Models

LangChain v1.0Welcome to the new LangChain documentation! If you encounter any issues or have feedback, please open an issue so we can improve. Archived v0 documentation can be found here.See the release notes and migration guide for a complete list of changes and instructions on how to upgrade your code.

LLMs are powerful AI tools that can interpret and generate text like humans. They’re versatile enough to write content, translate languages, summarize, and answer questions without needing specialized training for each task. In addition to text generation, many models support:

Tool calling - calling external tools (like databases queries or API calls) and use results in their responses.
Structured output - where the model’s response is constrained follow a defined format.
Multimodality - process and return data other than text, such as images, audio, and video.
Reasoning - models perform multi-step reasoning to arrive at a conclusion.

Models are the reasoning engine of agents. They drive the agent’s decision-making process, determining which tools to call, how to interpret results, and when to provide a final answer. The quality and capabilities of the model you choose directly impact your agent’s reliability and performance. Different models excel at different tasks - some are better at following complex instructions, others at structured reasoning, and some support larger context windows for handling more information. LangChain’s standard model interfaces give you access to many different provider integrations, which makes it easy to experiment with and switch between models to find the best fit for your case.

For provider-specific integration information and capabilities, see the provider’s integration page.

Basic usage

Models can be utilized in two ways:

With agents - Models can be dynamically specified when creating an agent. See the agents guide for more details on how to do this.
Standalone - Models can be called directly (outside of the agent loop) for tasks like text generation, classification, or extraction without the need for an agent framework.

The same model interface works in both contexts, which gives you the flexibility to start simple and scale up to more complex agent-based workflows as needed.

Initialize a model

The easiest way to get started with a standalone model in LangChain is to use initChatModel to initialize one from a provider of your choice (examples below):

OpenAI
Anthropic
Azure
Google Gemini
Bedrock Converse

👉 Read the OpenAI chat model integration docs

npm install @langchain/openai

import { initChatModel } from "langchain";

process.env.OPENAI_API_KEY = "your-api-key";

const model = await initChatModel("openai:gpt-4.1");

const response = await model.invoke("Why do parrots talk?");

See initChatModel for more detail, including information on how to pass model parameters.

Key methods

Invoke

The model takes messages as input and outputs messages after generating a complete response.

Stream

Invoke the model, but stream the output as it is generated in real-time.

Batch

Send multiple requests to a model in a batch for more efficient processing.

In addition to chat models, LangChain provides support for other adjacent technologies, such as embedding models and vector stores. See the integrations page for details.

Parameters

A chat model takes parameters that can be used to configure its behavior. The full set of supported parameters varies by model and provider, but standard ones include:

model

string

required

The name or identifier of the specific model you want to use with a provider.

apiKey

string

The key required for authenticating with the model’s provider. This is usually issued when you sign up for access to the model. Often accessed by setting an .

temperature

number

Controls the randomness of the model’s output. A higher number makes responses more creative; lower ones make them more deterministic.

timeout

number

The maximum time (in seconds) to wait for a response from the model before canceling the request.

maxTokens

number

Limits the total number of in the response, effectively controlling how long the output can be.

maxRetries

number

The maximum number of attempts the system will make to resend a request if it fails due to issues like network timeouts or rate limits.

Using initChatModel, pass these parameters as inline parameters:

Initialize using model parameters

const model = await initChatModel(
    "anthropic:claude-sonnet-4-5",
    { temperature: 0.7, timeout: 30, max_tokens: 1000 }
)

Each chat model integration may have additional params used to control provider-specific functionality. For example, @[ChatOpenAI] has use_responses_api to dictate whether to use the OpenAI Responses or Completions API.To find all the parameters supported by a given chat model, head to the chat model integrations page.

Invocation

A chat model must be invoked to generate an output. There are three primary invocation methods, each suited to different use cases.

Invoke

The most straightforward way to call a model is to use invoke() with a single message or a list of messages.

Single message

const response = await model.invoke("Why do parrots have colorful feathers?");
console.log(response);

A list of messages can be provided to a model to represent conversation history. Each message has a role that models use to indicate who sent the message in the conversation. See the messages guide for more detail on roles, types, and content.

Object format

const conversation = [
  { role: "system", content: "You are a helpful assistant that translates English to French." },
  { role: "user", content: "Translate: I love programming." },
  { role: "assistant", content: "J'adore la programmation." },
  { role: "user", content: "Translate: I love building applications." },
];

const response = await model.invoke(conversation);
console.log(response);  // AIMessage("J'adore créer des applications.")

Message objects

import { HumanMessage, AIMessage, SystemMessage } from "langchain";

const conversation = [
  new SystemMessage("You are a helpful assistant that translates English to French."),
  new HumanMessage("Translate: I love programming."),
  new AIMessage("J'adore la programmation."),
  new HumanMessage("Translate: I love building applications."),
];

const response = await model.invoke(conversation);
console.log(response);  // AIMessage("J'adore créer des applications.")

Stream

Most models can stream their output content while it is being generated. By displaying output progressively, streaming significantly improves user experience, particularly for longer responses. Calling stream() returns an that yields output chunks as they are produced. You can use a loop to process each chunk in real-time:

const stream = await model.stream("Why do parrots have colorful feathers?");
for await (const chunk of stream) {
  console.log(chunk.text)
}

As opposed to invoke(), which returns a single AIMessage after the model has finished generating its full response, stream() returns multiple AIMessageChunk objects, each containing a portion of the output text. Importantly, each chunk in a stream is designed to be gathered into a full message via summation:

Construct AIMessage

let full: AIMessageChunk | null = null;
for await (const chunk of stream) {
  full = full ? full.concat(chunk) : chunk;
  console.log(full.text);
}

// The
// The sky
// The sky is
// The sky is typically
// The sky is typically blue
// ...

console.log(full.contentBlocks);
// [{"type": "text", "text": "The sky is typically blue..."}]

The resulting message can be treated the same as a message that was generated with invoke() - for example, it can be aggregated into a message history and passed back to the model as conversational context.

Streaming only works if all steps in the program know how to process a stream of chunks. For instance, an application that isn’t streaming-capable would be one that needs to store the entire output in memory before it can be processed.

Advanced streaming topics

"Auto-streaming" chat models

LangChain simplifies streaming from chat models by automatically enabling streaming mode in certain cases, even when you’re not explicitly calling the streaming methods. This is particularly useful when you use the non-streaming invoke method but still want to stream the entire application, including intermediate results from the chat model.In LangGraph agents, for example, you can call model.invoke() within nodes, but LangChain will automatically delegate to streaming if running in a streaming mode.

How it works

When you invoke() a chat model, LangChain will automatically switch to an internal streaming mode if it detects that you are trying to stream the overall application. The result of the invocation will be the same as far as the code that was using invoke is concerned; however, while the chat model is being streamed, LangChain will take care of invoking @[on_llm_new_token] events in LangChain’s callback system.Callback events allow LangGraph stream() and streamEvents() to surface the chat model’s output in real-time.

Streaming events

LangChain chat models can also stream semantic events using [streamEvents()][BaseChatModel.streamEvents].This simplifies filtering based on event types and other metadata, and will aggregate the full message in the background. See below for an example.

const stream = await model.streamEvents("Hello");
for await (const event of stream) {
    if (event.event === "on_chat_model_start") {
        console.log(`Input: ${event.data.input}`);
    }
    if (event.event === "on_chat_model_stream") {
        console.log(`Token: ${event.data.chunk.text}`);
    }
    if (event.event === "on_chat_model_end") {
        console.log(`Full message: ${event.data.output.text}`);
    }
}

Input: Hello
Token: Hi
Token:  there
Token: !
Token:  How
Token:  can
Token:  I
...
Full message: Hi there! How can I help today?

See the streamEvents() reference for event types and other details.

Batch

Batching a collection of independent requests to a model can significantly improve performance and reduce costs, as the processing can be done in parallel:

Batch

const responses = await model.batch([
  "Why do parrots have colorful feathers?",
  "How do airplanes fly?",
  "What is quantum computing?",
  "Why do parrots have colorful feathers?",
  "How do airplanes fly?",
  "What is quantum computing?",
]);
for (const response of responses) {
  console.log(response);
}

When processing a large number of inputs using batch(), you may want to control the maximum number of parallel calls. This can be done by setting the maxConcurrency attribute in the RunnableConfig dictionary.

Batch with max concurrency

model.batch(
  listOfInputs,
  {
    maxConcurrency: 5,  // Limit to 5 parallel calls
  }
)

See the RunnableConfig reference for a full list of supported attributes.

For more details on batching, see the reference.

Tool calling

Models can request to call tools that perform tasks such as fetching data from a database, searching the web, or running code. Tools are pairings of:

A schema, including the name of the tool, a description, and/or argument definitions (often a JSON schema)
A function or to execute.

You may hear the term “function calling”. We use this interchangeably with “tool calling”.

To make tools that you have defined available for use by a model, you must bind them using bindTools(). In subsequent invocations, the model can choose to call any of the bound tools as needed. Some model providers offer built-in tools that can be enabled via model or invocation parameters (e.g. ChatOpenAI, ChatAnthropic). Check the respective provider reference for details.

See the tools guide for details and other options for creating tools.

Binding user tools

import { tool } from "langchain";
import * as z from "zod";
import { ChatOpenAI } from "@langchain/openai";

const getWeather = tool(
  (input) => `It's sunny in ${input.location}.`,
  {
    name: "get_weather",
    description: "Get the weather at a location.",
    schema: z.object({
      location: z.string().describe("The location to get the weather for"),
    }),
  },
);

const model = new ChatOpenAI({ model: "gpt-4o" });
const modelWithTools = model.bindTools([getWeather]);  

const response = await modelWithTools.invoke("What's the weather like in Boston?");
const toolCalls = response.tool_calls || [];
for (const tool_call of toolCalls) {
  // View tool calls made by the model
  console.log(`Tool: ${tool_call.name}`);
  console.log(`Args: ${tool_call.args}`);
}

When binding user-defined tools, the model’s response includes a request to execute a tool. When using a model separately from an agent, it is up to you to perform the requested action and return the result back to the model for use in subsequent reasoning. Note that when using an agent, the agent loop will handle the tool execution loop for you. Below, we show some common ways you can use tool calling.

Tool execution loop

When a model returns tool calls, you need to execute the tools and pass the results back to the model. This creates a conversation loop where the model can use tool results to generate its final response. LangChain includes agent abstractions that handle this orchestration for you.Here’s a simple example of how to do this:

Tool execution loop

// Bind (potentially multiple) tools to the model
const modelWithTools = model.bindTools([get_weather])

// Step 1: Model generates tool calls
const messages = [{"role": "user", "content": "What's the weather in Boston?"}]
const ai_msg = await modelWithTools.invoke(messages)
messages.push(ai_msg)

// Step 2: Execute tools and collect results
for (const tool_call of ai_msg.tool_calls) {
    // Execute the tool with the generated arguments
    const tool_result = await get_weather.invoke(tool_call)
    messages.push(tool_result)
}

// Step 3: Pass results back to model for final response
const final_response = await modelWithTools.invoke(messages)
console.log(final_response.text)
// "The current weather in Boston is 72°F and sunny."

Each @[ToolMessage] returned by the tool includes a tool_call_id that matches the original tool call, helping the model correlate results with requests.

Forcing tool calls

By default, the model has the freedom to choose which bound tool to use based on the user’s input. However, you might want to force choosing a tool, ensuring the model uses either a particular tool or any tool from a given list:

const modelWithTools = model.bindTools([tool_1], { toolChoice: "any" })

Parallel tool calls

Many models support calling multiple tools in parallel when appropriate. This allows the model to gather information from different sources simultaneously.

Parallel tool calls

const modelWithTools = model.bind_tools([get_weather])

const response = await modelWithTools.invoke(
    "What's the weather in Boston and Tokyo?"
)


// The model may generate multiple tool calls
console.log(response.tool_calls)
// [
//   { name: 'get_weather', args: { location: 'Boston' }, id: 'call_1' },
//   { name: 'get_time', args: { location: 'Tokyo' }, id: 'call_2' }
// ]


// Execute all tools (can be done in parallel with async)
const results = []
for (const tool_call of response.tool_calls || []) {
    if (tool_call.name === 'get_weather') {
        const result = await get_weather.invoke(tool_call)
        results.push(result)
    }
}

The model intelligently determines when parallel execution is appropriate based on the independence of the requested operations.

Most models supporting tool calling enable parallel tool calls by default. Some (including OpenAI and Anthropic) allow you to disable this feature. To do this, set parallel_tool_calls=False:

model.bind_tools([get_weather], parallel_tool_calls=False)

Streaming tool calls

When streaming responses, tool calls are progressively built through @[ToolCallChunk]. This allows you to see tool calls as they’re being generated rather than waiting for the complete response.

Streaming tool calls

const stream = await modelWithTools.stream(
    "What's the weather in Boston and Tokyo?"
)
for await (const chunk of stream) {
    // Tool call chunks arrive progressively
    if (chunk.tool_call_chunks) {
        for (const tool_chunk of chunk.tool_call_chunks) {
        console.log(`Tool: ${tool_chunk.get('name', '')}`)
        console.log(`Args: ${tool_chunk.get('args', '')}`)
        }
    }
}

// Output:
// Tool: get_weather
// Args:
// Tool:
// Args: {"loc
// Tool:
// Args: ation": "BOS"}
// Tool: get_time
// Args:
// Tool:
// Args: {"timezone": "Tokyo"}

You can accumulate chunks to build complete tool calls:

Accumulate tool calls

let full: AIMessageChunk | null = null
const stream = await modelWithTools.stream("What's the weather in Boston?")
for await (const chunk of stream) {
    full = full ? full.concat(chunk) : chunk
    console.log(full.contentBlocks)
}

Structured outputs

Models can be requested to provide their response in a format matching a given schema. This is useful for ensuring the output can be easily parsed and used in subsequent processing. LangChain supports multiple schema types and methods for enforcing structured outputs.

Zod
JSON Schema

A zod schema is the preferred method of defining an output schema. Note that when a zod schema is provided, the model output will also be validated against the schema using zod’s parse methods.

import * as z from "zod";

const Movie = z.object({
  title: z.string().describe("The title of the movie"),
  year: z.number().describe("The year the movie was released"),
  director: z.string().describe("The director of the movie"),
  rating: z.number().describe("The movie's rating out of 10"),
});

const modelWithStructure = model.withStructuredOutput(Movie);

const response = await modelWithStructure.invoke("Provide details about the movie Inception");
console.log(response);
// {
//   title: "Inception",
//   year: 2010,
//   director: "Christopher Nolan",
//   rating: 8.8,
// }

Key considerations for structured outputs:

Method parameter: Some providers support different methods ('jsonSchema', 'functionCalling', 'jsonMode')
Include raw: Use @[includeRaw: true][BaseChatModel.with_structured_output(include_raw)] to get both the parsed output and the raw AIMessage
Validation: Zod models provide automatic validation, while JSON Schema requires manual validation

Example: Message output alongside parsed structure

It can be useful to return the raw AIMessage object alongside the parsed representation to access response metadata such as token counts. To do this, set @[include_raw=True][BaseChatModel.with_structured_output(include_raw)] when calling @[with_structured_output][BaseChatModel.with_structured_output]:

import * as z from "zod";

const Movie = z.object({
  title: z.string().describe("The title of the movie"),
  year: z.number().describe("The year the movie was released"),
  director: z.string().describe("The director of the movie"),
  rating: z.number().describe("The movie's rating out of 10"),
  title: z.string().describe("The title of the movie"),
  year: z.number().describe("The year the movie was released"),
  director: z.string().describe("The director of the movie"),  
  rating: z.number().describe("The movie's rating out of 10"),
});

const modelWithStructure = model.withStructuredOutput(Movie, { includeRaw: true });

const response = await modelWithStructure.invoke("Provide details about the movie Inception");
console.log(response);
// {
//   raw: AIMessage { ... },
//   parsed: { title: "Inception", ... }
// }

Example: Nested structures

Schemas can be nested:

import * as z from "zod";

const Actor = z.object({
  name: str
  role: z.string(),
});

const MovieDetails = z.object({
  title: z.string(),
  year: z.number(),
  cast: z.array(Actor),
  genres: z.array(z.string()),
  budget: z.number().nullable().describe("Budget in millions USD"),
});

const modelWithStructure = model.withStructuredOutput(MovieDetails);

Supported models

LangChain supports all major model providers, including OpenAI, Anthropic, Google, Azure, AWS Bedrock, and more. Each provider offers a variety of models with different capabilities. For a full list of supported models in LangChain, see the integrations page.

Advanced topics

Multimodal

Certain models can process and return non-textual data such as images, audio, and video. You can pass non-textual data to a model by providing content blocks.

All LangChain chat models with underlying multimodal capabilities support:

Data in the cross-provider standard format (see our messages guide)
OpenAI chat completions format
Any format that is native to that specific provider (e.g., Anthropic models accept Anthropic native format)

See the multimodal section of the messages guide for details. Some models can also return multimodal data as part of their response. In such cases, the resulting AIMessage will have content blocks with multimodal types.

Multimodal output

const response = await model.invoke("Create a picture of a cat");
console.log(response.contentBlocks);
// [
//   { type: "text", text: "Here's a picture of a cat" },
//   { type: "image", data: "...", mimeType: "image/jpeg" },
// ]

See the integrations page for details on specific providers.

Reasoning

Newer models are capable of performing multi-step reasoning to arrive at a conclusion. This involves breaking down complex problems into smaller, more manageable steps. If supported by the underlying model, you can surface this reasoning process to better understand how the model arrived at its final answer.

const stream = model.stream("Why do parrots have colorful feathers?");
for await (const chunk of stream) {
    const reasoningSteps = chunk.contentBlocks.filter(b => b.type === "reasoning");
    console.log(reasoningSteps.length > 0 ? reasoningSteps : chunk.text);
}

Depending on the model, you can sometimes specify the level of effort it should put into reasoning. Similarly, you can request that the model turn off reasoning entirely. This may take the form of categorical “tiers” of reasoning (e.g., 'low' or 'high') or integer token budgets. For details, see the integrations page or reference for your respective chat model.

Local models

LangChain supports running models locally on your own hardware. This is useful for scenarios where either data privacy is critical, you want to invoke a custom model, or when you want to avoid the costs incurred when using a cloud-based model. Ollama is one of the easiest ways to run models locally. See the full list of local integrations on the integrations page.

Prompt caching

Many providers offer prompt caching features to reduce latency and cost on repeat processing of the same tokens. These features can be implicit or explicit:

Implicit prompt caching: providers will automatically pass on cost savings if a request hits a cache. Examples: OpenAI and Gemini (Gemini 2.5 and above).
Explicit caching: providers allow you to manually indicate cache points for greater control or to guarantee cost savings. Examples: @[ChatOpenAI] (via prompt_cache_key), Anthropic, AWS Bedrock, Gemini.

Prompt caching is often only engaged above a minimum input token threshold. See provider pages for details.

Cache usage will be reflected in the usage metadata of the model response.

Server-side tool use

Some providers support server-side tool-calling loops: models can interact with web search, code interpreters, and other tools and analyze the results in a single conversational turn. If a model invokes a tool server-side, the content of the response message will include content representing the invocation and result of the tool. Accessing the content blocks of the response will return the server-side tool calls and results in a provider-agnostic format:

import { initChatModel } from "langchain";

const model = await initChatModel("openai:gpt-4.1-mini");
const modelWithTools = model.bindTools([{ type: "web_search" }])

const message = await modelWithTools.invoke("What was a positive news story from today?");
console.log(message.contentBlocks);

This represents a single conversational turn; there are no associated ToolMessage objects that need to be passed in as in client-side tool-calling. See the integration page for your given provider for available tools and usage details.

Base URL or proxy

For many chat model integrations, you can configure the base URL for API requests, which allows you to use model providers that have OpenAI-compatible APIs or to use a proxy server.

Base URL

Many model providers offer OpenAI-compatible APIs (e.g., Together AI, vLLM). You can use initChatModel with these providers by specifying the appropriate base_url parameter:

model = initChatModel(
    "MODEL_NAME",
    {
        modelProvider: "openai",
        baseUrl: "BASE_URL",
        apiKey: "YOUR_API_KEY",
    }
)

When using direct chat model class instantiation, the parameter name may vary by provider. Check the respective reference for details.

Log probabilities

Certain models can be configured to return token-level log probabilities representing the likelihood of a given token by setting the logprobs parameter when initializing the model:

const model = new ChatOpenAI({
    model: "gpt-4o",
    logprobs: true,
});

const responseMessage = await model.invoke("Why do parrots talk?");

responseMessage.response_metadata.logprobs.content.slice(0, 5);

Token usage

A number of model providers return token usage information as part of the invocation response. When available, this information will be included on the AIMessage objects produced by the corresponding model. For more details, see the messages guide.

Some provider APIs, notably OpenAI and Azure OpenAI chat completions, require users opt-in to receiving token usage data in streaming contexts. See the streaming usage metadata section of the integration guide for details.

Invocation config

When invoking a model, you can pass additional configuration through the config parameter using a RunnableConfig object. This provides run-time control over execution behavior, callbacks, and metadata tracking. Common configuration options include:

Invocation with config

const response = await model.invoke(
    "Tell me a joke",
    {
        runName: "joke_generation",      // Custom name for this run
        tags: ["humor", "demo"],          // Tags for categorization
        metadata: {"user_id": "123"},     // Custom metadata
        callbacks: [my_callback_handler], // Callback handlers
    }
)

These configuration values are particularly useful when:

Debugging with LangSmith tracing
Implementing custom logging or monitoring
Controlling resource usage in production
Tracking invocations across complex pipelines

Key configuration attributes

runName

string

Identifies this specific invocation in logs and traces. Not inherited by sub-calls.

LangChain v1.0

Get started

Core components

Advanced usage

Use in production

Basic usage

Initialize a model

Key methods

Invoke

Stream

Batch

Parameters

Invocation

Invoke

Stream

How it works

Batch

Tool calling

Structured outputs

Supported models

Advanced topics

Multimodal

Reasoning

Local models

Prompt caching

Server-side tool use

Base URL or proxy

Log probabilities

Token usage

Invocation config

LangChain v1.0

Get started

Core components

Advanced usage

Use in production

​Basic usage

​Initialize a model

​Key methods

Invoke

Stream

Batch

​Parameters

​Invocation

​Invoke

​Stream

​How it works

​Batch

​Tool calling

​Structured outputs

​Supported models

​Advanced topics

​Multimodal

​Reasoning

​Local models

​Prompt caching

​Server-side tool use

​Base URL or proxy

​Log probabilities

​Token usage

​Invocation config

Basic usage

Initialize a model

Key methods

Parameters

Invocation

Invoke

Stream

How it works

Batch

Tool calling

Structured outputs

Supported models

Advanced topics

Multimodal

Reasoning

Local models

Prompt caching

Server-side tool use

Base URL or proxy

Log probabilities

Token usage

Invocation config