Ren Finlayson
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55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from agent_framework import HostedMCPTool, HostedWebSearchTool, TextReasoningContent, UsageContent
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from agent_framework.anthropic import AnthropicChatOptions, AnthropicClient
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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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"""
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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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agent = AnthropicClient[AnthropicChatOptions]().as_agent(
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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=[
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HostedMCPTool(
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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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HostedWebSearchTool(),
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],
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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_stream(query):
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for content in chunk.contents:
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if isinstance(content, TextReasoningContent):
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print(f"\033[32m{content.text}\033[0m", end="", flush=True)
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if isinstance(content, UsageContent):
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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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