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agent-framework/python/samples/getting_started/agents/anthropic/anthropic_advanced.py
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Eduard van Valkenburg 12d17acdc0 Python: Introducing the Anthropic Client (#1819)
* initial version of anthropic connector

* updated implementation and added tests

* fix type and readme

* mypy fix and int tests enabled

* add integration test setup

* updated based on comments

* improved function result handling

* added extra unordered test

* updated from review

* fix tool choice handling

* same fix for chat client
2025-11-03 19:32:28 +00:00

59 lines
1.9 KiB
Python

# Copyright (c) Microsoft. All rights reserved.
import asyncio
from agent_framework import HostedMCPTool, HostedWebSearchTool, TextReasoningContent, UsageContent
from agent_framework.anthropic import 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 streaming_example() -> None:
"""Example of streaming response (get results as they are generated)."""
agent = AnthropicClient().create_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(),
],
# anthropic needs a value for the max_tokens parameter
# we set it to 1024, but you can override like this:
max_tokens=20000,
additional_chat_options={"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.details}]\033[0m\n", end="", flush=True)
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
async def main() -> None:
print("=== Anthropic Example ===")
await streaming_example()
if __name__ == "__main__":
asyncio.run(main())