Merge pull request #758 from MicrosoftDocs/main

Merging from main to live.

Author: westey-m

agent-framework/tutorials/workflows/visualization.md

@@ -40,7 +40,7 @@ For basic text output (Mermaid and DOT), no additional dependencies are needed.
 
 ```bash
 # Install the viz extra
-pip install agent-framework[viz]
+pip install agent-framework graphviz
 
 # Install GraphViz binaries (required for image export)
 # On Ubuntu/Debian:

agent-framework/user-guide/agents/agent-types/TOC.yml

@@ -1,5 +1,7 @@
 - name: Overview
   href: index.md
+- name: Anthropic Agents
+  href: anthropic-agent.md
 - name: Azure AI Foundry Agents
   href: azure-ai-foundry-agent.md
 - name: Azure AI Foundry Models Agents via ChatCompletion

agent-framework/user-guide/agents/agent-types/anthropic-agent.md

@@ -0,0 +1,215 @@
+---
+title: Anthropic Agents
+description: Learn how to use the Microsoft Agent Framework with Anthropic's Claude models.
+zone_pivot_groups: programming-languages
+author: eavanvalkenburg
+ms.topic: tutorial
+ms.author: edvan
+ms.date: 11/05/2025
+ms.service: agent-framework
+---
+
+# Anthropic Agents
+
+The Microsoft Agent Framework supports creating agents that use [Anthropic's Claude models](https://www.anthropic.com/claude).
+
+::: zone pivot="programming-language-csharp"
+
+Coming soon...
+
+::: zone-end
+::: zone pivot="programming-language-python"
+
+## Prerequisites
+
+Install the Microsoft Agent Framework Anthropic package.
+
+```bash
+pip install agent-framework-anthropic --pre
+```
+
+## Configuration
+
+### Environment Variables
+
+Set up the required environment variables for Anthropic authentication:
+
+```bash
+# Required for Anthropic API access
+ANTHROPIC_API_KEY="your-anthropic-api-key"
+ANTHROPIC_CHAT_MODEL_ID="claude-sonnet-4-5-20250929"  # or your preferred model
+```
+
+Alternatively, you can use a `.env` file in your project root:
+
+```env
+ANTHROPIC_API_KEY=your-anthropic-api-key
+ANTHROPIC_CHAT_MODEL_ID=claude-sonnet-4-5-20250929
+```
+
+You can get an API key from the [Anthropic Console](https://console.anthropic.com/).
+
+## Getting Started
+
+Import the required classes from the Agent Framework:
+
+```python
+import asyncio
+from agent_framework.anthropic import AnthropicClient
+```
+
+## Creating an Anthropic Agent
+
+### Basic Agent Creation
+
+The simplest way to create an Anthropic agent:
+
+```python
+async def basic_example():
+    # Create an agent using Anthropic
+    agent = AnthropicClient().create_agent(
+        name="HelpfulAssistant",
+        instructions="You are a helpful assistant.",
+    )
+
+    result = await agent.run("Hello, how can you help me?")
+    print(result.text)
+```
+
+### Using Explicit Configuration
+
+You can provide explicit configuration instead of relying on environment variables:
+
+```python
+async def explicit_config_example():
+    agent = AnthropicClient(
+        model_id="claude-sonnet-4-5-20250929",
+        api_key="your-api-key-here",
+    ).create_agent(
+        name="HelpfulAssistant",
+        instructions="You are a helpful assistant.",
+    )
+
+    result = await agent.run("What can you do?")
+    print(result.text)
+```
+
+## Agent Features
+
+### Function Tools
+
+Equip your agent with custom functions:
+
+```python
+from typing import Annotated
+
+def get_weather(
+    location: Annotated[str, "The location to get the weather for."],
+) -> str:
+    """Get the weather for a given location."""
+    conditions = ["sunny", "cloudy", "rainy", "stormy"]
+    return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
+
+async def tools_example():
+    agent = AnthropicClient().create_agent(
+        name="WeatherAgent",
+        instructions="You are a helpful weather assistant.",
+        tools=get_weather,  # Add tools to the agent
+    )
+
+    result = await agent.run("What's the weather like in Seattle?")
+    print(result.text)
+```
+
+### Streaming Responses
+
+Get responses as they are generated for better user experience:
+
+```python
+async def streaming_example():
+    agent = AnthropicClient().create_agent(
+        name="WeatherAgent",
+        instructions="You are a helpful weather agent.",
+        tools=get_weather,
+    )
+
+    query = "What's the weather like in Portland and in Paris?"
+    print(f"User: {query}")
+    print("Agent: ", end="", flush=True)
+    async for chunk in agent.run_stream(query):
+        if chunk.text:
+            print(chunk.text, end="", flush=True)
+    print()
+```
+
+### Hosted Tools
+
+Anthropic agents support hosted tools such as web search, MCP (Model Context Protocol), and code execution:
+
+```python
+from agent_framework import HostedMCPTool, HostedWebSearchTool
+
+async def hosted_tools_example():
+    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(),
+        ],
+        max_tokens=20000,
+    )
+
+    result = await agent.run("Can you compare Python decorators with C# attributes?")
+    print(result.text)
+```
+
+### Extended Thinking (Reasoning)
+
+Anthropic supports extended thinking capabilities through the `thinking` feature, which allows the model to show its reasoning process:
+
+```python
+from agent_framework import TextReasoningContent, UsageContent
+
+async def thinking_example():
+    agent = AnthropicClient().create_agent(
+        name="DocsAgent",
+        instructions="You are a helpful agent.",
+        tools=[HostedWebSearchTool()],
+        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):
+                # Display thinking in a different color
+                print(f"\033[32m{content.text}\033[0m", end="", flush=True)
+            if isinstance(content, UsageContent):
+                print(f"\n\033[34m[Usage: {content.details}]\033[0m\n", end="", flush=True)
+        if chunk.text:
+            print(chunk.text, end="", flush=True)
+    print()
+```
+
+## Using the Agent
+
+The agent is a standard `BaseAgent` and supports all standard agent operations.
+
+See the [Agent getting started tutorials](../../../tutorials/overview.md) for more information on how to run and interact with agents.
+
+::: zone-end
+
+## Next steps
+
+> [!div class="nextstepaction"]
+> [Azure AI Agents](./azure-ai-foundry-agent.md)

agent-framework/user-guide/agents/agent-types/azure-ai-foundry-agent.md

@@ -106,7 +106,7 @@ Alternatively, you can provide these values directly in your code.
 Add the Agent Framework Azure AI package to your project:
 
 ```bash
-pip install agent-framework[azure-ai]
+pip install agent-framework-azure-ai
 ```
 
 ## Getting Started
@@ -219,7 +219,7 @@ async def main():
     async with (
         AzureCliCredential() as credential,
         AIProjectClient(
-            endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], 
+            endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
             credential=credential
         ) as project_client,
     ):