wehub-resource-sync
db620d33df
CodeQL / Analyze (csharp) (push) Waiting to run
CodeQL / Analyze (python) (push) Waiting to run
dotnet-build-and-test / dotnet-test-functions (push) Has been cancelled
dotnet-build-and-test / paths-filter (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Debug, windows-latest, net9.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Release, ubuntu-latest, net10.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Release, ubuntu-latest, net8.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-build (Release, windows-latest, net472) (push) Has been cancelled
dotnet-build-and-test / dotnet-test (Release, integration, true, ubuntu-latest, net10.0) (push) Has been cancelled
dotnet-build-and-test / dotnet-test (Release, integration, true, windows-latest, net472) (push) Has been cancelled
dotnet-build-and-test / dotnet-foundry-hosted-it (push) Has been cancelled
dotnet-build-and-test / dotnet-build-and-test-check (push) Has been cancelled
dotnet-build-and-test / Integration Test Report (push) Has been cancelled
73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
|
|
import asyncio
|
|
import os
|
|
|
|
from agent_framework import Agent
|
|
from agent_framework.anthropic import AnthropicClient
|
|
from dotenv import load_dotenv
|
|
|
|
# Load environment variables from .env file
|
|
load_dotenv()
|
|
|
|
"""
|
|
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.
|
|
|
|
Environment variables:
|
|
ANTHROPIC_API_KEY — Your Anthropic API key
|
|
ANTHROPIC_CHAT_MODEL — The Anthropic model to use (e.g., "claude-sonnet-4-6")
|
|
|
|
"""
|
|
|
|
|
|
async def main() -> None:
|
|
"""Example of streaming response (get results as they are generated)."""
|
|
client = AnthropicClient(
|
|
api_key=os.getenv("ANTHROPIC_API_KEY"),
|
|
model=os.getenv("ANTHROPIC_CHAT_MODEL"),
|
|
)
|
|
|
|
# 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" and content.text:
|
|
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())
|