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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)
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    *   [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)
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    *   [How to add ad-hoc tool calling capability to LLMs and Chat Models](/v0.2/docs/how_to/tools_prompting)
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    *   [How to pass through arguments from one step to the next](/v0.2/docs/how_to/passthrough)
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    *   [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)
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    *   [How to select examples by length](/v0.2/docs/how_to/example_selectors_length_based)
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    *   [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)
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    *   [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 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)
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*   How to create a custom LLM class

On this page

How to create a custom LLM class
================================

Prerequisites

This guide assumes familiarity with the following concepts:

*   [LLMs](/v0.2/docs/concepts/#llms)

This notebook goes over how to create a custom LLM wrapper, in case you want to use your own LLM or a different wrapper than one that is directly supported in LangChain.

There are a few required things that a custom LLM needs to implement after extending the [`LLM` class](https://v02.api.js.langchain.com/classes/langchain_core_language_models_llms.LLM.html):

*   A `_call` method that takes in a string and call options (which includes things like `stop` sequences), and returns a string.
*   A `_llmType` method that returns a string. Used for logging purposes only.

You can also implement the following optional method:

*   A `_streamResponseChunks` method that returns an `AsyncIterator` and yields [`GenerationChunks`](https://v02.api.js.langchain.com/classes/langchain_core_outputs.GenerationChunk.html). This allows the LLM to support streaming outputs.

Let’s implement a very simple custom LLM that just echoes back the first `n` characters of the input.

    import { LLM, type BaseLLMParams } from "@langchain/core/language_models/llms";import type { CallbackManagerForLLMRun } from "langchain/callbacks";import { GenerationChunk } from "langchain/schema";export interface CustomLLMInput extends BaseLLMParams {  n: number;}export class CustomLLM extends LLM {  n: number;  constructor(fields: CustomLLMInput) {    super(fields);    this.n = fields.n;  }  _llmType() {    return "custom";  }  async _call(    prompt: string,    options: this["ParsedCallOptions"],    runManager: CallbackManagerForLLMRun  ): Promise<string> {    // Pass `runManager?.getChild()` when invoking internal runnables to enable tracing    // await subRunnable.invoke(params, runManager?.getChild());    return prompt.slice(0, this.n);  }  async *_streamResponseChunks(    prompt: string,    options: this["ParsedCallOptions"],    runManager?: CallbackManagerForLLMRun  ): AsyncGenerator<GenerationChunk> {    // Pass `runManager?.getChild()` when invoking internal runnables to enable tracing    // await subRunnable.invoke(params, runManager?.getChild());    for (const letter of prompt.slice(0, this.n)) {      yield new GenerationChunk({        text: letter,      });      // Trigger the appropriate callback      await runManager?.handleLLMNewToken(letter);    }  }}

We can now use this as any other LLM:

    const llm = new CustomLLM({ n: 4 });await llm.invoke("I am an LLM");

    I am

And support streaming:

    const stream = await llm.stream("I am an LLM");for await (const chunk of stream) {  console.log(chunk);}

    Iam

Richer outputs[​](#richer-outputs "Direct link to Richer outputs")
------------------------------------------------------------------

If you want to take advantage of LangChain's callback system for functionality like token tracking, you can extend the [`BaseLLM`](https://v02.api.js.langchain.com/classes/langchain_core_language_models_llms.BaseLLM.html) class and implement the lower level `_generate` method. Rather than taking a single string as input and a single string output, it can take multiple input strings and map each to multiple string outputs. Additionally, it returns a `Generation` output with fields for additional metadata rather than just a string.

    import { CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager";import { LLMResult } from "@langchain/core/outputs";import {  BaseLLM,  BaseLLMCallOptions,  BaseLLMParams,} from "@langchain/core/language_models/llms";export interface AdvancedCustomLLMCallOptions extends BaseLLMCallOptions {}export interface AdvancedCustomLLMParams extends BaseLLMParams {  n: number;}export class AdvancedCustomLLM extends BaseLLM<AdvancedCustomLLMCallOptions> {  n: number;  constructor(fields: AdvancedCustomLLMParams) {    super(fields);    this.n = fields.n;  }  _llmType() {    return "advanced_custom_llm";  }  async _generate(    inputs: string[],    options: this["ParsedCallOptions"],    runManager?: CallbackManagerForLLMRun  ): Promise<LLMResult> {    const outputs = inputs.map((input) => input.slice(0, this.n));    // Pass `runManager?.getChild()` when invoking internal runnables to enable tracing    // await subRunnable.invoke(params, runManager?.getChild());    // One input could generate multiple outputs.    const generations = outputs.map((output) => [      {        text: output,        // Optional additional metadata for the generation        generationInfo: { outputCount: 1 },      },    ]);    const tokenUsage = {      usedTokens: this.n,    };    return {      generations,      llmOutput: { tokenUsage },    };  }}

This will pass the additional returned information in callback events and in the \`streamEvents method:

    const llm = new AdvancedCustomLLM({ n: 4 });const eventStream = await llm.streamEvents("I am an LLM", {  version: "v1",});for await (const event of eventStream) {  if (event.event === "on_llm_end") {    console.log(JSON.stringify(event, null, 2));  }}

    {  "event": "on_llm_end",  "name": "AdvancedCustomLLM",  "run_id": "a883a705-c651-4236-8095-cb515e2d4885",  "tags": [],  "metadata": {},  "data": {    "output": {      "generations": [        [          {            "text": "I am",            "generationInfo": {              "outputCount": 1            }          }        ]      ],      "llmOutput": {        "tokenUsage": {          "usedTokens": 4        }      }    }  }}

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