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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.

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Basic usage

The simplest way to use messages is to create message objects and pass them to a model when invoking. 
import { initChatModel, HumanMessage, SystemMessage } from "langchain";

const model = await initChatModel("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
Multi-turn agents accumulate long message histories. LangSmith records each turn, tool result, and model response so you can inspect the full conversation. Follow the tracing quickstart to enable tracing.We recommend you also set up LangSmith Engine which monitors your traces, detects issues, and proposes fixes.

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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

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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

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Dictionary format

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

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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

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

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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.

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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. Providers weigh/contextualize types of messages differently, which means it is sometimes helpful to manually 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);
​
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. See UsageMetadata.
​
response_metadata
ResponseMetadata | None
The response metadata of the message.

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Tool calls

When models make tool calls, they’re included in the AIMessage: 
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}`);
}
Other structured data, such as reasoning or citations, can also appear in message content.

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Token usage

An AIMessage can hold token counts and other usage metadata in its usage_metadata field: 
import { initChatModel } from "langchain";

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

const response = await model.invoke("Hello!");
console.log(response.usage_metadata);

{
  "output_tokens": 304,
  "input_tokens": 8,
  "total_tokens": 312,
  "input_token_details": {
    "cache_read": 0
  },
  "output_token_details": {
    "reasoning": 256
  }
}
See UsageMetadata for details.

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Streaming and chunks

During streaming, you’ll receive AIMessageChunk objects that can be combined into a full message object: 
import { AIMessageChunk } from "langchain";

let finalChunk: AIMessageChunk | undefined;
for (const chunk of chunks) {
  finalChunk = finalChunk ? finalChunk.concat(chunk) : chunk;
}
Learn more:

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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. Tools can generate ToolMessage objects directly. Below, we show a simple example. Read more in the tools guide. 
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
​
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. Must match the ID of the tool call in the AIMessage.
​
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.
For example, a retrieval tool could retrieve a passage from a document for reference by a model. Where message content contains text that the model will reference, an artifact can contain document identifiers or other metadata that an application can use (e.g., to render a page). See example below:
import { ToolMessage } from "langchain";

// Artifact available downstream
const artifact = { document_id: "doc_123", page: 0 };

const toolMessage = new ToolMessage({
  content: "It was the best of times, it was the worst of times.",
  tool_call_id: "call_123",
  name: "search_books",
  artifact
});
See the RAG tutorial for an end-to-end example of building retrieval agents with LangChain.

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Message content

You can think of a message’s content as the payload of data that gets sent to the model. Messages have a content attribute that is loosely-typed, supporting strings and lists of untyped objects (e.g., dictionaries). This allows support for provider-native structures directly in LangChain chat models, such as multimodal content and other data. Separately, LangChain provides dedicated content types for text, reasoning, citations, multi-modal data, server-side tool calls, and other message content. See content blocks below. LangChain chat models accept message content in the content attribute. This may contain either:
  1. A string
  2. A list of content blocks in a provider-native format
  3. A list of LangChain’s standard content blocks
See below for an example using multimodal inputs: 
import { HumanMessage } from "langchain";

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

// Provider-native format (e.g., OpenAI)
const humanMessage = new HumanMessage({
  content: [
    { type: "text", text: "Hello, how are you?" },
    {
      type: "image_url",
      image_url: { url: "https://example.com/image.jpg" },
    },
  ],
});

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

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Standard content blocks

LangChain provides a standard representation for message content that works across providers. Message objects implement a contentBlocks property that will lazily parse the content attribute into a standard, type-safe representation. For example, messages generated from ChatAnthropic or ChatOpenAI will include thinking or reasoning blocks in the format of the respective provider, but can be lazily parsed into a consistent ReasoningContentBlock representation: 
import { AIMessage } from "@langchain/core/messages";

const message = new AIMessage({
  content: [
    {
      "type": "thinking",
      "thinking": "...",
      "signature": "WaUjzkyp...",
    },
    {
      "type":"text",
      "text": "...",
      "id": "msg_abc123",
    },
  ],
  response_metadata: { model_provider: "anthropic" },
});

console.log(message.contentBlocks);
See the integrations guides to get started with the inference provider of your choice.
Serializing standard contentIf an application outside of LangChain needs access to the standard content block representation, you can opt-in to storing content blocks in message content.To do this, you can set the LC_OUTPUT_VERSION environment variable to v1. Or, initialize any chat model with outputVersion: "v1":
import { initChatModel } from "langchain";

const model = await initChatModel(
  "gpt-5-nano",
  { outputVersion: "v1" }
);

​
Multimodal

Multimodality refers to the ability to work with data that comes in different forms, such as text, audio, images, and video. LangChain includes standard types for these data that can be used across providers. Chat models can accept multimodal data as input and generate it as output. Below we show short examples of input messages featuring multimodal data.
Extra keys can be included top-level in the content block or nested in "extras": {"key": value}.OpenAI, for example, requires a filename for PDFs. See the provider page for your chosen model for specifics.
// From URL
const message = new HumanMessage({
  content: [
    { type: "text", text: "Describe the content of this image." },
    {
      type: "image",
      source_type: "url",
      url: "https://example.com/path/to/image.jpg"
    },
  ],
});

// From base64 data
const message = new HumanMessage({
  content: [
    { type: "text", text: "Describe the content of this image." },
    {
      type: "image",
      source_type: "base64",
      mime_type: "image/jpeg",
      data: "AAAAIGZ0eXBtcDQyAAAAAGlzb21tcDQyAAACAGlzb2...",
    },
  ],
});

// From provider-managed File ID
const message = new HumanMessage({
  content: [
    { type: "text", text: "Describe the content of this image." },
    { type: "image", source_type: "id", id: "file-abc123" },
  ],
});

// From URL
const message = new HumanMessage({
  content: [
    { type: "text", text: "Describe the content of this document." },
    { type: "file", source_type: "url", url: "https://example.com/path/to/document.pdf", mime_type: "application/pdf" },
  ],
});

// From base64 data
const message = new HumanMessage({
  content: [
    { type: "text", text: "Describe the content of this document." },
    {
      type: "file",
      source_type: "base64",
      data: "AAAAIGZ0eXBtcDQyAAAAAGlzb21tcDQyAAACAGlzb2...",
      mime_type: "application/pdf",
    },
  ],
});

// From provider-managed File ID
const message = new HumanMessage({
  content: [
    { type: "text", text: "Describe the content of this document." },
    { type: "file", source_type: "id", id: "file-abc123" },
  ],
});

// From base64 data
const message = new HumanMessage({
  content: [
    { type: "text", text: "Describe the content of this audio." },
    {
      type: "audio",
      source_type: "base64",
      data: "AAAAIGZ0eXBtcDQyAAAAAGlzb21tcDQyAAACAGlzb2...",
    },
  ],
});

// From provider-managed File ID
const message = new HumanMessage({
  content: [
    { type: "text", text: "Describe the content of this audio." },
    { type: "audio", source_type: "id", id: "file-abc123" },
  ],
});

// From base64 data
const message = new HumanMessage({
  content: [
    { type: "text", text: "Describe the content of this video." },
    {
      type: "video",
      source_type: "base64",
      data: "AAAAIGZ0eXBtcDQyAAAAAGlzb21tcDQyAAACAGlzb2...",
    },
  ],
});

// From provider-managed File ID
const message = new HumanMessage({
  content: [
    { type: "text", text: "Describe the content of this video." },
    { type: "video", source_type: "id", id: "file-abc123" },
  ],
});
Not all models support all file types. Check the model provider’s reference for supported formats and size limits.

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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:
Purpose: Standard text output
​
type
string
required
Always "text"
​
text
string
required
The text content
​
annotations
Citation[]
List of annotations for the text
Example:
{
    type: "text",
    text: "Hello world",
    annotations: []
}
Purpose: Model reasoning steps
​
type
string
required
Always "reasoning"
​
reasoning
string
required
The reasoning content
Example:
{
    type: "reasoning",
    reasoning: "The user is asking about..."
}
Purpose: Image data
​
type
string
required
Always "image"
​
url
string
URL pointing to the image location.
​
data
string
Base64-encoded image data.
​
fileId
string
Reference to the image in an external file storage system (e.g., OpenAI or Anthropic’s Files API).
​
mimeType
string
Image MIME type (e.g., image/jpeg, image/png). Required for base64 data.
Purpose: Audio data
​
type
string
required
Always "audio"
​
url
string
URL pointing to the audio location.
​
data
string
Base64-encoded audio data.
​
fileId
string
Reference to the audio file in an external file storage system (e.g., OpenAI or Anthropic’s Files API).
​
mimeType
string
Audio MIME type (e.g., audio/mpeg, audio/wav). Required for base64 data.
Purpose: Video data
​
type
string
required
Always "video"
​
url
string
URL pointing to the video location.
​
data
string
Base64-encoded video data.
​
fileId
string
Reference to the video file in an external file storage system (e.g., OpenAI or Anthropic’s Files API).
​
mimeType
string
Video MIME type (e.g., video/mp4, video/webm). Required for base64 data.
Purpose: Generic files (PDF, etc)
​
type
string
required
Always "file"
​
url
string
URL pointing to the file location.
​
data
string
Base64-encoded file data.
​
fileId
string
Reference to the file in an external file storage system (e.g., OpenAI or Anthropic’s Files API).
​
mimeType
string
File MIME type (e.g., application/pdf). Required for base64 data.
Purpose: Document text (.txt, .md)
​
type
string
required
Always "text-plain"
​
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)
Purpose: Function calls
​
type
string
required
Always "tool_call"
​
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"
}
Purpose: Streaming tool fragments
​
type
string
required
Always "tool_call_chunk"
​
name
string
Name of the tool being called
​
args
string
Partial tool arguments (may be incomplete JSON)
​
id
string
Tool call identifier
​
index
number | string
required
Position of this chunk in the stream
Purpose: Malformed calls
​
type
string
required
Always "invalid_tool_call"
​
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
Purpose: Tool call that is executed server-side.
​
type
string
required
Always "server_tool_call"
​
id
string
required
An identifier associated with the tool call.
​
name
string
required
The name of the tool to be called.
​
args
string
required
Partial tool arguments (may be incomplete JSON)
Purpose: Streaming server-side tool call fragments
​
type
string
required
Always "server_tool_call_chunk"
​
id
string
An identifier associated with the tool call.
​
name
string
Name of the tool being called
​
args
string
Partial tool arguments (may be incomplete JSON)
​
index
number | string
Position of this chunk in the stream
Purpose: Search results
​
type
string
required
Always "server_tool_result"
​
tool_call_id
string
required
Identifier of the corresponding server tool call.
​
id
string
Identifier associated with the server tool result.
​
status
string
required
Execution status of the server-side tool. "success" or "error".
​
output
Output of the executed tool.
Purpose: Provider-specific escape hatch
​
type
string
required
Always "non_standard"
​
value
object
required
Provider-specific data structure
Usage: For experimental or provider-unique features
Additional provider-specific content types may be found within the reference documentation of each model provider.
Each of these content blocks mentioned above are individually 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",
}
View the canonical type definitions in the API reference.
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.

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Use with chat models

Chat models accept a sequence of message objects as input and return an AIMessage as output. Interactions are often stateless, so that a simple conversational loop involves invoking a model with a growing list of messages. Refer to the below guides to learn more:
Connect these docs to Claude, VSCode, and more via MCP for real-time answers.
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