mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Eduard van Valkenburg
3446eb8d5d
* updates to final deprecated pieces and versions * fix mypy * fix readme links
73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from agent_framework import Agent
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from agent_framework.anthropic import AnthropicClient
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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"""
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Anthropic Chat Agent Example
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This sample demonstrates using Anthropic with:
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- Setting up an Anthropic-based agent with hosted tools.
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- Using the `thinking` feature.
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- Displaying both thinking and usage information during streaming responses.
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Environment variables:
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ANTHROPIC_API_KEY — Your Anthropic API key
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ANTHROPIC_CHAT_MODEL — The Anthropic model to use (e.g., "claude-sonnet-4-6")
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"""
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async def main() -> None:
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"""Example of streaming response (get results as they are generated)."""
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client = AnthropicClient(
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api_key=os.getenv("ANTHROPIC_API_KEY"),
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model=os.getenv("ANTHROPIC_CHAT_MODEL"),
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)
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# Create MCP tool configuration using instance method
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mcp_tool = client.get_mcp_tool(
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name="Microsoft_Learn_MCP",
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url="https://learn.microsoft.com/api/mcp",
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)
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# Create web search tool configuration using instance method
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web_search_tool = client.get_web_search_tool()
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agent = Agent(
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client=client,
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name="DocsAgent",
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instructions="You are a helpful agent for both Microsoft docs questions and general questions.",
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tools=[mcp_tool, web_search_tool],
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default_options={
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# anthropic needs a value for the max_tokens parameter
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# we set it to 1024, but you can override like this:
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"max_tokens": 20000,
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"thinking": {"type": "enabled", "budget_tokens": 10000},
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},
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)
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query = "Can you compare Python decorators with C# attributes?"
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print(f"User: {query}")
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print("Agent: ", end="", flush=True)
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async for chunk in agent.run(query, stream=True):
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for content in chunk.contents:
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if content.type == "text_reasoning" and content.text:
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print(f"\033[32m{content.text}\033[0m", end="", flush=True)
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if content.type == "usage":
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print(f"\n\033[34m[Usage so far: {content.usage_details}]\033[0m\n", end="", flush=True)
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if chunk.text:
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print(chunk.text, end="", flush=True)
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print("\n")
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if __name__ == "__main__":
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asyncio.run(main())
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