trim_messages

Parameters

Name	Type	Description
`messages`*	`Iterable[MessageLikeRepresentation] \| PromptValue`	Sequence of Message-like objects to trim.
`max_tokens`*	`int`	Max token count of trimmed messages.
`token_counter`*	`Callable[[list[BaseMessage]], int] \| Callable[[BaseMessage], int] \| BaseLanguageModel \| Literal['approximate']`
`strategy`	`Literal['first', 'last']`	Default:`'last'`
`allow_partial`	`bool`	Default:`False`
`end_on`	`str \| type[BaseMessage] \| Sequence[str \| type[BaseMessage]] \| None`	Default:`None`
`start_on`	`str \| type[BaseMessage] \| Sequence[str \| type[BaseMessage]] \| None`	Default:`None`
`include_system`	`bool`	Default:`False`
`text_splitter`	`Callable[[str], list[str]] \| TextSplitter \| None`	Default:`None`

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:

The resulting chat history should be valid. Most chat models expect that chat history starts with either (1) a 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.
It includes recent messages and drops old messages in the chat history. To achieve this set the strategy='last'.
Usually, the new chat history should include the 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.

Note

The examples below show how to configure trim_messages to achieve a behavior consistent with the above properties.

Example:

Trim chat history based on token count, keeping the SystemMessage if present, and ensuring that the chat history starts with a HumanMessage (or a SystemMessage followed by a HumanMessage).

from langchain_core.messages import (
    AIMessage,
    HumanMessage,
    BaseMessage,
    SystemMessage,
    trim_messages,
)

messages = [
    SystemMessage("you're a good assistant, you always respond with a joke."),
    HumanMessage("i wonder why it's called langchain"),
    AIMessage(
        'Well, I guess they thought "WordRope" and "SentenceString" just '
        "didn't have the same ring to it!"
    ),
    HumanMessage("and who is harrison chasing anyways"),
    AIMessage(
        "Hmmm let me think.\n\nWhy, he's probably chasing after the last "
        "cup of coffee in the office!"
    ),
    HumanMessage("what do you call a speechless parrot"),
]

trim_messages(
    messages,
    max_tokens=45,
    strategy="last",
    token_counter=ChatOpenAI(model="gpt-4o"),
    # Most chat models expect that chat history starts with either:
    # (1) a HumanMessage or
    # (2) a SystemMessage followed by a HumanMessage
    start_on="human",
    # Usually, we want to keep the SystemMessage
    # if it's present in the original history.
    # The SystemMessage has special instructions for the model.
    include_system=True,
    allow_partial=False,
)

[
    SystemMessage(
        content="you're a good assistant, you always respond with a joke."
    ),
    HumanMessage(content="what do you call a speechless parrot"),
]

Trim chat history using approximate token counting with 'approximate':

trim_messages(
    messages,
    max_tokens=45,
    strategy="last",
    # Using the "approximate" shortcut for fast token counting
    token_counter="approximate",
    start_on="human",
    include_system=True,
)

# This is equivalent to using `count_tokens_approximately` directly
from langchain_core.messages.utils import count_tokens_approximately

trim_messages(
    messages,
    max_tokens=45,
    strategy="last",
    token_counter=count_tokens_approximately,
    start_on="human",
    include_system=True,
)

Trim chat history based on the message count, keeping the SystemMessage if present, and ensuring that the chat history starts with a HumanMessage ( or a SystemMessage followed by a HumanMessage).

trim_messages(
    messages,
    # When `len` is passed in as the token counter function,
    # max_tokens will count the number of messages in the chat history.
    max_tokens=4,
    strategy="last",
    # Passing in `len` as a token counter function will
    # count the number of messages in the chat history.
    token_counter=len,
    # Most chat models expect that chat history starts with either:
    # (1) a HumanMessage or
    # (2) a SystemMessage followed by a HumanMessage
    start_on="human",
    # Usually, we want to keep the SystemMessage
    # if it's present in the original history.
    # The SystemMessage has special instructions for the model.
    include_system=True,
    allow_partial=False,
)

[
    SystemMessage(
        content="you're a good assistant, you always respond with a joke."
    ),
    HumanMessage(content="and who is harrison chasing anyways"),
    AIMessage(
        content="Hmmm let me think.\n\nWhy, he's probably chasing after "
        "the last cup of coffee in the office!"
    ),
    HumanMessage(content="what do you call a speechless parrot"),
]

Trim chat history using a custom token counter function that counts the number of tokens in each message.

messages = [
    SystemMessage("This is a 4 token text. The full message is 10 tokens."),
    HumanMessage(
        "This is a 4 token text. The full message is 10 tokens.", id="first"
    ),
    AIMessage(
        [
            {"type": "text", "text": "This is the FIRST 4 token block."},
            {"type": "text", "text": "This is the SECOND 4 token block."},
        ],
        id="second",
    ),
    HumanMessage(
        "This is a 4 token text. The full message is 10 tokens.", id="third"
    ),
    AIMessage(
        "This is a 4 token text. The full message is 10 tokens.",
        id="fourth",
    ),
]

def dummy_token_counter(messages: list[BaseMessage]) -> int:
    # treat each message like it adds 3 default tokens at the beginning
    # of the message and at the end of the message. 3 + 4 + 3 = 10 tokens
    # per message.

    default_content_len = 4
    default_msg_prefix_len = 3
    default_msg_suffix_len = 3

    count = 0
    for msg in messages:
        if isinstance(msg.content, str):
            count += (
                default_msg_prefix_len
                + default_content_len
                + default_msg_suffix_len
            )
        if isinstance(msg.content, list):
            count += (
                default_msg_prefix_len
                + len(msg.content) * default_content_len
                + default_msg_suffix_len
            )
    return count

First 30 tokens, allowing partial messages:

trim_messages(
    messages,
    max_tokens=30,
    token_counter=dummy_token_counter,
    strategy="first",
    allow_partial=True,
)

[
    SystemMessage("This is a 4 token text. The full message is 10 tokens."),
    HumanMessage(
        "This is a 4 token text. The full message is 10 tokens.",
        id="first",
    ),
    AIMessage(
        [{"type": "text", "text": "This is the FIRST 4 token block."}],
        id="second",
    ),
]

Function or llm for counting tokens in a BaseMessage or a list of BaseMessage.

If a BaseLanguageModel is passed in then BaseLanguageModel.get_num_tokens_from_messages() will be used. Set to len to count the number of messages in the chat history.

You can also use string shortcuts for convenience:

'approximate': Uses count_tokens_approximately for fast, approximate token counts.

Note

count_tokens_approximately (or the shortcut 'approximate') is recommended for using trim_messages on the hot path, where exact token counting is not necessary.

Strategy for trimming.

'first': Keep the first <= n_count tokens of the messages.
'last': Keep the last <= n_count tokens of the messages.

The message type to end on.

If specified then every message after the last occurrence of this type is ignored. If strategy='last' then this is done before we attempt to get the last max_tokens. If strategy='first' then this is done after we get the first max_tokens. Can be specified as string names (e.g. 'system', 'human', 'ai', ...) or as BaseMessage classes (e.g. SystemMessage, HumanMessage, AIMessage, ...). Can be a single type or a list of types.

The message type to start on.

Should only be specified if strategy='last'. If specified then every message before the first occurrence of this type is ignored. This is done after we trim the initial messages to the last max_tokens. Does not apply to a SystemMessage at index 0 if include_system=True. Can be specified as string names (e.g. 'system', 'human', 'ai', ...) or as BaseMessage classes (e.g. SystemMessage, HumanMessage, AIMessage, ...). Can be a single type or a list of types.

Function or langchain_text_splitters.TextSplitter for splitting the string contents of a message.

Only used if allow_partial=True. If strategy='last' then the last split tokens from a partial message will be included. if strategy='first' then the first split tokens from a partial message will be included. Token splitter assumes that separators are kept, so that split contents can be directly concatenated to recreate the original text. Defaults to splitting on newlines.

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