diff --git a/docs/docs/integrations/chat/writer.ipynb b/docs/docs/integrations/chat/writer.ipynb
new file mode 100644
index 0000000000000..455f8820ca936
--- /dev/null
+++ b/docs/docs/integrations/chat/writer.ipynb
@@ -0,0 +1,343 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "raw",
+   "source": [
+    "---\n",
+    "sidebar_label: Writer\n",
+    "---"
+   ],
+   "id": "85e07aae70a15572"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb4dd00a-8893-4a45-96f7-9a9fc341cd61",
+   "metadata": {},
+   "source": [
+    "# ChatWriter\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with Writer [chat models](/docs/concepts/#chat-models).\n",
+    "\n",
+    "Writer has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Writer docs](https://dev.writer.com/home/models).\n",
+    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| ChatWriter | langchain-community | ❌ | ❌ | ❌ | ❌ | ❌ |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | Image input | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access Writer models you'll need to create a Writer account, get an API key, and install the `writer-sdk` and `langchain-community` packages.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [Writer AI Studio](https://app.writer.com/aistudio/signup?utm_campaign=devrel) to sign up to OpenAI and generate an API key. Once you've done this set the WRITER_API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e817fe2e-4f1d-4533-b19e-2400b1cf6ce8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-24T13:51:54.323678Z",
+     "start_time": "2024-10-24T13:51:42.127404Z"
+    }
+   },
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.environ.get(\"WRITER_API_KEY\"):\n",
+    "    os.environ[\"WRITER_API_KEY\"] = getpass.getpass(\"Enter your Writer API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c59722a9-6dbb-45f7-ae59-5be50ca5733d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain Writer integration lives in the `langchain-community` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "2113471c-75d7-45df-b784-d78da4ef7aba",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-24T13:52:49.262240Z",
+     "start_time": "2024-10-24T13:52:47.564879Z"
+    }
+   },
+   "source": [
+    "%pip install -qU langchain-community writer-sdk"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1098bc9d-ce83-462b-8c19-f85bf3a159dc",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "522686de",
+   "metadata": {
+    "tags": [],
+    "ExecuteTime": {
+     "end_time": "2024-10-24T13:52:38.822950Z",
+     "start_time": "2024-10-24T13:52:38.674441Z"
+    }
+   },
+   "source": [
+    "from langchain_community.chat_models.writer import ChatWriter\n",
+    "\n",
+    "llm = ChatWriter(\n",
+    "    model=\"palmyra-x-004\",\n",
+    "    temperature=0.7,\n",
+    "    max_tokens=1000,\n",
+    "    # api_key=\"...\",  # if you prefer to pass api key in directly instaed of using env vars\n",
+    "    # base_url=\"...\",\n",
+    "    # other params...\n",
+    ")"
+   ],
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "cannot import name 'ChatWriter' from 'langchain_community.chat_models' (/home/yanomaly/PycharmProjects/whitesnake/writer/langсhain/libs/community/langchain_community/chat_models/__init__.py)",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mImportError\u001B[0m                               Traceback (most recent call last)",
+      "Cell \u001B[0;32mIn[3], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mlangchain_community\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mchat_models\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m ChatWriter\n\u001B[1;32m      3\u001B[0m llm \u001B[38;5;241m=\u001B[39m ChatWriter(\n\u001B[1;32m      4\u001B[0m     model\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpalmyra-x-004\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m      5\u001B[0m     temperature\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0.7\u001B[39m,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m      9\u001B[0m     \u001B[38;5;66;03m# other params...\u001B[39;00m\n\u001B[1;32m     10\u001B[0m )\n",
+      "\u001B[0;31mImportError\u001B[0m: cannot import name 'ChatWriter' from 'langchain_community.chat_models' (/home/yanomaly/PycharmProjects/whitesnake/writer/langсhain/libs/community/langchain_community/chat_models/__init__.py)"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6511982a-734a-4193-a47d-254f8dcaff5e",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that writes poems about the Python programming language.\",\n",
+    "    ),\n",
+    "    (\"human\", \"Write a poem about Python.\"),\n",
+    "]\n",
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2cd224b8-4499-41fb-a604-d53a7ff17b2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "778f912a-66ea-4a5d-b3de-6c7db4baba26",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbb043e6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that writes poems about the {input_language} programming language.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"Java\",\n",
+    "        \"input\": \"Write a poem about Java.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b1b52a5-b58d-40c9-bcdd-88eb8fb351e2",
+   "metadata": {},
+   "source": [
+    "## Tool calling\n",
+    "\n",
+    "Writer supports [tool calling](https://dev.writer.com/api-guides/tool-calling), which lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool.\n",
+    "\n",
+    "### ChatWriter.bind_tools()\n",
+    "\n",
+    "With `ChatWriter.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to tool schemas, which looks like:\n",
+    "```\n",
+    "{\n",
+    "    \"name\": \"...\",\n",
+    "    \"description\": \"...\",\n",
+    "    \"parameters\": {...}  # JSONSchema\n",
+    "}\n",
+    "```\n",
+    "and passed in every model invocation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b7ea7690-ec7a-4337-b392-e87d1f39a6ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "\n",
+    "class GetWeather(BaseModel):\n",
+    "    \"\"\"Get the current weather in a given location\"\"\"\n",
+    "\n",
+    "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
+    "\n",
+    "\n",
+    "llm_with_tools = llm.bind_tools([GetWeather])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d1ab955-6a68-42f8-bb5d-86eb1111478a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ai_msg = llm_with_tools.invoke(\n",
+    "    \"what is the weather like in New York City\",\n",
+    ")\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "768d1ae4-4b1a-48eb-a329-c8d5051067a3",
+   "metadata": {},
+   "source": [
+    "### AIMessage.tool_calls\n",
+    "Notice that the AIMessage has a `tool_calls` attribute. This contains in a standardized ToolCall format that is model-provider agnostic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "166cb7ce-831d-4a7c-9721-abc107f11084",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ai_msg.tool_calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e082c9ac-c7c7-4aff-a8ec-8e220262a59c",
+   "metadata": {},
+   "source": [
+    "For more on binding tools and tool call outputs, head to the [tool calling](/docs/how_to/function_calling) docs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a796d728-971b-408b-88d5-440015bbb941",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all Writer features, head to our [API reference](https://dev.writer.com/api-guides/api-reference/completion-api/chat-completion)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/libs/community/extended_testing_deps.txt b/libs/community/extended_testing_deps.txt
index 56caca04381cf..1214645947abf 100644
--- a/libs/community/extended_testing_deps.txt
+++ b/libs/community/extended_testing_deps.txt
@@ -95,4 +95,5 @@ xmltodict>=0.13.0,<0.14
 nanopq==0.2.1
 mlflow[genai]>=2.14.0
 databricks-sdk>=0.30.0
-websocket>=0.2.1,<1
\ No newline at end of file
+websocket>=0.2.1,<1
+writer-sdk>=1.2.0
diff --git a/libs/community/langchain_community/chat_models/writer.py b/libs/community/langchain_community/chat_models/writer.py
new file mode 100644
index 0000000000000..945b9d8b0b6d2
--- /dev/null
+++ b/libs/community/langchain_community/chat_models/writer.py
@@ -0,0 +1,317 @@
+"""Writer chat wrapper."""
+
+from __future__ import annotations
+
+import logging
+from typing import (
+    Any,
+    AsyncIterator,
+    Callable,
+    Dict,
+    Iterator,
+    List,
+    Literal,
+    Mapping,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
+)
+
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models import LanguageModelInput
+from langchain_core.language_models.chat_models import (
+    BaseChatModel,
+    agenerate_from_stream,
+    generate_from_stream,
+)
+from langchain_core.messages import (
+    AIMessage,
+    AIMessageChunk,
+    BaseMessage,
+    ChatMessage,
+    HumanMessage,
+    SystemMessage,
+    ToolMessage,
+)
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from langchain_core.runnables import Runnable
+from langchain_core.utils.function_calling import convert_to_openai_tool
+from pydantic import BaseModel, ConfigDict, Field, SecretStr
+
+logger = logging.getLogger(__name__)
+
+
+def _convert_message_to_dict(message: BaseMessage) -> dict:
+    """Convert a LangChain message to a Writer message dict."""
+    message_dict = {"role": "", "content": message.content}
+
+    if isinstance(message, ChatMessage):
+        message_dict["role"] = message.role
+    elif isinstance(message, HumanMessage):
+        message_dict["role"] = "user"
+    elif isinstance(message, AIMessage):
+        message_dict["role"] = "assistant"
+        if message.tool_calls:
+            message_dict["tool_calls"] = [
+                {
+                    "id": tool["id"],
+                    "type": "function",
+                    "function": {"name": tool["name"], "arguments": tool["args"]},
+                }
+                for tool in message.tool_calls
+            ]
+    elif isinstance(message, SystemMessage):
+        message_dict["role"] = "system"
+    elif isinstance(message, ToolMessage):
+        message_dict["role"] = "tool"
+        message_dict["tool_call_id"] = message.tool_call_id
+    else:
+        raise ValueError(f"Got unknown message type: {type(message)}")
+
+    if message.name:
+        message_dict["name"] = message.name
+
+    return message_dict
+
+
+def _convert_dict_to_message(response_dict: Dict[str, Any]) -> BaseMessage:
+    """Convert a Writer message dict to a LangChain message."""
+    role = response_dict["role"]
+    content = response_dict.get("content", "")
+
+    if role == "user":
+        return HumanMessage(content=content)
+    elif role == "assistant":
+        additional_kwargs = {}
+        if tool_calls := response_dict.get("tool_calls"):
+            additional_kwargs["tool_calls"] = tool_calls
+        return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
+    elif role == "system":
+        return SystemMessage(content=content)
+    elif role == "tool":
+        return ToolMessage(
+            content=content,
+            tool_call_id=response_dict["tool_call_id"],
+            name=response_dict.get("name"),
+        )
+    else:
+        return ChatMessage(content=content, role=role)
+
+
+class ChatWriter(BaseChatModel):
+    """Writer chat model.
+
+    To use, you should have the ``writer-sdk`` Python package installed, and the
+    environment variable ``WRITER_API_KEY`` set with your API key.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.chat_models import ChatWriter
+
+            chat = ChatWriter(model="palmyra-x-004")
+    """
+
+    client: Any = Field(default=None, exclude=True)  #: :meta private:
+    async_client: Any = Field(default=None, exclude=True)  #: :meta private:
+    model_name: str = Field(default="palmyra-x-004", alias="model")
+    """Model name to use."""
+    temperature: float = 0.7
+    """What sampling temperature to use."""
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Holds any model parameters valid for `create` call not explicitly specified."""
+    writer_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
+    """Writer API key."""
+    writer_api_base: Optional[str] = Field(default=None, alias="base_url")
+    """Base URL for API requests."""
+    streaming: bool = False
+    """Whether to stream the results or not."""
+    n: int = 1
+    """Number of chat completions to generate for each prompt."""
+    max_tokens: Optional[int] = None
+    """Maximum number of tokens to generate."""
+
+    model_config = ConfigDict(populate_by_name=True)
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of chat model."""
+        return "writer-chat"
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            "model_name": self.model_name,
+            "temperature": self.temperature,
+            "streaming": self.streaming,
+            **self.model_kwargs,
+        }
+
+    def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
+        generations = []
+        for choice in response["choices"]:
+            message = _convert_dict_to_message(choice["message"])
+            gen = ChatGeneration(
+                message=message,
+                generation_info=dict(finish_reason=choice.get("finish_reason")),
+            )
+            generations.append(gen)
+
+        token_usage = response.get("usage", {})
+        llm_output = {
+            "token_usage": token_usage,
+            "model_name": self.model_name,
+            "system_fingerprint": response.get("system_fingerprint", ""),
+        }
+
+        return ChatResult(generations=generations, llm_output=llm_output)
+
+    def _convert_messages_to_dicts(
+        self, messages: List[BaseMessage], stop: Optional[List[str]] = None
+    ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
+        params = {
+            "model": self.model_name,
+            "temperature": self.temperature,
+            "n": self.n,
+            "stream": self.streaming,
+            **self.model_kwargs,
+        }
+        if stop:
+            params["stop"] = stop
+        if self.max_tokens is not None:
+            params["max_tokens"] = self.max_tokens
+
+        message_dicts = [_convert_message_to_dict(m) for m in messages]
+        return message_dicts, params
+
+    def _stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        message_dicts, params = self._convert_messages_to_dicts(messages, stop)
+        params = {**params, **kwargs, "stream": True}
+
+        response = self.client.chat.chat(messages=message_dicts, **params)
+
+        for chunk in response:
+            delta = chunk["choices"][0].get("delta")
+            if not delta or not delta.get("content"):
+                continue
+            chunk = _convert_dict_to_message(
+                {"role": "assistant", "content": delta["content"]}
+            )
+            chunk = ChatGenerationChunk(message=chunk)
+
+            if run_manager:
+                run_manager.on_llm_new_token(chunk.text)
+
+            yield chunk
+
+    async def _astream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[ChatGenerationChunk]:
+        message_dicts, params = self._convert_messages_to_dicts(messages, stop)
+        params = {**params, **kwargs, "stream": True}
+
+        response = await self.async_client.chat.chat(messages=message_dicts, **params)
+
+        async for chunk in response:
+            delta = chunk["choices"][0].get("delta")
+            if not delta or not delta.get("content"):
+                continue
+            chunk = _convert_dict_to_message(
+                {"role": "assistant", "content": delta["content"]}
+            )
+            chunk = ChatGenerationChunk(message=chunk)
+
+            if run_manager:
+                await run_manager.on_llm_new_token(chunk.text)
+
+            yield chunk
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        if self.streaming:
+            return generate_from_stream(
+                self._stream(messages, stop, run_manager, **kwargs)
+            )
+
+        message_dicts, params = self._convert_messages_to_dicts(messages, stop)
+        params = {**params, **kwargs}
+        response = self.client.chat.chat(messages=message_dicts, **params)
+        return self._create_chat_result(response)
+
+    async def _agenerate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        if self.streaming:
+            return await agenerate_from_stream(
+                self._astream(messages, stop, run_manager, **kwargs)
+            )
+
+        message_dicts, params = self._convert_messages_to_dicts(messages, stop)
+        params = {**params, **kwargs}
+        response = await self.async_client.chat.chat(messages=message_dicts, **params)
+        return self._create_chat_result(response)
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling Writer API."""
+        return {
+            "model": self.model_name,
+            "temperature": self.temperature,
+            "stream": self.streaming,
+            "n": self.n,
+            "max_tokens": self.max_tokens,
+            **self.model_kwargs,
+        }
+
+    def bind_tools(
+        self,
+        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]],
+        *,
+        tool_choice: Optional[Union[str, Literal["auto", "none"]]] = None,
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, BaseMessage]:
+        """Bind tools to the chat model.
+
+        Args:
+            tools: Tools to bind to the model
+            tool_choice: Which tool to require ('auto', 'none', or specific tool name)
+            **kwargs: Additional parameters to pass to the chat model
+
+        Returns:
+            A runnable that will use the tools
+        """
+        formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
+
+        if tool_choice:
+            kwargs["tool_choice"] = (
+                (tool_choice)
+                if tool_choice in ("auto", "none")
+                else {"type": "function", "function": {"name": tool_choice}}
+            )
+
+        return super().bind(tools=formatted_tools, **kwargs)
diff --git a/libs/community/tests/unit_tests/chat_models/test_writer.py b/libs/community/tests/unit_tests/chat_models/test_writer.py
new file mode 100644
index 0000000000000..944a9dfeaba1f
--- /dev/null
+++ b/libs/community/tests/unit_tests/chat_models/test_writer.py
@@ -0,0 +1,303 @@
+"""Unit tests for Writer chat model integration."""
+
+import json
+from typing import Any, Dict, List
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+from langchain_core.callbacks.manager import CallbackManager
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
+from pydantic import SecretStr
+
+from langchain_community.chat_models.writer import ChatWriter, _convert_dict_to_message
+from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
+
+
+class TestChatWriter:
+    def test_writer_model_param(self) -> None:
+        """Test different ways to initialize the chat model."""
+        test_cases: List[dict] = [
+            {"model_name": "palmyra-x-004", "writer_api_key": "test-key"},
+            {"model": "palmyra-x-004", "writer_api_key": "test-key"},
+            {"model_name": "palmyra-x-004", "writer_api_key": "test-key"},
+            {
+                "model": "palmyra-x-004",
+                "writer_api_key": "test-key",
+                "temperature": 0.5,
+            },
+        ]
+
+        for case in test_cases:
+            chat = ChatWriter(**case)
+            assert chat.model_name == "palmyra-x-004"
+            assert chat.writer_api_key
+            assert chat.writer_api_key.get_secret_value() == "test-key"
+            assert chat.temperature == (0.5 if "temperature" in case else 0.7)
+
+    def test_convert_dict_to_message_human(self) -> None:
+        """Test converting a human message dict to a LangChain message."""
+        message = {"role": "user", "content": "Hello"}
+        result = _convert_dict_to_message(message)
+        assert isinstance(result, HumanMessage)
+        assert result.content == "Hello"
+
+    def test_convert_dict_to_message_ai(self) -> None:
+        """Test converting an AI message dict to a LangChain message."""
+        message = {"role": "assistant", "content": "Hello"}
+        result = _convert_dict_to_message(message)
+        assert isinstance(result, AIMessage)
+        assert result.content == "Hello"
+
+    def test_convert_dict_to_message_system(self) -> None:
+        """Test converting a system message dict to a LangChain message."""
+        message = {"role": "system", "content": "You are a helpful assistant"}
+        result = _convert_dict_to_message(message)
+        assert isinstance(result, SystemMessage)
+        assert result.content == "You are a helpful assistant"
+
+    def test_convert_dict_to_message_tool_call(self) -> None:
+        """Test converting a tool call message dict to a LangChain message."""
+        content = json.dumps({"result": 42})
+        message = {
+            "role": "tool",
+            "name": "get_number",
+            "content": content,
+            "tool_call_id": "call_abc123",
+        }
+        result = _convert_dict_to_message(message)
+        assert isinstance(result, ToolMessage)
+        assert result.name == "get_number"
+        assert result.content == content
+
+    def test_convert_dict_to_message_with_tool_calls(self) -> None:
+        """Test converting an AIMessage with tool calls."""
+        message = {
+            "role": "assistant",
+            "content": "",
+            "tool_calls": [
+                {
+                    "id": "call_abc123",
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "arguments": '{"location": "London"}',
+                    },
+                }
+            ],
+        }
+        result = _convert_dict_to_message(message)
+        assert isinstance(result, AIMessage)
+        assert result.tool_calls
+        assert len(result.tool_calls) == 1
+        assert result.tool_calls[0]["name"] == "get_weather"
+        assert result.tool_calls[0]["args"]["location"] == "London"
+
+    @pytest.fixture(autouse=True)
+    def mock_completion(self) -> Dict[str, Any]:
+        """Fixture providing a mock API response."""
+        return {
+            "id": "chat-12345",
+            "object": "chat.completion",
+            "created": 1699000000,
+            "model": "palmyra-x-004",
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {
+                        "role": "assistant",
+                        "content": "Hello! How can I help you?",
+                    },
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": {"prompt_tokens": 10, "completion_tokens": 8, "total_tokens": 18},
+        }
+
+    @pytest.fixture(autouse=True)
+    def mock_response(self) -> Dict[str, Any]:
+        response = {
+            "id": "chat-12345",
+            "choices": [
+                {
+                    "message": {
+                        "role": "assistant",
+                        "content": "",
+                        "tool_calls": [
+                            {
+                                "id": "call_abc123",
+                                "type": "function",
+                                "function": {
+                                    "name": "GetWeather",
+                                    "arguments": '{"location": "London"}',
+                                },
+                            }
+                        ],
+                    },
+                    "finish_reason": "tool_calls",
+                }
+            ],
+        }
+        return response
+
+    @pytest.fixture(autouse=True)
+    def mock_streaming_chunks(self) -> List[Dict[str, Any]]:
+        """Fixture providing mock streaming response chunks."""
+        return [
+            {
+                "id": "chat-12345",
+                "object": "chat.completion.chunk",
+                "created": 1699000000,
+                "model": "palmyra-x-004",
+                "choices": [
+                    {
+                        "index": 0,
+                        "delta": {
+                            "role": "assistant",
+                            "content": "Hello",
+                        },
+                        "finish_reason": None,
+                    }
+                ],
+            },
+            {
+                "id": "chat-12345",
+                "object": "chat.completion.chunk",
+                "created": 1699000000,
+                "model": "palmyra-x-004",
+                "choices": [
+                    {
+                        "index": 0,
+                        "delta": {
+                            "content": "!",
+                        },
+                        "finish_reason": "stop",
+                    }
+                ],
+            },
+        ]
+
+    def test_sync_completion(self, mock_completion: Dict[str, Any]) -> None:
+        """Test basic chat completion with mocked response."""
+        chat = ChatWriter(api_key=SecretStr("test-key"))
+        mock_client = MagicMock()
+        mock_client.chat.chat.return_value = mock_completion
+
+        with patch.object(chat, "client", mock_client):
+            message = HumanMessage(content="Hi there!")
+            response = chat.invoke([message])
+            assert isinstance(response, AIMessage)
+            assert response.content == "Hello! How can I help you?"
+
+    async def test_async_completion(self, mock_completion: Dict[str, Any]) -> None:
+        """Test async chat completion with mocked response."""
+        chat = ChatWriter(api_key=SecretStr("test-key"))
+        mock_client = AsyncMock()
+        mock_client.chat.chat.return_value = mock_completion
+
+        with patch.object(chat, "async_client", mock_client):
+            message = HumanMessage(content="Hi there!")
+            response = await chat.ainvoke([message])
+            assert isinstance(response, AIMessage)
+            assert response.content == "Hello! How can I help you?"
+
+    def test_sync_streaming(self, mock_streaming_chunks: List[Dict[str, Any]]) -> None:
+        """Test sync streaming with callback handler."""
+        callback_handler = FakeCallbackHandler()
+        callback_manager = CallbackManager([callback_handler])
+
+        chat = ChatWriter(
+            streaming=True,
+            callback_manager=callback_manager,
+            max_tokens=10,
+            api_key=SecretStr("test-key"),
+        )
+
+        mock_client = MagicMock()
+        mock_response = MagicMock()
+        mock_response.__iter__.return_value = mock_streaming_chunks
+        mock_client.chat.chat.return_value = mock_response
+
+        with patch.object(chat, "client", mock_client):
+            message = HumanMessage(content="Hi")
+            response = chat.invoke([message])
+
+            assert isinstance(response, AIMessage)
+            assert callback_handler.llm_streams > 0
+            assert response.content == "Hello!"
+
+    async def test_async_streaming(
+        self, mock_streaming_chunks: List[Dict[str, Any]]
+    ) -> None:
+        """Test async streaming with callback handler."""
+        callback_handler = FakeCallbackHandler()
+        callback_manager = CallbackManager([callback_handler])
+
+        chat = ChatWriter(
+            streaming=True,
+            callback_manager=callback_manager,
+            max_tokens=10,
+            api_key=SecretStr("test-key"),
+        )
+
+        mock_client = AsyncMock()
+        mock_response = AsyncMock()
+        mock_response.__aiter__.return_value = mock_streaming_chunks
+        mock_client.chat.chat.return_value = mock_response
+
+        with patch.object(chat, "async_client", mock_client):
+            message = HumanMessage(content="Hi")
+            response = await chat.ainvoke([message])
+
+            assert isinstance(response, AIMessage)
+            assert callback_handler.llm_streams > 0
+            assert response.content == "Hello!"
+
+    def test_sync_tool_calling(self, mock_response: Dict[str, Any]) -> None:
+        """Test synchronous tool calling functionality."""
+        from pydantic import BaseModel, Field
+
+        class GetWeather(BaseModel):
+            """Get the weather in a location."""
+
+            location: str = Field(..., description="The location to get weather for")
+
+        mock_client = MagicMock()
+        mock_client.chat.chat.return_value = mock_response
+
+        chat = ChatWriter(api_key=SecretStr("test-key"), client=mock_client)
+
+        chat_with_tools = chat.bind_tools(
+            tools=[GetWeather],
+            tool_choice="GetWeather",
+        )
+
+        response = chat_with_tools.invoke("What's the weather in London?")
+        assert isinstance(response, AIMessage)
+        assert response.tool_calls
+        assert response.tool_calls[0]["name"] == "GetWeather"
+        assert response.tool_calls[0]["args"]["location"] == "London"
+
+    async def test_async_tool_calling(self, mock_response: Dict[str, Any]) -> None:
+        """Test asynchronous tool calling functionality."""
+        from pydantic import BaseModel, Field
+
+        class GetWeather(BaseModel):
+            """Get the weather in a location."""
+
+            location: str = Field(..., description="The location to get weather for")
+
+        mock_client = AsyncMock()
+        mock_client.chat.chat.return_value = mock_response
+
+        chat = ChatWriter(api_key=SecretStr("test-key"), async_client=mock_client)
+
+        chat_with_tools = chat.bind_tools(
+            tools=[GetWeather],
+            tool_choice="GetWeather",
+        )
+
+        response = await chat_with_tools.ainvoke("What's the weather in London?")
+        assert isinstance(response, AIMessage)
+        assert response.tool_calls
+        assert response.tool_calls[0]["name"] == "GetWeather"
+        assert response.tool_calls[0]["args"]["location"] == "London"