system

Base abstract message class.

Messages are the inputs and outputs of a chat model.

Examples include HumanMessage, AIMessage, and SystemMessage.

Message chunk, which can be concatenated with other Message chunks.

Message for priming AI behavior.

The system message is usually passed in as the first of a sequence of input messages.

System Message chunk.

Standard, multimodal content blocks for Large Language Model I/O.

This module provides standardized data structures for representing inputs to and outputs from LLMs. The core abstraction is the Content Block, a TypedDict.

Rationale

Different LLM providers use distinct and incompatible API schemas. This module provides a unified, provider-agnostic format to facilitate these interactions. A message to or from a model is simply a list of content blocks, allowing for the natural interleaving of text, images, and other content in a single ordered sequence.

An adapter for a specific provider is responsible for translating this standard list of blocks into the format required by its API.

Extensibility

Data not yet mapped to a standard block may be represented using the NonStandardContentBlock, which allows for provider-specific data to be included without losing the benefits of type checking and validation.

Furthermore, provider-specific fields within a standard block are fully supported by default in the extras field of each block. This allows for additional metadata to be included without breaking the standard structure. For example, Google's thought signature:

AIMessage(
    content=[
        {
            "type": "text",
            "text": "J'adore la programmation.",
            "extras": {"signature": "EpoWCpc..."},  # Thought signature
        }
    ], ...
)

# Content block definition
# NOTE: `extra_items=Any`
class TextContentBlock(TypedDict, extra_items=Any):
    type: Literal["text"]
    id: NotRequired[str]
    text: str
    annotations: NotRequired[list[Annotation]]
    index: NotRequired[int]

from langchain_core.messages.content import TextContentBlock

# Create a text content block with provider-specific fields
my_block: TextContentBlock = {
    # Add required fields
    "type": "text",
    "text": "Hello, world!",
    # Additional fields not specified in the TypedDict
    # These are valid with PEP 728 and are typed as Any
    "openai_metadata": {"model": "gpt-4", "temperature": 0.7},
    "anthropic_usage": {"input_tokens": 10, "output_tokens": 20},
    "custom_field": "any value",
}

# Mutating an existing block to add provider-specific fields
openai_data = my_block["openai_metadata"]  # Type: Any

Example Usage

# Direct construction
from langchain_core.messages.content import TextContentBlock, ImageContentBlock

multimodal_message: AIMessage(
    content_blocks=[
        TextContentBlock(type="text", text="What is shown in this image?"),
        ImageContentBlock(
            type="image",
            url="https://www.langchain.com/images/brand/langchain_logo_text_w_white.png",
            mime_type="image/png",
        ),
    ]
)

# Using factories
from langchain_core.messages.content import create_text_block, create_image_block

multimodal_message: AIMessage(
    content=[
        create_text_block("What is shown in this image?"),
        create_image_block(
            url="https://www.langchain.com/images/brand/langchain_logo_text_w_white.png",
            mime_type="image/png",
        ),
    ]
)

Factory functions offer benefits such as:

• Automatic ID generation (when not provided)
• No need to manually specify the type field