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*   [Introduction](/v0.2/docs/introduction)
*   [Tutorials](/v0.2/docs/tutorials/)
    
    *   [Build a Question Answering application over a Graph Database](/v0.2/docs/tutorials/graph)
    *   [Tutorials](/v0.2/docs/tutorials/)
    *   [Build a Simple LLM Application](/v0.2/docs/tutorials/llm_chain)
    *   [Build a Query Analysis System](/v0.2/docs/tutorials/query_analysis)
    *   [Build a Chatbot](/v0.2/docs/tutorials/chatbot)
    *   [Build an Agent](/v0.2/docs/tutorials/agents)
    *   [Build an Extraction Chain](/v0.2/docs/tutorials/extraction)
    *   [Summarize Text](/v0.2/docs/tutorials/summarization)
    *   [Tagging](/v0.2/docs/tutorials/classification)
    *   [Build a Local RAG Application](/v0.2/docs/tutorials/local_rag)
    *   [Conversational RAG](/v0.2/docs/tutorials/qa_chat_history)
    *   [Build a Retrieval Augmented Generation (RAG) App](/v0.2/docs/tutorials/rag)
    *   [Build a Question/Answering system over SQL data](/v0.2/docs/tutorials/sql_qa)
*   [How-to guides](/v0.2/docs/how_to/)
    
    *   [How-to guides](/v0.2/docs/how_to/)
    *   [How to use example selectors](/v0.2/docs/how_to/example_selectors)
    *   [Installation](/v0.2/docs/how_to/installation)
    *   [How to stream responses from an LLM](/v0.2/docs/how_to/streaming_llm)
    *   [How to stream chat model responses](/v0.2/docs/how_to/chat_streaming)
    *   [How to embed text data](/v0.2/docs/how_to/embed_text)
    *   [How to use few shot examples in chat models](/v0.2/docs/how_to/few_shot_examples_chat)
    *   [How to cache model responses](/v0.2/docs/how_to/llm_caching)
    *   [How to cache chat model responses](/v0.2/docs/how_to/chat_model_caching)
    *   [How to create a custom LLM class](/v0.2/docs/how_to/custom_llm)
    *   [How to use few shot examples](/v0.2/docs/how_to/few_shot_examples)
    *   [How to use output parsers to parse an LLM response into structured format](/v0.2/docs/how_to/output_parser_structured)
    *   [How to return structured data from a model](/v0.2/docs/how_to/structured_output)
    *   [How to add ad-hoc tool calling capability to LLMs and Chat Models](/v0.2/docs/how_to/tools_prompting)
    *   [How to create a custom chat model class](/v0.2/docs/how_to/custom_chat)
    *   [How to do per-user retrieval](/v0.2/docs/how_to/qa_per_user)
    *   [How to track token usage](/v0.2/docs/how_to/chat_token_usage_tracking)
    *   [How to track token usage](/v0.2/docs/how_to/llm_token_usage_tracking)
    *   [How to pass through arguments from one step to the next](/v0.2/docs/how_to/passthrough)
    *   [How to compose prompts together](/v0.2/docs/how_to/prompts_composition)
    *   [How to use legacy LangChain Agents (AgentExecutor)](/v0.2/docs/how_to/agent_executor)
    *   [How to add values to a chain's state](/v0.2/docs/how_to/assign)
    *   [How to attach runtime arguments to a Runnable](/v0.2/docs/how_to/binding)
    *   [How to cache embedding results](/v0.2/docs/how_to/caching_embeddings)
    *   [How to split by character](/v0.2/docs/how_to/character_text_splitter)
    *   [How to manage memory](/v0.2/docs/how_to/chatbots_memory)
    *   [How to do retrieval](/v0.2/docs/how_to/chatbots_retrieval)
    *   [How to use tools](/v0.2/docs/how_to/chatbots_tools)
    *   [How to split code](/v0.2/docs/how_to/code_splitter)
    *   [How to do retrieval with contextual compression](/v0.2/docs/how_to/contextual_compression)
    *   [How to write a custom retriever class](/v0.2/docs/how_to/custom_retriever)
    *   [How to create custom Tools](/v0.2/docs/how_to/custom_tools)
    *   [How to debug your LLM apps](/v0.2/docs/how_to/debugging)
    *   [How to load CSV data](/v0.2/docs/how_to/document_loader_csv)
    *   [How to write a custom document loader](/v0.2/docs/how_to/document_loader_custom)
    *   [How to load data from a directory](/v0.2/docs/how_to/document_loader_directory)
    *   [How to load PDF files](/v0.2/docs/how_to/document_loader_pdf)
    *   [How to load JSON data](/v0.2/docs/how_to/document_loaders_json)
    *   [How to select examples by length](/v0.2/docs/how_to/example_selectors_length_based)
    *   [How to select examples by similarity](/v0.2/docs/how_to/example_selectors_similarity)
    *   [How to use reference examples](/v0.2/docs/how_to/extraction_examples)
    *   [How to handle long text](/v0.2/docs/how_to/extraction_long_text)
    *   [How to do extraction without using function calling](/v0.2/docs/how_to/extraction_parse)
    *   [Fallbacks](/v0.2/docs/how_to/fallbacks)
    *   [Few Shot Prompt Templates](/v0.2/docs/how_to/few_shot)
    *   [How to run custom functions](/v0.2/docs/how_to/functions)
    *   [How to construct knowledge graphs](/v0.2/docs/how_to/graph_constructing)
    *   [How to map values to a database](/v0.2/docs/how_to/graph_mapping)
    *   [How to improve results with prompting](/v0.2/docs/how_to/graph_prompting)
    *   [How to add a semantic layer over the database](/v0.2/docs/how_to/graph_semantic)
    *   [How to reindex data to keep your vectorstore in-sync with the underlying data source](/v0.2/docs/how_to/indexing)
    *   [How to get log probabilities](/v0.2/docs/how_to/logprobs)
    *   [How to add message history](/v0.2/docs/how_to/message_history)
    *   [How to generate multiple embeddings per document](/v0.2/docs/how_to/multi_vector)
    *   [How to generate multiple queries to retrieve data for](/v0.2/docs/how_to/multiple_queries)
    *   [How to parse JSON output](/v0.2/docs/how_to/output_parser_json)
    *   [How to retry when output parsing errors occur](/v0.2/docs/how_to/output_parser_retry)
    *   [How to parse XML output](/v0.2/docs/how_to/output_parser_xml)
    *   [How to invoke runnables in parallel](/v0.2/docs/how_to/parallel)
    *   [How to retrieve the whole document for a chunk](/v0.2/docs/how_to/parent_document_retriever)
    *   [How to partially format prompt templates](/v0.2/docs/how_to/prompts_partial)
    *   [How to add chat history to a question-answering chain](/v0.2/docs/how_to/qa_chat_history_how_to)
    *   [How to return citations](/v0.2/docs/how_to/qa_citations)
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    *   [How to stream from a question-answering chain](/v0.2/docs/how_to/qa_streaming)
    *   [How to construct filters](/v0.2/docs/how_to/query_constructing_filters)
    *   [How to add examples to the prompt](/v0.2/docs/how_to/query_few_shot)
    *   [How to deal with high cardinality categorical variables](/v0.2/docs/how_to/query_high_cardinality)
    *   [How to handle multiple queries](/v0.2/docs/how_to/query_multiple_queries)
    *   [How to handle multiple retrievers](/v0.2/docs/how_to/query_multiple_retrievers)
    *   [How to handle cases where no queries are generated](/v0.2/docs/how_to/query_no_queries)
    *   [How to recursively split text by characters](/v0.2/docs/how_to/recursive_text_splitter)
    *   [How to reduce retrieval latency](/v0.2/docs/how_to/reduce_retrieval_latency)
    *   [How to route execution within a chain](/v0.2/docs/how_to/routing)
    *   [How to chain runnables](/v0.2/docs/how_to/sequence)
    *   [How to split text by tokens](/v0.2/docs/how_to/split_by_token)
    *   [How to deal with large databases](/v0.2/docs/how_to/sql_large_db)
    *   [How to use prompting to improve results](/v0.2/docs/how_to/sql_prompting)
    *   [How to do query validation](/v0.2/docs/how_to/sql_query_checking)
    *   [How to stream](/v0.2/docs/how_to/streaming)
    *   [How to create a time-weighted retriever](/v0.2/docs/how_to/time_weighted_vectorstore)
    *   [How to use a chat model to call tools](/v0.2/docs/how_to/tool_calling)
    *   [How to call tools with multi-modal data](/v0.2/docs/how_to/tool_calls_multi_modal)
    *   [How to use LangChain tools](/v0.2/docs/how_to/tools_builtin)
    *   [How use a vector store to retrieve data](/v0.2/docs/how_to/vectorstore_retriever)
    *   [How to create and query vector stores](/v0.2/docs/how_to/vectorstores)
*   [Conceptual guide](/v0.2/docs/concepts)
*   Ecosystem
    
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*   Versions
    
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    *   [Release Policy](/v0.2/docs/versions/release_policy)
    *   [Packages](/v0.2/docs/versions/packages)
*   [Security](/v0.2/docs/security)

*   [](/v0.2/)
*   [How-to guides](/v0.2/docs/how_to/)
*   How to track token usage

On this page

How to track token usage
========================

Prerequisites

This guide assumes familiarity with the following concepts:

*   [Chat models](/v0.2/docs/concepts/#chat-models)

This notebook goes over how to track your token usage for specific calls.

Using `AIMessage.response_metadata`[​](#using-aimessageresponse_metadata "Direct link to using-aimessageresponse_metadata")
---------------------------------------------------------------------------------------------------------------------------

A number of model providers return token usage information as part of the chat generation response. When available, this is included in the `AIMessage.response_metadata` field. Here's an example with OpenAI:

tip

See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai

    yarn add @langchain/openai

    pnpm add @langchain/openai

    import { ChatOpenAI } from "@langchain/openai";const chatModel = new ChatOpenAI({  model: "gpt-4-turbo",});const res = await chatModel.invoke("Tell me a joke.");console.log(res.response_metadata);/*  {    tokenUsage: { completionTokens: 15, promptTokens: 12, totalTokens: 27 },    finish_reason: 'stop'  }*/

#### API Reference:

*   [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai`

And here's an example with Anthropic:

*   npm
*   Yarn
*   pnpm

    npm install @langchain/anthropic

    yarn add @langchain/anthropic

    pnpm add @langchain/anthropic

    import { ChatAnthropic } from "@langchain/anthropic";const chatModel = new ChatAnthropic({  model: "claude-3-sonnet-20240229",});const res = await chatModel.invoke("Tell me a joke.");console.log(res.response_metadata);/*  {    id: 'msg_017Mgz6HdgNbi3cwL1LNB9Dw',    model: 'claude-3-sonnet-20240229',    stop_sequence: null,    usage: { input_tokens: 12, output_tokens: 30 },    stop_reason: 'end_turn'  }*/

#### API Reference:

*   [ChatAnthropic](https://v02.api.js.langchain.com/classes/langchain_anthropic.ChatAnthropic.html) from `@langchain/anthropic`

Using callbacks[​](#using-callbacks "Direct link to Using callbacks")
---------------------------------------------------------------------

You can also use the `handleLLMEnd` callback to get the full output from the LLM, including token usage for supported models. Here's an example of how you could do that:

    import { ChatOpenAI } from "@langchain/openai";const chatModel = new ChatOpenAI({  model: "gpt-4-turbo",  callbacks: [    {      handleLLMEnd(output) {        console.log(JSON.stringify(output, null, 2));      },    },  ],});await chatModel.invoke("Tell me a joke.");/*  {    "generations": [      [        {          "text": "Why did the scarecrow win an award?\n\nBecause he was outstanding in his field!",          "message": {            "lc": 1,            "type": "constructor",            "id": [              "langchain_core",              "messages",              "AIMessage"            ],            "kwargs": {              "content": "Why did the scarecrow win an award?\n\nBecause he was outstanding in his field!",              "tool_calls": [],              "invalid_tool_calls": [],              "additional_kwargs": {},              "response_metadata": {                "tokenUsage": {                  "completionTokens": 17,                  "promptTokens": 12,                  "totalTokens": 29                },                "finish_reason": "stop"              }            }          },          "generationInfo": {            "finish_reason": "stop"          }        }      ]    ],    "llmOutput": {      "tokenUsage": {        "completionTokens": 17,        "promptTokens": 12,        "totalTokens": 29      }    }  }*/

#### API Reference:

*   [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai`

Next steps[​](#next-steps "Direct link to Next steps")
------------------------------------------------------

You've now seen a few examples of how to track chat model token usage for supported providers.

Next, check out the other how-to guides on chat models in this section, like [how to get a model to return structured output](/v0.2/docs/how_to/structured_output) or [how to add caching to your chat models](/v0.2/docs/how_to/chat_model_caching).

* * *

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*   [Using `AIMessage.response_metadata`](#using-aimessageresponse_metadata)
*   [Using callbacks](#using-callbacks)
*   [Next steps](#next-steps)

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