chore: import upstream snapshot with attribution

python/samples/02-agents/providers/anthropic/anthropic_foundry.py

@@ -0,0 +1,76 @@
+# Copyright (c) Microsoft. All rights reserved.
+import asyncio
+
+from agent_framework import Agent
+from agent_framework.foundry import AnthropicFoundryClient
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+"""
+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_RESOURCE
+    Should be the resource name portion of your Foundry Anthropic URL, such as <your-resource-name>.
+- ANTHROPIC_FOUNDRY_BASE_URL
+    Optional alternative to ANTHROPIC_FOUNDRY_RESOURCE. Should be something like
+    https://<your-resource-name>.services.ai.azure.com/anthropic/
+- ANTHROPIC_CHAT_MODEL
+    Should be something like claude-haiku-4-5
+"""
+
+
+async def main() -> None:
+    """Example of streaming response (get results as they are generated)."""
+    client = AnthropicFoundryClient()
+
+    # Create MCP tool configuration using instance method
+    mcp_tool = client.get_mcp_tool(
+        name="Microsoft_Learn_MCP",
+        url="https://learn.microsoft.com/api/mcp",
+    )
+
+    # Create web search tool configuration using instance method
+    web_search_tool = client.get_web_search_tool()
+
+    agent = Agent(
+        client=client,
+        name="DocsAgent",
+        instructions="You are a helpful agent for both Microsoft docs questions and general questions.",
+        tools=[mcp_tool, web_search_tool],
+        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(query, stream=True):
+        for content in chunk.contents:
+            if content.type == "text_reasoning":
+                print(f"\033[32m{content.text}\033[0m", end="", flush=True)
+            if content.type == "usage":
+                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())