Messages

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Overview

Messages are the fundamental unit of context for models in LangChain. They represent the input and output of models, carrying both the content and metadata needed to represent the state of a conversation when interacting with an LLM.

Messages are objects that contain:

• Role - Identifies the message type (e.g. system, user)
• Content - Represents the actual content of the message (like text, images, audio, documents, etc.)
• Metadata - Optional fields such as response information, message IDs, and token usage

LangChain provides a standard message type that works across all model providers, ensuring consistent behavior regardless of the model being called.

Message types

System Message

Tells the model how to behave and provide context for interactions

Human Message

Represents user input and interactions with the model

AI Message

Responses generated by the model, including text content, tool calls, and metadata

Tool Message

Represents the outputs of tool calls

Basic usage

The simplest way to use messages is to create message objects and pass them to a model when invoking.

import { initChatModel } from "langchain";

const model = await initChatModel("openai:gpt-5-nano");

const systemMsg = new SystemMessage("You are a helpful assistant.");
const humanMsg = new HumanMessage("Hello, how are you?");

const messages = [systemMsg, humanMsg];
const response = await model.invoke(messages);  // Returns AIMessage

Text prompts

Text prompts are strings - ideal for straightforward generation tasks where you don’t need to retain conversation history.

const response = await model.invoke("Write a haiku about spring");

Use text prompts when:

• You have a single, standalone request
• You don’t need conversation history
• You want minimal code complexity

Message prompts

Alternatively, you can pass in a list of messages to the model by providing a list of message objects.

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

const messages = [
    new SystemMessage("You are a poetry expert"),
    new HumanMessage("Write a haiku about spring"),
    new AIMessage("Cherry blossoms bloom...")
]
const response = await model.invoke(messages);

Use message prompts when:

• Managing multi-turn conversations
• Working with multimodal content (images, audio, files)
• Including system instructions

Dictionary format

You can also specify messages directly in OpenAI chat completions format.

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

const messages = [
    { role: "system", content: "You are a poetry expert" },
    { role: "user", content: "Write a haiku about spring" },
    { role: "assistant", content: "Cherry blossoms bloom..." }
]
const response = await model.invoke(messages);

Message types

System Message

A SystemMessage represent an initial set of instructions that primes the model’s behavior. You can use a system message to set the tone, define the model’s role, and establish guidelines for responses.

Basic instructions

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

const systemMsg = new SystemMessage("You are a helpful coding assistant.");

const messages = [
    systemMsg,
    new HumanMessage("How do I create a REST API?")
]
const response = await model.invoke(messages);

Detailed persona

import { SystemMessage, HumanMessage } from "langchain";

const systemMsg = new SystemMessage(`
You are a senior TypeScript developer with expertise in web frameworks.
Always provide code examples and explain your reasoning.
Be concise but thorough in your explanations.
`);

const messages = [
    systemMsg,
    new HumanMessage("How do I create a REST API?")
]
const response = await model.invoke(messages);

Human Message

A HumanMessage represents user input and interactions. They can contain text, images, audio, files, and any other amount of multimodal content.

Text content

Message object

const humanMsg = new HumanMessage("What is machine learning?");
const response = await model.invoke([humanMsg]);

String shortcut

const response = await model.invoke("What is machine learning?");

Message metadata

Add metadata

const humanMsg = new HumanMessage({
    content: "Hello!",
    name: "alice",
    id: "msg_123"
});

The name field behavior varies by provider - some use it for user identification, others ignore it. To check, refer to the model provider’s reference.

AI Message

An AIMessage represents the output of a model invocation. They can include multimodal data, tool calls, and provider-specific metadata that you can later access.

const response = await model.invoke("Explain AI");
console.log(typeof response);  // AIMessage

AIMessage objects are returned by the model when calling it, which contains all of the associated metadata in the response. However, that doesn’t mean that’s the only place they can be created/ modified from.

Providers weight/contextualize types of messages differently, which means it is sometimes helpful to create a new AIMessage object and insert it into the message history as if it came from the model.

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

const aiMsg = new AIMessage("I'd be happy to help you with that question!");

const messages = [
    new SystemMessage("You are a helpful assistant"),
    new HumanMessage("Can you help me?"),
    aiMsg,  // Insert as if it came from the model
    new HumanMessage("Great! What's 2+2?")
]

const response = await model.invoke(messages);

Attributes

text · string

The text content of the message.

content · string | ContentBlock[]

The raw content of the message.

content_blocks · ContentBlock.Standard[]

The standardized content blocks of the message. (See Content)

tool_calls · ToolCall[] | None

The tool calls made by the model. Empty if no tools are called.

id · string

A unique identifier for the message (either automatically generated by LangChain or returned in the provider response)

usage_metadata · UsageMetadata | None

The usage metadata of the message, which can contain token counts when available.

response_metadata · ResponseMetadata | None

The response metadata of the message.

Tool calling responses

When models make tool calls, they’re included in the AI message:

const modelWithTools = model.bindTools([getWeather]);
const response = await modelWithTools.invoke("What's the weather in Paris?");

for (const toolCall of response.tool_calls) {
    console.log(`Tool: ${toolCall.name}`);
    console.log(`Args: ${toolCall.args}`);
    console.log(`ID: ${toolCall.id}`);
}

Streaming and chunks

During streaming, you’ll receive AIMessageChunk objects that can be combined:

Stream and collect chunks

const chunks = [];
for await (const chunk of model.stream("Write a poem")) {
    chunks.push(chunk);
    console.log(chunk.text);
}

Tool Message

For models that support tool calling, AI messages can contain tool calls. Tool messages are used to pass the results of a single tool execution back to the model.

import { AIMessage, ToolMessage } from "langchain";

const aiMessage = new AIMessage({
    content: [],
    tool_calls: [{
        name: "get_weather",
        args: { location: "San Francisco" },
        id: "call_123"
    }]
});

const toolMessage = new ToolMessage({
    content: "Sunny, 72°F",
    tool_call_id: "call_123"
});

const messages = [
    new HumanMessage("What's the weather in San Francisco?"),
    aiMessage,  // Model's tool call
    toolMessage,  // Tool execution result
];

const response = await model.invoke(messages);  // Model processes the result

Attributes

content · string · required

The stringified output of the tool call.

tool_call_id · string · required

The ID of the tool call that this message is responding to. (this must match the ID of the tool call in the AI message)

name · string · required

The name of the tool that was called.

artifact · dict

Additional data not sent to the model but can be accessed programmatically.

The artifact field stores supplementary data that won’t be sent to the model but can be accessed programmatically. This is useful for storing raw results, debugging information, or data for downstream processing without cluttering the model’s context.

Example: Using artifact for raw data

import { ToolMessage } from "langchain";

// Tool execution returns structured data
const rawData = { temperature: 72, condition: "Sunny" };
const toolMessage = new ToolMessage({
    content: "The weather is sunny with a temperature of 72°F.",
    tool_call_id: "call_123",
    name: "get_weather",
    artifact: { raw_data: rawData }  // Store structured data
});

// Later, access the raw data programmatically
const weatherInfo = toolMessage.artifact.raw_data;
console.log(`Raw weather data: ${JSON.stringify(weatherInfo)}`);

Content

You can think of a message’s content as the payload of data that gets sent to the model. Within a message, you can content either as a string or a list of LangChain content blocks.

import { HumanMessage } from "langchain";

// String content
const humanMessage = new HumanMessage("Hello, how are you?");

// List of content blocks
const humanMessage = new HumanMessage({
    contentBlocks: [
        { type: "text", text: "Hello, how are you?" },
        { type: "image", url: "https://example.com/image.jpg" },
    ],
});

Most providers have an opinionated format of representing message content. This makes it difficult to build applications that need to work across multiple AI providers, as each structure means you have to write custom code to handle format.

// OpenAI format
const openaiMessage = {
    role: "user",
    content: [
        { type: "text", text: "What's in this image?" },
        { type: "image_url", image_url: { url: "..." } }
    ]
};

// Anthropic format
const anthropicMessage = {
    role: "user",
    content: [
        { type: "text", text: "What's in this image?" },
        { type: "image", source: { type: "url", media_type: "image/jpeg", url: "..." } }
    ]
};

By default, AIMessage objects will store the output of the model inside of the content field. If you want to access content in a way that won’t change between providers, you can access the .contentBlocks field, which will return a list of content blocks that adhere to the standard content format.

Access content blocks

const message = await model.invoke("Why do parrots have different colors?");
const contentBlocks = message.contentBlocks;

Because each provider handles content differently, you can also initialize a message with a list of content blocks. This will ensure that the message is always in the standard format, regardless of the provider.

const universalMessage = new HumanMessage({
    contentBlocks: [
        { type: "text", text: "What's in this image?" },
        { type: "image", url: "..." }
    ],
});

Examples

Multimodal message

const universalMessage = new HumanMessage({
    contentBlocks: [
        { type: "text", text: "Compare this image and document:" },
        { type: "image", url: "chart.jpg" },
        { type: "file", base64: pdfData, mimeType: "application/pdf" },
        { type: "text", text: "Which data source is more reliable?" }
    ],
});

const response = await model.invoke([universalMessage]);

PDF document analysis

import { readFileSync } from "node:fs";
import { HumanMessage } from "langchain";

// Read and encode PDF
const pdfData = readFileSync("report.pdf");
const pdfBase64 = pdfData.toString("base64");

const message = new HumanMessage({
    contentBlocks: [
        { type: "text", text: "Summarize the key findings in this report" },
        { type: "file", data: pdfBase64, mimeType: "application/pdf" }
    ]
});

const response = await model.invoke([message]);

Not all models support all file types. Check the model provider’s reference for supported formats and size limits.

Content block reference

Content blocks are represented (either when creating a message or accessing the contentBlocks field) as a list of typed objects. Each item in the list must adhere to one of the following block types:

Core

ContentBlock.Text

Purpose: Standard text output

text · string · required

The text content

annotations · Citation[]

List of annotations for the text

Example:

{
    type: "text",
    text: "Hello world",
    annotations: []
}

ContentBlock.Reasoning

Purpose: Model reasoning steps

reasoning · string · required

The reasoning content

Example:

{
    type: "reasoning",
    reasoning: "The user is asking about..."
}

Multimodal

ContentBlock.Multimodal.PlainText

Purpose: Document text (.txt, .md)

text · string · required

The text content

title · string

Title of the text content

mimeType · string

MIME type of the text (e.g., text/plain, text/markdown)

ContentBlock.Multimodal.Image

Purpose: Image data

url · string

URL pointing to the image location.

data · string

Base64-encoded image data.

fileId · string

Reference ID to an externally stored image (e.g., in a provider’s file system or in a bucket).

mimeType · string

Image MIME type (e.g., image/jpeg, image/png)

ContentBlock.Multimodal.Audio

Purpose: Audio data

url · string

URL pointing to the audio location.

data · string

Base64-encoded audio data.

fileId · string

Reference ID to an externally stored audio file (e.g., in a provider’s file system or in a bucket).

mimeType · string

Audio MIME type (e.g., audio/mpeg, audio/wav)

ContentBlock.Multimodal.Video

Purpose: Video data

url · string

URL pointing to the video location.

data · string

Base64-encoded video data.

fileId · string

Reference ID to an externally stored video file (e.g., in a provider’s file system or in a bucket).

mimeType · string

Video MIME type (e.g., video/mp4, video/webm)

ContentBlock.Multimodal.File

Purpose: Generic files (PDF, etc)

url · string

URL pointing to the file location.

data · string

Base64-encoded file data.

fileId · string

Reference ID to an externally stored file (e.g., in a provider’s file system or in a bucket).

mimeType · string

File MIME type (e.g., application/pdf)

Tool Calling

ContentBlock.Tools.ToolCall

Purpose: Function calls

name · string · required

Name of the tool to call

args · object · required

Arguments to pass to the tool

id · string · required

Unique identifier for this tool call

Example:

{
    type: "tool_call",
    name: "search",
    args: { query: "weather" },
    id: "call_123"
}

ContentBlock.Tools.ToolCallChunk

Purpose: Streaming tool fragments

name · string

Name of the tool being called

args · string

Partial tool arguments (may be incomplete JSON)

id · string

Tool call identifier

index · number · required

Position of this chunk in the stream

ContentBlock.Tools.InvalidToolCall

Purpose: Malformed calls

name · string

Name of the tool that failed to be called

args · string

Raw arguments that failed to parse

error · string · required

Description of what went wrong

Common errors: Invalid JSON, missing required fields

Server-Side Tool Execution

ContentBlock.Tools.WebSearchCall

Purpose: Built-in web search

query · string · required

The search query to execute

ContentBlock.Tools.WebSearchResult

Purpose: Search results

urls · string[] · required

URLs of the search results

Returns: Top search results with optional snippets

ContentBlock.Tools.CodeInterpreterCall

Purpose: Code execution

language · string · required

Programming language to execute (e.g. python, javascript, sql)

code · string · required

Code to execute

ContentBlock.Tools.CodeInterpreterResult

Purpose: Execution results

output · string · required

Output from the code execution

error · string

Error message if execution failed

Each of these content blocks mentioned above are indvidually addressable as types when importing the ContentBlock type.

import { ContentBlock } from "langchain";

// Text block
const textBlock: ContentBlock.Text = {
    type: "text",
    text: "Hello world",
}

// Image block
const imageBlock: ContentBlock.Multimodal.Image = {
    type: "image",
    url: "https://example.com/image.png",
    mimeType: "image/png",
}

Content blocks were introduced as a new property on messages in LangChain v1 to standardize content formats across providers while maintaining backward compatibility with existing code. Content blocks are not a replacement for the content property, but rather a new property that can be used to access the content of a message in a standardized format.

Examples

Multi-turn conversations

Building conversational applications requires managing message history and context:

Basic chat loop

import { HumanMessage, SystemMessage } from "langchain";

// Initialize conversation
const messages = [
    new SystemMessage("You are a helpful assistant specializing in Python programming")
]

// Simulate multi-turn conversation
while (true) {
    const userInput = await getNextMessage();
    if (userInput.toLowerCase() === "quit") {
        break;
    }

    // Add user message
    messages.push(new HumanMessage(userInput));

    // Get model response
    const response = await model.invoke(messages);

    // Add assistant response to history
    messages.push(response);

    console.log(`Assistant: ${response.content}`);
}