ausführen
app = workflow.compile()

result = await app.ainvoke({
    "messages": [{"role": "user", "content": "Recherchiere AI Agent Trends 2025"}],
    "next_step": "research"
})

Multi-Agent with Subgraphs

from langgraph.graph import StateGraph
from langgraph.prebuilt import create_react_agent

# Spezialisierte Agents als Subgraphs
researcher = create_react_agent(
    model=ChatOpenAI(model="gpt-4o"),
    tools=[TavilySearchResults()],
    state_modifier="Du bist ein Research-Spezialist."
)

writer = create_react_agent(
    model=ChatOpenAI(model="gpt-4o"),
    tools=[],
    state_modifier="Du bist ein Content-Writer."
)

# Supervisor-Graph
class SupervisorState(TypedDict):
    messages: list
    current_agent: str
    task_complete: bool

supervisor_graph = StateGraph(SupervisorState)
supervisor_graph.add_node("researcher", researcher)
supervisor_graph.add_node("writer", writer)
supervisor_graph.add_node("supervisor", supervisor_node)

# Routing vom Supervisor
supervisor_graph.add_conditional_edges(
    "supervisor",
    lambda s: s["current_agent"],
    {
        "researcher": "researcher",
        "writer": "writer",
        "FINISH": END
    }
)

When to Choose LangGraph?

• Complex, stateful workflows
• Explicit control over agent behavior
• Production-grade with checkpointing
• Human-in-the-loop required
• Team with LangChain experience

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5. Vercel AI SDK

Overview

The Vercel AI SDK is geared toward web developers and offers seamless integration with Next.js, React, and other frontend frameworks. Version 6 (2025) introduced an agent abstraction.

npm Downloads: 500k+/week | Languages: TypeScript/JavaScript

Core Features

• Agent Abstraction: ToolLoopAgent for agent workflows
• Streaming: Native streaming support
• Multi-Provider: OpenAI, Anthropic, Google, etc.
• MCP Tools: Integration via plugins
• Fluid Compute: Optimized for Vercel deployment

Code Example: Agent with Tools

import { generateText, tool } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

// Tool definieren
const weatherTool = tool({
  description: 'Ruft aktuelle Wetterdaten ab',
  parameters: z.object({
    city: z.string().describe('Stadt für Wetterabfrage')
  }),
  execute: async ({ city }) => {
    // API-Call zu Wetter-Service
    const response = await fetch(
      `https://api.weather.com/v1/current?city=${city}`
    );
    return response.json();
  }
});

// Agent-Loop
const result = await generateText({
  model: openai('gpt-4o'),
  tools: { weather: weatherTool },
  maxSteps: 5,
  system: 'Du bist ein hilfreicher Wetter-Assistent.',
  prompt: 'Wie ist das Wetter in Berlin und München?'
});

console.log(result.text);

ToolLoopAgent Class

import { ToolLoopAgent } from 'ai/agents';
import { anthropic } from '@ai-sdk/anthropic';

const agent = new ToolLoopAgent({
  model: anthropic('claude-sonnet-4-5'),
  tools: {
    search: searchTool,
    calculate: calculatorTool,
    writeFile: fileWriteTool
  },
  maxIterations: 10,
  stopWhen: (state) => state.taskComplete
});

const result = await agent.run({
  task: 'Recherchiere AI-Trends und erstelle einen Report als Markdown-Datei'
});

Next.js Integration

// app/api/agent/route.ts
import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

export async function POST(req: Request) {
  const { messages } = await req.json();

  const result = streamText({
    model: openai('gpt-4o'),
    messages,
    tools: {
      // Tools hier
    },
    maxSteps: 5
  });

  return result.toDataStreamResponse();
}

When to Choose Vercel AI SDK?

• Web applications with Next.js/React
• Streaming-first requirements
• Multi-provider flexibility
• Vercel deployment planned
• Frontend-focused team

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6. CrewAI

Overview

CrewAI specializes in multi-agent collaboration with a "crew" concept — teams of specialized agents working together like a real team.

GitHub Stars: ~25k+ | Languages: Python

Core Features

• Role-based Agents: Each agent has a defined role
• Crew Orchestration: Parallel and sequential workflows
• Memory: Shared and individual memory
• Task Delegation: Automatic task distribution

Code Example

from crewai import Agent, Task, Crew, Process

# Agents mit Rollen definieren
researcher = Agent(
    role="Senior Research Analyst",
    goal="Finde die wichtigsten AI-Trends und Insights",
    backstory="""Du bist ein erfahrener Analyst mit 15 Jahren
    Erfahrung in Technology Research.""",
    tools=[search_tool, scrape_tool],
    verbose=True
)

writer = Agent(
    role="Content Strategist",
    goal="Erstelle compelling Content aus Research-Ergebnissen",
    backstory="""Du bist ein preisgekrönter Tech-Journalist
    mit Expertise in AI-Themen.""",
    tools=[],
    verbose=True
)

editor = Agent(
    role="Editor-in-Chief",
    goal="Stelle höchste Qualität und Faktentreue sicher",
    backstory="""Du bist ein erfahrener Editor mit strengen
    journalistischen Standards.""",
    tools=[],
    verbose=True
)

# Tasks definieren
research_task = Task(
    description="Recherchiere die Top 5 AI Agent SDKs 2025",
    expected_output="Detaillierter Research Report mit Quellen",
    agent=researcher
)

writing_task = Task(
    description="Schreibe einen Blog-Artikel basierend auf dem Research",
    expected_output="2000-Wort Blog-Post im Markdown-Format",
    agent=writer,
    context=[research_task]
)

editing_task = Task(
    description="Reviewe und verbessere den Artikel",
    expected_output="Finaler, publikationsreifer Artikel",
    agent=editor,
    context=[writing_task]
)

# Crew zusammenstellen
crew = Crew(
    agents=[researcher, writer, editor],
    tasks=[research_task, writing_task, editing_task],
    process=Process.sequential,
    verbose=True
)

# Ausführen
result = crew.kickoff()
print(result)

When to Choose CrewAI?

• Complex tasks with multiple specialists
• Role-based workflow desired
• Parallel agent execution
• Team-based analogy fits the use case

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7. Microsoft AutoGen + Semantic Kernel

Overview

Microsoft offers two complementary frameworks with AutoGen and Semantic Kernel, which are unified into an enterprise-ready Agent Service in Azure AI Foundry.

AutoGen GitHub Stars: 35k+ | Semantic Kernel Stars: 22k+

AutoGen: Multi-Agent Conversations

from autogen import AssistantAgent, UserProxyAgent

# Agents erstellen
assistant = AssistantAgent(
    name="assistant",
    llm_config={
        "model": "gpt-4o",
        "temperature": 0.7
    },
    system_message="Du bist ein hilfreicher AI-Assistent."
)

user_proxy = UserProxyAgent(
    name="user_proxy",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=10,
    code_execution_config={
        "work_dir": "workspace",
        "use_docker": False
    }
)

# Konversation starten
user_proxy.initiate_chat(
    assistant,
    message="Schreibe ein Python-Script für Web-Scraping"
)

Semantic Kernel: Enterprise Integration

import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion

# Kernel erstellen
kernel = sk.Kernel()

# Azure OpenAI hinzufügen
kernel.add_service(AzureChatCompletion(
    deployment_name="gpt-4o",
    endpoint="https://your-resource.openai.azure.com/",
    api_key="your-api-key"
))

# Plugin laden
kernel.add_plugin(
    parent_directory="plugins",
    plugin_name="WriterPlugin"
)

# Funktion ausführen
result = await kernel.invoke(
    kernel.plugins["WriterPlugin"]["WriteArticle"],
    topic="AI Agent SDKs 2025"
)

When to Choose Microsoft Frameworks?

• Azure enterprise environment
• Strong .NET/C# preference (Semantic Kernel)
• Multi-agent conversations (AutoGen)
• Enterprise security and compliance

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8. Other Notable Frameworks

Strands Agents (AWS)

Model-agnostic framework with strong AWS Bedrock integration:

from strands import Agent

agent = Agent(
    model="bedrock/anthropic.claude-v3",
    tools=[/* AWS tools */]
)

PydanticAI

Type-safe agent development with Pydantic:

from pydantic_ai import Agent

agent = Agent(
    model="openai:gpt-4o",
    result_type=MyResultModel  # Pydantic Model
)

Smolagents (Hugging Face)

Minimalist framework for rapid prototypes:

from smolagents import CodeAgent, HfApiModel

agent = CodeAgent(
    tools=[],
    model=HfApiModel()
)

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Comparison Matrix: Choosing the Right Framework

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Decision Guide: Which SDK for Which Use Case?

Scenario 1: Rapid Prototype with OpenAI

Recommendation: OpenAI Agents SDK

• Simplest API
• Hosted MCP without setup
• Fast iteration

Scenario 2: Production Coding Assistant

Recommendation: Claude Agent SDK

• Proven infrastructure (Claude Code)
• Strong code generation
• Permission framework

Scenario 3: Enterprise Multi-Agent on GCP

Recommendation: Google ADK

• Vertex AI integration
• A2A for agent communication
• Enterprise compliance

Scenario 4: Complex Workflow with Human-in-the-Loop

Recommendation: LangGraph

• Explicit state control
• Checkpointing
• Interrupt handling

Scenario 5: Web App with Streaming

Recommendation: Vercel AI SDK

• React/Next.js native
• Streaming-first
• Edge deployment

Scenario 6: Team-based Tasks

Recommendation: CrewAI

• Role-based agents
• Parallel execution
• Intuitive metaphor

Scenario 7: Azure Enterprise

Recommendation: AutoGen + Semantic Kernel

• Azure integration
• Enterprise security
• .NET support

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Best Practices for Agent Development in 2025

1. Use MCP as the Connectivity Standard

# MCP für standardisierte Tool-Integration
mcp_servers = [
    MCPServer("filesystem", "/path/to/allowed"),
    MCPServer("database", connection_string),
    MCPServer("api", api_config)
]

agent = Agent(mcp_servers=mcp_servers)

2. Observability from the Start

• LangSmith for LangGraph
• Langfuse for framework-agnostic
• Vercel Observability for AI SDK
• Built-in Tracing for OpenAI SDK

3. Error Handling and Fallbacks

try:
    result = await agent.run(task)
except AgentTimeoutError:
    result = await fallback_agent.run(task)
except ToolExecutionError as e:
    logger.error(f"Tool failed: {e}")
    result = await agent.run(task, skip_failed_tools=True)

4. Cost Management

• Set token limits per agent
• Use cheaper models for simple tasks
• Implement caching for repeated queries
• Implement rate limiting

5. Security First

• Follow MCP security best practices
• OAuth 2.1 for authenticated tools
• Sandboxing for code execution
• Input sanitization

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Outlook: What's Coming in 2026?

Q1 2026:

• Expanded A2A protocol adoption
• Cross-framework interoperability

Q2 2026:

• Native multi-modal agents
• Real-time streaming improvements

Q3 2026:

• Semantic caching standards
• Context compression

Q4 2026:

• Built-in compliance features
• Multi-tenancy primitives

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Conclusion

The AI Agent SDK landscape of 2025 offers the right framework for every use case:

• Claude Agent SDK for production-grade autonomous agents
• OpenAI Agents SDK for rapid prototypes in the OpenAI ecosystem
• Google ADK for enterprise on GCP
• LangGraph for complex, stateful workflows
• Vercel AI SDK for web applications
• CrewAI for team-based multi-agent systems
• AutoGen/SK for Azure enterprise

The most important trend: MCP as the universal standard connects all frameworks and enables portable tools and integrations.

My advice: Start with the simplest framework that meets your requirements. Adding complexity is easier than reducing it.

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This article was researched in December 2025 and reflects the current state of the AI Agent SDK landscape.