> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-opensw-1781706951-9d0138e.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# ChatDeepSeek integration

> Integrate with the ChatDeepSeek chat model using LangChain Python.

This will help you get started with DeepSeek's hosted [chat models](/oss/python/langchain/models).

<Tip>
  **API Reference**

  For detailed documentation of all features and configuration options, head to the [`ChatDeepSeek`](https://reference.langchain.com/python/langchain-deepseek/chat_models/ChatDeepSeek) API reference.
</Tip>

<Tip>
  **DeepSeek's models are open source and can be run locally (e.g. in [Ollama](/oss/python/integrations/chat/ollama)) or on other inference providers (e.g. , [Together](/oss/python/integrations/chat/together)) as well.**
</Tip>

## Overview

### Integration details

| Class                                                                                                | Package                                                                            | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/deepseek) |                                              Downloads                                              |                                              Version                                             |
| :--------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------- | :----------: | :--------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------: |
| [`ChatDeepSeek`](https://reference.langchain.com/python/langchain-deepseek/chat_models/ChatDeepSeek) | [`langchain-deepseek`](https://reference.langchain.com/python/langchain-deepseek/) |     beta     |                                    ✅                                   | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-deepseek?style=flat-square\&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-deepseek?style=flat-square\&label=%20) |

### Model features

| [Tool calling](/oss/python/langchain/tools) | [Structured output](/oss/python/langchain/structured-output) | [Image input](/oss/python/langchain/messages#multimodal) | Audio input | Video input | [Token-level streaming](/oss/python/langchain/streaming/) | Native async | [Token usage](/oss/python/langchain/models#token-usage) | [Logprobs](/oss/python/langchain/models#log-probabilities) |
| :-----------------------------------------: | :----------------------------------------------------------: | :------------------------------------------------------: | :---------: | :---------: | :-------------------------------------------------------: | :----------: | :-----------------------------------------------------: | :--------------------------------------------------------: |
|                      ✅                      |                               ✅                              |                             ❌                            |      ❌      |      ❌      |                             ✅                             |       ✅      |                            ✅                            |                              ❌                             |

<Note>
  **DeepSeek-R1, specified via `model="deepseek-reasoner"`, does not support tool calling or structured output. Those features [are supported](https://api-docs.deepseek.com/guides/function_calling) by DeepSeek-V3 (specified via `model="deepseek-chat"`).**
</Note>

## Setup

To access DeepSeek models you'll need to create a/an DeepSeek account, get an API key, and install the `langchain-deepseek` integration package.

### Credentials

Head to [DeepSeek's API Key page](https://platform.deepseek.com/api_keys) to sign up to DeepSeek and generate an API key. Once you've done this set the `DEEPSEEK_API_KEY` environment variable:

```python theme={null}
import getpass
import os

if not os.getenv("DEEPSEEK_API_KEY"):
    os.environ["DEEPSEEK_API_KEY"] = getpass.getpass("Enter your DeepSeek API key: ")
```

To enable automated tracing of your model calls, set your [LangSmith](/langsmith/observability) API key:

```python theme={null}
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
```

### Installation

The LangChain DeepSeek integration lives in the `langchain-deepseek` package:

```python theme={null}
pip install -qU langchain-deepseek
```

## Instantiation

Now we can instantiate our model object and generate chat completions:

```python theme={null}
from langchain_deepseek import ChatDeepSeek

llm = ChatDeepSeek(
    model="deepseek-chat",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    # other params...
)
```

## Invocation

```python theme={null}
messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg.content
```

***

## API reference

For detailed documentation of all `ChatDeepSeek` features and configurations head to the [API Reference](https://reference.langchain.com/python/langchain-deepseek/chat_models/ChatDeepSeek).

***

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