Alpha Notice: These docs cover the v1-alpha release. Content is incomplete and subject to change.For the latest stable version, see the current LangChain Python or LangChain JavaScript docs.

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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.

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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

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

model = init_chat_model("openai: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

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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

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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. 
from langchain_core.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

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/images/vendor/openai.svg Dictionary format

You can also specify messages directly in OpenAI chat completions format. 
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage

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)

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Message types

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

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

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Human Message

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

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Text content

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

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Message metadata

human_msg = HumanMessage(
    content="Hello!",
    name="alice",  # Optional: identify different users
    id="msg_123",  # Optional: unique identifier for tracing
)
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. 
response = model.invoke("Explain AI")
print(type(response))  # <class 'langchain_core.messages.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. 
from langchain_core.messages import AIMessage, SystemMessage, HumanMessage

# Create an AI message manually (e.g., for conversation history)
ai_msg = AIMessage(
    content="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)

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AIMessage attributes

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text
string
The text content of the message.
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content
string | dict[]
The raw content of the message.
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content_blocks
ContentBlock[]
The standardized content blocks of the message. (See Content)
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tool_calls
dict[] | None
The tool calls made by the model. Empty if no tools are called.
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id
string
A unique identifier for the message (either automatically generated by LangChain or returned in the provider response)
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usage_metadata
dict | None
The usage metadata of the message, which can contain token counts when available.
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response_metadata
ResponseMetadata | None
The response metadata of the message.

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Tool calling responses

When models make tool calls, they’re included in the AI message:
Tool calling
model_with_tools = model.bind_tools([GetWeather])
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']}")

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Streaming and chunks

During streaming, you’ll receive AIMessageChunk objects that can be combined: 
chunks = []
for chunk in model.stream("Write a poem"):
    chunks.append(chunk)
    print(chunk.text)

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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. 
# After a model makes a tool call
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

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ToolMessage attributes

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content
string
required
The stringified output of the tool call.
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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)
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name
string
required
The name of the tool that was called.
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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.

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Content

You can think of a message’s content as the actual payload of data that gets sent to the model. Within a message, you can content either as a string or a list of content blocks. 
from langchain_core.messages import HumanMessage

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

# List of content blocks
human_message = HumanMessage(content_blocks=[
    {"type": "text", "text": "Hello, how are you?"},
    {"type": "image", "url": "https://example.com/image.jpg"},
])
Each provider has their own opinionated format for 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 each provider’s format.
Provider-specific format examples
# OpenAI format
openai_message = {
    "role": "user", 
    "content": [
        {"type": "text", "text": "What's in this image?"}, 
        {"type": "image_url", "image_url": {"url": "..."}}
    ]
}

# Anthropic format
anthropic_message = {
    "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 attribute. If you want to access content in a way that won’t change between providers, you can access the content_blocks attribute, which will return a list of content blocks that adhere to the standard content format.
Access content blocks
message = model.invoke("Why do parrots have different colors?")
message.content_blocks
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.
Initialize with content blocks
universal_message = HumanMessage(content_blocks=[
    {"type": "text", "text": "What's in this image?"},
    {"type": "image", "url": "..."}  # Works with any provider
])

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Examples

Multimodal message
multimodal_message = HumanMessage(content_blocks=[
    {"type": "text", "text": "Compare this image and document:"},
    {"type": "image", "url": "chart.jpg"},
    {"type": "file", "base64": pdf_data, "mime_type": "application/pdf"},
    {"type": "text", "text": "Which data source is more reliable?"}
])

response = model.invoke([multimodal_message])

import base64
from langchain_core.messages import HumanMessage

# Read and encode PDF
with open("report.pdf", "rb") as f:
    pdf_data = base64.b64encode(f.read()).decode()

message = HumanMessage(content_blocks=[
    {"type": "text", "text": "Summarize the key findings in this report"},
    {"type": "file", "base64": pdf_data, "mime_type": "application/pdf"}
])

response = model.invoke([message])
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 content_blocks attribute) as a list of typed dictionaries. Each item in the list must adhere to one of the following block types:

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Examples

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Multi-turn conversations

Building conversational applications requires managing message history and context: 
from langchain_core.messages import HumanMessage, AIMessage

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

# Simulate multi-turn conversation
while True:
    user_input = input("You: ")
    if user_input.lower() == "quit":
        break
    
    # Add user message
    messages.append(HumanMessage(user_input))
    
    # Get model response
    response = model.invoke(messages)
    
    # Add assistant response to history
    messages.append(response)
    
    print(f"Assistant: {response.content}")

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Message output versions: v0 vs v1

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. .content_blocks is a computed property that outputs standard blocks based on .content. Because of this content blocks will not be included in the output when a message gets stringified/serialized by default. If you want to serialize a message that uses standard content blocks (e.g. if you were passing messages to a client), there’s a couple of different options to instruct the model to output a message using standard content blocks: 
# Default v0 (backward compatible)
model = ChatOpenAI()

# Explicit v1
model = ChatOpenAI(output_version="v1")
When instructed, the model will output a message with .content as a list of standard content blocks (the same as if you were accessing .content_blocks). 
model = init_chat_model(model="", provider="", output_version="v1")
response = model.invoke("Analyze the sales data and show your reasoning")

response.content  # List of ContentBlock objects
response.content_blocks  # Same as above, but typed
Models can interpret both v0 and v1 content formats, which means that you can use the same model instance to invoke messages that might have different content formats.
Aspectv0v1 (Standard)
Default behaviorYesAccessible through .content_blocks
Serializationcontent is raw model outputcontent is a list of ContentBlock objects
Content formatProvider-specificStandardized ContentBlock types
Type safetyLimitedFully typed
Multimodal supportBasicComprehensive