Messages are objects used in prompts and chat conversations.
LangChain auto-generated ID prefix for messages and content blocks.
Internal tracing/callback system identifier.
Used for:
A type representing any defined Message or MessageChunk type.
Import an attribute from a module located in a package.
This utility function is used in custom __getattr__ methods within __init__.py
files to dynamically import attributes.
Ensure the ID is a valid string, generating a new UUID if not provided.
Auto-generated UUIDs are prefixed by 'lc_' to indicate they are
LangChain-generated IDs.
Merge multiple message contents.
Convert a Message to a dictionary.
Convert a sequence of Messages to a list of dictionaries.
Format standard data content block to format expected by OpenAI.
"Standard data content block" can include old-style LangChain v0 blocks (URLContentBlock, Base64ContentBlock, IDContentBlock) or new ones.
Convert ImageContentBlock to format expected by OpenAI Chat Completions.
Check if the provided content block is a data content block.
Returns True for both v0 (old-style) and v1 (new-style) multimodal data blocks.
Convert a sequence of messages to a list of messages.
Convert LangChain messages into OpenAI message dicts.
Filter messages based on name, type or id.
Convert a sequence of messages to strings and concatenate them into one string.
Merge consecutive Messages of the same type.
ToolMessage objects are not merged, as each has a distinct tool call id that
can't be merged.
Convert a message chunk to a Message.
Convert a sequence of messages from dicts to Message objects.
Trim messages to be below a token count.
trim_messages can be used to reduce the size of a chat history to a specified
token or message count.
In either case, if passing the trimmed chat history back into a chat model directly, the resulting chat history should usually satisfy the following properties:
HumanMessage or (2) a SystemMessage
followed by a HumanMessage. To achieve this, set start_on='human'.
In addition, generally a ToolMessage can only appear after an AIMessage
that involved a tool call.strategy='last'.SystemMessage if it
was present in the original chat history since the SystemMessage includes
special instructions to the chat model. The SystemMessage is almost always
the first message in the history if present. To achieve this set the
include_system=True.The examples below show how to configure trim_messages to achieve a behavior
consistent with the above properties.
Message from an AI.
An AIMessage is returned from a chat model as a response to a prompt.
This message represents the output of the model and consists of both the raw output as returned by the model and standardized fields (e.g., tool calls, usage metadata) added by the LangChain framework.
Message chunk from an AI (yielded when streaming).
Breakdown of input token counts.
Does not need to sum to full input token count. Does not need to have all keys.
Breakdown of output token counts.
Does not need to sum to full output token count. Does not need to have all keys.
Usage metadata for a message, such as token counts.
This is a standard representation of token usage that is consistent across models.
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 that can be assigned an arbitrary speaker (i.e. role).
Chat Message chunk.
Audio data.
create_audio_block may also be used as a factory to create an
AudioContentBlock. Benefits include:
Annotation for citing data from a document.
start/end indices refer to the response text,
not the source text. This means that the indices are relative to the model's
response, not the original document (as specified in the url).
create_citation may also be used as a factory to create a Citation.
Benefits include:
File data that doesn't fit into other multimodal block types.
This block is intended for files that are not images, audio, or plaintext. For example, it can be used for PDFs, Word documents, etc.
If the file is an image, audio, or plaintext, you should use the corresponding
content block type (e.g., ImageContentBlock, AudioContentBlock,
PlainTextContentBlock).
create_file_block may also be used as a factory to create a
FileContentBlock. Benefits include:
Image data.
create_image_block may also be used as a factory to create an
ImageContentBlock. Benefits include:
Allowance for errors made by LLM.
Here we add an error key to surface errors made during generation
(e.g., invalid JSON arguments.)
Provider-specific annotation format.
Provider-specific content data.
This block contains data for which there is not yet a standard type.
The purpose of this block should be to simply hold a provider-specific payload.
If a provider's non-standard output includes reasoning and tool calls, it should be
the adapter's job to parse that payload and emit the corresponding standard
ReasoningContentBlock and ToolCalls.
Has no extras field, as provider-specific data should be included in the
value field.
create_non_standard_block may also be used as a factory to create a
NonStandardContentBlock. Benefits include:
Plaintext data (e.g., from a .txt or .md document).
A PlainTextContentBlock existed in langchain-core<1.0.0. Although the
name has carried over, the structure has changed significantly. The only shared
keys between the old and new versions are type and text, though the
type value has changed from 'text' to 'text-plain'.
Title and context are optional fields that may be passed to the model. See Anthropic example.
create_plaintext_block may also be used as a factory to create a
PlainTextContentBlock. Benefits include:
Reasoning output from a LLM.
create_reasoning_block may also be used as a factory to create a
ReasoningContentBlock. Benefits include:
Tool call that is executed server-side.
For example: code execution, web search, etc.
A chunk of a server-side tool call (yielded when streaming).
Result of a server-side tool call.
Text output from a LLM.
This typically represents the main text content of a message, such as the response from a language model or the text of a user message.
create_text_block may also be used as a factory to create a
TextContentBlock. Benefits include:
Video data.
create_video_block may also be used as a factory to create a
VideoContentBlock. Benefits include:
Message for passing the result of executing a tool back to a model.
FunctionMessage are an older version of the ToolMessage schema, and
do not contain the tool_call_id field.
The tool_call_id field is used to associate the tool call request with the
tool call response. Useful in situations where a chat model is able
to request multiple tool calls in parallel.
Function Message chunk.
Message from the user.
A HumanMessage is a message that is passed in from a user to the model.
Human Message chunk.
Message responsible for deleting other messages.
Message for priming AI behavior.
The system message is usually passed in as the first of a sequence of input messages.
System Message chunk.
Represents an AI's request to call a tool.
A chunk of a tool call (yielded when streaming).
When merging ToolCallChunk objects (e.g., via AIMessageChunk.__add__), all
string attributes are concatenated. Chunks are only merged if their values of
index are equal and not None.
Example:
left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)]
right_chunks = [ToolCallChunk(name=None, args="1}", index=0)]
(
AIMessageChunk(content="", tool_call_chunks=left_chunks)
+ AIMessageChunk(content="", tool_call_chunks=right_chunks)
).tool_call_chunks == [ToolCallChunk(name="foo", args='{"a":1}', index=0)]Message for passing the result of executing a tool back to a model.
ToolMessage objects contain the result of a tool invocation. Typically, the result
is encoded inside the content field.
tool_call_id is used to associate the tool call request with the tool call
response. Useful in situations where a chat model is able to request multiple tool
calls in parallel.
Tool Message chunk.
A union of all defined Annotation types.
A union of all defined ContentBlock types and aliases.
A union of all defined multimodal data ContentBlock types.
A type representing the various ways a message can be represented.
AI message.
Module contains utility functions for working with messages.
Some examples of what you can do with these functions include:
Messages for tools.
Function Message.
Chat Message.
Human message.
Message responsible for deleting other messages.
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
}
], ...
)Following widespread adoption of PEP 728, we
intend to add extra_items=Any as a param to Content Blocks. This will signify to
type checkers that additional provider-specific fields are allowed outside of the
extras field, and that will become the new standard approach to adding
provider-specific metadata.
Example with PEP 728 provider-specific fields:
# 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: AnyExample 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:
type fieldSystem message.
Base message.
Derivations of standard content blocks from provider content.
AIMessage will first attempt to use a provider-specific translator if
model_provider is set in response_metadata on the message. Consequently, each
provider translator must handle all possible content response types from the provider,
including text.
If no provider is set, or if the provider does not have a registered translator,
AIMessage will fall back to best-effort parsing of the content into blocks using
the implementation in BaseMessage.