Agent SDKs Compared: OpenAI, Claude, Google ADK and More

import asyncio

from agents import Agent, Runner

agent = Agent(
    name="History tutor",
    instructions="You answer history questions clearly and concisely.",
    model="gpt-6-astra",
)

async def main() -> None:
    result = await Runner.run(agent, "When did the Roman Empire fall?")
    print(result.final_output)

if __name__ == "__main__":
    asyncio.run(main())

From the official quickstart (pip install openai-agents). Source: OpenAI Agents SDK quickstart (opens in new tab)

import asyncio
from claude_agent_sdk import query, ClaudeAgentOptions, ResultMessage

async def main():
    async for message in query(
        prompt="Review utils.py for bugs that would cause crashes. Fix any issues you find.",
        options=ClaudeAgentOptions(
            allowed_tools=["Read", "Edit", "Glob"],  # Auto-approve these tools
            permission_mode="acceptEdits",  # Auto-approve file edits
        ),
    ):
        if isinstance(message, ResultMessage):
            print(f"Done: {message.subtype}")

asyncio.run(main())

Trimmed from the official quickstart (pip install claude-agent-sdk). Source: Claude Agent SDK quickstart (opens in new tab)

from google.adk.agents.llm_agent import Agent

# Mock tool implementation
def get_current_time(city: str) -> dict:
    """Returns the current time in a specified city."""
    return {"status": "success", "city": city, "time": "10:30 AM"}

root_agent = Agent(
    model='gemini-flash-latest',
    name='root_agent',
    description="Tells the current time in a specified city.",
    instruction="You are a helpful assistant that tells the current time in cities. Use the 'get_current_time' tool for this purpose.",
    tools=[get_current_time],
)

From the official Python quickstart (pip install google-adk; run with adk run my_agent). Source: ADK Python quickstart (opens in new tab)

import asyncio
from agent_framework import Agent
from agent_framework.foundry import FoundryChatClient
from azure.identity import AzureCliCredential

agent = Agent(
    client=FoundryChatClient(
        project_endpoint="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project",
        model="gpt-5.4-mini",
        credential=AzureCliCredential(),
    ),
    name="HelloAgent",
    instructions="You are a friendly assistant. Keep your answers brief.",
)

async def main():
    result = await agent.run("What is the largest city in France?")
    print(f"Agent: {result}")

asyncio.run(main())

Adapted from the official overview (pip install agent-framework); the two doc snippets are joined in one async main. Source: Agent Framework overview (opens in new tab)

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

def mock_llm(state: MessagesState):
    return {"messages": [{"role": "ai", "content": "hello world"}]}

graph = StateGraph(MessagesState)
graph.add_node(mock_llm)
graph.add_edge(START, "mock_llm")
graph.add_edge("mock_llm", END)
graph = graph.compile()

graph.invoke({"messages": [{"role": "user", "content": "hi!"}]})

Official hello world (pip install -U langgraph); uses a mock LLM node. Source: LangGraph overview (opens in new tab)

from crewai import Agent
from crewai_tools import SerperDevTool

researcher = Agent(
    role="AI Technology Researcher",
    goal="Research the latest AI developments",
    tools=[SerperDevTool()],
    verbose=True,
)

# Use kickoff() to interact directly with the agent
result = researcher.kickoff("What are the latest developments in language models?")
print(result.raw)

From the official Agents docs (pip install crewai crewai-tools; needs SERPER_API_KEY). Source: CrewAI Agents: kickoff() (opens in new tab)

import { ToolLoopAgent, tool } from 'ai';
import { z } from 'zod';

const weatherAgent = new ToolLoopAgent({
  model: "anthropic/claude-sonnet-5.5",
  tools: {
    weather: tool({
      description: 'Get the weather in a location (in Fahrenheit)',
      inputSchema: z.object({
        location: z.string().describe('The location to get the weather for'),
      }),
      execute: async ({ location }) => ({
        location,
        temperature: 72 + Math.floor(Math.random() * 21) - 10,
      }),
    }),
  },
});

const result = await weatherAgent.generate({
  prompt: 'What is the weather in San Francisco?',
});

console.log(result.text);

Trimmed from the official Agents overview (npm install ai zod). Source: AI SDK Agents overview (opens in new tab)

import { Agent } from '@mastra/core/agent'

export const weatherAgent = new Agent({
  id: 'weather-agent',
  name: 'Weather Agent',
  instructions: 'You are a helpful weather assistant that provides accurate weather information.',
  // Use a string in provider/model format
  model: 'openai/gpt-5.6-sol',
})

const response = await weatherAgent.generate('Weather in SF')
console.log(response.text)

From the official Get started page (npm install @mastra/core zod); tools omitted. Source: Mastra: Get started (opens in new tab)

from typing import Literal

from pydantic import BaseModel, Field

from pydantic_ai import Agent, RunContext

class Sentiment(BaseModel):
    label: Literal['positive', 'negative', 'neutral']
    score: float = Field(ge=-1, le=1)

agent = Agent('openai:gpt-6.1-sol', output_type=Sentiment)

@agent.tool
def recent_reviews(ctx: RunContext, product: str) -> list[str]:
    """Fetch recent review snippets for a product."""
    return ['The new release fixed everything I complained about!']

result = agent.run_sync('How are people feeling about the Extract app?')
print(result.output)

From the official docs home (pip install pydantic-ai). Source: Pydantic AI docs (opens in new tab)