langchain-exa

Modules:

Name	Description
retrievers
tools	Tool for the Exa Search API.

Classes:

Name	Description
ExaSearchRetriever	Exa Search retriever.
ExaFindSimilarResults	Tool that queries the Metaphor Search API and gets back json.
ExaSearchResults	Exa Search tool.

ExaSearchRetriever

Bases: BaseRetriever

Exa Search retriever.

Methods:

Name	Description
get_name	Get the name of the Runnable.
get_input_schema	Get a pydantic model that can be used to validate input to the Runnable.
get_input_jsonschema	Get a JSON schema that represents the input to the Runnable.
get_output_schema	Get a pydantic model that can be used to validate output to the Runnable.
get_output_jsonschema	Get a JSON schema that represents the output of the Runnable.
config_schema	The type of config this Runnable accepts specified as a pydantic model.
get_config_jsonschema	Get a JSON schema that represents the config of the Runnable.
get_graph	Return a graph representation of this Runnable.
get_prompts	Return a list of prompts used by this Runnable.
__or__	Runnable "or" operator.
__ror__	Runnable "reverse-or" operator.
pipe	Pipe runnables.
pick	Pick keys from the output dict of this Runnable.
assign	Assigns new fields to the dict output of this Runnable.
invoke	Invoke the retriever to get relevant documents.
ainvoke	Asynchronously invoke the retriever to get relevant documents.
batch	Default implementation runs invoke in parallel using a thread pool executor.
batch_as_completed	Run invoke in parallel on a list of inputs.
abatch	Default implementation runs ainvoke in parallel using asyncio.gather.
abatch_as_completed	Run ainvoke in parallel on a list of inputs.
stream	Default implementation of stream, which calls invoke.
astream	Default implementation of astream, which calls ainvoke.
astream_log	Stream all output from a Runnable, as reported to the callback system.
astream_events	Generate a stream of events.
transform	Transform inputs to outputs.
atransform	Transform inputs to outputs.
bind	Bind arguments to a Runnable, returning a new Runnable.
with_config	Bind config to a Runnable, returning a new Runnable.
with_listeners	Bind lifecycle listeners to a Runnable, returning a new Runnable.
with_alisteners	Bind async lifecycle listeners to a Runnable.
with_types	Bind input and output types to a Runnable, returning a new Runnable.
with_retry	Create a new Runnable that retries the original Runnable on exceptions.
map	Return a new Runnable that maps a list of inputs to a list of outputs.
with_fallbacks	Add fallbacks to a Runnable, returning a new Runnable.
as_tool	Create a BaseTool from a Runnable.
__init__
is_lc_serializable	Is this class serializable?
get_lc_namespace	Get the namespace of the langchain object.
lc_id	Return a unique identifier for this class for serialization purposes.
to_json	Serialize the Runnable to JSON.
to_json_not_implemented	Serialize a "not implemented" object.
configurable_fields	Configure particular Runnable fields at runtime.
configurable_alternatives	Configure alternatives for Runnables that can be set at runtime.
get_relevant_documents	Retrieve documents relevant to a query.
aget_relevant_documents	Asynchronously get documents relevant to a query.
validate_environment	Validate the environment.

Attributes:

Name	Type	Description
InputType	type[Input]	Input type.
OutputType	type[Output]	Output Type.
input_schema	type[BaseModel]	The type of input this Runnable accepts specified as a pydantic model.
output_schema	type[BaseModel]	Output schema.
config_specs	list[ConfigurableFieldSpec]	List configurable fields for this Runnable.
lc_secrets	dict[str, str]	A map of constructor argument names to secret ids.
lc_attributes	dict	List of attribute names that should be included in the serialized kwargs.
tags	list[str] | None	Optional list of tags associated with the retriever. Defaults to None.
metadata	dict[str, Any] | None	Optional metadata associated with the retriever. Defaults to None.
k	int	The number of search results to return (1 to 100).
include_domains	Optional[list[str]]	A list of domains to include in the search.
exclude_domains	Optional[list[str]]	A list of domains to exclude from the search.
start_crawl_date	Optional[str]	The start date for the crawl (in YYYY-MM-DD format).
end_crawl_date	Optional[str]	The end date for the crawl (in YYYY-MM-DD format).
start_published_date	Optional[str]	The start date for when the document was published (in YYYY-MM-DD format).
end_published_date	Optional[str]	The end date for when the document was published (in YYYY-MM-DD format).
use_autoprompt	Optional[bool]	Whether to use autoprompt for the search.
type	str	The type of search, 'keyword', 'neural', or 'auto'. Default: neural
highlights	Optional[Union[HighlightsContentsOptions, bool]]	Whether to set the page content to the highlights of the results.
text_contents_options	Union[TextContentsOptions, dict[str, Any], Literal[True]]	How to set the page content of the results. Can be True or a dict with options
livecrawl	Optional[Literal['always', 'fallback', 'never']]	Option to crawl live webpages if content is not in the index. Options: "always",
summary	Optional[Union[bool, dict[str, str]]]	Whether to include a summary of the content. Can be a boolean or a dict with a

InputType property

InputType: type[Input]

Input type.

The type of input this Runnable accepts specified as a type annotation.

Raises:

Type	Description
TypeError	If the input type cannot be inferred.

OutputType property

OutputType: type[Output]

Output Type.

The type of output this Runnable produces specified as a type annotation.

Raises:

Type	Description
TypeError	If the output type cannot be inferred.

input_schema property

input_schema: type[BaseModel]

The type of input this Runnable accepts specified as a pydantic model.

output_schema property

output_schema: type[BaseModel]

Output schema.

The type of output this Runnable produces specified as a pydantic model.

config_specs property

config_specs: list[ConfigurableFieldSpec]

List configurable fields for this Runnable.

lc_secrets property

lc_secrets: dict[str, str]

A map of constructor argument names to secret ids.

For example,

lc_attributes property

lc_attributes: dict

List of attribute names that should be included in the serialized kwargs.

These attributes must be accepted by the constructor. Default is an empty dictionary.

tags class-attribute instance-attribute

tags: list[str] | None = None

Optional list of tags associated with the retriever. Defaults to None. These tags will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a retriever with its use case.

metadata class-attribute instance-attribute

metadata: dict[str, Any] | None = None

Optional metadata associated with the retriever. Defaults to None. This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a retriever with its use case.

k class-attribute instance-attribute

k: int = 10

The number of search results to return (1 to 100).

include_domains class-attribute instance-attribute

include_domains: Optional[list[str]] = None

A list of domains to include in the search.

exclude_domains class-attribute instance-attribute

exclude_domains: Optional[list[str]] = None

A list of domains to exclude from the search.

start_crawl_date class-attribute instance-attribute

start_crawl_date: Optional[str] = None

The start date for the crawl (in YYYY-MM-DD format).

end_crawl_date class-attribute instance-attribute

end_crawl_date: Optional[str] = None

The end date for the crawl (in YYYY-MM-DD format).

start_published_date class-attribute instance-attribute

start_published_date: Optional[str] = None

The start date for when the document was published (in YYYY-MM-DD format).

end_published_date class-attribute instance-attribute

end_published_date: Optional[str] = None

The end date for when the document was published (in YYYY-MM-DD format).

use_autoprompt class-attribute instance-attribute

use_autoprompt: Optional[bool] = None

Whether to use autoprompt for the search.

type class-attribute instance-attribute

type: str = 'neural'

The type of search, 'keyword', 'neural', or 'auto'. Default: neural

highlights class-attribute instance-attribute

highlights: Optional[
    Union[HighlightsContentsOptions, bool]
] = None

Whether to set the page content to the highlights of the results.

text_contents_options class-attribute instance-attribute

text_contents_options: Union[
    TextContentsOptions, dict[str, Any], Literal[True]
] = True

How to set the page content of the results. Can be True or a dict with options like max_characters.

livecrawl class-attribute instance-attribute

livecrawl: Optional[
    Literal["always", "fallback", "never"]
] = None

Option to crawl live webpages if content is not in the index. Options: "always", "fallback", "never".

summary class-attribute instance-attribute

summary: Optional[Union[bool, dict[str, str]]] = None

Whether to include a summary of the content. Can be a boolean or a dict with a custom query.

get_name

get_name(
    suffix: str | None = None, *, name: str | None = None
) -> str

Get the name of the Runnable.

Parameters:

Name	Type	Description	Default
suffix	str | None	An optional suffix to append to the name.	None
name	str | None	An optional name to use instead of the Runnable's name.	None

Returns:

Type	Description
str	The name of the Runnable.

get_input_schema

get_input_schema(
    config: RunnableConfig | None = None,
) -> type[BaseModel]

Get a pydantic model that can be used to validate input to the Runnable.

Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic input schema that depends on which configuration the Runnable is invoked with.

This method allows to get an input schema for a specific configuration.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	A config to use when generating the schema.	None

Returns:

Type	Description
type[BaseModel]	A pydantic model that can be used to validate input.

get_input_jsonschema

get_input_jsonschema(
    config: RunnableConfig | None = None,
) -> dict[str, Any]

Get a JSON schema that represents the input to the Runnable.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	A config to use when generating the schema.	None

Returns:

Type	Description
dict[str, Any]	A JSON schema that represents the input to the Runnable.

Example

from langchain_core.runnables import RunnableLambda


def add_one(x: int) -> int:
    return x + 1


runnable = RunnableLambda(add_one)

print(runnable.get_input_jsonschema())

Added in version 0.3.0

get_output_schema

get_output_schema(
    config: RunnableConfig | None = None,
) -> type[BaseModel]

Get a pydantic model that can be used to validate output to the Runnable.

Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic output schema that depends on which configuration the Runnable is invoked with.

This method allows to get an output schema for a specific configuration.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	A config to use when generating the schema.	None

Returns:

Type	Description
type[BaseModel]	A pydantic model that can be used to validate output.

get_output_jsonschema

get_output_jsonschema(
    config: RunnableConfig | None = None,
) -> dict[str, Any]

Get a JSON schema that represents the output of the Runnable.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	A config to use when generating the schema.	None

Returns:

Type	Description
dict[str, Any]	A JSON schema that represents the output of the Runnable.

Example

from langchain_core.runnables import RunnableLambda


def add_one(x: int) -> int:
    return x + 1


runnable = RunnableLambda(add_one)

print(runnable.get_output_jsonschema())

Added in version 0.3.0

config_schema

config_schema(
    *, include: Sequence[str] | None = None
) -> type[BaseModel]

The type of config this Runnable accepts specified as a pydantic model.

To mark a field as configurable, see the configurable_fields and configurable_alternatives methods.

Parameters:

Name	Type	Description	Default
include	Sequence[str] | None	A list of fields to include in the config schema.	None

Returns:

Type	Description
type[BaseModel]	A pydantic model that can be used to validate config.

get_config_jsonschema

get_config_jsonschema(
    *, include: Sequence[str] | None = None
) -> dict[str, Any]

Get a JSON schema that represents the config of the Runnable.

Parameters:

Name	Type	Description	Default
include	Sequence[str] | None	A list of fields to include in the config schema.	None

Returns:

Type	Description
dict[str, Any]	A JSON schema that represents the config of the Runnable.

Added in version 0.3.0

get_graph

get_graph(config: RunnableConfig | None = None) -> Graph

Return a graph representation of this Runnable.

get_prompts

get_prompts(
    config: RunnableConfig | None = None,
) -> list[BasePromptTemplate]

Return a list of prompts used by this Runnable.

__or__

__or__(
    other: (
        Runnable[Any, Other]
        | Callable[[Iterator[Any]], Iterator[Other]]
        | Callable[
            [AsyncIterator[Any]], AsyncIterator[Other]
        ]
        | Callable[[Any], Other]
        | Mapping[
            str,
            Runnable[Any, Other]
            | Callable[[Any], Other]
            | Any,
        ]
    ),
) -> RunnableSerializable[Input, Other]

Runnable "or" operator.

Compose this Runnable with another object to create a RunnableSequence.

Parameters:

Name	Type	Description	Default
other	Runnable[Any, Other] | Callable[[Iterator[Any]], Iterator[Other]] | Callable[[AsyncIterator[Any]], AsyncIterator[Other]] | Callable[[Any], Other] | Mapping[str, Runnable[Any, Other] | Callable[[Any], Other] | Any]	Another Runnable or a Runnable-like object.	required

Returns:

Type	Description
RunnableSerializable[Input, Other]	A new Runnable.

__ror__

__ror__(
    other: (
        Runnable[Other, Any]
        | Callable[[Iterator[Other]], Iterator[Any]]
        | Callable[
            [AsyncIterator[Other]], AsyncIterator[Any]
        ]
        | Callable[[Other], Any]
        | Mapping[
            str,
            Runnable[Other, Any]
            | Callable[[Other], Any]
            | Any,
        ]
    ),
) -> RunnableSerializable[Other, Output]

Runnable "reverse-or" operator.

Compose this Runnable with another object to create a RunnableSequence.

Parameters:

Name	Type	Description	Default
other	Runnable[Other, Any] | Callable[[Iterator[Other]], Iterator[Any]] | Callable[[AsyncIterator[Other]], AsyncIterator[Any]] | Callable[[Other], Any] | Mapping[str, Runnable[Other, Any] | Callable[[Other], Any] | Any]	Another Runnable or a Runnable-like object.	required

Returns:

Type	Description
RunnableSerializable[Other, Output]	A new Runnable.

pipe

pipe(
    *others: Runnable[Any, Other] | Callable[[Any], Other],
    name: str | None = None
) -> RunnableSerializable[Input, Other]

Pipe runnables.

Compose this Runnable with Runnable-like objects to make a RunnableSequence.

Equivalent to RunnableSequence(self, *others) or self | others[0] | ...

Example

from langchain_core.runnables import RunnableLambda


def add_one(x: int) -> int:
    return x + 1


def mul_two(x: int) -> int:
    return x * 2


runnable_1 = RunnableLambda(add_one)
runnable_2 = RunnableLambda(mul_two)
sequence = runnable_1.pipe(runnable_2)
# Or equivalently:
# sequence = runnable_1 | runnable_2
# sequence = RunnableSequence(first=runnable_1, last=runnable_2)
sequence.invoke(1)
await sequence.ainvoke(1)
# -> 4

sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
# -> [4, 6, 8]

Parameters:

Name	Type	Description	Default
*others	Runnable[Any, Other] | Callable[[Any], Other]	Other Runnable or Runnable-like objects to compose	()
name	str | None	An optional name for the resulting RunnableSequence.	None

Returns:

Type	Description
RunnableSerializable[Input, Other]	A new Runnable.

pick

pick(
    keys: str | list[str],
) -> RunnableSerializable[Any, Any]

Pick keys from the output dict of this Runnable.

Pick single key:

```python
import json

from langchain_core.runnables import RunnableLambda, RunnableMap

as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
chain = RunnableMap(str=as_str, json=as_json)

chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}

json_only_chain = chain.pick("json")
json_only_chain.invoke("[1, 2, 3]")
# -> [1, 2, 3]
```

Pick list of keys:

```python
from typing import Any

import json

from langchain_core.runnables import RunnableLambda, RunnableMap

as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)


def as_bytes(x: Any) -> bytes:
    return bytes(x, "utf-8")


chain = RunnableMap(
    str=as_str, json=as_json, bytes=RunnableLambda(as_bytes)
)

chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}

json_and_bytes_chain = chain.pick(["json", "bytes"])
json_and_bytes_chain.invoke("[1, 2, 3]")
# -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
```

Parameters:

Name	Type	Description	Default
keys	str | list[str]	A key or list of keys to pick from the output dict.	required

Returns:

Type	Description
RunnableSerializable[Any, Any]	a new Runnable.

assign

assign(
    **kwargs: (
        Runnable[dict[str, Any], Any]
        | Callable[[dict[str, Any]], Any]
        | Mapping[
            str,
            Runnable[dict[str, Any], Any]
            | Callable[[dict[str, Any]], Any],
        ]
    ),
) -> RunnableSerializable[Any, Any]

Assigns new fields to the dict output of this Runnable.

from langchain_community.llms.fake import FakeStreamingListLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import SystemMessagePromptTemplate
from langchain_core.runnables import Runnable
from operator import itemgetter

prompt = (
    SystemMessagePromptTemplate.from_template("You are a nice assistant.")
    + "{question}"
)
llm = FakeStreamingListLLM(responses=["foo-lish"])

chain: Runnable = prompt | llm | {"str": StrOutputParser()}

chain_with_assign = chain.assign(hello=itemgetter("str") | llm)

print(chain_with_assign.input_schema.model_json_schema())
# {'title': 'PromptInput', 'type': 'object', 'properties':
{'question': {'title': 'Question', 'type': 'string'}}}
print(chain_with_assign.output_schema.model_json_schema())
# {'title': 'RunnableSequenceOutput', 'type': 'object', 'properties':
{'str': {'title': 'Str',
'type': 'string'}, 'hello': {'title': 'Hello', 'type': 'string'}}}

Parameters:

Name	Type	Description	Default
**kwargs	Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any] | Mapping[str, Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any]]	A mapping of keys to Runnable or Runnable-like objects that will be invoked with the entire output dict of this Runnable.	{}

Returns:

Type	Description
RunnableSerializable[Any, Any]	A new Runnable.

invoke

invoke(
    input: str,
    config: RunnableConfig | None = None,
    **kwargs: Any
) -> list[Document]

Invoke the retriever to get relevant documents.

Main entry point for synchronous retriever invocations.

Parameters:

Name	Type	Description	Default
input	str	The query string.	required
config	RunnableConfig | None	Configuration for the retriever. Defaults to None.	None
kwargs	Any	Additional arguments to pass to the retriever.	{}

Returns:

Type	Description
list[Document]	List of relevant documents.

Examples:

.. code-block:: python

retriever.invoke("query")

ainvoke async

ainvoke(
    input: str,
    config: RunnableConfig | None = None,
    **kwargs: Any
) -> list[Document]

Asynchronously invoke the retriever to get relevant documents.

Main entry point for asynchronous retriever invocations.

Parameters:

Name	Type	Description	Default
input	str	The query string.	required
config	RunnableConfig | None	Configuration for the retriever. Defaults to None.	None
kwargs	Any	Additional arguments to pass to the retriever.	{}

Returns:

Type	Description
list[Document]	List of relevant documents.

Examples:

.. code-block:: python

await retriever.ainvoke("query")

batch

batch(
    inputs: list[Input],
    config: (
        RunnableConfig | list[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> list[Output]

Default implementation runs invoke in parallel using a thread pool executor.

The default implementation of batch works well for IO bound runnables.

Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

Parameters:

Name	Type	Description	Default
inputs	list[Input]	A list of inputs to the Runnable.	required
config	RunnableConfig | list[RunnableConfig] | None	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.	None
return_exceptions	bool	Whether to return exceptions instead of raising them. Defaults to False.	False
**kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Returns:

Type	Description
list[Output]	A list of outputs from the Runnable.

batch_as_completed

batch_as_completed(
    inputs: Sequence[Input],
    config: (
        RunnableConfig | Sequence[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> Iterator[tuple[int, Output | Exception]]

Run invoke in parallel on a list of inputs.

Yields results as they complete.

Parameters:

Name	Type	Description	Default
inputs	Sequence[Input]	A list of inputs to the Runnable.	required
config	RunnableConfig | Sequence[RunnableConfig] | None	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.	None
return_exceptions	bool	Whether to return exceptions instead of raising them. Defaults to False.	False
**kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
tuple[int, Output | Exception]	Tuples of the index of the input and the output from the Runnable.

abatch async

abatch(
    inputs: list[Input],
    config: (
        RunnableConfig | list[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> list[Output]

Default implementation runs ainvoke in parallel using asyncio.gather.

The default implementation of batch works well for IO bound runnables.

Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

Parameters:

Name	Type	Description	Default
inputs	list[Input]	A list of inputs to the Runnable.	required
config	RunnableConfig | list[RunnableConfig] | None	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.	None
return_exceptions	bool	Whether to return exceptions instead of raising them. Defaults to False.	False
**kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Returns:

Type	Description
list[Output]	A list of outputs from the Runnable.

abatch_as_completed async

abatch_as_completed(
    inputs: Sequence[Input],
    config: (
        RunnableConfig | Sequence[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> AsyncIterator[tuple[int, Output | Exception]]

Run ainvoke in parallel on a list of inputs.

Yields results as they complete.

Parameters:

Name	Type	Description	Default
inputs	Sequence[Input]	A list of inputs to the Runnable.	required
config	RunnableConfig | Sequence[RunnableConfig] | None	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.	None
return_exceptions	bool	Whether to return exceptions instead of raising them. Defaults to False.	False
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
AsyncIterator[tuple[int, Output | Exception]]	A tuple of the index of the input and the output from the Runnable.

stream

stream(
    input: Input,
    config: RunnableConfig | None = None,
    **kwargs: Any | None
) -> Iterator[Output]

Default implementation of stream, which calls invoke.

Subclasses should override this method if they support streaming output.

Parameters:

Name	Type	Description	Default
input	Input	The input to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable. Defaults to None.	None
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
Output	The output of the Runnable.

astream async

astream(
    input: Input,
    config: RunnableConfig | None = None,
    **kwargs: Any | None
) -> AsyncIterator[Output]

Default implementation of astream, which calls ainvoke.

Subclasses should override this method if they support streaming output.

Parameters:

Name	Type	Description	Default
input	Input	The input to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable. Defaults to None.	None
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
AsyncIterator[Output]	The output of the Runnable.

astream_log async

astream_log(
    input: Any,
    config: RunnableConfig | None = None,
    *,
    diff: bool = True,
    with_streamed_output_list: bool = True,
    include_names: Sequence[str] | None = None,
    include_types: Sequence[str] | None = None,
    include_tags: Sequence[str] | None = None,
    exclude_names: Sequence[str] | None = None,
    exclude_types: Sequence[str] | None = None,
    exclude_tags: Sequence[str] | None = None,
    **kwargs: Any
) -> AsyncIterator[RunLogPatch] | AsyncIterator[RunLog]

Stream all output from a Runnable, as reported to the callback system.

This includes all inner runs of LLMs, Retrievers, Tools, etc.

Output is streamed as Log objects, which include a list of Jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run.

The Jsonpatch ops can be applied in order to construct state.

Parameters:

Name	Type	Description	Default
input	Any	The input to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable.	None
diff	bool	Whether to yield diffs between each step or the current state.	True
with_streamed_output_list	bool	Whether to yield the streamed_output list.	True
include_names	Sequence[str] | None	Only include logs with these names.	None
include_types	Sequence[str] | None	Only include logs with these types.	None
include_tags	Sequence[str] | None	Only include logs with these tags.	None
exclude_names	Sequence[str] | None	Exclude logs with these names.	None
exclude_types	Sequence[str] | None	Exclude logs with these types.	None
exclude_tags	Sequence[str] | None	Exclude logs with these tags.	None
kwargs	Any	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
AsyncIterator[RunLogPatch] | AsyncIterator[RunLog]	A RunLogPatch or RunLog object.

astream_events async

astream_events(
    input: Any,
    config: RunnableConfig | None = None,
    *,
    version: Literal["v1", "v2"] = "v2",
    include_names: Sequence[str] | None = None,
    include_types: Sequence[str] | None = None,
    include_tags: Sequence[str] | None = None,
    exclude_names: Sequence[str] | None = None,
    exclude_types: Sequence[str] | None = None,
    exclude_tags: Sequence[str] | None = None,
    **kwargs: Any
) -> AsyncIterator[StreamEvent]

Generate a stream of events.

Use to create an iterator over StreamEvents that provide real-time information about the progress of the Runnable, including StreamEvents from intermediate results.

A StreamEvent is a dictionary with the following schema:

• event: str - Event names are of the format: on_[runnable_type]_(start|stream|end).
• name: str - The name of the Runnable that generated the event.
• run_id: str - randomly generated ID associated with the given execution of the Runnable that emitted the event. A child Runnable that gets invoked as part of the execution of a parent Runnable is assigned its own unique ID.
• parent_ids: list[str] - The IDs of the parent runnables that generated the event. The root Runnable will have an empty list. The order of the parent IDs is from the root to the immediate parent. Only available for v2 version of the API. The v1 version of the API will return an empty list.
• tags: Optional[list[str]] - The tags of the Runnable that generated the event.
• metadata: Optional[dict[str, Any]] - The metadata of the Runnable that generated the event.
• data: dict[str, Any]

Below is a table that illustrates some events that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.

Note

This reference table is for the v2 version of the schema.

+--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | event | name | chunk | input | output | +==========================+==================+=====================================+===================================================+=====================================================+ | on_chat_model_start | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chat_model_stream | [model name] | AIMessageChunk(content="hello") | | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chat_model_end | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | AIMessageChunk(content="hello world") | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_llm_start | [model name] | | {'input': 'hello'} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_llm_stream | [model name] | 'Hello' | | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_llm_end | [model name] | | 'Hello human!' | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chain_start | format_docs | | | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chain_stream | format_docs | 'hello world!, goodbye world!' | | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chain_end | format_docs | | [Document(...)] | 'hello world!, goodbye world!' | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_tool_start | some_tool | | {"x": 1, "y": "2"} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_tool_end | some_tool | | | {"x": 1, "y": "2"} | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_retriever_start | [retriever name] | | {"query": "hello"} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_retriever_end | [retriever name] | | {"query": "hello"} | [Document(...), ..] | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_prompt_start | [template_name] | | {"question": "hello"} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_prompt_end | [template_name] | | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+

In addition to the standard events, users can also dispatch custom events (see example below).

Custom events will be only be surfaced with in the v2 version of the API!

A custom event has following format:

+-----------+------+-----------------------------------------------------------------------------------------------------------+ | Attribute | Type | Description | +===========+======+===========================================================================================================+ | name | str | A user defined name for the event. | +-----------+------+-----------------------------------------------------------------------------------------------------------+ | data | Any | The data associated with the event. This can be anything, though we suggest making it JSON serializable. | +-----------+------+-----------------------------------------------------------------------------------------------------------+

Here are declarations associated with the standard events shown above:

format_docs:

def format_docs(docs: list[Document]) -> str:
    '''Format the docs.'''
    return ", ".join([doc.page_content for doc in docs])


format_docs = RunnableLambda(format_docs)

some_tool:

@tool
def some_tool(x: int, y: str) -> dict:
    '''Some_tool.'''
    return {"x": x, "y": y}

prompt:

template = ChatPromptTemplate.from_messages(
    [
        ("system", "You are Cat Agent 007"),
        ("human", "{question}"),
    ]
).with_config({"run_name": "my_template", "tags": ["my_template"]})

Example:

from langchain_core.runnables import RunnableLambda


async def reverse(s: str) -> str:
    return s[::-1]


chain = RunnableLambda(func=reverse)

events = [event async for event in chain.astream_events("hello", version="v2")]

# will produce the following events (run_id, and parent_ids
# has been omitted for brevity):
[
    {
        "data": {"input": "hello"},
        "event": "on_chain_start",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"chunk": "olleh"},
        "event": "on_chain_stream",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"output": "olleh"},
        "event": "on_chain_end",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
]

Example: Dispatch Custom Event

from langchain_core.callbacks.manager import (
    adispatch_custom_event,
)
from langchain_core.runnables import RunnableLambda, RunnableConfig
import asyncio


async def slow_thing(some_input: str, config: RunnableConfig) -> str:
    """Do something that takes a long time."""
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 1 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 2 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    return "Done"

slow_thing = RunnableLambda(slow_thing)

async for event in slow_thing.astream_events("some_input", version="v2"):
    print(event)

Parameters:

Name	Type	Description	Default
input	Any	The input to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable.	None
version	Literal['v1', 'v2']	The version of the schema to use either 'v2' or 'v1'. Users should use 'v2'. 'v1' is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in 'v2'.	'v2'
include_names	Sequence[str] | None	Only include events from Runnables with matching names.	None
include_types	Sequence[str] | None	Only include events from Runnables with matching types.	None
include_tags	Sequence[str] | None	Only include events from Runnables with matching tags.	None
exclude_names	Sequence[str] | None	Exclude events from Runnables with matching names.	None
exclude_types	Sequence[str] | None	Exclude events from Runnables with matching types.	None
exclude_tags	Sequence[str] | None	Exclude events from Runnables with matching tags.	None
kwargs	Any	Additional keyword arguments to pass to the Runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.	{}

Yields:

Type	Description
AsyncIterator[StreamEvent]	An async stream of StreamEvents.

Raises:

Type	Description
NotImplementedError	If the version is not 'v1' or 'v2'.

transform

transform(
    input: Iterator[Input],
    config: RunnableConfig | None = None,
    **kwargs: Any | None
) -> Iterator[Output]

Transform inputs to outputs.

Default implementation of transform, which buffers input and calls astream.

Subclasses should override this method if they can start producing output while input is still being generated.

Parameters:

Name	Type	Description	Default
input	Iterator[Input]	An iterator of inputs to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable. Defaults to None.	None
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
Output	The output of the Runnable.

atransform async

atransform(
    input: AsyncIterator[Input],
    config: RunnableConfig | None = None,
    **kwargs: Any | None
) -> AsyncIterator[Output]

Transform inputs to outputs.

Default implementation of atransform, which buffers input and calls astream.

Subclasses should override this method if they can start producing output while input is still being generated.

Parameters:

Name	Type	Description	Default
input	AsyncIterator[Input]	An async iterator of inputs to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable. Defaults to None.	None
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
AsyncIterator[Output]	The output of the Runnable.

bind

bind(**kwargs: Any) -> Runnable[Input, Output]

Bind arguments to a Runnable, returning a new Runnable.

Useful when a Runnable in a chain requires an argument that is not in the output of the previous Runnable or included in the user input.

Parameters:

Name	Type	Description	Default
kwargs	Any	The arguments to bind to the Runnable.	{}

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the arguments bound.

Example

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

llm = ChatOllama(model="llama3.1")

# Without bind.
chain = llm | StrOutputParser()

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.'

# With bind.
chain = llm.bind(stop=["three"]) | StrOutputParser()

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'

with_config

with_config(
    config: RunnableConfig | None = None, **kwargs: Any
) -> Runnable[Input, Output]

Bind config to a Runnable, returning a new Runnable.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	The config to bind to the Runnable.	None
kwargs	Any	Additional keyword arguments to pass to the Runnable.	{}

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the config bound.

with_listeners

with_listeners(
    *,
    on_start: (
        Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None
    ) = None,
    on_end: (
        Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None
    ) = None,
    on_error: (
        Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None
    ) = None
) -> Runnable[Input, Output]

Bind lifecycle listeners to a Runnable, returning a new Runnable.

The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.

Parameters:

Name	Type	Description	Default
on_start	Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None	Called before the Runnable starts running, with the Run object. Defaults to None.	None
on_end	Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None	Called after the Runnable finishes running, with the Run object. Defaults to None.	None
on_error	Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None	Called if the Runnable throws an error, with the Run object. Defaults to None.	None

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the listeners bound.

Example

from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run

import time


def test_runnable(time_to_sleep: int):
    time.sleep(time_to_sleep)


def fn_start(run_obj: Run):
    print("start_time:", run_obj.start_time)


def fn_end(run_obj: Run):
    print("end_time:", run_obj.end_time)


chain = RunnableLambda(test_runnable).with_listeners(
    on_start=fn_start, on_end=fn_end
)
chain.invoke(2)

with_alisteners

with_alisteners(
    *,
    on_start: AsyncListener | None = None,
    on_end: AsyncListener | None = None,
    on_error: AsyncListener | None = None
) -> Runnable[Input, Output]

Bind async lifecycle listeners to a Runnable.

Returns a new Runnable.

The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.

Parameters:

Name	Type	Description	Default
on_start	AsyncListener | None	Called asynchronously before the Runnable starts running, with the Run object. Defaults to None.	None
on_end	AsyncListener | None	Called asynchronously after the Runnable finishes running, with the Run object. Defaults to None.	None
on_error	AsyncListener | None	Called asynchronously if the Runnable throws an error, with the Run object. Defaults to None.	None

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the listeners bound.

Example

from langchain_core.runnables import RunnableLambda, Runnable
from datetime import datetime, timezone
import time
import asyncio

def format_t(timestamp: float) -> str:
    return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()

async def test_runnable(time_to_sleep: int):
    print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
    await asyncio.sleep(time_to_sleep)
    print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")

async def fn_start(run_obj: Runnable):
    print(f"on start callback starts at {format_t(time.time())}")
    await asyncio.sleep(3)
    print(f"on start callback ends at {format_t(time.time())}")

async def fn_end(run_obj: Runnable):
    print(f"on end callback starts at {format_t(time.time())}")
    await asyncio.sleep(2)
    print(f"on end callback ends at {format_t(time.time())}")

runnable = RunnableLambda(test_runnable).with_alisteners(
    on_start=fn_start,
    on_end=fn_end
)
async def concurrent_runs():
    await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))

asyncio.run(concurrent_runs())
Result:
on start callback starts at 2025-03-01T07:05:22.875378+00:00
on start callback starts at 2025-03-01T07:05:22.875495+00:00
on start callback ends at 2025-03-01T07:05:25.878862+00:00
on start callback ends at 2025-03-01T07:05:25.878947+00:00
Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
on end callback starts at 2025-03-01T07:05:27.882360+00:00
Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
on end callback starts at 2025-03-01T07:05:28.882428+00:00
on end callback ends at 2025-03-01T07:05:29.883893+00:00
on end callback ends at 2025-03-01T07:05:30.884831+00:00

with_types

with_types(
    *,
    input_type: type[Input] | None = None,
    output_type: type[Output] | None = None
) -> Runnable[Input, Output]

Bind input and output types to a Runnable, returning a new Runnable.

Parameters:

Name	Type	Description	Default
input_type	type[Input] | None	The input type to bind to the Runnable. Defaults to None.	None
output_type	type[Output] | None	The output type to bind to the Runnable. Defaults to None.	None

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the types bound.

with_retry

with_retry(
    *,
    retry_if_exception_type: tuple[
        type[BaseException], ...
    ] = (Exception,),
    wait_exponential_jitter: bool = True,
    exponential_jitter_params: (
        ExponentialJitterParams | None
    ) = None,
    stop_after_attempt: int = 3
) -> Runnable[Input, Output]

Create a new Runnable that retries the original Runnable on exceptions.

Parameters:

Name	Type	Description	Default
retry_if_exception_type	tuple[type[BaseException], ...]	A tuple of exception types to retry on. Defaults to (Exception,).	(Exception,)
wait_exponential_jitter	bool	Whether to add jitter to the wait time between retries. Defaults to True.	True
stop_after_attempt	int	The maximum number of attempts to make before giving up. Defaults to 3.	3
exponential_jitter_params	ExponentialJitterParams | None	Parameters for tenacity.wait_exponential_jitter. Namely: initial, max, exp_base, and jitter (all float values).	None

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable that retries the original Runnable on exceptions.

Example

from langchain_core.runnables import RunnableLambda

count = 0


def _lambda(x: int) -> None:
    global count
    count = count + 1
    if x == 1:
        raise ValueError("x is 1")
    else:
        pass


runnable = RunnableLambda(_lambda)
try:
    runnable.with_retry(
        stop_after_attempt=2,
        retry_if_exception_type=(ValueError,),
    ).invoke(1)
except ValueError:
    pass

assert count == 2

map

map() -> Runnable[list[Input], list[Output]]

Return a new Runnable that maps a list of inputs to a list of outputs.

Calls invoke with each input.

Returns:

Type	Description
Runnable[list[Input], list[Output]]	A new Runnable that maps a list of inputs to a list of outputs.

Example

from langchain_core.runnables import RunnableLambda


def _lambda(x: int) -> int:
    return x + 1


runnable = RunnableLambda(_lambda)
print(runnable.map().invoke([1, 2, 3]))  # [2, 3, 4]

with_fallbacks

with_fallbacks(
    fallbacks: Sequence[Runnable[Input, Output]],
    *,
    exceptions_to_handle: tuple[
        type[BaseException], ...
    ] = (Exception,),
    exception_key: str | None = None
) -> RunnableWithFallbacks[Input, Output]

Add fallbacks to a Runnable, returning a new Runnable.

The new Runnable will try the original Runnable, and then each fallback in order, upon failures.

Parameters:

Name	Type	Description	Default
fallbacks	Sequence[Runnable[Input, Output]]	A sequence of runnables to try if the original Runnable fails.	required
exceptions_to_handle	tuple[type[BaseException], ...]	A tuple of exception types to handle. Defaults to (Exception,).	(Exception,)
exception_key	str | None	If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input. Defaults to None.	None

Returns:

Type	Description
RunnableWithFallbacks[Input, Output]	A new Runnable that will try the original Runnable, and then each
RunnableWithFallbacks[Input, Output]	fallback in order, upon failures.

Example

from typing import Iterator

from langchain_core.runnables import RunnableGenerator


def _generate_immediate_error(input: Iterator) -> Iterator[str]:
    raise ValueError()
    yield ""


def _generate(input: Iterator) -> Iterator[str]:
    yield from "foo bar"


runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
    [RunnableGenerator(_generate)]
)
print("".join(runnable.stream({})))  # foo bar

Parameters:

Name	Type	Description	Default
fallbacks	Sequence[Runnable[Input, Output]]	A sequence of runnables to try if the original Runnable fails.	required
exceptions_to_handle	tuple[type[BaseException], ...]	A tuple of exception types to handle.	(Exception,)
exception_key	str | None	If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input.	None

Returns:

Type	Description
RunnableWithFallbacks[Input, Output]	A new Runnable that will try the original Runnable, and then each
RunnableWithFallbacks[Input, Output]	fallback in order, upon failures.

as_tool

as_tool(
    args_schema: type[BaseModel] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    arg_types: dict[str, type] | None = None
) -> BaseTool

Create a BaseTool from a Runnable.

as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. Where possible, schemas are inferred from runnable.get_input_schema. Alternatively (e.g., if the Runnable takes a dict as input and the specific dict keys are not typed), the schema can be specified directly with args_schema. You can also pass arg_types to just specify the required arguments and their types.

Parameters:

Name	Type	Description	Default
args_schema	type[BaseModel] | None	The schema for the tool. Defaults to None.	None
name	str | None	The name of the tool. Defaults to None.	None
description	str | None	The description of the tool. Defaults to None.	None
arg_types	dict[str, type] | None	A dictionary of argument names to types. Defaults to None.	None

Returns:

Type	Description
BaseTool	A BaseTool instance.

Typed dict input:

from typing_extensions import TypedDict
from langchain_core.runnables import RunnableLambda


class Args(TypedDict):
    a: int
    b: list[int]


def f(x: Args) -> str:
    return str(x["a"] * max(x["b"]))


runnable = RunnableLambda(f)
as_tool = runnable.as_tool()
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via args_schema:

from typing import Any
from pydantic import BaseModel, Field
from langchain_core.runnables import RunnableLambda

def f(x: dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))

class FSchema(BaseModel):
    """Apply a function to an integer and list of integers."""

    a: int = Field(..., description="Integer")
    b: list[int] = Field(..., description="List of ints")

runnable = RunnableLambda(f)
as_tool = runnable.as_tool(FSchema)
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via arg_types:

from typing import Any
from langchain_core.runnables import RunnableLambda


def f(x: dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))


runnable = RunnableLambda(f)
as_tool = runnable.as_tool(arg_types={"a": int, "b": list[int]})
as_tool.invoke({"a": 3, "b": [1, 2]})

String input:

from langchain_core.runnables import RunnableLambda


def f(x: str) -> str:
    return x + "a"


def g(x: str) -> str:
    return x + "z"


runnable = RunnableLambda(f) | g
as_tool = runnable.as_tool()
as_tool.invoke("b")

Added in version 0.2.14

__init__

__init__(*args: Any, **kwargs: Any) -> None

is_lc_serializable classmethod

is_lc_serializable() -> bool

Is this class serializable?

By design, even if a class inherits from Serializable, it is not serializable by default. This is to prevent accidental serialization of objects that should not be serialized.

Returns:

Type	Description
bool	Whether the class is serializable. Default is False.

get_lc_namespace classmethod

get_lc_namespace() -> list[str]

Get the namespace of the langchain object.

For example, if the class is langchain.llms.openai.OpenAI, then the namespace is ["langchain", "llms", "openai"]

Returns:

Type	Description
list[str]	The namespace as a list of strings.

lc_id classmethod

lc_id() -> list[str]

Return a unique identifier for this class for serialization purposes.

The unique identifier is a list of strings that describes the path to the object. For example, for the class langchain.llms.openai.OpenAI, the id is ["langchain", "llms", "openai", "OpenAI"].

to_json

to_json() -> (
    SerializedConstructor | SerializedNotImplemented
)

Serialize the Runnable to JSON.

Returns:

Type	Description
SerializedConstructor | SerializedNotImplemented	A JSON-serializable representation of the Runnable.

to_json_not_implemented

to_json_not_implemented() -> SerializedNotImplemented

Serialize a "not implemented" object.

Returns:

Type	Description
SerializedNotImplemented	SerializedNotImplemented.

configurable_fields

configurable_fields(
    **kwargs: AnyConfigurableField,
) -> RunnableSerializable[Input, Output]

Configure particular Runnable fields at runtime.

Parameters:

Name	Type	Description	Default
**kwargs	AnyConfigurableField	A dictionary of ConfigurableField instances to configure.	{}

Raises:

Type	Description
ValueError	If a configuration key is not found in the Runnable.

Returns:

Type	Description
RunnableSerializable[Input, Output]	A new Runnable with the fields configured.

from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatOpenAI(max_tokens=20).configurable_fields(
    max_tokens=ConfigurableField(
        id="output_token_number",
        name="Max tokens in the output",
        description="The maximum number of tokens in the output",
    )
)

# max_tokens = 20
print("max_tokens_20: ", model.invoke("tell me something about chess").content)

# max_tokens = 200
print(
    "max_tokens_200: ",
    model.with_config(configurable={"output_token_number": 200})
    .invoke("tell me something about chess")
    .content,
)

configurable_alternatives

configurable_alternatives(
    which: ConfigurableField,
    *,
    default_key: str = "default",
    prefix_keys: bool = False,
    **kwargs: (
        Runnable[Input, Output]
        | Callable[[], Runnable[Input, Output]]
    )
) -> RunnableSerializable[Input, Output]

Configure alternatives for Runnables that can be set at runtime.

Parameters:

Name	Type	Description	Default
which	ConfigurableField	The ConfigurableField instance that will be used to select the alternative.	required
default_key	str	The default key to use if no alternative is selected. Defaults to 'default'.	'default'
prefix_keys	bool	Whether to prefix the keys with the ConfigurableField id. Defaults to False.	False
**kwargs	Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]	A dictionary of keys to Runnable instances or callables that return Runnable instances.	{}

Returns:

Type	Description
RunnableSerializable[Input, Output]	A new Runnable with the alternatives configured.

from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatAnthropic(
    model_name="claude-3-7-sonnet-20250219"
).configurable_alternatives(
    ConfigurableField(id="llm"),
    default_key="anthropic",
    openai=ChatOpenAI(),
)

# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)

# uses ChatOpenAI
print(
    model.with_config(configurable={"llm": "openai"})
    .invoke("which organization created you?")
    .content
)

get_relevant_documents

get_relevant_documents(
    query: str,
    *,
    callbacks: Callbacks = None,
    tags: list[str] | None = None,
    metadata: dict[str, Any] | None = None,
    run_name: str | None = None,
    **kwargs: Any
) -> list[Document]

Retrieve documents relevant to a query.

Users should favor using .invoke or .batch rather than get_relevant_documents directly.

Parameters:

Name	Type	Description	Default
query	str	string to find relevant documents for.	required
callbacks	Callbacks	Callback manager or list of callbacks. Defaults to None.	None
tags	list[str] | None	Optional list of tags associated with the retriever. These tags will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Defaults to None.	None
metadata	dict[str, Any] | None	Optional metadata associated with the retriever. This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Defaults to None.	None
run_name	str | None	Optional name for the run. Defaults to None.	None
kwargs	Any	Additional arguments to pass to the retriever.	{}

Returns:

Type	Description
list[Document]	List of relevant documents.

aget_relevant_documents async

aget_relevant_documents(
    query: str,
    *,
    callbacks: Callbacks = None,
    tags: list[str] | None = None,
    metadata: dict[str, Any] | None = None,
    run_name: str | None = None,
    **kwargs: Any
) -> list[Document]

Asynchronously get documents relevant to a query.

Users should favor using .ainvoke or .abatch rather than aget_relevant_documents directly.

Parameters:

Name	Type	Description	Default
query	str	string to find relevant documents for.	required
callbacks	Callbacks	Callback manager or list of callbacks.	None
tags	list[str] | None	Optional list of tags associated with the retriever. These tags will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Defaults to None.	None
metadata	dict[str, Any] | None	Optional metadata associated with the retriever. This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Defaults to None.	None
run_name	str | None	Optional name for the run. Defaults to None.	None
kwargs	Any	Additional arguments to pass to the retriever.	{}

Returns:

Type	Description
list[Document]	List of relevant documents.

validate_environment classmethod

validate_environment(values: dict) -> Any

Validate the environment.

ExaFindSimilarResults

Bases: BaseTool

Tool that queries the Metaphor Search API and gets back json.

Methods:

Name	Description
get_name	Get the name of the Runnable.
get_input_schema	The tool's input schema.
get_input_jsonschema	Get a JSON schema that represents the input to the Runnable.
get_output_schema	Get a pydantic model that can be used to validate output to the Runnable.
get_output_jsonschema	Get a JSON schema that represents the output of the Runnable.
config_schema	The type of config this Runnable accepts specified as a pydantic model.
get_config_jsonschema	Get a JSON schema that represents the config of the Runnable.
get_graph	Return a graph representation of this Runnable.
get_prompts	Return a list of prompts used by this Runnable.
__or__	Runnable "or" operator.
__ror__	Runnable "reverse-or" operator.
pipe	Pipe runnables.
pick	Pick keys from the output dict of this Runnable.
assign	Assigns new fields to the dict output of this Runnable.
batch	Default implementation runs invoke in parallel using a thread pool executor.
batch_as_completed	Run invoke in parallel on a list of inputs.
abatch	Default implementation runs ainvoke in parallel using asyncio.gather.
abatch_as_completed	Run ainvoke in parallel on a list of inputs.
stream	Default implementation of stream, which calls invoke.
astream	Default implementation of astream, which calls ainvoke.
astream_log	Stream all output from a Runnable, as reported to the callback system.
astream_events	Generate a stream of events.
transform	Transform inputs to outputs.
atransform	Transform inputs to outputs.
bind	Bind arguments to a Runnable, returning a new Runnable.
with_config	Bind config to a Runnable, returning a new Runnable.
with_listeners	Bind lifecycle listeners to a Runnable, returning a new Runnable.
with_alisteners	Bind async lifecycle listeners to a Runnable.
with_types	Bind input and output types to a Runnable, returning a new Runnable.
with_retry	Create a new Runnable that retries the original Runnable on exceptions.
map	Return a new Runnable that maps a list of inputs to a list of outputs.
with_fallbacks	Add fallbacks to a Runnable, returning a new Runnable.
as_tool	Create a BaseTool from a Runnable.
__init__	Initialize the tool.
is_lc_serializable	Is this class serializable?
get_lc_namespace	Get the namespace of the langchain object.
lc_id	Return a unique identifier for this class for serialization purposes.
to_json	Serialize the Runnable to JSON.
to_json_not_implemented	Serialize a "not implemented" object.
configurable_fields	Configure particular Runnable fields at runtime.
configurable_alternatives	Configure alternatives for Runnables that can be set at runtime.
__init_subclass__	Validate the tool class definition during subclass creation.
run	Run the tool.
arun	Run the tool asynchronously.
validate_environment	Validate the environment.

Attributes:

Name	Type	Description
InputType	type[Input]	Input type.
OutputType	type[Output]	Output Type.
input_schema	type[BaseModel]	The type of input this Runnable accepts specified as a pydantic model.
output_schema	type[BaseModel]	Output schema.
config_specs	list[ConfigurableFieldSpec]	List configurable fields for this Runnable.
lc_secrets	dict[str, str]	A map of constructor argument names to secret ids.
lc_attributes	dict	List of attribute names that should be included in the serialized kwargs.
args_schema	Annotated[ArgsSchema | None, SkipValidation()]	Pydantic model class to validate and parse the tool's input arguments.
return_direct	bool	Whether to return the tool's output directly.
verbose	bool	Whether to log the tool's progress.
callbacks	Callbacks	Callbacks to be called during tool execution.
tags	list[str] | None	Optional list of tags associated with the tool. Defaults to None.
metadata	dict[str, Any] | None	Optional metadata associated with the tool. Defaults to None.
handle_tool_error	bool | str | Callable[[ToolException], str] | None	Handle the content of the ToolException thrown.
handle_validation_error	bool | str | Callable[[ValidationError | ValidationError], str] | None	Handle the content of the ValidationError thrown.
response_format	Literal['content', 'content_and_artifact']	The tool response format. Defaults to 'content'.
is_single_input	bool	Check if the tool accepts only a single input argument.
args	dict	Get the tool's input arguments schema.
tool_call_schema	ArgsSchema	Get the schema for tool calls, excluding injected arguments.

InputType property

InputType: type[Input]

Input type.

The type of input this Runnable accepts specified as a type annotation.

Raises:

Type	Description
TypeError	If the input type cannot be inferred.

OutputType property

OutputType: type[Output]

Output Type.

The type of output this Runnable produces specified as a type annotation.

Raises:

Type	Description
TypeError	If the output type cannot be inferred.

input_schema property

input_schema: type[BaseModel]

The type of input this Runnable accepts specified as a pydantic model.

output_schema property

output_schema: type[BaseModel]

Output schema.

The type of output this Runnable produces specified as a pydantic model.

config_specs property

config_specs: list[ConfigurableFieldSpec]

List configurable fields for this Runnable.

lc_secrets property

lc_secrets: dict[str, str]

A map of constructor argument names to secret ids.

For example,

lc_attributes property

lc_attributes: dict

List of attribute names that should be included in the serialized kwargs.

These attributes must be accepted by the constructor. Default is an empty dictionary.

args_schema class-attribute instance-attribute

args_schema: Annotated[
    ArgsSchema | None, SkipValidation()
] = Field(default=None, description="The tool schema.")

Pydantic model class to validate and parse the tool's input arguments.

Args schema should be either:

• A subclass of pydantic.BaseModel.
• A subclass of pydantic.v1.BaseModel if accessing v1 namespace in pydantic 2
• a JSON schema dict

return_direct class-attribute instance-attribute

return_direct: bool = False

Whether to return the tool's output directly.

Setting this to True means that after the tool is called, the AgentExecutor will stop looping.

verbose class-attribute instance-attribute

verbose: bool = False

Whether to log the tool's progress.

callbacks class-attribute instance-attribute

callbacks: Callbacks = Field(default=None, exclude=True)

Callbacks to be called during tool execution.

tags class-attribute instance-attribute

tags: list[str] | None = None

Optional list of tags associated with the tool. Defaults to None. These tags will be associated with each call to this tool, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a tool with its use case.

metadata class-attribute instance-attribute

metadata: dict[str, Any] | None = None

Optional metadata associated with the tool. Defaults to None. This metadata will be associated with each call to this tool, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a tool with its use case.

handle_tool_error class-attribute instance-attribute

handle_tool_error: (
    bool | str | Callable[[ToolException], str] | None
) = False

Handle the content of the ToolException thrown.

handle_validation_error class-attribute instance-attribute

handle_validation_error: (
    bool
    | str
    | Callable[[ValidationError | ValidationError], str]
    | None
) = False

Handle the content of the ValidationError thrown.

response_format class-attribute instance-attribute

response_format: Literal[
    "content", "content_and_artifact"
] = "content"

The tool response format. Defaults to 'content'.

If "content" then the output of the tool is interpreted as the contents of a ToolMessage. If "content_and_artifact" then the output is expected to be a two-tuple corresponding to the (content, artifact) of a ToolMessage.

is_single_input property

is_single_input: bool

Check if the tool accepts only a single input argument.

Returns:

Type	Description
bool	True if the tool has only one input argument, False otherwise.

args property

args: dict

Get the tool's input arguments schema.

Returns:

Type	Description
dict	Dictionary containing the tool's argument properties.

tool_call_schema property

tool_call_schema: ArgsSchema

Get the schema for tool calls, excluding injected arguments.

Returns:

Type	Description
ArgsSchema	The schema that should be used for tool calls from language models.

get_name

get_name(
    suffix: str | None = None, *, name: str | None = None
) -> str

Get the name of the Runnable.

Parameters:

Name	Type	Description	Default
suffix	str | None	An optional suffix to append to the name.	None
name	str | None	An optional name to use instead of the Runnable's name.	None

Returns:

Type	Description
str	The name of the Runnable.

get_input_schema

get_input_schema(
    config: RunnableConfig | None = None,
) -> type[BaseModel]

The tool's input schema.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	The configuration for the tool.	None

Returns:

Type	Description
type[BaseModel]	The input schema for the tool.

get_input_jsonschema

get_input_jsonschema(
    config: RunnableConfig | None = None,
) -> dict[str, Any]

Get a JSON schema that represents the input to the Runnable.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	A config to use when generating the schema.	None

Returns:

Type	Description
dict[str, Any]	A JSON schema that represents the input to the Runnable.

Example

from langchain_core.runnables import RunnableLambda


def add_one(x: int) -> int:
    return x + 1


runnable = RunnableLambda(add_one)

print(runnable.get_input_jsonschema())

Added in version 0.3.0

get_output_schema

get_output_schema(
    config: RunnableConfig | None = None,
) -> type[BaseModel]

Get a pydantic model that can be used to validate output to the Runnable.

Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic output schema that depends on which configuration the Runnable is invoked with.

This method allows to get an output schema for a specific configuration.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	A config to use when generating the schema.	None

Returns:

Type	Description
type[BaseModel]	A pydantic model that can be used to validate output.

get_output_jsonschema

get_output_jsonschema(
    config: RunnableConfig | None = None,
) -> dict[str, Any]

Get a JSON schema that represents the output of the Runnable.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	A config to use when generating the schema.	None

Returns:

Type	Description
dict[str, Any]	A JSON schema that represents the output of the Runnable.

Example

from langchain_core.runnables import RunnableLambda


def add_one(x: int) -> int:
    return x + 1


runnable = RunnableLambda(add_one)

print(runnable.get_output_jsonschema())

Added in version 0.3.0

config_schema

config_schema(
    *, include: Sequence[str] | None = None
) -> type[BaseModel]

The type of config this Runnable accepts specified as a pydantic model.

To mark a field as configurable, see the configurable_fields and configurable_alternatives methods.

Parameters:

Name	Type	Description	Default
include	Sequence[str] | None	A list of fields to include in the config schema.	None

Returns:

Type	Description
type[BaseModel]	A pydantic model that can be used to validate config.

get_config_jsonschema

get_config_jsonschema(
    *, include: Sequence[str] | None = None
) -> dict[str, Any]

Get a JSON schema that represents the config of the Runnable.

Parameters:

Name	Type	Description	Default
include	Sequence[str] | None	A list of fields to include in the config schema.	None

Returns:

Type	Description
dict[str, Any]	A JSON schema that represents the config of the Runnable.

Added in version 0.3.0

get_graph

get_graph(config: RunnableConfig | None = None) -> Graph

Return a graph representation of this Runnable.

get_prompts

get_prompts(
    config: RunnableConfig | None = None,
) -> list[BasePromptTemplate]

Return a list of prompts used by this Runnable.

__or__

__or__(
    other: (
        Runnable[Any, Other]
        | Callable[[Iterator[Any]], Iterator[Other]]
        | Callable[
            [AsyncIterator[Any]], AsyncIterator[Other]
        ]
        | Callable[[Any], Other]
        | Mapping[
            str,
            Runnable[Any, Other]
            | Callable[[Any], Other]
            | Any,
        ]
    ),
) -> RunnableSerializable[Input, Other]

Runnable "or" operator.

Compose this Runnable with another object to create a RunnableSequence.

Parameters:

Name	Type	Description	Default
other	Runnable[Any, Other] | Callable[[Iterator[Any]], Iterator[Other]] | Callable[[AsyncIterator[Any]], AsyncIterator[Other]] | Callable[[Any], Other] | Mapping[str, Runnable[Any, Other] | Callable[[Any], Other] | Any]	Another Runnable or a Runnable-like object.	required

Returns:

Type	Description
RunnableSerializable[Input, Other]	A new Runnable.

__ror__

__ror__(
    other: (
        Runnable[Other, Any]
        | Callable[[Iterator[Other]], Iterator[Any]]
        | Callable[
            [AsyncIterator[Other]], AsyncIterator[Any]
        ]
        | Callable[[Other], Any]
        | Mapping[
            str,
            Runnable[Other, Any]
            | Callable[[Other], Any]
            | Any,
        ]
    ),
) -> RunnableSerializable[Other, Output]

Runnable "reverse-or" operator.

Compose this Runnable with another object to create a RunnableSequence.

Parameters:

Name	Type	Description	Default
other	Runnable[Other, Any] | Callable[[Iterator[Other]], Iterator[Any]] | Callable[[AsyncIterator[Other]], AsyncIterator[Any]] | Callable[[Other], Any] | Mapping[str, Runnable[Other, Any] | Callable[[Other], Any] | Any]	Another Runnable or a Runnable-like object.	required

Returns:

Type	Description
RunnableSerializable[Other, Output]	A new Runnable.

pipe

pipe(
    *others: Runnable[Any, Other] | Callable[[Any], Other],
    name: str | None = None
) -> RunnableSerializable[Input, Other]

Pipe runnables.

Compose this Runnable with Runnable-like objects to make a RunnableSequence.

Equivalent to RunnableSequence(self, *others) or self | others[0] | ...

Example

from langchain_core.runnables import RunnableLambda


def add_one(x: int) -> int:
    return x + 1


def mul_two(x: int) -> int:
    return x * 2


runnable_1 = RunnableLambda(add_one)
runnable_2 = RunnableLambda(mul_two)
sequence = runnable_1.pipe(runnable_2)
# Or equivalently:
# sequence = runnable_1 | runnable_2
# sequence = RunnableSequence(first=runnable_1, last=runnable_2)
sequence.invoke(1)
await sequence.ainvoke(1)
# -> 4

sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
# -> [4, 6, 8]

Parameters:

Name	Type	Description	Default
*others	Runnable[Any, Other] | Callable[[Any], Other]	Other Runnable or Runnable-like objects to compose	()
name	str | None	An optional name for the resulting RunnableSequence.	None

Returns:

Type	Description
RunnableSerializable[Input, Other]	A new Runnable.

pick

pick(
    keys: str | list[str],
) -> RunnableSerializable[Any, Any]

Pick keys from the output dict of this Runnable.

Pick single key:

```python
import json

from langchain_core.runnables import RunnableLambda, RunnableMap

as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
chain = RunnableMap(str=as_str, json=as_json)

chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}

json_only_chain = chain.pick("json")
json_only_chain.invoke("[1, 2, 3]")
# -> [1, 2, 3]
```

Pick list of keys:

```python
from typing import Any

import json

from langchain_core.runnables import RunnableLambda, RunnableMap

as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)


def as_bytes(x: Any) -> bytes:
    return bytes(x, "utf-8")


chain = RunnableMap(
    str=as_str, json=as_json, bytes=RunnableLambda(as_bytes)
)

chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}

json_and_bytes_chain = chain.pick(["json", "bytes"])
json_and_bytes_chain.invoke("[1, 2, 3]")
# -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
```

Parameters:

Name	Type	Description	Default
keys	str | list[str]	A key or list of keys to pick from the output dict.	required

Returns:

Type	Description
RunnableSerializable[Any, Any]	a new Runnable.

assign

assign(
    **kwargs: (
        Runnable[dict[str, Any], Any]
        | Callable[[dict[str, Any]], Any]
        | Mapping[
            str,
            Runnable[dict[str, Any], Any]
            | Callable[[dict[str, Any]], Any],
        ]
    ),
) -> RunnableSerializable[Any, Any]

Assigns new fields to the dict output of this Runnable.

from langchain_community.llms.fake import FakeStreamingListLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import SystemMessagePromptTemplate
from langchain_core.runnables import Runnable
from operator import itemgetter

prompt = (
    SystemMessagePromptTemplate.from_template("You are a nice assistant.")
    + "{question}"
)
llm = FakeStreamingListLLM(responses=["foo-lish"])

chain: Runnable = prompt | llm | {"str": StrOutputParser()}

chain_with_assign = chain.assign(hello=itemgetter("str") | llm)

print(chain_with_assign.input_schema.model_json_schema())
# {'title': 'PromptInput', 'type': 'object', 'properties':
{'question': {'title': 'Question', 'type': 'string'}}}
print(chain_with_assign.output_schema.model_json_schema())
# {'title': 'RunnableSequenceOutput', 'type': 'object', 'properties':
{'str': {'title': 'Str',
'type': 'string'}, 'hello': {'title': 'Hello', 'type': 'string'}}}

Parameters:

Name	Type	Description	Default
**kwargs	Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any] | Mapping[str, Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any]]	A mapping of keys to Runnable or Runnable-like objects that will be invoked with the entire output dict of this Runnable.	{}

Returns:

Type	Description
RunnableSerializable[Any, Any]	A new Runnable.

batch

batch(
    inputs: list[Input],
    config: (
        RunnableConfig | list[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> list[Output]

Default implementation runs invoke in parallel using a thread pool executor.

The default implementation of batch works well for IO bound runnables.

Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

Parameters:

Name	Type	Description	Default
inputs	list[Input]	A list of inputs to the Runnable.	required
config	RunnableConfig | list[RunnableConfig] | None	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.	None
return_exceptions	bool	Whether to return exceptions instead of raising them. Defaults to False.	False
**kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Returns:

Type	Description
list[Output]	A list of outputs from the Runnable.

batch_as_completed

batch_as_completed(
    inputs: Sequence[Input],
    config: (
        RunnableConfig | Sequence[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> Iterator[tuple[int, Output | Exception]]

Run invoke in parallel on a list of inputs.

Yields results as they complete.

Parameters:

Name	Type	Description	Default
inputs	Sequence[Input]	A list of inputs to the Runnable.	required
config	RunnableConfig | Sequence[RunnableConfig] | None	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.	None
return_exceptions	bool	Whether to return exceptions instead of raising them. Defaults to False.	False
**kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
tuple[int, Output | Exception]	Tuples of the index of the input and the output from the Runnable.

abatch async

abatch(
    inputs: list[Input],
    config: (
        RunnableConfig | list[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> list[Output]

Default implementation runs ainvoke in parallel using asyncio.gather.

The default implementation of batch works well for IO bound runnables.

Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

Parameters:

Name	Type	Description	Default
inputs	list[Input]	A list of inputs to the Runnable.	required
config	RunnableConfig | list[RunnableConfig] | None	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.	None
return_exceptions	bool	Whether to return exceptions instead of raising them. Defaults to False.	False
**kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Returns:

Type	Description
list[Output]	A list of outputs from the Runnable.

abatch_as_completed async

abatch_as_completed(
    inputs: Sequence[Input],
    config: (
        RunnableConfig | Sequence[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> AsyncIterator[tuple[int, Output | Exception]]

Run ainvoke in parallel on a list of inputs.

Yields results as they complete.

Parameters:

Name	Type	Description	Default
inputs	Sequence[Input]	A list of inputs to the Runnable.	required
config	RunnableConfig | Sequence[RunnableConfig] | None	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.	None
return_exceptions	bool	Whether to return exceptions instead of raising them. Defaults to False.	False
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
AsyncIterator[tuple[int, Output | Exception]]	A tuple of the index of the input and the output from the Runnable.

stream

stream(
    input: Input,
    config: RunnableConfig | None = None,
    **kwargs: Any | None
) -> Iterator[Output]

Default implementation of stream, which calls invoke.

Subclasses should override this method if they support streaming output.

Parameters:

Name	Type	Description	Default
input	Input	The input to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable. Defaults to None.	None
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
Output	The output of the Runnable.

astream async

astream(
    input: Input,
    config: RunnableConfig | None = None,
    **kwargs: Any | None
) -> AsyncIterator[Output]

Default implementation of astream, which calls ainvoke.

Subclasses should override this method if they support streaming output.

Parameters:

Name	Type	Description	Default
input	Input	The input to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable. Defaults to None.	None
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
AsyncIterator[Output]	The output of the Runnable.

astream_log async

astream_log(
    input: Any,
    config: RunnableConfig | None = None,
    *,
    diff: bool = True,
    with_streamed_output_list: bool = True,
    include_names: Sequence[str] | None = None,
    include_types: Sequence[str] | None = None,
    include_tags: Sequence[str] | None = None,
    exclude_names: Sequence[str] | None = None,
    exclude_types: Sequence[str] | None = None,
    exclude_tags: Sequence[str] | None = None,
    **kwargs: Any
) -> AsyncIterator[RunLogPatch] | AsyncIterator[RunLog]

Stream all output from a Runnable, as reported to the callback system.

This includes all inner runs of LLMs, Retrievers, Tools, etc.

Output is streamed as Log objects, which include a list of Jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run.

The Jsonpatch ops can be applied in order to construct state.

Parameters:

Name	Type	Description	Default
input	Any	The input to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable.	None
diff	bool	Whether to yield diffs between each step or the current state.	True
with_streamed_output_list	bool	Whether to yield the streamed_output list.	True
include_names	Sequence[str] | None	Only include logs with these names.	None
include_types	Sequence[str] | None	Only include logs with these types.	None
include_tags	Sequence[str] | None	Only include logs with these tags.	None
exclude_names	Sequence[str] | None	Exclude logs with these names.	None
exclude_types	Sequence[str] | None	Exclude logs with these types.	None
exclude_tags	Sequence[str] | None	Exclude logs with these tags.	None
kwargs	Any	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
AsyncIterator[RunLogPatch] | AsyncIterator[RunLog]	A RunLogPatch or RunLog object.

astream_events async

astream_events(
    input: Any,
    config: RunnableConfig | None = None,
    *,
    version: Literal["v1", "v2"] = "v2",
    include_names: Sequence[str] | None = None,
    include_types: Sequence[str] | None = None,
    include_tags: Sequence[str] | None = None,
    exclude_names: Sequence[str] | None = None,
    exclude_types: Sequence[str] | None = None,
    exclude_tags: Sequence[str] | None = None,
    **kwargs: Any
) -> AsyncIterator[StreamEvent]

Generate a stream of events.

Use to create an iterator over StreamEvents that provide real-time information about the progress of the Runnable, including StreamEvents from intermediate results.

A StreamEvent is a dictionary with the following schema:

• event: str - Event names are of the format: on_[runnable_type]_(start|stream|end).
• name: str - The name of the Runnable that generated the event.
• run_id: str - randomly generated ID associated with the given execution of the Runnable that emitted the event. A child Runnable that gets invoked as part of the execution of a parent Runnable is assigned its own unique ID.
• parent_ids: list[str] - The IDs of the parent runnables that generated the event. The root Runnable will have an empty list. The order of the parent IDs is from the root to the immediate parent. Only available for v2 version of the API. The v1 version of the API will return an empty list.
• tags: Optional[list[str]] - The tags of the Runnable that generated the event.
• metadata: Optional[dict[str, Any]] - The metadata of the Runnable that generated the event.
• data: dict[str, Any]

Below is a table that illustrates some events that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.

Note

This reference table is for the v2 version of the schema.

+--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | event | name | chunk | input | output | +==========================+==================+=====================================+===================================================+=====================================================+ | on_chat_model_start | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chat_model_stream | [model name] | AIMessageChunk(content="hello") | | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chat_model_end | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | AIMessageChunk(content="hello world") | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_llm_start | [model name] | | {'input': 'hello'} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_llm_stream | [model name] | 'Hello' | | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_llm_end | [model name] | | 'Hello human!' | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chain_start | format_docs | | | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chain_stream | format_docs | 'hello world!, goodbye world!' | | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chain_end | format_docs | | [Document(...)] | 'hello world!, goodbye world!' | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_tool_start | some_tool | | {"x": 1, "y": "2"} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_tool_end | some_tool | | | {"x": 1, "y": "2"} | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_retriever_start | [retriever name] | | {"query": "hello"} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_retriever_end | [retriever name] | | {"query": "hello"} | [Document(...), ..] | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_prompt_start | [template_name] | | {"question": "hello"} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_prompt_end | [template_name] | | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+

In addition to the standard events, users can also dispatch custom events (see example below).

Custom events will be only be surfaced with in the v2 version of the API!

A custom event has following format:

+-----------+------+-----------------------------------------------------------------------------------------------------------+ | Attribute | Type | Description | +===========+======+===========================================================================================================+ | name | str | A user defined name for the event. | +-----------+------+-----------------------------------------------------------------------------------------------------------+ | data | Any | The data associated with the event. This can be anything, though we suggest making it JSON serializable. | +-----------+------+-----------------------------------------------------------------------------------------------------------+

Here are declarations associated with the standard events shown above:

format_docs:

def format_docs(docs: list[Document]) -> str:
    '''Format the docs.'''
    return ", ".join([doc.page_content for doc in docs])


format_docs = RunnableLambda(format_docs)

some_tool:

@tool
def some_tool(x: int, y: str) -> dict:
    '''Some_tool.'''
    return {"x": x, "y": y}

prompt:

template = ChatPromptTemplate.from_messages(
    [
        ("system", "You are Cat Agent 007"),
        ("human", "{question}"),
    ]
).with_config({"run_name": "my_template", "tags": ["my_template"]})

Example:

from langchain_core.runnables import RunnableLambda


async def reverse(s: str) -> str:
    return s[::-1]


chain = RunnableLambda(func=reverse)

events = [event async for event in chain.astream_events("hello", version="v2")]

# will produce the following events (run_id, and parent_ids
# has been omitted for brevity):
[
    {
        "data": {"input": "hello"},
        "event": "on_chain_start",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"chunk": "olleh"},
        "event": "on_chain_stream",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"output": "olleh"},
        "event": "on_chain_end",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
]

Example: Dispatch Custom Event

from langchain_core.callbacks.manager import (
    adispatch_custom_event,
)
from langchain_core.runnables import RunnableLambda, RunnableConfig
import asyncio


async def slow_thing(some_input: str, config: RunnableConfig) -> str:
    """Do something that takes a long time."""
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 1 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 2 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    return "Done"

slow_thing = RunnableLambda(slow_thing)

async for event in slow_thing.astream_events("some_input", version="v2"):
    print(event)

Parameters:

Name	Type	Description	Default
input	Any	The input to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable.	None
version	Literal['v1', 'v2']	The version of the schema to use either 'v2' or 'v1'. Users should use 'v2'. 'v1' is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in 'v2'.	'v2'
include_names	Sequence[str] | None	Only include events from Runnables with matching names.	None
include_types	Sequence[str] | None	Only include events from Runnables with matching types.	None
include_tags	Sequence[str] | None	Only include events from Runnables with matching tags.	None
exclude_names	Sequence[str] | None	Exclude events from Runnables with matching names.	None
exclude_types	Sequence[str] | None	Exclude events from Runnables with matching types.	None
exclude_tags	Sequence[str] | None	Exclude events from Runnables with matching tags.	None
kwargs	Any	Additional keyword arguments to pass to the Runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.	{}

Yields:

Type	Description
AsyncIterator[StreamEvent]	An async stream of StreamEvents.

Raises:

Type	Description
NotImplementedError	If the version is not 'v1' or 'v2'.

transform

transform(
    input: Iterator[Input],
    config: RunnableConfig | None = None,
    **kwargs: Any | None
) -> Iterator[Output]

Transform inputs to outputs.

Default implementation of transform, which buffers input and calls astream.

Subclasses should override this method if they can start producing output while input is still being generated.

Parameters:

Name	Type	Description	Default
input	Iterator[Input]	An iterator of inputs to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable. Defaults to None.	None
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
Output	The output of the Runnable.

atransform async

atransform(
    input: AsyncIterator[Input],
    config: RunnableConfig | None = None,
    **kwargs: Any | None
) -> AsyncIterator[Output]

Transform inputs to outputs.

Default implementation of atransform, which buffers input and calls astream.

Subclasses should override this method if they can start producing output while input is still being generated.

Parameters:

Name	Type	Description	Default
input	AsyncIterator[Input]	An async iterator of inputs to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable. Defaults to None.	None
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
AsyncIterator[Output]	The output of the Runnable.

bind

bind(**kwargs: Any) -> Runnable[Input, Output]

Bind arguments to a Runnable, returning a new Runnable.

Useful when a Runnable in a chain requires an argument that is not in the output of the previous Runnable or included in the user input.

Parameters:

Name	Type	Description	Default
kwargs	Any	The arguments to bind to the Runnable.	{}

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the arguments bound.

Example

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

llm = ChatOllama(model="llama3.1")

# Without bind.
chain = llm | StrOutputParser()

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.'

# With bind.
chain = llm.bind(stop=["three"]) | StrOutputParser()

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'

with_config

with_config(
    config: RunnableConfig | None = None, **kwargs: Any
) -> Runnable[Input, Output]

Bind config to a Runnable, returning a new Runnable.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	The config to bind to the Runnable.	None
kwargs	Any	Additional keyword arguments to pass to the Runnable.	{}

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the config bound.

with_listeners

with_listeners(
    *,
    on_start: (
        Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None
    ) = None,
    on_end: (
        Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None
    ) = None,
    on_error: (
        Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None
    ) = None
) -> Runnable[Input, Output]

Bind lifecycle listeners to a Runnable, returning a new Runnable.

The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.

Parameters:

Name	Type	Description	Default
on_start	Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None	Called before the Runnable starts running, with the Run object. Defaults to None.	None
on_end	Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None	Called after the Runnable finishes running, with the Run object. Defaults to None.	None
on_error	Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None	Called if the Runnable throws an error, with the Run object. Defaults to None.	None

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the listeners bound.

Example

from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run

import time


def test_runnable(time_to_sleep: int):
    time.sleep(time_to_sleep)


def fn_start(run_obj: Run):
    print("start_time:", run_obj.start_time)


def fn_end(run_obj: Run):
    print("end_time:", run_obj.end_time)


chain = RunnableLambda(test_runnable).with_listeners(
    on_start=fn_start, on_end=fn_end
)
chain.invoke(2)

with_alisteners

with_alisteners(
    *,
    on_start: AsyncListener | None = None,
    on_end: AsyncListener | None = None,
    on_error: AsyncListener | None = None
) -> Runnable[Input, Output]

Bind async lifecycle listeners to a Runnable.

Returns a new Runnable.

The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.

Parameters:

Name	Type	Description	Default
on_start	AsyncListener | None	Called asynchronously before the Runnable starts running, with the Run object. Defaults to None.	None
on_end	AsyncListener | None	Called asynchronously after the Runnable finishes running, with the Run object. Defaults to None.	None
on_error	AsyncListener | None	Called asynchronously if the Runnable throws an error, with the Run object. Defaults to None.	None

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the listeners bound.

Example

from langchain_core.runnables import RunnableLambda, Runnable
from datetime import datetime, timezone
import time
import asyncio

def format_t(timestamp: float) -> str:
    return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()

async def test_runnable(time_to_sleep: int):
    print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
    await asyncio.sleep(time_to_sleep)
    print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")

async def fn_start(run_obj: Runnable):
    print(f"on start callback starts at {format_t(time.time())}")
    await asyncio.sleep(3)
    print(f"on start callback ends at {format_t(time.time())}")

async def fn_end(run_obj: Runnable):
    print(f"on end callback starts at {format_t(time.time())}")
    await asyncio.sleep(2)
    print(f"on end callback ends at {format_t(time.time())}")

runnable = RunnableLambda(test_runnable).with_alisteners(
    on_start=fn_start,
    on_end=fn_end
)
async def concurrent_runs():
    await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))

asyncio.run(concurrent_runs())
Result:
on start callback starts at 2025-03-01T07:05:22.875378+00:00
on start callback starts at 2025-03-01T07:05:22.875495+00:00
on start callback ends at 2025-03-01T07:05:25.878862+00:00
on start callback ends at 2025-03-01T07:05:25.878947+00:00
Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
on end callback starts at 2025-03-01T07:05:27.882360+00:00
Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
on end callback starts at 2025-03-01T07:05:28.882428+00:00
on end callback ends at 2025-03-01T07:05:29.883893+00:00
on end callback ends at 2025-03-01T07:05:30.884831+00:00

with_types

with_types(
    *,
    input_type: type[Input] | None = None,
    output_type: type[Output] | None = None
) -> Runnable[Input, Output]

Bind input and output types to a Runnable, returning a new Runnable.

Parameters:

Name	Type	Description	Default
input_type	type[Input] | None	The input type to bind to the Runnable. Defaults to None.	None
output_type	type[Output] | None	The output type to bind to the Runnable. Defaults to None.	None

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the types bound.

with_retry

with_retry(
    *,
    retry_if_exception_type: tuple[
        type[BaseException], ...
    ] = (Exception,),
    wait_exponential_jitter: bool = True,
    exponential_jitter_params: (
        ExponentialJitterParams | None
    ) = None,
    stop_after_attempt: int = 3
) -> Runnable[Input, Output]

Create a new Runnable that retries the original Runnable on exceptions.

Parameters:

Name	Type	Description	Default
retry_if_exception_type	tuple[type[BaseException], ...]	A tuple of exception types to retry on. Defaults to (Exception,).	(Exception,)
wait_exponential_jitter	bool	Whether to add jitter to the wait time between retries. Defaults to True.	True
stop_after_attempt	int	The maximum number of attempts to make before giving up. Defaults to 3.	3
exponential_jitter_params	ExponentialJitterParams | None	Parameters for tenacity.wait_exponential_jitter. Namely: initial, max, exp_base, and jitter (all float values).	None

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable that retries the original Runnable on exceptions.

Example

from langchain_core.runnables import RunnableLambda

count = 0


def _lambda(x: int) -> None:
    global count
    count = count + 1
    if x == 1:
        raise ValueError("x is 1")
    else:
        pass


runnable = RunnableLambda(_lambda)
try:
    runnable.with_retry(
        stop_after_attempt=2,
        retry_if_exception_type=(ValueError,),
    ).invoke(1)
except ValueError:
    pass

assert count == 2

map

map() -> Runnable[list[Input], list[Output]]

Return a new Runnable that maps a list of inputs to a list of outputs.

Calls invoke with each input.

Returns:

Type	Description
Runnable[list[Input], list[Output]]	A new Runnable that maps a list of inputs to a list of outputs.

Example

from langchain_core.runnables import RunnableLambda


def _lambda(x: int) -> int:
    return x + 1


runnable = RunnableLambda(_lambda)
print(runnable.map().invoke([1, 2, 3]))  # [2, 3, 4]

with_fallbacks

with_fallbacks(
    fallbacks: Sequence[Runnable[Input, Output]],
    *,
    exceptions_to_handle: tuple[
        type[BaseException], ...
    ] = (Exception,),
    exception_key: str | None = None
) -> RunnableWithFallbacks[Input, Output]

Add fallbacks to a Runnable, returning a new Runnable.

The new Runnable will try the original Runnable, and then each fallback in order, upon failures.

Parameters:

Name	Type	Description	Default
fallbacks	Sequence[Runnable[Input, Output]]	A sequence of runnables to try if the original Runnable fails.	required
exceptions_to_handle	tuple[type[BaseException], ...]	A tuple of exception types to handle. Defaults to (Exception,).	(Exception,)
exception_key	str | None	If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input. Defaults to None.	None

Returns:

Type	Description
RunnableWithFallbacks[Input, Output]	A new Runnable that will try the original Runnable, and then each
RunnableWithFallbacks[Input, Output]	fallback in order, upon failures.

Example

from typing import Iterator

from langchain_core.runnables import RunnableGenerator


def _generate_immediate_error(input: Iterator) -> Iterator[str]:
    raise ValueError()
    yield ""


def _generate(input: Iterator) -> Iterator[str]:
    yield from "foo bar"


runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
    [RunnableGenerator(_generate)]
)
print("".join(runnable.stream({})))  # foo bar

Parameters:

Name	Type	Description	Default
fallbacks	Sequence[Runnable[Input, Output]]	A sequence of runnables to try if the original Runnable fails.	required
exceptions_to_handle	tuple[type[BaseException], ...]	A tuple of exception types to handle.	(Exception,)
exception_key	str | None	If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input.	None

Returns:

Type	Description
RunnableWithFallbacks[Input, Output]	A new Runnable that will try the original Runnable, and then each
RunnableWithFallbacks[Input, Output]	fallback in order, upon failures.

as_tool

as_tool(
    args_schema: type[BaseModel] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    arg_types: dict[str, type] | None = None
) -> BaseTool

Create a BaseTool from a Runnable.

as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. Where possible, schemas are inferred from runnable.get_input_schema. Alternatively (e.g., if the Runnable takes a dict as input and the specific dict keys are not typed), the schema can be specified directly with args_schema. You can also pass arg_types to just specify the required arguments and their types.

Parameters:

Name	Type	Description	Default
args_schema	type[BaseModel] | None	The schema for the tool. Defaults to None.	None
name	str | None	The name of the tool. Defaults to None.	None
description	str | None	The description of the tool. Defaults to None.	None
arg_types	dict[str, type] | None	A dictionary of argument names to types. Defaults to None.	None

Returns:

Type	Description
BaseTool	A BaseTool instance.

Typed dict input:

from typing_extensions import TypedDict
from langchain_core.runnables import RunnableLambda


class Args(TypedDict):
    a: int
    b: list[int]


def f(x: Args) -> str:
    return str(x["a"] * max(x["b"]))


runnable = RunnableLambda(f)
as_tool = runnable.as_tool()
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via args_schema:

from typing import Any
from pydantic import BaseModel, Field
from langchain_core.runnables import RunnableLambda

def f(x: dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))

class FSchema(BaseModel):
    """Apply a function to an integer and list of integers."""

    a: int = Field(..., description="Integer")
    b: list[int] = Field(..., description="List of ints")

runnable = RunnableLambda(f)
as_tool = runnable.as_tool(FSchema)
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via arg_types:

from typing import Any
from langchain_core.runnables import RunnableLambda


def f(x: dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))


runnable = RunnableLambda(f)
as_tool = runnable.as_tool(arg_types={"a": int, "b": list[int]})
as_tool.invoke({"a": 3, "b": [1, 2]})

String input:

from langchain_core.runnables import RunnableLambda


def f(x: str) -> str:
    return x + "a"


def g(x: str) -> str:
    return x + "z"


runnable = RunnableLambda(f) | g
as_tool = runnable.as_tool()
as_tool.invoke("b")

Added in version 0.2.14

__init__

__init__(**kwargs: Any) -> None

Initialize the tool.

Raises:

Type	Description
TypeError	If args_schema is not a subclass of pydantic BaseModel or dict.

is_lc_serializable classmethod

is_lc_serializable() -> bool

Is this class serializable?

By design, even if a class inherits from Serializable, it is not serializable by default. This is to prevent accidental serialization of objects that should not be serialized.

Returns:

Type	Description
bool	Whether the class is serializable. Default is False.

get_lc_namespace classmethod

get_lc_namespace() -> list[str]

Get the namespace of the langchain object.

For example, if the class is langchain.llms.openai.OpenAI, then the namespace is ["langchain", "llms", "openai"]

Returns:

Type	Description
list[str]	The namespace as a list of strings.

lc_id classmethod

lc_id() -> list[str]

Return a unique identifier for this class for serialization purposes.

The unique identifier is a list of strings that describes the path to the object. For example, for the class langchain.llms.openai.OpenAI, the id is ["langchain", "llms", "openai", "OpenAI"].

to_json

to_json() -> (
    SerializedConstructor | SerializedNotImplemented
)

Serialize the Runnable to JSON.

Returns:

Type	Description
SerializedConstructor | SerializedNotImplemented	A JSON-serializable representation of the Runnable.

to_json_not_implemented

to_json_not_implemented() -> SerializedNotImplemented

Serialize a "not implemented" object.

Returns:

Type	Description
SerializedNotImplemented	SerializedNotImplemented.

configurable_fields

configurable_fields(
    **kwargs: AnyConfigurableField,
) -> RunnableSerializable[Input, Output]

Configure particular Runnable fields at runtime.

Parameters:

Name	Type	Description	Default
**kwargs	AnyConfigurableField	A dictionary of ConfigurableField instances to configure.	{}

Raises:

Type	Description
ValueError	If a configuration key is not found in the Runnable.

Returns:

Type	Description
RunnableSerializable[Input, Output]	A new Runnable with the fields configured.

from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatOpenAI(max_tokens=20).configurable_fields(
    max_tokens=ConfigurableField(
        id="output_token_number",
        name="Max tokens in the output",
        description="The maximum number of tokens in the output",
    )
)

# max_tokens = 20
print("max_tokens_20: ", model.invoke("tell me something about chess").content)

# max_tokens = 200
print(
    "max_tokens_200: ",
    model.with_config(configurable={"output_token_number": 200})
    .invoke("tell me something about chess")
    .content,
)

configurable_alternatives

configurable_alternatives(
    which: ConfigurableField,
    *,
    default_key: str = "default",
    prefix_keys: bool = False,
    **kwargs: (
        Runnable[Input, Output]
        | Callable[[], Runnable[Input, Output]]
    )
) -> RunnableSerializable[Input, Output]

Configure alternatives for Runnables that can be set at runtime.

Parameters:

Name	Type	Description	Default
which	ConfigurableField	The ConfigurableField instance that will be used to select the alternative.	required
default_key	str	The default key to use if no alternative is selected. Defaults to 'default'.	'default'
prefix_keys	bool	Whether to prefix the keys with the ConfigurableField id. Defaults to False.	False
**kwargs	Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]	A dictionary of keys to Runnable instances or callables that return Runnable instances.	{}

Returns:

Type	Description
RunnableSerializable[Input, Output]	A new Runnable with the alternatives configured.

from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatAnthropic(
    model_name="claude-3-7-sonnet-20250219"
).configurable_alternatives(
    ConfigurableField(id="llm"),
    default_key="anthropic",
    openai=ChatOpenAI(),
)

# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)

# uses ChatOpenAI
print(
    model.with_config(configurable={"llm": "openai"})
    .invoke("which organization created you?")
    .content
)

__init_subclass__

__init_subclass__(**kwargs: Any) -> None

Validate the tool class definition during subclass creation.

Parameters:

Name	Type	Description	Default
**kwargs	Any	Additional keyword arguments passed to the parent class.	{}

Raises:

Type	Description
SchemaAnnotationError	If args_schema has incorrect type annotation.

run

run(
    tool_input: str | dict[str, Any],
    verbose: bool | None = None,
    start_color: str | None = "green",
    color: str | None = "green",
    callbacks: Callbacks = None,
    *,
    tags: list[str] | None = None,
    metadata: dict[str, Any] | None = None,
    run_name: str | None = None,
    run_id: UUID | None = None,
    config: RunnableConfig | None = None,
    tool_call_id: str | None = None,
    **kwargs: Any
) -> Any

Run the tool.

Parameters:

Name	Type	Description	Default
tool_input	str | dict[str, Any]	The input to the tool.	required
verbose	bool | None	Whether to log the tool's progress. Defaults to None.	None
start_color	str | None	The color to use when starting the tool. Defaults to 'green'.	'green'
color	str | None	The color to use when ending the tool. Defaults to 'green'.	'green'
callbacks	Callbacks	Callbacks to be called during tool execution. Defaults to None.	None
tags	list[str] | None	Optional list of tags associated with the tool. Defaults to None.	None
metadata	dict[str, Any] | None	Optional metadata associated with the tool. Defaults to None.	None
run_name	str | None	The name of the run. Defaults to None.	None
run_id	UUID | None	The id of the run. Defaults to None.	None
config	RunnableConfig | None	The configuration for the tool. Defaults to None.	None
tool_call_id	str | None	The id of the tool call. Defaults to None.	None
kwargs	Any	Keyword arguments to be passed to tool callbacks (event handler)	{}

Returns:

Type	Description
Any	The output of the tool.

Raises:

Type	Description
ToolException	If an error occurs during tool execution.

arun async

arun(
    tool_input: str | dict,
    verbose: bool | None = None,
    start_color: str | None = "green",
    color: str | None = "green",
    callbacks: Callbacks = None,
    *,
    tags: list[str] | None = None,
    metadata: dict[str, Any] | None = None,
    run_name: str | None = None,
    run_id: UUID | None = None,
    config: RunnableConfig | None = None,
    tool_call_id: str | None = None,
    **kwargs: Any
) -> Any

Run the tool asynchronously.

Parameters:

Name	Type	Description	Default
tool_input	str | dict	The input to the tool.	required
verbose	bool | None	Whether to log the tool's progress. Defaults to None.	None
start_color	str | None	The color to use when starting the tool. Defaults to 'green'.	'green'
color	str | None	The color to use when ending the tool. Defaults to 'green'.	'green'
callbacks	Callbacks	Callbacks to be called during tool execution. Defaults to None.	None
tags	list[str] | None	Optional list of tags associated with the tool. Defaults to None.	None
metadata	dict[str, Any] | None	Optional metadata associated with the tool. Defaults to None.	None
run_name	str | None	The name of the run. Defaults to None.	None
run_id	UUID | None	The id of the run. Defaults to None.	None
config	RunnableConfig | None	The configuration for the tool. Defaults to None.	None
tool_call_id	str | None	The id of the tool call. Defaults to None.	None
kwargs	Any	Keyword arguments to be passed to tool callbacks	{}

Returns:

Type	Description
Any	The output of the tool.

Raises:

Type	Description
ToolException	If an error occurs during tool execution.

validate_environment classmethod

validate_environment(values: dict) -> Any

Validate the environment.

ExaSearchResults

Bases: BaseTool

Exa Search tool.

Setup

Install langchain-exa and set environment variable EXA_API_KEY.

.. code-block:: bash

pip install -U langchain-exa
export EXA_API_KEY="your-api-key"

Instantiation

.. code-block:: python

from langchain-exa import ExaSearchResults

tool = ExaSearchResults()

Invocation with args

.. code-block:: python

tool.invoke({"query": "what is the weather in SF", "num_results": 1})

.. code-block:: python

SearchResponse(
    results=[
        Result(
            url="https://www.wunderground.com/weather/37.8,-122.4",
            id="https://www.wunderground.com/weather/37.8,-122.4",
            title="San Francisco, CA Weather Conditionsstar_ratehome",
            score=0.1843988299369812,
            published_date="2023-02-23T01:17:06.594Z",
            author=None,
            text="The time period when the sun is no more than 6 degrees below the horizon at either sunrise or sunset. The horizon should be clearly defined and the brightest stars should be visible under good atmospheric conditions (i.e. no moonlight, or other lights). One still should be able to carry on ordinary outdoor activities. The time period when the sun is between 6 and 12 degrees below the horizon at either sunrise or sunset. The horizon is well defined and the outline of objects might be visible without artificial light. Ordinary outdoor activities are not possible at this time without extra illumination. The time period when the sun is between 12 and 18 degrees below the horizon at either sunrise or sunset. The sun does not contribute to the illumination of the sky before this time in the morning, or after this time in the evening. In the beginning of morning astronomical twilight and at the end of astronomical twilight in the evening, sky illumination is very faint, and might be undetectable. The time of Civil Sunset minus the time of Civil Sunrise. The time of Actual Sunset minus the time of Actual Sunrise. The change in length of daylight between today and tomorrow is also listed when available.",
            highlights=None,
            highlight_scores=None,
            summary=None,
        )
    ],
    autoprompt_string=None,
)

Invocation with ToolCall:

.. code-block:: python

    tool.invoke(
        {
            "args": {"query": "what is the weather in SF", "num_results": 1},
            "id": "1",
            "name": tool.name,
            "type": "tool_call",
        }
    )

.. code-block:: python

    ToolMessage(
        content="Title: San Francisco, CA Weather Conditionsstar_ratehome\nURL: https://www.wunderground.com/weather/37.8,-122.4\nID: https://www.wunderground.com/weather/37.8,-122.4\nScore: 0.1843988299369812\nPublished Date: 2023-02-23T01:17:06.594Z\nAuthor: None\nText: The time period when the sun is no more than 6 degrees below the horizon at either sunrise or sunset. The horizon should be clearly defined and the brightest stars should be visible under good atmospheric conditions (i.e. no moonlight, or other lights). One still should be able to carry on ordinary outdoor activities. The time period when the sun is between 6 and 12 degrees below the horizon at either sunrise or sunset. The horizon is well defined and the outline of objects might be visible without artificial light. Ordinary outdoor activities are not possible at this time without extra illumination. The time period when the sun is between 12 and 18 degrees below the horizon at either sunrise or sunset. The sun does not contribute to the illumination of the sky before this time in the morning, or after this time in the evening. In the beginning of morning astronomical twilight and at the end of astronomical twilight in the evening, sky illumination is very faint, and might be undetectable. The time of Civil Sunset minus the time of Civil Sunrise. The time of Actual Sunset minus the time of Actual Sunrise. The change in length of daylight between today and tomorrow is also listed when available.\nHighlights: None\nHighlight Scores: None\nSummary: None\n",
        name="exa_search_results_json",
        tool_call_id="1",
    )

Methods:

Name	Description
get_name	Get the name of the Runnable.
get_input_schema	The tool's input schema.
get_input_jsonschema	Get a JSON schema that represents the input to the Runnable.
get_output_schema	Get a pydantic model that can be used to validate output to the Runnable.
get_output_jsonschema	Get a JSON schema that represents the output of the Runnable.
config_schema	The type of config this Runnable accepts specified as a pydantic model.
get_config_jsonschema	Get a JSON schema that represents the config of the Runnable.
get_graph	Return a graph representation of this Runnable.
get_prompts	Return a list of prompts used by this Runnable.
__or__	Runnable "or" operator.
__ror__	Runnable "reverse-or" operator.
pipe	Pipe runnables.
pick	Pick keys from the output dict of this Runnable.
assign	Assigns new fields to the dict output of this Runnable.
batch	Default implementation runs invoke in parallel using a thread pool executor.
batch_as_completed	Run invoke in parallel on a list of inputs.
abatch	Default implementation runs ainvoke in parallel using asyncio.gather.
abatch_as_completed	Run ainvoke in parallel on a list of inputs.
stream	Default implementation of stream, which calls invoke.
astream	Default implementation of astream, which calls ainvoke.
astream_log	Stream all output from a Runnable, as reported to the callback system.
astream_events	Generate a stream of events.
transform	Transform inputs to outputs.
atransform	Transform inputs to outputs.
bind	Bind arguments to a Runnable, returning a new Runnable.
with_config	Bind config to a Runnable, returning a new Runnable.
with_listeners	Bind lifecycle listeners to a Runnable, returning a new Runnable.
with_alisteners	Bind async lifecycle listeners to a Runnable.
with_types	Bind input and output types to a Runnable, returning a new Runnable.
with_retry	Create a new Runnable that retries the original Runnable on exceptions.
map	Return a new Runnable that maps a list of inputs to a list of outputs.
with_fallbacks	Add fallbacks to a Runnable, returning a new Runnable.
as_tool	Create a BaseTool from a Runnable.
__init__	Initialize the tool.
is_lc_serializable	Is this class serializable?
get_lc_namespace	Get the namespace of the langchain object.
lc_id	Return a unique identifier for this class for serialization purposes.
to_json	Serialize the Runnable to JSON.
to_json_not_implemented	Serialize a "not implemented" object.
configurable_fields	Configure particular Runnable fields at runtime.
configurable_alternatives	Configure alternatives for Runnables that can be set at runtime.
__init_subclass__	Validate the tool class definition during subclass creation.
run	Run the tool.
arun	Run the tool asynchronously.
validate_environment	Validate the environment.

Attributes:

Name	Type	Description
InputType	type[Input]	Input type.
OutputType	type[Output]	Output Type.
input_schema	type[BaseModel]	The type of input this Runnable accepts specified as a pydantic model.
output_schema	type[BaseModel]	Output schema.
config_specs	list[ConfigurableFieldSpec]	List configurable fields for this Runnable.
lc_secrets	dict[str, str]	A map of constructor argument names to secret ids.
lc_attributes	dict	List of attribute names that should be included in the serialized kwargs.
args_schema	Annotated[ArgsSchema | None, SkipValidation()]	Pydantic model class to validate and parse the tool's input arguments.
return_direct	bool	Whether to return the tool's output directly.
verbose	bool	Whether to log the tool's progress.
callbacks	Callbacks	Callbacks to be called during tool execution.
tags	list[str] | None	Optional list of tags associated with the tool. Defaults to None.
metadata	dict[str, Any] | None	Optional metadata associated with the tool. Defaults to None.
handle_tool_error	bool | str | Callable[[ToolException], str] | None	Handle the content of the ToolException thrown.
handle_validation_error	bool | str | Callable[[ValidationError | ValidationError], str] | None	Handle the content of the ValidationError thrown.
response_format	Literal['content', 'content_and_artifact']	The tool response format. Defaults to 'content'.
is_single_input	bool	Check if the tool accepts only a single input argument.
args	dict	Get the tool's input arguments schema.
tool_call_schema	ArgsSchema	Get the schema for tool calls, excluding injected arguments.

InputType property

InputType: type[Input]

Input type.

The type of input this Runnable accepts specified as a type annotation.

Raises:

Type	Description
TypeError	If the input type cannot be inferred.

OutputType property

OutputType: type[Output]

Output Type.

The type of output this Runnable produces specified as a type annotation.

Raises:

Type	Description
TypeError	If the output type cannot be inferred.

input_schema property

input_schema: type[BaseModel]

The type of input this Runnable accepts specified as a pydantic model.

output_schema property

output_schema: type[BaseModel]

Output schema.

The type of output this Runnable produces specified as a pydantic model.

config_specs property

config_specs: list[ConfigurableFieldSpec]

List configurable fields for this Runnable.

lc_secrets property

lc_secrets: dict[str, str]

A map of constructor argument names to secret ids.

For example,

lc_attributes property

lc_attributes: dict

List of attribute names that should be included in the serialized kwargs.

These attributes must be accepted by the constructor. Default is an empty dictionary.

args_schema class-attribute instance-attribute

args_schema: Annotated[
    ArgsSchema | None, SkipValidation()
] = Field(default=None, description="The tool schema.")

Pydantic model class to validate and parse the tool's input arguments.

Args schema should be either:

• A subclass of pydantic.BaseModel.
• A subclass of pydantic.v1.BaseModel if accessing v1 namespace in pydantic 2
• a JSON schema dict

return_direct class-attribute instance-attribute

return_direct: bool = False

Whether to return the tool's output directly.

Setting this to True means that after the tool is called, the AgentExecutor will stop looping.

verbose class-attribute instance-attribute

verbose: bool = False

Whether to log the tool's progress.

callbacks class-attribute instance-attribute

callbacks: Callbacks = Field(default=None, exclude=True)

Callbacks to be called during tool execution.

tags class-attribute instance-attribute

tags: list[str] | None = None

Optional list of tags associated with the tool. Defaults to None. These tags will be associated with each call to this tool, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a tool with its use case.

metadata class-attribute instance-attribute

metadata: dict[str, Any] | None = None

Optional metadata associated with the tool. Defaults to None. This metadata will be associated with each call to this tool, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a tool with its use case.

handle_tool_error class-attribute instance-attribute

handle_tool_error: (
    bool | str | Callable[[ToolException], str] | None
) = False

Handle the content of the ToolException thrown.

handle_validation_error class-attribute instance-attribute

handle_validation_error: (
    bool
    | str
    | Callable[[ValidationError | ValidationError], str]
    | None
) = False

Handle the content of the ValidationError thrown.

response_format class-attribute instance-attribute

response_format: Literal[
    "content", "content_and_artifact"
] = "content"

The tool response format. Defaults to 'content'.

If "content" then the output of the tool is interpreted as the contents of a ToolMessage. If "content_and_artifact" then the output is expected to be a two-tuple corresponding to the (content, artifact) of a ToolMessage.

is_single_input property

is_single_input: bool

Check if the tool accepts only a single input argument.

Returns:

Type	Description
bool	True if the tool has only one input argument, False otherwise.

args property

args: dict

Get the tool's input arguments schema.

Returns:

Type	Description
dict	Dictionary containing the tool's argument properties.

tool_call_schema property

tool_call_schema: ArgsSchema

Get the schema for tool calls, excluding injected arguments.

Returns:

Type	Description
ArgsSchema	The schema that should be used for tool calls from language models.

get_name

get_name(
    suffix: str | None = None, *, name: str | None = None
) -> str

Get the name of the Runnable.

Parameters:

Name	Type	Description	Default
suffix	str | None	An optional suffix to append to the name.	None
name	str | None	An optional name to use instead of the Runnable's name.	None

Returns:

Type	Description
str	The name of the Runnable.

get_input_schema

get_input_schema(
    config: RunnableConfig | None = None,
) -> type[BaseModel]

The tool's input schema.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	The configuration for the tool.	None

Returns:

Type	Description
type[BaseModel]	The input schema for the tool.

get_input_jsonschema

get_input_jsonschema(
    config: RunnableConfig | None = None,
) -> dict[str, Any]

Get a JSON schema that represents the input to the Runnable.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	A config to use when generating the schema.	None

Returns:

Type	Description
dict[str, Any]	A JSON schema that represents the input to the Runnable.

Example

from langchain_core.runnables import RunnableLambda


def add_one(x: int) -> int:
    return x + 1


runnable = RunnableLambda(add_one)

print(runnable.get_input_jsonschema())

Added in version 0.3.0

get_output_schema

get_output_schema(
    config: RunnableConfig | None = None,
) -> type[BaseModel]

Get a pydantic model that can be used to validate output to the Runnable.

Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic output schema that depends on which configuration the Runnable is invoked with.

This method allows to get an output schema for a specific configuration.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	A config to use when generating the schema.	None

Returns:

Type	Description
type[BaseModel]	A pydantic model that can be used to validate output.

get_output_jsonschema

get_output_jsonschema(
    config: RunnableConfig | None = None,
) -> dict[str, Any]

Get a JSON schema that represents the output of the Runnable.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	A config to use when generating the schema.	None

Returns:

Type	Description
dict[str, Any]	A JSON schema that represents the output of the Runnable.

Example

from langchain_core.runnables import RunnableLambda


def add_one(x: int) -> int:
    return x + 1


runnable = RunnableLambda(add_one)

print(runnable.get_output_jsonschema())

Added in version 0.3.0

config_schema

config_schema(
    *, include: Sequence[str] | None = None
) -> type[BaseModel]

The type of config this Runnable accepts specified as a pydantic model.

To mark a field as configurable, see the configurable_fields and configurable_alternatives methods.

Parameters:

Name	Type	Description	Default
include	Sequence[str] | None	A list of fields to include in the config schema.	None

Returns:

Type	Description
type[BaseModel]	A pydantic model that can be used to validate config.

get_config_jsonschema

get_config_jsonschema(
    *, include: Sequence[str] | None = None
) -> dict[str, Any]

Get a JSON schema that represents the config of the Runnable.

Parameters:

Name	Type	Description	Default
include	Sequence[str] | None	A list of fields to include in the config schema.	None

Returns:

Type	Description
dict[str, Any]	A JSON schema that represents the config of the Runnable.

Added in version 0.3.0

get_graph

get_graph(config: RunnableConfig | None = None) -> Graph

Return a graph representation of this Runnable.

get_prompts

get_prompts(
    config: RunnableConfig | None = None,
) -> list[BasePromptTemplate]

Return a list of prompts used by this Runnable.

__or__

__or__(
    other: (
        Runnable[Any, Other]
        | Callable[[Iterator[Any]], Iterator[Other]]
        | Callable[
            [AsyncIterator[Any]], AsyncIterator[Other]
        ]
        | Callable[[Any], Other]
        | Mapping[
            str,
            Runnable[Any, Other]
            | Callable[[Any], Other]
            | Any,
        ]
    ),
) -> RunnableSerializable[Input, Other]

Runnable "or" operator.

Compose this Runnable with another object to create a RunnableSequence.

Parameters:

Name	Type	Description	Default
other	Runnable[Any, Other] | Callable[[Iterator[Any]], Iterator[Other]] | Callable[[AsyncIterator[Any]], AsyncIterator[Other]] | Callable[[Any], Other] | Mapping[str, Runnable[Any, Other] | Callable[[Any], Other] | Any]	Another Runnable or a Runnable-like object.	required

Returns:

Type	Description
RunnableSerializable[Input, Other]	A new Runnable.

__ror__

__ror__(
    other: (
        Runnable[Other, Any]
        | Callable[[Iterator[Other]], Iterator[Any]]
        | Callable[
            [AsyncIterator[Other]], AsyncIterator[Any]
        ]
        | Callable[[Other], Any]
        | Mapping[
            str,
            Runnable[Other, Any]
            | Callable[[Other], Any]
            | Any,
        ]
    ),
) -> RunnableSerializable[Other, Output]

Runnable "reverse-or" operator.

Compose this Runnable with another object to create a RunnableSequence.

Parameters:

Name	Type	Description	Default
other	Runnable[Other, Any] | Callable[[Iterator[Other]], Iterator[Any]] | Callable[[AsyncIterator[Other]], AsyncIterator[Any]] | Callable[[Other], Any] | Mapping[str, Runnable[Other, Any] | Callable[[Other], Any] | Any]	Another Runnable or a Runnable-like object.	required

Returns:

Type	Description
RunnableSerializable[Other, Output]	A new Runnable.

pipe

pipe(
    *others: Runnable[Any, Other] | Callable[[Any], Other],
    name: str | None = None
) -> RunnableSerializable[Input, Other]

Pipe runnables.

Compose this Runnable with Runnable-like objects to make a RunnableSequence.

Equivalent to RunnableSequence(self, *others) or self | others[0] | ...

Example

from langchain_core.runnables import RunnableLambda


def add_one(x: int) -> int:
    return x + 1


def mul_two(x: int) -> int:
    return x * 2


runnable_1 = RunnableLambda(add_one)
runnable_2 = RunnableLambda(mul_two)
sequence = runnable_1.pipe(runnable_2)
# Or equivalently:
# sequence = runnable_1 | runnable_2
# sequence = RunnableSequence(first=runnable_1, last=runnable_2)
sequence.invoke(1)
await sequence.ainvoke(1)
# -> 4

sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
# -> [4, 6, 8]

Parameters:

Name	Type	Description	Default
*others	Runnable[Any, Other] | Callable[[Any], Other]	Other Runnable or Runnable-like objects to compose	()
name	str | None	An optional name for the resulting RunnableSequence.	None

Returns:

Type	Description
RunnableSerializable[Input, Other]	A new Runnable.

pick

pick(
    keys: str | list[str],
) -> RunnableSerializable[Any, Any]

Pick keys from the output dict of this Runnable.

Pick single key:

```python
import json

from langchain_core.runnables import RunnableLambda, RunnableMap

as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
chain = RunnableMap(str=as_str, json=as_json)

chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}

json_only_chain = chain.pick("json")
json_only_chain.invoke("[1, 2, 3]")
# -> [1, 2, 3]
```

Pick list of keys:

```python
from typing import Any

import json

from langchain_core.runnables import RunnableLambda, RunnableMap

as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)


def as_bytes(x: Any) -> bytes:
    return bytes(x, "utf-8")


chain = RunnableMap(
    str=as_str, json=as_json, bytes=RunnableLambda(as_bytes)
)

chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}

json_and_bytes_chain = chain.pick(["json", "bytes"])
json_and_bytes_chain.invoke("[1, 2, 3]")
# -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
```

Parameters:

Name	Type	Description	Default
keys	str | list[str]	A key or list of keys to pick from the output dict.	required

Returns:

Type	Description
RunnableSerializable[Any, Any]	a new Runnable.

assign

assign(
    **kwargs: (
        Runnable[dict[str, Any], Any]
        | Callable[[dict[str, Any]], Any]
        | Mapping[
            str,
            Runnable[dict[str, Any], Any]
            | Callable[[dict[str, Any]], Any],
        ]
    ),
) -> RunnableSerializable[Any, Any]

Assigns new fields to the dict output of this Runnable.

from langchain_community.llms.fake import FakeStreamingListLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import SystemMessagePromptTemplate
from langchain_core.runnables import Runnable
from operator import itemgetter

prompt = (
    SystemMessagePromptTemplate.from_template("You are a nice assistant.")
    + "{question}"
)
llm = FakeStreamingListLLM(responses=["foo-lish"])

chain: Runnable = prompt | llm | {"str": StrOutputParser()}

chain_with_assign = chain.assign(hello=itemgetter("str") | llm)

print(chain_with_assign.input_schema.model_json_schema())
# {'title': 'PromptInput', 'type': 'object', 'properties':
{'question': {'title': 'Question', 'type': 'string'}}}
print(chain_with_assign.output_schema.model_json_schema())
# {'title': 'RunnableSequenceOutput', 'type': 'object', 'properties':
{'str': {'title': 'Str',
'type': 'string'}, 'hello': {'title': 'Hello', 'type': 'string'}}}

Parameters:

Name	Type	Description	Default
**kwargs	Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any] | Mapping[str, Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any]]	A mapping of keys to Runnable or Runnable-like objects that will be invoked with the entire output dict of this Runnable.	{}

Returns:

Type	Description
RunnableSerializable[Any, Any]	A new Runnable.

batch

batch(
    inputs: list[Input],
    config: (
        RunnableConfig | list[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> list[Output]

Default implementation runs invoke in parallel using a thread pool executor.

The default implementation of batch works well for IO bound runnables.

Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

Parameters:

Name	Type	Description	Default
inputs	list[Input]	A list of inputs to the Runnable.	required
config	RunnableConfig | list[RunnableConfig] | None	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.	None
return_exceptions	bool	Whether to return exceptions instead of raising them. Defaults to False.	False
**kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Returns:

Type	Description
list[Output]	A list of outputs from the Runnable.

batch_as_completed

batch_as_completed(
    inputs: Sequence[Input],
    config: (
        RunnableConfig | Sequence[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> Iterator[tuple[int, Output | Exception]]

Run invoke in parallel on a list of inputs.

Yields results as they complete.

Parameters:

Name	Type	Description	Default
inputs	Sequence[Input]	A list of inputs to the Runnable.	required
config	RunnableConfig | Sequence[RunnableConfig] | None	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.	None
return_exceptions	bool	Whether to return exceptions instead of raising them. Defaults to False.	False
**kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
tuple[int, Output | Exception]	Tuples of the index of the input and the output from the Runnable.

abatch async

abatch(
    inputs: list[Input],
    config: (
        RunnableConfig | list[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> list[Output]

Default implementation runs ainvoke in parallel using asyncio.gather.

The default implementation of batch works well for IO bound runnables.

Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

Parameters:

Name	Type	Description	Default
inputs	list[Input]	A list of inputs to the Runnable.	required
config	RunnableConfig | list[RunnableConfig] | None	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.	None
return_exceptions	bool	Whether to return exceptions instead of raising them. Defaults to False.	False
**kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Returns:

Type	Description
list[Output]	A list of outputs from the Runnable.

abatch_as_completed async

abatch_as_completed(
    inputs: Sequence[Input],
    config: (
        RunnableConfig | Sequence[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> AsyncIterator[tuple[int, Output | Exception]]

Run ainvoke in parallel on a list of inputs.

Yields results as they complete.

Parameters:

Name	Type	Description	Default
inputs	Sequence[Input]	A list of inputs to the Runnable.	required
config	RunnableConfig | Sequence[RunnableConfig] | None	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.	None
return_exceptions	bool	Whether to return exceptions instead of raising them. Defaults to False.	False
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
AsyncIterator[tuple[int, Output | Exception]]	A tuple of the index of the input and the output from the Runnable.

stream

stream(
    input: Input,
    config: RunnableConfig | None = None,
    **kwargs: Any | None
) -> Iterator[Output]

Default implementation of stream, which calls invoke.

Subclasses should override this method if they support streaming output.

Parameters:

Name	Type	Description	Default
input	Input	The input to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable. Defaults to None.	None
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
Output	The output of the Runnable.

astream async

astream(
    input: Input,
    config: RunnableConfig | None = None,
    **kwargs: Any | None
) -> AsyncIterator[Output]

Default implementation of astream, which calls ainvoke.

Subclasses should override this method if they support streaming output.

Parameters:

Name	Type	Description	Default
input	Input	The input to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable. Defaults to None.	None
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
AsyncIterator[Output]	The output of the Runnable.

astream_log async

astream_log(
    input: Any,
    config: RunnableConfig | None = None,
    *,
    diff: bool = True,
    with_streamed_output_list: bool = True,
    include_names: Sequence[str] | None = None,
    include_types: Sequence[str] | None = None,
    include_tags: Sequence[str] | None = None,
    exclude_names: Sequence[str] | None = None,
    exclude_types: Sequence[str] | None = None,
    exclude_tags: Sequence[str] | None = None,
    **kwargs: Any
) -> AsyncIterator[RunLogPatch] | AsyncIterator[RunLog]

Stream all output from a Runnable, as reported to the callback system.

This includes all inner runs of LLMs, Retrievers, Tools, etc.

Output is streamed as Log objects, which include a list of Jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run.

The Jsonpatch ops can be applied in order to construct state.

Parameters:

Name	Type	Description	Default
input	Any	The input to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable.	None
diff	bool	Whether to yield diffs between each step or the current state.	True
with_streamed_output_list	bool	Whether to yield the streamed_output list.	True
include_names	Sequence[str] | None	Only include logs with these names.	None
include_types	Sequence[str] | None	Only include logs with these types.	None
include_tags	Sequence[str] | None	Only include logs with these tags.	None
exclude_names	Sequence[str] | None	Exclude logs with these names.	None
exclude_types	Sequence[str] | None	Exclude logs with these types.	None
exclude_tags	Sequence[str] | None	Exclude logs with these tags.	None
kwargs	Any	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
AsyncIterator[RunLogPatch] | AsyncIterator[RunLog]	A RunLogPatch or RunLog object.

astream_events async

astream_events(
    input: Any,
    config: RunnableConfig | None = None,
    *,
    version: Literal["v1", "v2"] = "v2",
    include_names: Sequence[str] | None = None,
    include_types: Sequence[str] | None = None,
    include_tags: Sequence[str] | None = None,
    exclude_names: Sequence[str] | None = None,
    exclude_types: Sequence[str] | None = None,
    exclude_tags: Sequence[str] | None = None,
    **kwargs: Any
) -> AsyncIterator[StreamEvent]

Generate a stream of events.

Use to create an iterator over StreamEvents that provide real-time information about the progress of the Runnable, including StreamEvents from intermediate results.

A StreamEvent is a dictionary with the following schema:

• event: str - Event names are of the format: on_[runnable_type]_(start|stream|end).
• name: str - The name of the Runnable that generated the event.
• run_id: str - randomly generated ID associated with the given execution of the Runnable that emitted the event. A child Runnable that gets invoked as part of the execution of a parent Runnable is assigned its own unique ID.
• parent_ids: list[str] - The IDs of the parent runnables that generated the event. The root Runnable will have an empty list. The order of the parent IDs is from the root to the immediate parent. Only available for v2 version of the API. The v1 version of the API will return an empty list.
• tags: Optional[list[str]] - The tags of the Runnable that generated the event.
• metadata: Optional[dict[str, Any]] - The metadata of the Runnable that generated the event.
• data: dict[str, Any]

Below is a table that illustrates some events that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.

Note

This reference table is for the v2 version of the schema.

+--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | event | name | chunk | input | output | +==========================+==================+=====================================+===================================================+=====================================================+ | on_chat_model_start | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chat_model_stream | [model name] | AIMessageChunk(content="hello") | | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chat_model_end | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | AIMessageChunk(content="hello world") | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_llm_start | [model name] | | {'input': 'hello'} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_llm_stream | [model name] | 'Hello' | | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_llm_end | [model name] | | 'Hello human!' | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chain_start | format_docs | | | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chain_stream | format_docs | 'hello world!, goodbye world!' | | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_chain_end | format_docs | | [Document(...)] | 'hello world!, goodbye world!' | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_tool_start | some_tool | | {"x": 1, "y": "2"} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_tool_end | some_tool | | | {"x": 1, "y": "2"} | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_retriever_start | [retriever name] | | {"query": "hello"} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_retriever_end | [retriever name] | | {"query": "hello"} | [Document(...), ..] | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_prompt_start | [template_name] | | {"question": "hello"} | | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ | on_prompt_end | [template_name] | | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) | +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+

In addition to the standard events, users can also dispatch custom events (see example below).

Custom events will be only be surfaced with in the v2 version of the API!

A custom event has following format:

+-----------+------+-----------------------------------------------------------------------------------------------------------+ | Attribute | Type | Description | +===========+======+===========================================================================================================+ | name | str | A user defined name for the event. | +-----------+------+-----------------------------------------------------------------------------------------------------------+ | data | Any | The data associated with the event. This can be anything, though we suggest making it JSON serializable. | +-----------+------+-----------------------------------------------------------------------------------------------------------+

Here are declarations associated with the standard events shown above:

format_docs:

def format_docs(docs: list[Document]) -> str:
    '''Format the docs.'''
    return ", ".join([doc.page_content for doc in docs])


format_docs = RunnableLambda(format_docs)

some_tool:

@tool
def some_tool(x: int, y: str) -> dict:
    '''Some_tool.'''
    return {"x": x, "y": y}

prompt:

template = ChatPromptTemplate.from_messages(
    [
        ("system", "You are Cat Agent 007"),
        ("human", "{question}"),
    ]
).with_config({"run_name": "my_template", "tags": ["my_template"]})

Example:

from langchain_core.runnables import RunnableLambda


async def reverse(s: str) -> str:
    return s[::-1]


chain = RunnableLambda(func=reverse)

events = [event async for event in chain.astream_events("hello", version="v2")]

# will produce the following events (run_id, and parent_ids
# has been omitted for brevity):
[
    {
        "data": {"input": "hello"},
        "event": "on_chain_start",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"chunk": "olleh"},
        "event": "on_chain_stream",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"output": "olleh"},
        "event": "on_chain_end",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
]

Example: Dispatch Custom Event

from langchain_core.callbacks.manager import (
    adispatch_custom_event,
)
from langchain_core.runnables import RunnableLambda, RunnableConfig
import asyncio


async def slow_thing(some_input: str, config: RunnableConfig) -> str:
    """Do something that takes a long time."""
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 1 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 2 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    return "Done"

slow_thing = RunnableLambda(slow_thing)

async for event in slow_thing.astream_events("some_input", version="v2"):
    print(event)

Parameters:

Name	Type	Description	Default
input	Any	The input to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable.	None
version	Literal['v1', 'v2']	The version of the schema to use either 'v2' or 'v1'. Users should use 'v2'. 'v1' is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in 'v2'.	'v2'
include_names	Sequence[str] | None	Only include events from Runnables with matching names.	None
include_types	Sequence[str] | None	Only include events from Runnables with matching types.	None
include_tags	Sequence[str] | None	Only include events from Runnables with matching tags.	None
exclude_names	Sequence[str] | None	Exclude events from Runnables with matching names.	None
exclude_types	Sequence[str] | None	Exclude events from Runnables with matching types.	None
exclude_tags	Sequence[str] | None	Exclude events from Runnables with matching tags.	None
kwargs	Any	Additional keyword arguments to pass to the Runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.	{}

Yields:

Type	Description
AsyncIterator[StreamEvent]	An async stream of StreamEvents.

Raises:

Type	Description
NotImplementedError	If the version is not 'v1' or 'v2'.

transform

transform(
    input: Iterator[Input],
    config: RunnableConfig | None = None,
    **kwargs: Any | None
) -> Iterator[Output]

Transform inputs to outputs.

Default implementation of transform, which buffers input and calls astream.

Subclasses should override this method if they can start producing output while input is still being generated.

Parameters:

Name	Type	Description	Default
input	Iterator[Input]	An iterator of inputs to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable. Defaults to None.	None
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
Output	The output of the Runnable.

atransform async

atransform(
    input: AsyncIterator[Input],
    config: RunnableConfig | None = None,
    **kwargs: Any | None
) -> AsyncIterator[Output]

Transform inputs to outputs.

Default implementation of atransform, which buffers input and calls astream.

Subclasses should override this method if they can start producing output while input is still being generated.

Parameters:

Name	Type	Description	Default
input	AsyncIterator[Input]	An async iterator of inputs to the Runnable.	required
config	RunnableConfig | None	The config to use for the Runnable. Defaults to None.	None
kwargs	Any | None	Additional keyword arguments to pass to the Runnable.	{}

Yields:

Type	Description
AsyncIterator[Output]	The output of the Runnable.

bind

bind(**kwargs: Any) -> Runnable[Input, Output]

Bind arguments to a Runnable, returning a new Runnable.

Useful when a Runnable in a chain requires an argument that is not in the output of the previous Runnable or included in the user input.

Parameters:

Name	Type	Description	Default
kwargs	Any	The arguments to bind to the Runnable.	{}

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the arguments bound.

Example

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

llm = ChatOllama(model="llama3.1")

# Without bind.
chain = llm | StrOutputParser()

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.'

# With bind.
chain = llm.bind(stop=["three"]) | StrOutputParser()

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'

with_config

with_config(
    config: RunnableConfig | None = None, **kwargs: Any
) -> Runnable[Input, Output]

Bind config to a Runnable, returning a new Runnable.

Parameters:

Name	Type	Description	Default
config	RunnableConfig | None	The config to bind to the Runnable.	None
kwargs	Any	Additional keyword arguments to pass to the Runnable.	{}

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the config bound.

with_listeners

with_listeners(
    *,
    on_start: (
        Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None
    ) = None,
    on_end: (
        Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None
    ) = None,
    on_error: (
        Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None
    ) = None
) -> Runnable[Input, Output]

Bind lifecycle listeners to a Runnable, returning a new Runnable.

The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.

Parameters:

Name	Type	Description	Default
on_start	Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None	Called before the Runnable starts running, with the Run object. Defaults to None.	None
on_end	Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None	Called after the Runnable finishes running, with the Run object. Defaults to None.	None
on_error	Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None	Called if the Runnable throws an error, with the Run object. Defaults to None.	None

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the listeners bound.

Example

from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run

import time


def test_runnable(time_to_sleep: int):
    time.sleep(time_to_sleep)


def fn_start(run_obj: Run):
    print("start_time:", run_obj.start_time)


def fn_end(run_obj: Run):
    print("end_time:", run_obj.end_time)


chain = RunnableLambda(test_runnable).with_listeners(
    on_start=fn_start, on_end=fn_end
)
chain.invoke(2)

with_alisteners

with_alisteners(
    *,
    on_start: AsyncListener | None = None,
    on_end: AsyncListener | None = None,
    on_error: AsyncListener | None = None
) -> Runnable[Input, Output]

Bind async lifecycle listeners to a Runnable.

Returns a new Runnable.

The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.

Parameters:

Name	Type	Description	Default
on_start	AsyncListener | None	Called asynchronously before the Runnable starts running, with the Run object. Defaults to None.	None
on_end	AsyncListener | None	Called asynchronously after the Runnable finishes running, with the Run object. Defaults to None.	None
on_error	AsyncListener | None	Called asynchronously if the Runnable throws an error, with the Run object. Defaults to None.	None

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the listeners bound.

Example

from langchain_core.runnables import RunnableLambda, Runnable
from datetime import datetime, timezone
import time
import asyncio

def format_t(timestamp: float) -> str:
    return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()

async def test_runnable(time_to_sleep: int):
    print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
    await asyncio.sleep(time_to_sleep)
    print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")

async def fn_start(run_obj: Runnable):
    print(f"on start callback starts at {format_t(time.time())}")
    await asyncio.sleep(3)
    print(f"on start callback ends at {format_t(time.time())}")

async def fn_end(run_obj: Runnable):
    print(f"on end callback starts at {format_t(time.time())}")
    await asyncio.sleep(2)
    print(f"on end callback ends at {format_t(time.time())}")

runnable = RunnableLambda(test_runnable).with_alisteners(
    on_start=fn_start,
    on_end=fn_end
)
async def concurrent_runs():
    await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))

asyncio.run(concurrent_runs())
Result:
on start callback starts at 2025-03-01T07:05:22.875378+00:00
on start callback starts at 2025-03-01T07:05:22.875495+00:00
on start callback ends at 2025-03-01T07:05:25.878862+00:00
on start callback ends at 2025-03-01T07:05:25.878947+00:00
Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
on end callback starts at 2025-03-01T07:05:27.882360+00:00
Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
on end callback starts at 2025-03-01T07:05:28.882428+00:00
on end callback ends at 2025-03-01T07:05:29.883893+00:00
on end callback ends at 2025-03-01T07:05:30.884831+00:00

with_types

with_types(
    *,
    input_type: type[Input] | None = None,
    output_type: type[Output] | None = None
) -> Runnable[Input, Output]

Bind input and output types to a Runnable, returning a new Runnable.

Parameters:

Name	Type	Description	Default
input_type	type[Input] | None	The input type to bind to the Runnable. Defaults to None.	None
output_type	type[Output] | None	The output type to bind to the Runnable. Defaults to None.	None

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable with the types bound.

with_retry

with_retry(
    *,
    retry_if_exception_type: tuple[
        type[BaseException], ...
    ] = (Exception,),
    wait_exponential_jitter: bool = True,
    exponential_jitter_params: (
        ExponentialJitterParams | None
    ) = None,
    stop_after_attempt: int = 3
) -> Runnable[Input, Output]

Create a new Runnable that retries the original Runnable on exceptions.

Parameters:

Name	Type	Description	Default
retry_if_exception_type	tuple[type[BaseException], ...]	A tuple of exception types to retry on. Defaults to (Exception,).	(Exception,)
wait_exponential_jitter	bool	Whether to add jitter to the wait time between retries. Defaults to True.	True
stop_after_attempt	int	The maximum number of attempts to make before giving up. Defaults to 3.	3
exponential_jitter_params	ExponentialJitterParams | None	Parameters for tenacity.wait_exponential_jitter. Namely: initial, max, exp_base, and jitter (all float values).	None

Returns:

Type	Description
Runnable[Input, Output]	A new Runnable that retries the original Runnable on exceptions.

Example

from langchain_core.runnables import RunnableLambda

count = 0


def _lambda(x: int) -> None:
    global count
    count = count + 1
    if x == 1:
        raise ValueError("x is 1")
    else:
        pass


runnable = RunnableLambda(_lambda)
try:
    runnable.with_retry(
        stop_after_attempt=2,
        retry_if_exception_type=(ValueError,),
    ).invoke(1)
except ValueError:
    pass

assert count == 2

map

map() -> Runnable[list[Input], list[Output]]

Return a new Runnable that maps a list of inputs to a list of outputs.

Calls invoke with each input.

Returns:

Type	Description
Runnable[list[Input], list[Output]]	A new Runnable that maps a list of inputs to a list of outputs.

Example

from langchain_core.runnables import RunnableLambda


def _lambda(x: int) -> int:
    return x + 1


runnable = RunnableLambda(_lambda)
print(runnable.map().invoke([1, 2, 3]))  # [2, 3, 4]

with_fallbacks

with_fallbacks(
    fallbacks: Sequence[Runnable[Input, Output]],
    *,
    exceptions_to_handle: tuple[
        type[BaseException], ...
    ] = (Exception,),
    exception_key: str | None = None
) -> RunnableWithFallbacks[Input, Output]

Add fallbacks to a Runnable, returning a new Runnable.

The new Runnable will try the original Runnable, and then each fallback in order, upon failures.

Parameters:

Name	Type	Description	Default
fallbacks	Sequence[Runnable[Input, Output]]	A sequence of runnables to try if the original Runnable fails.	required
exceptions_to_handle	tuple[type[BaseException], ...]	A tuple of exception types to handle. Defaults to (Exception,).	(Exception,)
exception_key	str | None	If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input. Defaults to None.	None

Returns:

Type	Description
RunnableWithFallbacks[Input, Output]	A new Runnable that will try the original Runnable, and then each
RunnableWithFallbacks[Input, Output]	fallback in order, upon failures.

Example

from typing import Iterator

from langchain_core.runnables import RunnableGenerator


def _generate_immediate_error(input: Iterator) -> Iterator[str]:
    raise ValueError()
    yield ""


def _generate(input: Iterator) -> Iterator[str]:
    yield from "foo bar"


runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
    [RunnableGenerator(_generate)]
)
print("".join(runnable.stream({})))  # foo bar

Parameters:

Name	Type	Description	Default
fallbacks	Sequence[Runnable[Input, Output]]	A sequence of runnables to try if the original Runnable fails.	required
exceptions_to_handle	tuple[type[BaseException], ...]	A tuple of exception types to handle.	(Exception,)
exception_key	str | None	If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input.	None

Returns:

Type	Description
RunnableWithFallbacks[Input, Output]	A new Runnable that will try the original Runnable, and then each
RunnableWithFallbacks[Input, Output]	fallback in order, upon failures.

as_tool

as_tool(
    args_schema: type[BaseModel] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    arg_types: dict[str, type] | None = None
) -> BaseTool

Create a BaseTool from a Runnable.

as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. Where possible, schemas are inferred from runnable.get_input_schema. Alternatively (e.g., if the Runnable takes a dict as input and the specific dict keys are not typed), the schema can be specified directly with args_schema. You can also pass arg_types to just specify the required arguments and their types.

Parameters:

Name	Type	Description	Default
args_schema	type[BaseModel] | None	The schema for the tool. Defaults to None.	None
name	str | None	The name of the tool. Defaults to None.	None
description	str | None	The description of the tool. Defaults to None.	None
arg_types	dict[str, type] | None	A dictionary of argument names to types. Defaults to None.	None

Returns:

Type	Description
BaseTool	A BaseTool instance.

Typed dict input:

from typing_extensions import TypedDict
from langchain_core.runnables import RunnableLambda


class Args(TypedDict):
    a: int
    b: list[int]


def f(x: Args) -> str:
    return str(x["a"] * max(x["b"]))


runnable = RunnableLambda(f)
as_tool = runnable.as_tool()
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via args_schema:

from typing import Any
from pydantic import BaseModel, Field
from langchain_core.runnables import RunnableLambda

def f(x: dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))

class FSchema(BaseModel):
    """Apply a function to an integer and list of integers."""

    a: int = Field(..., description="Integer")
    b: list[int] = Field(..., description="List of ints")

runnable = RunnableLambda(f)
as_tool = runnable.as_tool(FSchema)
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via arg_types:

from typing import Any
from langchain_core.runnables import RunnableLambda


def f(x: dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))


runnable = RunnableLambda(f)
as_tool = runnable.as_tool(arg_types={"a": int, "b": list[int]})
as_tool.invoke({"a": 3, "b": [1, 2]})

String input:

from langchain_core.runnables import RunnableLambda


def f(x: str) -> str:
    return x + "a"


def g(x: str) -> str:
    return x + "z"


runnable = RunnableLambda(f) | g
as_tool = runnable.as_tool()
as_tool.invoke("b")

Added in version 0.2.14

__init__

__init__(**kwargs: Any) -> None

Initialize the tool.

Raises:

Type	Description
TypeError	If args_schema is not a subclass of pydantic BaseModel or dict.

is_lc_serializable classmethod

is_lc_serializable() -> bool

Is this class serializable?

By design, even if a class inherits from Serializable, it is not serializable by default. This is to prevent accidental serialization of objects that should not be serialized.

Returns:

Type	Description
bool	Whether the class is serializable. Default is False.

get_lc_namespace classmethod

get_lc_namespace() -> list[str]

Get the namespace of the langchain object.

For example, if the class is langchain.llms.openai.OpenAI, then the namespace is ["langchain", "llms", "openai"]

Returns:

Type	Description
list[str]	The namespace as a list of strings.

lc_id classmethod

lc_id() -> list[str]

Return a unique identifier for this class for serialization purposes.

The unique identifier is a list of strings that describes the path to the object. For example, for the class langchain.llms.openai.OpenAI, the id is ["langchain", "llms", "openai", "OpenAI"].

to_json

to_json() -> (
    SerializedConstructor | SerializedNotImplemented
)

Serialize the Runnable to JSON.

Returns:

Type	Description
SerializedConstructor | SerializedNotImplemented	A JSON-serializable representation of the Runnable.

to_json_not_implemented

to_json_not_implemented() -> SerializedNotImplemented

Serialize a "not implemented" object.

Returns:

Type	Description
SerializedNotImplemented	SerializedNotImplemented.

configurable_fields

configurable_fields(
    **kwargs: AnyConfigurableField,
) -> RunnableSerializable[Input, Output]

Configure particular Runnable fields at runtime.

Parameters:

Name	Type	Description	Default
**kwargs	AnyConfigurableField	A dictionary of ConfigurableField instances to configure.	{}

Raises:

Type	Description
ValueError	If a configuration key is not found in the Runnable.

Returns:

Type	Description
RunnableSerializable[Input, Output]	A new Runnable with the fields configured.

from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatOpenAI(max_tokens=20).configurable_fields(
    max_tokens=ConfigurableField(
        id="output_token_number",
        name="Max tokens in the output",
        description="The maximum number of tokens in the output",
    )
)

# max_tokens = 20
print("max_tokens_20: ", model.invoke("tell me something about chess").content)

# max_tokens = 200
print(
    "max_tokens_200: ",
    model.with_config(configurable={"output_token_number": 200})
    .invoke("tell me something about chess")
    .content,
)

configurable_alternatives

configurable_alternatives(
    which: ConfigurableField,
    *,
    default_key: str = "default",
    prefix_keys: bool = False,
    **kwargs: (
        Runnable[Input, Output]
        | Callable[[], Runnable[Input, Output]]
    )
) -> RunnableSerializable[Input, Output]

Configure alternatives for Runnables that can be set at runtime.

Parameters:

Name	Type	Description	Default
which	ConfigurableField	The ConfigurableField instance that will be used to select the alternative.	required
default_key	str	The default key to use if no alternative is selected. Defaults to 'default'.	'default'
prefix_keys	bool	Whether to prefix the keys with the ConfigurableField id. Defaults to False.	False
**kwargs	Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]	A dictionary of keys to Runnable instances or callables that return Runnable instances.	{}

Returns:

Type	Description
RunnableSerializable[Input, Output]	A new Runnable with the alternatives configured.

from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatAnthropic(
    model_name="claude-3-7-sonnet-20250219"
).configurable_alternatives(
    ConfigurableField(id="llm"),
    default_key="anthropic",
    openai=ChatOpenAI(),
)

# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)

# uses ChatOpenAI
print(
    model.with_config(configurable={"llm": "openai"})
    .invoke("which organization created you?")
    .content
)

__init_subclass__

__init_subclass__(**kwargs: Any) -> None

Validate the tool class definition during subclass creation.

Parameters:

Name	Type	Description	Default
**kwargs	Any	Additional keyword arguments passed to the parent class.	{}

Raises:

Type	Description
SchemaAnnotationError	If args_schema has incorrect type annotation.

run

run(
    tool_input: str | dict[str, Any],
    verbose: bool | None = None,
    start_color: str | None = "green",
    color: str | None = "green",
    callbacks: Callbacks = None,
    *,
    tags: list[str] | None = None,
    metadata: dict[str, Any] | None = None,
    run_name: str | None = None,
    run_id: UUID | None = None,
    config: RunnableConfig | None = None,
    tool_call_id: str | None = None,
    **kwargs: Any
) -> Any

Run the tool.

Parameters:

Name	Type	Description	Default
tool_input	str | dict[str, Any]	The input to the tool.	required
verbose	bool | None	Whether to log the tool's progress. Defaults to None.	None
start_color	str | None	The color to use when starting the tool. Defaults to 'green'.	'green'
color	str | None	The color to use when ending the tool. Defaults to 'green'.	'green'
callbacks	Callbacks	Callbacks to be called during tool execution. Defaults to None.	None
tags	list[str] | None	Optional list of tags associated with the tool. Defaults to None.	None
metadata	dict[str, Any] | None	Optional metadata associated with the tool. Defaults to None.	None
run_name	str | None	The name of the run. Defaults to None.	None
run_id	UUID | None	The id of the run. Defaults to None.	None
config	RunnableConfig | None	The configuration for the tool. Defaults to None.	None
tool_call_id	str | None	The id of the tool call. Defaults to None.	None
kwargs	Any	Keyword arguments to be passed to tool callbacks (event handler)	{}

Returns:

Type	Description
Any	The output of the tool.

Raises:

Type	Description
ToolException	If an error occurs during tool execution.

arun async

arun(
    tool_input: str | dict,
    verbose: bool | None = None,
    start_color: str | None = "green",
    color: str | None = "green",
    callbacks: Callbacks = None,
    *,
    tags: list[str] | None = None,
    metadata: dict[str, Any] | None = None,
    run_name: str | None = None,
    run_id: UUID | None = None,
    config: RunnableConfig | None = None,
    tool_call_id: str | None = None,
    **kwargs: Any
) -> Any

Run the tool asynchronously.

Parameters:

Name	Type	Description	Default
tool_input	str | dict	The input to the tool.	required
verbose	bool | None	Whether to log the tool's progress. Defaults to None.	None
start_color	str | None	The color to use when starting the tool. Defaults to 'green'.	'green'
color	str | None	The color to use when ending the tool. Defaults to 'green'.	'green'
callbacks	Callbacks	Callbacks to be called during tool execution. Defaults to None.	None
tags	list[str] | None	Optional list of tags associated with the tool. Defaults to None.	None
metadata	dict[str, Any] | None	Optional metadata associated with the tool. Defaults to None.	None
run_name	str | None	The name of the run. Defaults to None.	None
run_id	UUID | None	The id of the run. Defaults to None.	None
config	RunnableConfig | None	The configuration for the tool. Defaults to None.	None
tool_call_id	str | None	The id of the tool call. Defaults to None.	None
kwargs	Any	Keyword arguments to be passed to tool callbacks	{}

Returns:

Type	Description
Any	The output of the tool.

Raises:

Type	Description
ToolException	If an error occurs during tool execution.

validate_environment classmethod

validate_environment(values: dict) -> Any

Validate the environment.