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python/samples/getting_started/agents/anthropic/README.md

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+# Anthropic Examples
+
+This folder contains examples demonstrating how to use Anthropic's Claude models with the Agent Framework.
+
+## Examples
+
+| File | Description |
+|------|-------------|
+| [`anthropic_basic.py`](anthropic_basic.py) | Demonstrates how to setup a simple agent using the AnthropicClient, with both streaming and non-streaming responses. |
+| [`anthropic_advanced.py`](anthropic_advanced.py) | Shows advanced usage of the AnthropicClient, including hosted tools and `thinking`. |
+| [`anthropic_skills.py`](anthropic_skills.py) | Illustrates how to use Anthropic-managed Skills with an agent, including the Code Interpreter tool and file generation and saving. |
+| [`anthropic_foundry.py`](anthropic_foundry.py) | Example of using Foundry's Anthropic integration with the Agent Framework. |
+
+## Environment Variables
+
+Set the following environment variables before running the examples:
+
+- `ANTHROPIC_API_KEY`: Your Anthropic API key (get one from [Anthropic Console](https://console.anthropic.com/))
+- `ANTHROPIC_CHAT_MODEL_ID`: The Claude model to use (e.g., `claude-haiku-4-5`, `claude-sonnet-4-5-20250929`)
+
+Or, for Foundry:
+- `ANTHROPIC_FOUNDRY_API_KEY`: Your Foundry Anthropic API key
+- `ANTHROPIC_FOUNDRY_ENDPOINT`: The endpoint URL for your Foundry Anthropic resource
+- `ANTHROPIC_CHAT_MODEL_ID`: The Claude model to use in Foundry (e.g., `claude-haiku-4-5`)

python/samples/getting_started/agents/anthropic/anthropic_advanced.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from agent_framework import HostedMCPTool, HostedWebSearchTool, TextReasoningContent, UsageContent
+from agent_framework.anthropic import AnthropicChatOptions, AnthropicClient
+
+"""
+Anthropic Chat Agent Example
+
+This sample demonstrates using Anthropic with:
+- Setting up an Anthropic-based agent with hosted tools.
+- Using the `thinking` feature.
+- Displaying both thinking and usage information during streaming responses.
+"""
+
+
+async def main() -> None:
+    """Example of streaming response (get results as they are generated)."""
+    agent = AnthropicClient[AnthropicChatOptions]().as_agent(
+        name="DocsAgent",
+        instructions="You are a helpful agent for both Microsoft docs questions and general questions.",
+        tools=[
+            HostedMCPTool(
+                name="Microsoft Learn MCP",
+                url="https://learn.microsoft.com/api/mcp",
+            ),
+            HostedWebSearchTool(),
+        ],
+        default_options={
+            # anthropic needs a value for the max_tokens parameter
+            # we set it to 1024, but you can override like this:
+            "max_tokens": 20000,
+            "thinking": {"type": "enabled", "budget_tokens": 10000},
+        },
+    )
+
+    query = "Can you compare Python decorators with C# attributes?"
+    print(f"User: {query}")
+    print("Agent: ", end="", flush=True)
+    async for chunk in agent.run_stream(query):
+        for content in chunk.contents:
+            if isinstance(content, TextReasoningContent):
+                print(f"\033[32m{content.text}\033[0m", end="", flush=True)
+            if isinstance(content, UsageContent):
+                print(f"\n\033[34m[Usage so far: {content.usage_details}]\033[0m\n", end="", flush=True)
+        if chunk.text:
+            print(chunk.text, end="", flush=True)
+
+    print("\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/getting_started/agents/anthropic/anthropic_basic.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+from random import randint
+from typing import Annotated
+
+from agent_framework.anthropic import AnthropicClient
+
+"""
+Anthropic Chat Agent Example
+
+This sample demonstrates using Anthropic with an agent and a single custom tool.
+"""
+
+
+def get_weather(
+    location: Annotated[str, "The location to get the weather for."],
+) -> str:
+    """Get the weather for a given location."""
+    conditions = ["sunny", "cloudy", "rainy", "stormy"]
+    return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
+
+
+async def non_streaming_example() -> None:
+    """Example of non-streaming response (get the complete result at once)."""
+    print("=== Non-streaming Response Example ===")
+
+    agent = AnthropicClient(
+    ).as_agent(
+        name="WeatherAgent",
+        instructions="You are a helpful weather agent.",
+        tools=get_weather,
+    )
+
+    query = "What's the weather like in Seattle?"
+    print(f"User: {query}")
+    result = await agent.run(query)
+    print(f"Result: {result}\n")
+
+
+async def streaming_example() -> None:
+    """Example of streaming response (get results as they are generated)."""
+    print("=== Streaming Response Example ===")
+
+    agent = AnthropicClient(
+    ).as_agent(
+        name="WeatherAgent",
+        instructions="You are a helpful weather agent.",
+        tools=get_weather,
+    )
+
+    query = "What's the weather like in Portland and in Paris?"
+    print(f"User: {query}")
+    print("Agent: ", end="", flush=True)
+    async for chunk in agent.run_stream(query):
+        if chunk.text:
+            print(chunk.text, end="", flush=True)
+    print("\n")
+
+
+async def main() -> None:
+    print("=== Anthropic Example ===")
+
+    await streaming_example()
+    await non_streaming_example()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/getting_started/agents/anthropic/anthropic_foundry.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from agent_framework import HostedMCPTool, HostedWebSearchTool, TextReasoningContent, UsageContent
+from agent_framework.anthropic import AnthropicClient
+from anthropic import AsyncAnthropicFoundry
+
+"""
+Anthropic Foundry Chat Agent Example
+
+This sample demonstrates using Anthropic with:
+- Setting up an Anthropic-based agent with hosted tools.
+- Using the `thinking` feature.
+- Displaying both thinking and usage information during streaming responses.
+
+This example requires `anthropic>=0.74.0` and an endpoint in Foundry for Anthropic.
+
+To use the Foundry integration ensure you have the following environment variables set:
+- ANTHROPIC_FOUNDRY_API_KEY
+    Alternatively you can pass in a azure_ad_token_provider function to the AsyncAnthropicFoundry constructor.
+- ANTHROPIC_FOUNDRY_ENDPOINT
+    Should be something like https://<your-resource-name>.services.ai.azure.com/anthropic/
+- ANTHROPIC_CHAT_MODEL_ID
+    Should be something like claude-haiku-4-5
+"""
+
+
+async def main() -> None:
+    """Example of streaming response (get results as they are generated)."""
+    agent = AnthropicClient(anthropic_client=AsyncAnthropicFoundry()).as_agent(
+        name="DocsAgent",
+        instructions="You are a helpful agent for both Microsoft docs questions and general questions.",
+        tools=[
+            HostedMCPTool(
+                name="Microsoft Learn MCP",
+                url="https://learn.microsoft.com/api/mcp",
+            ),
+            HostedWebSearchTool(),
+        ],
+        default_options={
+            # anthropic needs a value for the max_tokens parameter
+            # we set it to 1024, but you can override like this:
+            "max_tokens": 20000,
+            "thinking": {"type": "enabled", "budget_tokens": 10000},
+        },
+    )
+
+    query = "Can you compare Python decorators with C# attributes?"
+    print(f"User: {query}")
+    print("Agent: ", end="", flush=True)
+    async for chunk in agent.run_stream(query):
+        for content in chunk.contents:
+            if isinstance(content, TextReasoningContent):
+                print(f"\033[32m{content.text}\033[0m", end="", flush=True)
+            if isinstance(content, UsageContent):
+                print(f"\n\033[34m[Usage so far: {content.usage_details}]\033[0m\n", end="", flush=True)
+        if chunk.text:
+            print(chunk.text, end="", flush=True)
+
+    print("\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/getting_started/agents/anthropic/anthropic_skills.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import logging
+from pathlib import Path
+
+from agent_framework import HostedCodeInterpreterTool, HostedFileContent
+from agent_framework.anthropic import AnthropicChatOptions, AnthropicClient
+
+logger = logging.getLogger(__name__)
+"""
+Anthropic Skills Agent Example
+
+This sample demonstrates using Anthropic with:
+- Listing and using Anthropic-managed Skills.
+- One approach to add additional beta flags.
+    You can also set additonal_chat_options with "additional_beta_flags" per request.
+- Creating an agent with the Code Interpreter tool and a Skill.
+- Catching and downloading generated files from the agent.
+"""
+
+
+async def main() -> None:
+    """Example of streaming response (get results as they are generated)."""
+    client = AnthropicClient[AnthropicChatOptions](additional_beta_flags=["skills-2025-10-02"])
+
+    # List Anthropic-managed Skills
+    skills = await client.anthropic_client.beta.skills.list(source="anthropic", betas=["skills-2025-10-02"])
+    for skill in skills.data:
+        print(f"{skill.source}: {skill.id} (version: {skill.latest_version})")
+
+    # Create a agent with the pptx skill enabled
+    # Skills also need the code interpreter tool to function
+    agent = client.as_agent(
+        name="DocsAgent",
+        instructions="You are a helpful agent for creating powerpoint presentations.",
+        tools=HostedCodeInterpreterTool(),
+        default_options={
+            "max_tokens": 20000,
+            "thinking": {"type": "enabled", "budget_tokens": 10000},
+            "container": {"skills": [{"type": "anthropic", "skill_id": "pptx", "version": "latest"}]},
+        },
+    )
+
+    print(
+        "The agent output will use the following colors:\n"
+        "\033[0mUser: (default)\033[0m\n"
+        "\033[0mAgent: (default)\033[0m\n"
+        "\033[32mAgent Reasoning: (green)\033[0m\n"
+        "\033[34mUsage: (blue)\033[0m\n"
+    )
+    query = "Create a presentation about renewable energy with 5 slides"
+    print(f"User: {query}")
+    print("Agent: ", end="", flush=True)
+    files: list[HostedFileContent] = []
+    async for chunk in agent.run_stream(query):
+        for content in chunk.contents:
+            match content.type:
+                case "text":
+                    print(content.text, end="", flush=True)
+                case "text_reasoning":
+                    print(f"\033[32m{content.text}\033[0m", end="", flush=True)
+                case "usage":
+                    print(f"\n\033[34m[Usage so far: {content.usage_details}]\033[0m\n", end="", flush=True)
+                case "hosted_file":
+                    # Catch generated files
+                    files.append(content)
+                case _:
+                    logger.debug("Unhandled content type: %s", content.type)
+                    pass
+
+    print("\n")
+    if files:
+        # Save to a new file (will be in the folder where you are running this script)
+        # When running this sample multiple times, the files will be overritten
+        # Since I'm using the pptx skill, the files will be PowerPoint presentations
+        print("Generated files:")
+        for idx, file in enumerate(files):
+            file_content = await client.anthropic_client.beta.files.download(
+                file_id=file.file_id, betas=["files-api-2025-04-14"]
+            )
+            with open(Path(__file__).parent / f"renewable_energy-{idx}.pptx", "wb") as f:
+                await file_content.write_to_file(f.name)
+            print(f"File {idx}: renewable_energy-{idx}.pptx saved to disk.")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())