This will help you getting started with DeepSeek chat models. For detailed documentation of all ChatDeepSeek features and configurations head to the API reference.

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Overview

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Integration details

ClassPackageLocalSerializablePY supportPackage downloadsPackage latest
ChatDeepSeek@langchain/deepseek❌ (see Ollama)beta✅NPM - DownloadsNPM - Version

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Model features

See the links in the table headers below for guides on how to use specific features.
Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingToken usageLogprobs
✅✅✅❌❌❌✅✅✅
Note that as of 1/27/25, tool calling and structured output are not currently supported for deepseek-reasoner.

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Setup

To access DeepSeek models you’ll need to create a DeepSeek account, get an API key, and install the @langchain/deepseek integration package. You can also access the DeepSeek API through providers like Together AI or Ollama.

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Credentials

Head to https://deepseek.com/ to sign up to DeepSeek and generate an API key. Once you’ve done this set the DEEPSEEK_API_KEY environment variable: 
export DEEPSEEK_API_KEY="your-api-key"
If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below: 
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

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Installation

The LangChain ChatDeepSeek integration lives in the @langchain/deepseek package: 
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  @langchain/deepseek @langchain/core
</Npm2Yarn>

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Instantiation

Now we can instantiate our model object and generate chat completions: 
import { ChatDeepSeek } from "@langchain/deepseek";

const llm = new ChatDeepSeek({
  model: "deepseek-reasoner",
  temperature: 0,
  // other params...
})

const aiMsg = await llm.invoke([
  [
    "system",
    "You are a helpful assistant that translates English to French. Translate the user sentence.",
  ],
  ["human", "I love programming."],
])
aiMsg

AIMessage {
  "id": "e2874482-68a7-4552-8154-b6a245bab429",
  "content": "J'adore la programmation.",
  "additional_kwargs": {
    "reasoning_content": "..."
  },
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 23,
      "completionTokens": 7,
      "totalTokens": 30
    },
    "finish_reason": "stop",
    "model_name": "deepseek-reasoner",
    "usage": {
      "prompt_tokens": 23,
      "completion_tokens": 7,
      "total_tokens": 30,
      "prompt_tokens_details": {
        "cached_tokens": 0
      },
      "prompt_cache_hit_tokens": 0,
      "prompt_cache_miss_tokens": 23
    },
    "system_fingerprint": "fp_3a5770e1b4"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 7,
    "input_tokens": 23,
    "total_tokens": 30,
    "input_token_details": {
      "cache_read": 0
    },
    "output_token_details": {}
  }
}

console.log(aiMsg.content)

J'adore la programmation.

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Chaining

We can chain our model with a prompt template like so: 
import { ChatPromptTemplate } from "@langchain/core/prompts"

const prompt = ChatPromptTemplate.fromMessages(
  [
    [
      "system",
      "You are a helpful assistant that translates {input_language} to {output_language}.",
    ],
    ["human", "{input}"],
  ]
)

const chain = prompt.pipe(llm);
await chain.invoke(
  {
    input_language: "English",
    output_language: "German",
    input: "I love programming.",
  }
)

AIMessage {
  "id": "6e7f6f8c-8d7a-4dad-be07-425384038fd4",
  "content": "Ich liebe es zu programmieren.",
  "additional_kwargs": {
    "reasoning_content": "..."
  },
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 18,
      "completionTokens": 9,
      "totalTokens": 27
    },
    "finish_reason": "stop",
    "model_name": "deepseek-reasoner",
    "usage": {
      "prompt_tokens": 18,
      "completion_tokens": 9,
      "total_tokens": 27,
      "prompt_tokens_details": {
        "cached_tokens": 0
      },
      "prompt_cache_hit_tokens": 0,
      "prompt_cache_miss_tokens": 18
    },
    "system_fingerprint": "fp_3a5770e1b4"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 9,
    "input_tokens": 18,
    "total_tokens": 27,
    "input_token_details": {
      "cache_read": 0
    },
    "output_token_details": {}
  }
}

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API reference

For detailed documentation of all ChatDeepSeek features and configurations head to the API reference: https://api.js.langchain.com/classes/_langchain_deepseek.ChatDeepSeek.html