pip install agent-framework --pre
pip install agent-framework-anthropic

import os
from agent_framework.anthropic import AnthropicClient

# Set ANTHROPIC_API_KEY environment variable
# The client will automatically read from the environment
client = AnthropicClient()

from anthropic.foundry import AnthropicFoundryClient
from anthropic.foundry import AnthropicFoundryIdentityTokenCredentials
from azure.identity import DefaultAzureCredential
from agent_framework.anthropic import AnthropicClient

# Using Azure managed identity
credential = DefaultAzureCredential()
foundry_creds = AnthropicFoundryIdentityTokenCredentials(
    credential,
    resource="your-resource-name",
    scopes=["https://ai.azure.com/.default"]
)

client = AnthropicFoundryClient(foundry_creds)

import asyncio
from agent_framework.anthropic import AnthropicClient

async def main():
    # Create agent with Anthropic
    agent = AnthropicClient().as_agent(
        name="Assistant",
        instructions="You are a helpful assistant.",
    )
    
    # Non-streaming response
    result = await agent.run("What is the capital of France?")
    print(result)

asyncio.run(main())

import asyncio
from agent_framework.anthropic import AnthropicClient

async def main():
    agent = AnthropicClient().as_agent(
        name="Assistant",
        instructions="You are a helpful assistant.",
    )
    
    # Streaming response
    query = "Write a short story about AI agents."
    print("Agent: ", end="", flush=True)
    async for chunk in agent.run(query, stream=True):
        if chunk.text:
            print(chunk.text, end="", flush=True)
    print()

asyncio.run(main())

import asyncio
from typing import Annotated
from agent_framework import tool
from agent_framework.anthropic import AnthropicClient

@tool(approval_mode="never_require")  # Use "always_require" in production
def get_weather(location: Annotated[str, "City name"]) -> str:
    """Get the weather for a location."""
    return f"Weather in {location}: Sunny, 72°F"

@tool(approval_mode="never_require")
def search_web(query: Annotated[str, "Search query"]) -> str:
    """Search the web for information."""
    return f"Search results for: {query}"

async def main():
    agent = AnthropicClient().as_agent(
        name="Assistant",
        instructions="You are a helpful assistant with access to tools.",
        tools=[get_weather, search_web],
    )
    
    result = await agent.run(
        "What's the weather in Seattle and find recent news about it?"
    )
    print(result)

asyncio.run(main())

import asyncio
from agent_framework.anthropic import AnthropicClient

async def main():
    agent = AnthropicClient(
        model_id="claude-sonnet-4-20250514"
    ).as_agent(
        instructions="You are a reasoning assistant. Think step by step.",
    )
    
    result = await agent.run(
        "Solve this logic puzzle: Three people are in a room. "
        "Alice says Bob is lying. Bob says Charlie is lying. "
        "Charlie says both Alice and Bob are lying. Who is telling the truth?"
    )
    print(result)

asyncio.run(main())

import asyncio
from agent_framework import Message
from agent_framework.anthropic import AnthropicClient

async def main():
    agent = AnthropicClient().as_agent(
        instructions="You analyze images in detail.",
    )
    
    message = Message(
        role="user",
        text="What's in this image? Describe it in detail.",
        images=["https://example.com/image.jpg"],
    )
    
    result = await agent.run(message)
    print(result)

asyncio.run(main())

import asyncio
from agent_framework.anthropic import AnthropicClient

async def main():
    agent = AnthropicClient().as_agent(
        instructions="You analyze long documents.",
    )
    
    # Claude can handle very long inputs
    with open("long_document.txt", "r") as f:
        document = f.read()  # Could be 100k+ tokens
    
    result = await agent.run(
        f"Summarize this document and identify key themes:\n\n{document}"
    )
    print(result)

asyncio.run(main())