Messages

{/* TODO: section on metadata types (response and usage) */}

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.

Basic usage

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

from langchain.chat_models import init_chat_model
from langchain.messages import HumanMessage, AIMessage, SystemMessage

model = init_chat_model("gpt-5-nano")

system_msg = SystemMessage("You are a helpful assistant.")
human_msg = HumanMessage("Hello, how are you?")

# Use with chat models
messages = [system_msg, human_msg]
response = model.invoke(messages)  # Returns AIMessage

Tip
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.

Text prompts

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

response = 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.

from langchain.messages import SystemMessage, HumanMessage, AIMessage

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

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

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

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.

system_msg = SystemMessage("You are a helpful coding assistant.")

messages = [
    system_msg,
    HumanMessage("How do I create a REST API?")
]
response = model.invoke(messages)

from langchain.messages import SystemMessage, HumanMessage

system_msg = SystemMessage("""
You are a senior Python developer with expertise in web frameworks.
Always provide code examples and explain your reasoning.
Be concise but thorough in your explanations.
""")

messages = [
    system_msg,
    HumanMessage("How do I create a REST API?")
]
response = 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

response = model.invoke([
  HumanMessage("What is machine learning?")
])

String shortcut

# Using a string is a shortcut for a single HumanMessage
response = model.invoke("What is machine learning?")

Message metadata

human_msg = HumanMessage(
    content="Hello!",
    name="alice",  # Optional: identify different users
    id="msg_123",  # Optional: unique identifier for tracing
)

Note
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.

response = model.invoke("Explain AI")
print(type(response))  # <class 'langchain.messages.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.

from langchain.messages import AIMessage, SystemMessage, HumanMessage

# Create an AI message manually (e.g., for conversation history)
ai_msg = AIMessage("I'd be happy to help you with that question!")

# Add to conversation history
messages = [
    SystemMessage("You are a helpful assistant"),
    HumanMessage("Can you help me?"),
    ai_msg,  # Insert as if it came from the model
    HumanMessage("Great! What's 2+2?")
]

response = model.invoke(messages)

Attributes

`text`

Type: string

The text content of the message.

`content`

Type: string | dict[]

The raw content of the message.

`content_blocks`

Type: ContentBlock[]

The standardized content blocks of the message.

`tool_calls`

Type: dict[] | None

The tool calls made by the model.

Empty if no tools are called.

`id`

Type: string

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

`usage_metadata`

Type: dict | None

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

`response_metadata`

Type: ResponseMetadata | None

The response metadata of the message.

Tool calls

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

from langchain.chat_models import init_chat_model

model = init_chat_model("gpt-5-nano")

def get_weather(location: str) -> str:
    """Get the weather at a location."""
    ...

model_with_tools = model.bind_tools([get_weather])
response = model_with_tools.invoke("What's the weather in Paris?")

for tool_call in response.tool_calls:
    print(f"Tool: {tool_call['name']}")
    print(f"Args: {tool_call['args']}")
    print(f"ID: {tool_call['id']}")

Other structured data, such as reasoning or citations, can also appear in message content.

Token usage

An AIMessage can hold token counts and other usage metadata in its usage_metadata field:

from langchain.chat_models import init_chat_model

model = init_chat_model("gpt-5-nano")

response = model.invoke("Hello!")
response.usage_metadata

{'input_tokens': 8,
 'output_tokens': 304,
 'total_tokens': 312,
 'input_token_details': {'audio': 0, 'cache_read': 0},
 'output_token_details': {'audio': 0, 'reasoning': 256}}

See UsageMetadata for details.

Streaming and chunks

During streaming, you’ll receive AIMessageChunk objects that can be combined into a full message object:

chunks = []
full_message = None
for chunk in model.stream("Hi"):
    chunks.append(chunk)
    print(chunk.text)
    full_message = chunk if full_message is None else full_message + chunk

Note
Learn more:
Streaming tokens from chat models
Streaming tokens and/or steps from agents

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.

from langchain.messages import AIMessage
from langchain.messages import ToolMessage

# After a model makes a tool call
# (Here, we demonstrate manually creating the messages for brevity)
ai_message = AIMessage(
    content=[],
    tool_calls=[{
        "name": "get_weather",
        "args": {"location": "San Francisco"},
        "id": "call_123"
    }]
)

# Execute tool and create result message
weather_result = "Sunny, 72°F"
tool_message = ToolMessage(
    content=weather_result,
    tool_call_id="call_123"  # Must match the call ID
)

# Continue conversation
messages = [
    HumanMessage("What's the weather in San Francisco?"),
    ai_message,  # Model's tool call
    tool_message,  # Tool execution result
]
response = model.invoke(messages)  # Model processes the result

Attributes

`content`

Type: string

The stringified output of the tool call.

`tool_call_id`

Type: string

The ID of the tool call that this message is responding to. Must match the ID of the tool call in the AIMessage.

`name`

Type: string

The name of the tool that was called.

`artifact`

Type: dict

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

Note
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 retrieval metadata
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:
from langchain.messages import ToolMessage

# Sent to model
message_content = "It was the best of times, it was the worst of times."

# Artifact available downstream
artifact = {"document_id": "doc_123", "page": 0}

tool_message = ToolMessage(
    content=message_content,
    tool_call_id="call_123",
    name="search_books",
    artifact=artifact,
)
See the RAG tutorial for an end-to-end example of building retrieval agents with LangChain.

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:

A string
A list of content blocks in a provider-native format
A list of LangChain’s standard content blocks

See below for an example using multimodal inputs:

from langchain.messages import HumanMessage

# String content
human_message = HumanMessage("Hello, how are you?")

# Provider-native format (e.g., OpenAI)
human_message = 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
human_message = HumanMessage(content_blocks=[
    {"type": "text", "text": "Hello, how are you?"},
    {"type": "image", "url": "https://example.com/image.jpg"},
])

Tip
Specifying content_blocks when initializing a message will still populate message content, but provides a type-safe interface for doing so.

Standard content blocks

LangChain provides a standard representation for message content that works across providers.

Message objects implement a content_blocks 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:

Anthropic

from langchain.messages import AIMessage

message = AIMessage(
    content=[
        {"type": "thinking", "thinking": "...", "signature": "WaUjzkyp..."},
        {"type": "text", "text": "..."},
    ],
    response_metadata={"model_provider": "anthropic"}
)
message.content_blocks

[{'type': 'reasoning',
  'reasoning': '...',
  'extras': {'signature': 'WaUjzkyp...'}},
 {'type': 'text', 'text': '...'}]

OpenAI

from langchain.messages import AIMessage

message = AIMessage(
    content=[
        {
            "type": "reasoning",
            "id": "rs_abc123",
            "summary": [
                {"type": "summary_text", "text": "summary 1"},
                {"type": "summary_text", "text": "summary 2"},
            ],
        },
        {"type": "text", "text": "...", "id": "msg_abc123"},
    ],
    response_metadata={"model_provider": "openai"}
)
message.content_blocks

[{'type': 'reasoning', 'id': 'rs_abc123', 'reasoning': 'summary 1'},
 {'type': 'reasoning', 'id': 'rs_abc123', 'reasoning': 'summary 2'},
 {'type': 'text', 'text': '...', 'id': 'msg_abc123'}]

See the integrations guides to get started with the inference provider of your choice.

Note
Serializing standard content
If 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 output_version="v1":
from langchain.chat_models import init_chat_model

model = init_chat_model("gpt-5-nano", output_version="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.

Note
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.

Image input

# From URL
message = {
    "role": "user",
    "content": [
        {"type": "text", "text": "Describe the content of this image."},
        {"type": "image", "url": "https://example.com/path/to/image.jpg"},
    ]
}

# From base64 data
message = {
    "role": "user",
    "content": [
        {"type": "text", "text": "Describe the content of this image."},
        {
            "type": "image",
            "base64": "AAAAIGZ0eXBtcDQyAAAAAGlzb21tcDQyAAACAGlzb2...",
            "mime_type": "image/jpeg",
        },
    ]
}

# From provider-managed File ID
message = {
    "role": "user",
    "content": [
        {"type": "text", "text": "Describe the content of this image."},
        {"type": "image", "file_id": "file-abc123"},
    ]
}

PDF document input

# From URL
message = {
    "role": "user",
    "content": [
        {"type": "text", "text": "Describe the content of this document."},
        {"type": "file", "url": "https://example.com/path/to/document.pdf"},
    ]
}

# From base64 data
message = {
    "role": "user",
    "content": [
        {"type": "text", "text": "Describe the content of this document."},
        {
            "type": "file",
            "base64": "AAAAIGZ0eXBtcDQyAAAAAGlzb21tcDQyAAACAGlzb2...",
            "mime_type": "application/pdf",
        },
    ]
}

# From provider-managed File ID
message = {
    "role": "user",
    "content": [
        {"type": "text", "text": "Describe the content of this document."},
        {"type": "file", "file_id": "file-abc123"},
    ]
}

Audio input

# From base64 data
message = {
    "role": "user",
    "content": [
        {"type": "text", "text": "Describe the content of this audio."},
        {
            "type": "audio",
            "base64": "AAAAIGZ0eXBtcDQyAAAAAGlzb21tcDQyAAACAGlzb2...",
            "mime_type": "audio/wav",
        },
    ]
}

# From provider-managed File ID
message = {
    "role": "user",
    "content": [
        {"type": "text", "text": "Describe the content of this audio."},
        {"type": "audio", "file_id": "file-abc123"},
    ]
}

Video input

# From base64 data
message = {
    "role": "user",
    "content": [
        {"type": "text", "text": "Describe the content of this video."},
        {
            "type": "video",
            "base64": "AAAAIGZ0eXBtcDQyAAAAAGlzb21tcDQyAAACAGlzb2...",
            "mime_type": "video/mp4",
        },
    ]
}

# From provider-managed File ID
message = {
    "role": "user",
    "content": [
        {"type": "text", "text": "Describe the content of this video."},
        {"type": "video", "file_id": "file-abc123"},
    ]
}

Warning
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 content_blocks property) as a list of typed dictionaries. Each item in the list must adhere to one of the following block types:

Core

TextContentBlock

            **Purpose:** Standard text output

`type`

Type: string

Always "text"

`text`

Type: string

The text content

`annotations`

Type: object[]

List of annotations for the text

`extras`

Type: object

Additional provider-specific data

            **Example:**
            ```python
            {
                "type": "text",
                "text": "Hello world",
                "annotations": []
            }
            ```

ReasoningContentBlock

            **Purpose:** Model reasoning steps

`type`

Type: string

Always "reasoning"

`reasoning`

Type: string

The reasoning content

`extras`

Type: object

Additional provider-specific data

            **Example:**
            ```python
            {
                "type": "reasoning",
                "reasoning": "The user is asking about...",
                "extras": {"signature": "abc123"},
            }
            ```

Multimodal

ImageContentBlock

            **Purpose:** Image data

`type`

Type: string

Always "image"

`url`

Type: string

URL pointing to the image location.

`base64`

Type: string

Base64-encoded image data.

`id`

Type: string

Unique identifier for this content block (either generated by the provider or by LangChain).

`mime_type`

Type: string

Image MIME type (e.g., image/jpeg, image/png). Required for base64 data.

AudioContentBlock

            **Purpose:** Audio data

`type`

Type: string

Always "audio"

`url`

Type: string

URL pointing to the audio location.

`base64`

Type: string

Base64-encoded audio data.

`id`

Type: string

Unique identifier for this content block (either generated by the provider or by LangChain).

`mime_type`

Type: string

Audio MIME type (e.g., audio/mpeg, audio/wav). Required for base64 data.

VideoContentBlock

            **Purpose:** Video data

`type`

Type: string

Always "video"

`url`

Type: string

URL pointing to the video location.

`base64`

Type: string

Base64-encoded video data.

`id`

Type: string

Unique identifier for this content block (either generated by the provider or by LangChain).

`mime_type`

Type: string

Video MIME type (e.g., video/mp4, video/webm). Required for base64 data.

FileContentBlock

            **Purpose:** Generic files (PDF, etc)

`type`

Type: string

Always "file"

`url`

Type: string

URL pointing to the file location.

`base64`

Type: string

Base64-encoded file data.

`id`

Type: string

Unique identifier for this content block (either generated by the provider or by LangChain).

`mime_type`

Type: string

File MIME type (e.g., application/pdf). Required for base64 data.

PlainTextContentBlock

            **Purpose:** Document text (`.txt`, `.md`)

`type`

Type: string

Always "text-plain"

`text`

Type: string

The text content

`mime_type`

Type: string

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

Tool Calling

ToolCall

            **Purpose:** Function calls

`type`

Type: string

Always "tool_call"

`name`

Type: string

Name of the tool to call

`args`

Type: object

Arguments to pass to the tool

`id`

Type: string

Unique identifier for this tool call

            **Example:**
            ```python
            {
                "type": "tool_call",
                "name": "search",
                "args": {"query": "weather"},
                "id": "call_123"
            }
            ```

ToolCallChunk

            **Purpose:** Streaming tool call fragments

`type`

Type: string

Always "tool_call_chunk"

`name`

Type: string

Name of the tool being called

`args`

Type: string

Partial tool arguments (may be incomplete JSON)

`id`

Type: string

Tool call identifier

`index`

Type: number | string

Position of this chunk in the stream

InvalidToolCall

            **Purpose:** Malformed calls, intended to catch JSON parsing errors.

`type`

Type: string

Always "invalid_tool_call"

`name`

Type: string

Name of the tool that failed to be called

`args`

Type: object

Arguments to pass to the tool

`error`

Type: string

Description of what went wrong

Server-Side Tool Execution

ServerToolCall

            **Purpose:** Tool call that is executed server-side.

`type`

Type: string

Always "server_tool_call"

`id`

Type: string

An identifier associated with the tool call.

`name`

Type: string

The name of the tool to be called.

`args`

Type: string

Partial tool arguments (may be incomplete JSON)

ServerToolCallChunk

            **Purpose:** Streaming server-side tool call fragments

`type`

Type: string

Always "server_tool_call_chunk"

`id`

Type: string

An identifier associated with the tool call.

`name`

Type: string

Name of the tool being called

`args`

Type: string

Partial tool arguments (may be incomplete JSON)

`index`

Type: number | string

Position of this chunk in the stream

ServerToolResult

            **Purpose:** Search results

`type`

Type: string

Always "server_tool_result"

`tool_call_id`

Type: string

Identifier of the corresponding server tool call.

`id`

Type: string

Identifier associated with the server tool result.

`status`

Type: string

Execution status of the server-side tool. "success" or "error".

`output`

Output of the executed tool.

Provider-Specific Blocks

NonStandardContentBlock

        **Purpose:** Provider-specific escape hatch

`type`

Type: string

Always "non_standard"

`value`

Type: object

Provider-specific data structure

        **Usage:** For experimental or provider-unique features

    Additional provider-specific content types may be found within the [reference documentation](https://docs.langchain.com/oss/python/integrations/providers/overview) of each model provider.

Tip
View the canonical type definitions in the API reference.

Info
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.

Serialization

You can serialize messages to plain objects for storage and deserialize back to message instances. This is useful for persisting conversation history and resuming sessions.

from langchain.messages import HumanMessage
from langchain_core.load import dumpd, load

message = HumanMessage("What is the capital of France?")

# Serialize to a plain dict
serialized = dumpd(message)

# Deserialize back to a message object
restored = load(serialized)

Warning
load() instantiates Python objects and can trigger side effects during deserialization. Never call load() on data from an untrusted or unauthenticated source.

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:

Built-in features for persisting and managing conversation histories
Strategies for managing context windows, including trimming and summarizing messages

Long-term memory Models