diff --git a/docs/api/models/grok.md b/docs/api/models/grok.md
new file mode 100644
index 0000000000..699c1e95f9
--- /dev/null
+++ b/docs/api/models/grok.md
@@ -0,0 +1,7 @@
+# `pydantic_ai.models.grok`
+
+## Setup
+
+For details on how to set up authentication with this model, see [model configuration for Grokq](../../models/grokq.md).
+
+::: pydantic_ai.models.grok
diff --git a/docs/models/grok.md b/docs/models/grok.md
new file mode 100644
index 0000000000..db04423493
--- /dev/null
+++ b/docs/models/grok.md
@@ -0,0 +1,77 @@
+# Groq
+
+## Install
+
+To use `GroqModel`, you need to either install `pydantic-ai`, or install `pydantic-ai-slim` with the `groq` optional group:
+
+```bash
+pip/uv-add "pydantic-ai-slim[groq]"
+```
+
+## Configuration
+
+To use [Groq](https://groq.com/) through their API, go to [console.groq.com/keys](https://console.groq.com/keys) and follow your nose until you find the place to generate an API key.
+
+`GroqModelName` contains a list of available Groq models.
+
+## Environment variable
+
+Once you have the API key, you can set it as an environment variable:
+
+```bash
+export GROQ_API_KEY='your-api-key'
+```
+
+You can then use `GroqModel` by name:
+
+```python
+from pydantic_ai import Agent
+
+agent = Agent('groq:llama-3.3-70b-versatile')
+...
+```
+
+Or initialise the model directly with just the model name:
+
+```python
+from pydantic_ai import Agent
+from pydantic_ai.models.groq import GroqModel
+
+model = GroqModel('llama-3.3-70b-versatile')
+agent = Agent(model)
+...
+```
+
+## `provider` argument
+
+You can provide a custom `Provider` via the `provider` argument:
+
+```python
+from pydantic_ai import Agent
+from pydantic_ai.models.groq import GroqModel
+from pydantic_ai.providers.groq import GroqProvider
+
+model = GroqModel(
+    'llama-3.3-70b-versatile', provider=GroqProvider(api_key='your-api-key')
+)
+agent = Agent(model)
+...
+```
+
+You can also customize the `GroqProvider` with a custom `httpx.AsyncHTTPClient`:
+
+```python
+from httpx import AsyncClient
+
+from pydantic_ai import Agent
+from pydantic_ai.models.groq import GroqModel
+from pydantic_ai.providers.groq import GroqProvider
+
+custom_http_client = AsyncClient(timeout=30)
+model = GroqModel(
+    'llama-3.3-70b-versatile',
+    provider=GroqProvider(api_key='your-api-key', http_client=custom_http_client),
+)
+agent = Agent(model)
+...
+```
diff --git a/docs/models/overview.md b/docs/models/overview.md
index 75cf954b11..8e7b4aadc9 100644
--- a/docs/models/overview.md
+++ b/docs/models/overview.md
@@ -5,6 +5,7 @@ Pydantic AI is model-agnostic and has built-in support for multiple model provid
 * [OpenAI](openai.md)
 * [Anthropic](anthropic.md)
 * [Gemini](google.md) (via two different APIs: Generative Language API and VertexAI API)
+* [Grok](grok.md)
 * [Groq](groq.md)
 * [Mistral](mistral.md)
 * [Cohere](cohere.md)
diff --git a/examples/pydantic_ai_examples/flight_booking_grok.py b/examples/pydantic_ai_examples/flight_booking_grok.py
new file mode 100644
index 0000000000..1acb497927
--- /dev/null
+++ b/examples/pydantic_ai_examples/flight_booking_grok.py
@@ -0,0 +1,248 @@
+"""Example of a multi-agent flow where one agent delegates work to another.
+
+In this scenario, a group of agents work together to find flights for a user.
+"""
+
+import datetime
+from dataclasses import dataclass
+from typing import Literal
+import os
+import logfire
+from pydantic import BaseModel, Field
+from rich.prompt import Prompt
+
+from pydantic_ai import Agent, ModelRetry, RunContext, RunUsage, UsageLimits
+from pydantic_ai.messages import ModelMessage
+
+# Import local GrokModel
+from pydantic_ai.models.grok import GrokModel
+
+logfire.configure()
+logfire.instrument_pydantic_ai()
+logfire.instrument_httpx()
+
+# Configure for xAI API
+xai_api_key = os.getenv("XAI_API_KEY")
+if not xai_api_key:
+    raise ValueError("XAI_API_KEY environment variable is required")
+
+
+# Create the model using the new GrokModelpwd
+model = GrokModel("grok-4-fast-non-reasoning", api_key=xai_api_key)
+
+
+class FlightDetails(BaseModel):
+    """Details of the most suitable flight."""
+
+    flight_number: str
+    price: int
+    origin: str = Field(description="Three-letter airport code")
+    destination: str = Field(description="Three-letter airport code")
+    date: datetime.date
+
+
+class NoFlightFound(BaseModel):
+    """When no valid flight is found."""
+
+
+@dataclass
+class Deps:
+    web_page_text: str
+    req_origin: str
+    req_destination: str
+    req_date: datetime.date
+
+
+# This agent is responsible for controlling the flow of the conversation.
+search_agent = Agent[Deps, FlightDetails | NoFlightFound](
+    model=model,
+    output_type=FlightDetails | NoFlightFound,  # type: ignore
+    retries=4,
+    system_prompt=("Your job is to find the cheapest flight for the user on the given date. "),
+)
+
+
+# This agent is responsible for extracting flight details from web page text.
+extraction_agent = Agent(
+    model=model,
+    output_type=list[FlightDetails],
+    system_prompt="Extract all the flight details from the given text.",
+)
+
+
+@search_agent.tool
+async def extract_flights(ctx: RunContext[Deps]) -> list[FlightDetails]:
+    """Get details of all flights."""
+    # we pass the usage to the search agent so requests within this agent are counted
+    result = await extraction_agent.run(ctx.deps.web_page_text, usage=ctx.usage)
+    logfire.info("found {flight_count} flights", flight_count=len(result.output))
+    return result.output
+
+
+@search_agent.output_validator
+async def validate_output(
+    ctx: RunContext[Deps], output: FlightDetails | NoFlightFound
+) -> FlightDetails | NoFlightFound:
+    """Procedural validation that the flight meets the constraints."""
+    if isinstance(output, NoFlightFound):
+        return output
+
+    errors: list[str] = []
+    if output.origin != ctx.deps.req_origin:
+        errors.append(f"Flight should have origin {ctx.deps.req_origin}, not {output.origin}")
+    if output.destination != ctx.deps.req_destination:
+        errors.append(
+            f"Flight should have destination {ctx.deps.req_destination}, not {output.destination}"
+        )
+    if output.date != ctx.deps.req_date:
+        errors.append(f"Flight should be on {ctx.deps.req_date}, not {output.date}")
+
+    if errors:
+        raise ModelRetry("\n".join(errors))
+    else:
+        return output
+
+
+class SeatPreference(BaseModel):
+    row: int = Field(ge=1, le=30)
+    seat: Literal["A", "B", "C", "D", "E", "F"]
+
+
+class Failed(BaseModel):
+    """Unable to extract a seat selection."""
+
+
+# This agent is responsible for extracting the user's seat selection
+seat_preference_agent = Agent[None, SeatPreference | Failed](
+    model=model,
+    output_type=SeatPreference | Failed,
+    system_prompt=(
+        "Extract the user's seat preference. "
+        "Seats A and F are window seats. "
+        "Row 1 is the front row and has extra leg room. "
+        "Rows 14, and 20 also have extra leg room. "
+    ),
+)
+
+
+# in reality this would be downloaded from a booking site,
+# potentially using another agent to navigate the site
+flights_web_page = """
+1. Flight SFO-AK123
+- Price: $350
+- Origin: San Francisco International Airport (SFO)
+- Destination: Ted Stevens Anchorage International Airport (ANC)
+- Date: January 10, 2025
+
+2. Flight SFO-AK456
+- Price: $370
+- Origin: San Francisco International Airport (SFO)
+- Destination: Fairbanks International Airport (FAI)
+- Date: January 10, 2025
+
+3. Flight SFO-AK789
+- Price: $400
+- Origin: San Francisco International Airport (SFO)
+- Destination: Juneau International Airport (JNU)
+- Date: January 20, 2025
+
+4. Flight NYC-LA101
+- Price: $250
+- Origin: San Francisco International Airport (SFO)
+- Destination: Ted Stevens Anchorage International Airport (ANC)
+- Date: January 10, 2025
+
+5. Flight CHI-MIA202
+- Price: $200
+- Origin: Chicago O'Hare International Airport (ORD)
+- Destination: Miami International Airport (MIA)
+- Date: January 12, 2025
+
+6. Flight BOS-SEA303
+- Price: $120
+- Origin: Boston Logan International Airport (BOS)
+- Destination: Ted Stevens Anchorage International Airport (ANC)
+- Date: January 12, 2025
+
+7. Flight DFW-DEN404
+- Price: $150
+- Origin: Dallas/Fort Worth International Airport (DFW)
+- Destination: Denver International Airport (DEN)
+- Date: January 10, 2025
+
+8. Flight ATL-HOU505
+- Price: $180
+- Origin: Hartsfield-Jackson Atlanta International Airport (ATL)
+- Destination: George Bush Intercontinental Airport (IAH)
+- Date: January 10, 2025
+"""
+
+# restrict how many requests this app can make to the LLM
+usage_limits = UsageLimits(request_limit=15)
+
+
+async def main():
+    deps = Deps(
+        web_page_text=flights_web_page,
+        req_origin="SFO",
+        req_destination="ANC",
+        req_date=datetime.date(2025, 1, 10),
+    )
+    message_history: list[ModelMessage] | None = None
+    usage: RunUsage = RunUsage()
+    # run the agent until a satisfactory flight is found
+    while True:
+        result = await search_agent.run(
+            f"Find me a flight from {deps.req_origin} to {deps.req_destination} on {deps.req_date}",
+            deps=deps,
+            usage=usage,
+            message_history=message_history,
+            usage_limits=usage_limits,
+        )
+        if isinstance(result.output, NoFlightFound):
+            print("No flight found")
+            break
+        else:
+            flight = result.output
+            print(f"Flight found: {flight}")
+            answer = Prompt.ask(
+                "Do you want to buy this flight, or keep searching? (buy/*search)",
+                choices=["buy", "search", ""],
+                show_choices=False,
+            )
+            if answer == "buy":
+                seat = await find_seat(usage)
+                await buy_tickets(flight, seat)
+                break
+            else:
+                message_history = result.all_messages(
+                    output_tool_return_content="Please suggest another flight"
+                )
+
+
+async def find_seat(usage: RunUsage) -> SeatPreference:
+    message_history: list[ModelMessage] | None = None
+    while True:
+        answer = Prompt.ask("What seat would you like?")
+
+        result = await seat_preference_agent.run(
+            answer,
+            message_history=message_history,
+            usage=usage,
+            usage_limits=usage_limits,
+        )
+        if isinstance(result.output, SeatPreference):
+            return result.output
+        else:
+            print("Could not understand seat preference. Please try again.")
+            message_history = result.all_messages()
+
+
+async def buy_tickets(flight_details: FlightDetails, seat: SeatPreference):
+    print(f"Purchasing flight {flight_details=!r} {seat=!r}...")
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(main())
diff --git a/pydantic_ai_slim/pydantic_ai/models/grok.py b/pydantic_ai_slim/pydantic_ai/models/grok.py
new file mode 100644
index 0000000000..5ec8c0ebf6
--- /dev/null
+++ b/pydantic_ai_slim/pydantic_ai/models/grok.py
@@ -0,0 +1,340 @@
+"""Grok model implementation using xAI SDK."""
+
+import os
+from contextlib import asynccontextmanager
+from typing import Any, AsyncIterator
+
+from .._run_context import RunContext
+from ..messages import (
+    ModelMessage,
+    ModelRequest,
+    ModelResponse,
+    SystemPromptPart,
+    UserPromptPart,
+    ToolReturnPart,
+    TextPart,
+    ToolCallPart,
+    FinishReason,
+)
+from ..models import (
+    Model,
+    ModelRequestParameters,
+    ModelSettings,
+    StreamedResponse,
+)
+from ..usage import RequestUsage
+from .._utils import now_utc
+
+# Import xai_sdk components
+from xai_sdk import AsyncClient
+from xai_sdk.chat import system, user, assistant, tool, tool_result
+import xai_sdk.chat as chat_types
+
+
+class GrokModel(Model):
+    """A model that uses the xAI SDK to interact with Grok."""
+
+    _model_name: str
+    _api_key: str
+
+    def __init__(
+        self,
+        model_name: str,
+        *,
+        api_key: str | None = None,
+        settings: ModelSettings | None = None,
+    ):
+        """Initialize the Grok model.
+
+        Args:
+            model_name: The name of the Grok model to use (e.g., "grok-3", "grok-4-fast-non-reasoning")
+            api_key: The xAI API key. If not provided, uses XAI_API_KEY environment variable.
+            settings: Optional model settings.
+        """
+        super().__init__(settings=settings)
+        self._model_name = model_name
+        self._api_key = api_key or os.getenv("XAI_API_KEY") or ""
+        if not self._api_key:
+            raise ValueError("XAI API key is required")
+
+    @property
+    def model_name(self) -> str:
+        """The model name."""
+        return self._model_name
+
+    @property
+    def system(self) -> str:
+        """The model provider."""
+        return "xai"
+
+    def _map_messages(self, messages: list[ModelMessage]) -> list[chat_types.chat_pb2.Message]:
+        """Convert pydantic_ai messages to xAI SDK messages."""
+        xai_messages = []
+
+        for message in messages:
+            if isinstance(message, ModelRequest):
+                for part in message.parts:
+                    if isinstance(part, SystemPromptPart):
+                        xai_messages.append(system(part.content))
+                    elif isinstance(part, UserPromptPart):
+                        # Handle user prompt content
+                        if isinstance(part.content, str):
+                            xai_messages.append(user(part.content))
+                        else:
+                            # Handle complex content (images, etc.)
+                            # For now, just concatenate text content
+                            text_parts = []
+                            for item in part.content:
+                                if isinstance(item, str):
+                                    text_parts.append(item)
+                            if text_parts:
+                                xai_messages.append(user(" ".join(text_parts)))
+                    elif isinstance(part, ToolReturnPart):
+                        xai_messages.append(tool_result(part.model_response_str()))
+            elif isinstance(message, ModelResponse):
+                content_parts = []
+                for part in message.parts:
+                    if isinstance(part, TextPart):
+                        content_parts.append(part.content)
+                    elif isinstance(part, ToolCallPart):
+                        # Tool calls will be handled separately in the response processing
+                        pass
+
+                if content_parts:
+                    xai_messages.append(assistant(" ".join(content_parts)))
+
+        return xai_messages
+
+    def _map_tools(
+        self, model_request_parameters: ModelRequestParameters
+    ) -> list[chat_types.chat_pb2.Tool]:
+        """Convert pydantic_ai tool definitions to xAI SDK tools."""
+        tools = []
+        for tool_def in model_request_parameters.tool_defs.values():
+            xai_tool = tool(
+                name=tool_def.name,
+                description=tool_def.description or "",
+                parameters=tool_def.parameters_json_schema,
+            )
+            tools.append(xai_tool)
+        return tools
+
+    async def request(
+        self,
+        messages: list[ModelMessage],
+        model_settings: ModelSettings | None,
+        model_request_parameters: ModelRequestParameters,
+    ) -> ModelResponse:
+        """Make a request to the Grok model."""
+        # Create client in the current async context to avoid event loop issues
+        client = AsyncClient(api_key=self._api_key)
+
+        # Convert messages to xAI format
+        xai_messages = self._map_messages(messages)
+
+        # Convert tools if any
+        tools = (
+            self._map_tools(model_request_parameters)
+            if model_request_parameters.tool_defs
+            else None
+        )
+
+        # Filter model settings to only include xAI SDK compatible parameters
+        xai_settings = {}
+        if model_settings:
+            # Map pydantic_ai settings to xAI SDK parameters
+            if "temperature" in model_settings:
+                xai_settings["temperature"] = model_settings["temperature"]
+            if "top_p" in model_settings:
+                xai_settings["top_p"] = model_settings["top_p"]
+            if "max_tokens" in model_settings:
+                xai_settings["max_tokens"] = model_settings["max_tokens"]
+            if "stop_sequences" in model_settings:
+                xai_settings["stop"] = model_settings["stop_sequences"]
+            if "seed" in model_settings:
+                xai_settings["seed"] = model_settings["seed"]
+
+        # Create chat instance
+        chat = client.chat.create(
+            model=self._model_name, messages=xai_messages, tools=tools, **xai_settings
+        )
+
+        # Sample the response
+        response = await chat.sample()
+
+        # Convert response to pydantic_ai format
+        return self._process_response(response)
+
+    @asynccontextmanager
+    async def request_stream(
+        self,
+        messages: list[ModelMessage],
+        model_settings: ModelSettings | None,
+        model_request_parameters: ModelRequestParameters,
+        run_context: RunContext[Any] | None = None,
+    ) -> AsyncIterator[StreamedResponse]:
+        """Make a streaming request to the Grok model."""
+        # Create client in the current async context to avoid event loop issues
+        client = AsyncClient(api_key=self._api_key)
+
+        # Convert messages to xAI format
+        xai_messages = self._map_messages(messages)
+
+        # Convert tools if any
+        tools = (
+            self._map_tools(model_request_parameters)
+            if model_request_parameters.tool_defs
+            else None
+        )
+
+        # Filter model settings to only include xAI SDK compatible parameters
+        xai_settings = {}
+        if model_settings:
+            # Map pydantic_ai settings to xAI SDK parameters
+            if "temperature" in model_settings:
+                xai_settings["temperature"] = model_settings["temperature"]
+            if "top_p" in model_settings:
+                xai_settings["top_p"] = model_settings["top_p"]
+            if "max_tokens" in model_settings:
+                xai_settings["max_tokens"] = model_settings["max_tokens"]
+            if "stop_sequences" in model_settings:
+                xai_settings["stop"] = model_settings["stop_sequences"]
+            if "seed" in model_settings:
+                xai_settings["seed"] = model_settings["seed"]
+
+        # Create chat instance
+        chat = client.chat.create(
+            model=self._model_name, messages=xai_messages, tools=tools, **xai_settings
+        )
+
+        # Stream the response
+        response_stream = chat.stream()
+        streamed_response = GrokStreamedResponse(model_request_parameters)
+        streamed_response._model_name = self._model_name
+        streamed_response._response = response_stream
+        streamed_response._timestamp = now_utc()
+        streamed_response._provider_name = "xai"
+        yield streamed_response
+
+    def _process_response(self, response: chat_types.Response) -> ModelResponse:
+        """Convert xAI SDK response to pydantic_ai ModelResponse."""
+        from typing import cast
+
+        parts = []
+
+        # Add text content
+        if response.content:
+            parts.append(TextPart(content=response.content))
+
+        # Add tool calls
+        for tool_call in response.tool_calls:
+            parts.append(
+                ToolCallPart(
+                    tool_name=tool_call.function.name,
+                    args=tool_call.function.arguments,
+                    tool_call_id=tool_call.id,
+                )
+            )
+
+        # Convert usage - try to access attributes, default to 0 if not available
+        input_tokens = getattr(response.usage, "input_tokens", 0)
+        output_tokens = getattr(response.usage, "output_tokens", 0)
+        usage = RequestUsage(input_tokens=input_tokens, output_tokens=output_tokens)
+
+        # Map finish reason
+        finish_reason_map = {
+            "stop": "stop",
+            "length": "length",
+            "content_filter": "content_filter",
+            "max_output_tokens": "length",
+            "cancelled": "error",
+            "failed": "error",
+        }
+        raw_finish_reason = response.finish_reason
+        mapped_reason = (
+            finish_reason_map.get(raw_finish_reason, "stop")
+            if isinstance(raw_finish_reason, str)
+            else "stop"
+        )
+        finish_reason = cast(FinishReason, mapped_reason)
+
+        return ModelResponse(
+            parts=parts,
+            usage=usage,
+            model_name=self._model_name,
+            timestamp=now_utc(),
+            provider_name="xai",
+            finish_reason=finish_reason,
+        )
+
+
+class GrokStreamedResponse(StreamedResponse):
+    """Implementation of `StreamedResponse` for xAI SDK."""
+
+    _model_name: str
+    _response: Any  # xai_sdk chat stream
+    _timestamp: Any
+    _provider_name: str
+    _usage: RequestUsage
+    provider_response_id: str | None
+    finish_reason: Any
+
+    async def _get_event_iterator(self):
+        """Iterate over streaming events from xAI SDK."""
+        from typing import cast
+
+        async for response, chunk in self._response:
+            # Update usage if available
+            if hasattr(response, "usage"):
+                input_tokens = getattr(response.usage, "input_tokens", 0)
+                output_tokens = getattr(response.usage, "output_tokens", 0)
+                self._usage = RequestUsage(input_tokens=input_tokens, output_tokens=output_tokens)
+
+            # Set provider response ID
+            if hasattr(response, "id") and self.provider_response_id is None:
+                self.provider_response_id = response.id
+
+            # Handle finish reason
+            if hasattr(response, "finish_reason") and response.finish_reason:
+                finish_reason_map = {
+                    "stop": "stop",
+                    "length": "length",
+                    "content_filter": "content_filter",
+                    "max_output_tokens": "length",
+                    "cancelled": "error",
+                    "failed": "error",
+                }
+                mapped_reason = finish_reason_map.get(response.finish_reason, "stop")
+                self.finish_reason = cast(FinishReason, mapped_reason)
+
+            # Handle text content
+            if hasattr(chunk, "content") and chunk.content:
+                yield self._parts_manager.handle_text_delta(
+                    vendor_part_id="content",
+                    content=chunk.content,
+                )
+
+            # Handle tool calls
+            if hasattr(chunk, "tool_calls"):
+                for tool_call in chunk.tool_calls:
+                    yield self._parts_manager.handle_tool_call_part(
+                        vendor_part_id=tool_call.id,
+                        tool_name=tool_call.function.name,
+                        args=tool_call.function.arguments,
+                        tool_call_id=tool_call.id,
+                    )
+
+    @property
+    def model_name(self) -> str:
+        """Get the model name of the response."""
+        return self._model_name
+
+    @property
+    def provider_name(self) -> str:
+        """Get the provider name."""
+        return self._provider_name
+
+    @property
+    def timestamp(self):
+        """Get the timestamp of the response."""
+        return self._timestamp
diff --git a/pydantic_ai_slim/pydantic_ai/profiles/grok.py b/pydantic_ai_slim/pydantic_ai/profiles/grok.py
index 3b7c4a3746..9a1a9317c4 100644
--- a/pydantic_ai_slim/pydantic_ai/profiles/grok.py
+++ b/pydantic_ai_slim/pydantic_ai/profiles/grok.py
@@ -1,8 +1,24 @@
 from __future__ import annotations as _annotations
 
+from dataclasses import dataclass
+
 from . import ModelProfile
 
+@dataclass(kw_only=True)
+class GrokModelProfile(ModelProfile):
+    """Profile for models used with GroqModel.
+
+    ALL FIELDS MUST BE `groq_` PREFIXED SO YOU CAN MERGE THEM WITH OTHER MODELS.
+    """
+
+    builtin_tool: bool = False
+    """Whether the model always has the web search built-in tool available."""
+
+
 
 def grok_model_profile(model_name: str) -> ModelProfile | None:
     """Get the model profile for a Grok model."""
-    return None
+    return GrokModelProfile(
+        # Support tool calling for building tools
+        builtin_tool=model_name.startswith('grok-4'),
+    )
diff --git a/pydantic_ai_slim/pydantic_ai/providers/grok.py b/pydantic_ai_slim/pydantic_ai/providers/grok.py
index 604a38abbf..65ae8946d7 100644
--- a/pydantic_ai_slim/pydantic_ai/providers/grok.py
+++ b/pydantic_ai_slim/pydantic_ai/providers/grok.py
@@ -25,6 +25,8 @@
 GrokModelName = Literal[
     'grok-4',
     'grok-4-0709',
+    'grok-4-fast-reasoning',
+    'grok-4-fast-non-reasoning',
     'grok-3',
     'grok-3-mini',
     'grok-3-fast',
diff --git a/pydantic_ai_slim/pyproject.toml b/pydantic_ai_slim/pyproject.toml
index 79a3982c02..85fb2a7e0c 100644
--- a/pydantic_ai_slim/pyproject.toml
+++ b/pydantic_ai_slim/pyproject.toml
@@ -72,6 +72,7 @@ cohere = ["cohere>=5.18.0; platform_system != 'Emscripten'"]
 vertexai = ["google-auth>=2.36.0", "requests>=2.32.2"]
 google = ["google-genai>=1.46.0"]
 anthropic = ["anthropic>=0.70.0"]
+grok = ["xai-sdk>=1.4.0"]
 groq = ["groq>=0.25.0"]
 mistral = ["mistralai>=1.9.10"]
 bedrock = ["boto3>=1.39.0"]
diff --git a/pyproject.toml b/pyproject.toml
index 3c13afdece..b8aa47e7f3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -46,7 +46,7 @@ requires-python = ">=3.10"
 
 [tool.hatch.metadata.hooks.uv-dynamic-versioning]
 dependencies = [
-    "pydantic-ai-slim[openai,vertexai,google,groq,anthropic,mistral,cohere,bedrock,huggingface,cli,mcp,fastmcp,evals,ag-ui,retries,temporal,logfire,ui]=={{ version }}",
+    "pydantic-ai-slim[openai,vertexai,google,grok,groq,anthropic,mistral,cohere,bedrock,huggingface,cli,mcp,fastmcp,evals,ag-ui,retries,temporal,logfire,ui]=={{ version }}",
 ]
 
 [tool.hatch.metadata.hooks.uv-dynamic-versioning.optional-dependencies]
diff --git a/uv.lock b/uv.lock
index 7d94519296..6ae5258d6f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13' and platform_python_implementation == 'PyPy'",
@@ -1410,18 +1410,6 @@ wheels = [
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 ]
 
-[[package]]
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-version = "1.2.18"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "wrapt" },
-]
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-wheels = [
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-]
-
 [[package]]
 name = "depyf"
 version = "0.19.0"
@@ -2015,14 +2003,14 @@ wheels = [
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.68.0"
+version = "1.72.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "protobuf" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/e5/7b/adfd75544c415c487b33061fe7ae526165241c1ea133f9a9125a56b39fd8/googleapis_common_protos-1.72.0.tar.gz", hash = "sha256:e55a601c1b32b52d7a3e65f43563e2aa61bcd737998ee672ac9b951cd49319f5", size = 147433, upload-time = "2025-11-06T18:29:24.087Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/c4/ab/09169d5a4612a5f92490806649ac8d41e3ec9129c636754575b3553f4ea4/googleapis_common_protos-1.72.0-py3-none-any.whl", hash = "sha256:4299c5a82d5ae1a9702ada957347726b167f9f8d1fc352477702a1e851ff4038", size = 297515, upload-time = "2025-11-06T18:29:13.14Z" },
 ]
 
 [[package]]
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 ]
 
+[[package]]
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+version = "1.76.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
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 source = { editable = "." }
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@@ -5555,6 +5606,9 @@ fastmcp = [
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