Add AnthropicSamplingHandler

docs/clients/sampling.mdx

@@ -177,11 +177,15 @@ client = Client(
 )
 ```
 
-## Using the OpenAI Handler
+## Built-in Handlers
 
-<VersionBadge version="2.14.0" />
+FastMCP provides built-in sampling handlers for OpenAI and Anthropic APIs. These handlers support the full sampling API including tool use, handling message conversion and response formatting automatically.
 
-For full-featured sampling with tool support, use the built-in OpenAI handler. It handles message conversion, tool calls, and response formatting automatically:
+### OpenAI Handler
+
+<VersionBadge version="2.11.0" />
+
+The OpenAI handler works with OpenAI's API and any OpenAI-compatible provider:
 
 ```python
 from fastmcp import Client
@@ -193,7 +197,7 @@ client = Client(
 )
 ```
 
-The handler works with any OpenAI-compatible API by passing a custom client:
+For OpenAI-compatible APIs (like local models), pass a custom client:
 
 ```python
 from openai import AsyncOpenAI
@@ -208,9 +212,47 @@ client = Client(
 ```
 
 <Note>
-Tool execution happens on the server side. The client's role is to pass tools to the LLM and return the LLM's response (which may include tool use requests). The server then executes the tools and may send follow-up sampling requests with tool results.
+Install the OpenAI handler with `pip install fastmcp[openai]`.
 </Note>
 
+### Anthropic Handler
+
+<VersionBadge version="2.14.1" />
+
+The Anthropic handler uses Claude models via the Anthropic API:
+
+```python
+from fastmcp import Client
+from fastmcp.client.sampling.handlers.anthropic import AnthropicSamplingHandler
+
+client = Client(
+    "my_mcp_server.py",
+    sampling_handler=AnthropicSamplingHandler(default_model="claude-sonnet-4-5"),
+)
+```
+
+You can pass a custom client for advanced configuration:
+
+```python
+from anthropic import AsyncAnthropic
+
+client = Client(
+    "my_mcp_server.py",
+    sampling_handler=AnthropicSamplingHandler(
+        default_model="claude-sonnet-4-5",
+        client=AsyncAnthropic(),  # Uses ANTHROPIC_API_KEY env var
+    ),
+)
+```
+
+<Note>
+Install the Anthropic handler with `pip install fastmcp[anthropic]`.
+</Note>
+
+### Tool Execution
+
+Tool execution happens on the server side. The client's role is to pass tools to the LLM and return the LLM's response (which may include tool use requests). The server then executes the tools and may send follow-up sampling requests with tool results.
+
 <Tip>
-To implement a custom sampling handler, see the [OpenAISamplingHandler source code](https://github.com/jlowin/fastmcp/blob/main/src/fastmcp/client/sampling/handlers/openai.py) as a reference.
+To implement a custom sampling handler, see the [handler source code](https://github.com/jlowin/fastmcp/tree/main/src/fastmcp/client/sampling/handlers) as a reference.
 </Tip>

docs/servers/sampling.mdx

@@ -443,32 +443,32 @@ tool_result = ToolResultContent(
 
 Client support for sampling is optional—some clients may not implement it. To ensure your tools work regardless of client capabilities, configure a `sampling_handler` that sends requests directly to an LLM provider.
 
-### OpenAI Handler
-
-FastMCP provides an OpenAI-compatible handler that works with OpenAI's API and compatible providers. It supports the full sampling API including tools, automatically converting your Python functions to OpenAI's function calling format.
+FastMCP provides built-in handlers for [OpenAI and Anthropic APIs](/clients/sampling#built-in-handlers). These handlers support the full sampling API including tools, automatically converting your Python functions to each provider's format.
 
 <Note>
-  The OpenAI handler requires the `openai` package. Install it with:
-  ```bash
-  pip install fastmcp[openai]
-  # or
-  pip install openai
-  ```
-  You'll also need to set the `OPENAI_API_KEY` environment variable or pass it directly to the client.
+Install handlers with `pip install fastmcp[openai]` or `pip install fastmcp[anthropic]`.
 </Note>
 
 ```python
-import os
-from openai import OpenAI
 from fastmcp import FastMCP
 from fastmcp.client.sampling.handlers.openai import OpenAISamplingHandler
 
 server = FastMCP(
     name="My Server",
-    sampling_handler=OpenAISamplingHandler(
-        default_model="gpt-4o-mini",
-        client=OpenAI(api_key=os.getenv("OPENAI_API_KEY")),
-    ),
+    sampling_handler=OpenAISamplingHandler(default_model="gpt-4o-mini"),
+    sampling_handler_behavior="fallback",
+)
+```
+
+Or with Anthropic:
+
+```python
+from fastmcp import FastMCP
+from fastmcp.client.sampling.handlers.anthropic import AnthropicSamplingHandler
+
+server = FastMCP(
+    name="My Server",
+    sampling_handler=AnthropicSamplingHandler(default_model="claude-sonnet-4-5"),
     sampling_handler_behavior="fallback",
 )
 ```

pyproject.toml

@@ -47,12 +47,13 @@ classifiers = [
 ]
 
 [project.optional-dependencies]
+anthropic = ["anthropic>=0.40.0"]
 openai = ["openai>=1.102.0"]
 
 [dependency-groups]
 dev = [
     "dirty-equals>=0.9.0",
-    "fastmcp[openai]",
+    "fastmcp[anthropic,openai]",
     # add optional dependencies for fastmcp dev
     "fastapi>=0.115.12",
     "inline-snapshot[dirty-equals]>=0.27.2",