Runnables

langchain_core.runnables.base.Runnable ¶

Bases: ABC, Generic[Input, Output]

A unit of work that can be invoked, batched, streamed, transformed and composed.

Key Methods¶

invoke/ainvoke: Transforms a single input into an output.
batch/abatch: Efficiently transforms multiple inputs into outputs.
stream/astream: Streams output from a single input as it's produced.
astream_log: Streams output and selected intermediate results from an input.

Built-in optimizations:

Batch: By default, batch runs invoke() in parallel using a thread pool executor. Override to optimize batching.
Async: Methods with 'a' suffix are asynchronous. By default, they execute the sync counterpart using asyncio's thread pool. Override for native async.

All methods accept an optional config argument, which can be used to configure execution, add tags and metadata for tracing and debugging etc.

Runnables expose schematic information about their input, output and config via the input_schema property, the output_schema property and config_schema method.

Composition¶

Runnable objects can be composed together to create chains in a declarative way.

Any chain constructed this way will automatically have sync, async, batch, and streaming support.

The main composition primitives are RunnableSequence and RunnableParallel.

RunnableSequence invokes a series of runnables sequentially, with one Runnable's output serving as the next's input. Construct using the | operator or by passing a list of runnables to RunnableSequence.

RunnableParallel invokes runnables concurrently, providing the same input to each. Construct it using a dict literal within a sequence or by passing a dict to RunnableParallel.

For example,

from langchain_core.runnables import RunnableLambda

# A RunnableSequence constructed using the `|` operator
sequence = RunnableLambda(lambda x: x + 1) | RunnableLambda(lambda x: x * 2)
sequence.invoke(1)  # 4
sequence.batch([1, 2, 3])  # [4, 6, 8]


# A sequence that contains a RunnableParallel constructed using a dict literal
sequence = RunnableLambda(lambda x: x + 1) | {
    "mul_2": RunnableLambda(lambda x: x * 2),
    "mul_5": RunnableLambda(lambda x: x * 5),
}
sequence.invoke(1)  # {'mul_2': 4, 'mul_5': 10}

Standard Methods¶

All Runnables expose additional methods that can be used to modify their behavior (e.g., add a retry policy, add lifecycle listeners, make them configurable, etc.).

These methods will work on any Runnable, including Runnable chains constructed by composing other Runnables. See the individual methods for details.

For example,

from langchain_core.runnables import RunnableLambda

import random

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


def buggy_double(y: int) -> int:
    """Buggy code that will fail 70% of the time"""
    if random.random() > 0.3:
        print('This code failed, and will probably be retried!')  # noqa: T201
        raise ValueError('Triggered buggy code')
    return y * 2

sequence = (
    RunnableLambda(add_one) |
    RunnableLambda(buggy_double).with_retry( # Retry on failure
        stop_after_attempt=10,
        wait_exponential_jitter=False
    )
)

print(sequence.input_schema.model_json_schema()) # Show inferred input schema
print(sequence.output_schema.model_json_schema()) # Show inferred output schema
print(sequence.invoke(2)) # invoke the sequence (note the retry above!!)

Debugging and tracing¶

As the chains get longer, it can be useful to be able to see intermediate results to debug and trace the chain.

You can set the global debug flag to True to enable debug output for all chains:

from langchain_core.globals import set_debug

set_debug(True)

Alternatively, you can pass existing or custom callbacks to any given chain:

from langchain_core.tracers import ConsoleCallbackHandler

chain.invoke(..., config={"callbacks": [ConsoleCallbackHandler()]})

For a UI (and much more) checkout LangSmith.

METHOD	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 `Runnable` objects.
`pick`	Pick keys from the output `dict` of this `Runnable`.
`assign`	Assigns new fields to the `dict` output of this `Runnable`.
`invoke`	Transform a single input into an output.
`ainvoke`	Transform a single input into an output.
`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`.

name `instance-attribute` ¶

name: str | None

The name of the Runnable. Used for debugging and tracing.

InputType `property` ¶

InputType: type[Input]

Input type.

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

RAISES	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	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.

get_name ¶

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

Get the name of the Runnable.

PARAMETER	DESCRIPTION
`suffix`	An optional suffix to append to the name. TYPE: `str \| None` DEFAULT: `None`
`name`	An optional name to use instead of the `Runnable`'s name. TYPE: `str \| None` DEFAULT: `None`

RETURNS	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.

Runnable objects 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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

Runnable objects 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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	DESCRIPTION
`RunnableSerializable[Other, Output]`	A new `Runnable`.

pipe ¶

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

Pipe Runnable objects.

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]

PARAMETER	DESCRIPTION
`*others`	Other `Runnable` or `Runnable`-like objects to compose TYPE: `Runnable[Any, Other] \| Callable[[Any], Other]` DEFAULT: `()`
`name`	An optional name for the resulting `RunnableSequence`. TYPE: `str \| None` DEFAULT: `None`

RETURNS	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 a single key:

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 a list of keys:

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]"}

PARAMETER	DESCRIPTION
`keys`	A key or list of keys to pick from the output dict. TYPE: `str \| list[str]`

RETURNS	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}"
)
model = FakeStreamingListLLM(responses=["foo-lish"])

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

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

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'}}}

PARAMETER	DESCRIPTION
`**kwargs`	A mapping of keys to `Runnable` or `Runnable`-like objects that will be invoked with the entire output dict of this `Runnable`. TYPE: `Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any] \| Mapping[str, Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any]]` DEFAULT: `{}`

RETURNS	DESCRIPTION
`RunnableSerializable[Any, Any]`	A new `Runnable`.

invoke `abstractmethod` ¶

invoke(input: Input, config: RunnableConfig | None = None, **kwargs: Any) -> Output

Transform a single input into an output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	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. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Output`	The output of the `Runnable`.

ainvoke `async` ¶

ainvoke(input: Input, config: RunnableConfig | None = None, **kwargs: Any) -> Output

Transform a single input into an output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	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. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Output`	The output of the `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 must override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `list[Input]`
`config`	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. TYPE: `RunnableConfig \| list[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `Sequence[Input]`
`config`	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. TYPE: `RunnableConfig \| Sequence[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

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

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `list[Input]`
`config`	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. TYPE: `RunnableConfig \| list[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `Sequence[Input]`
`config`	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. TYPE: `RunnableConfig \| Sequence[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	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 must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	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 must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	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.

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

YIELDS	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 StreamEvent that provide real-time information about the progress of the Runnable, including StreamEvent from intermediate results.

A StreamEvent is a dictionary with the following schema:

event: Event names are of the format: on_[runnable_type]_(start|stream|end).
name: The name of the Runnable that generated the event.
run_id: 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: 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: The tags of the Runnable that generated the event.
metadata: The metadata of the Runnable that generated the event.
data: The data associated with the event. The contents of this field depend on the type of event. See the table below for more details.

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"]})

For instance:

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)

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Any`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`version`	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'`. TYPE: `Literal['v1', 'v2']` DEFAULT: `'v2'`
`include_names`	Only include events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_types`	Only include events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_tags`	Only include events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_names`	Exclude events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_types`	Exclude events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_tags`	Exclude events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`**kwargs`	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`. TYPE: `Any` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[StreamEvent]`	An async stream of `StreamEvent`.

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

PARAMETER	DESCRIPTION
`input`	An iterator of inputs to the `Runnable`. TYPE: `Iterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

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

PARAMETER	DESCRIPTION
`input`	An async iterator of inputs to the `Runnable`. TYPE: `AsyncIterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	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.

PARAMETER	DESCRIPTION
`**kwargs`	The arguments to bind to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`Runnable[Input, Output]`	A new `Runnable` with the arguments bound.

Example

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

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

# Without bind
chain = model | StrOutputParser()

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

# With bind
chain = model.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.

PARAMETER	DESCRIPTION
`config`	The config to bind to the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called before the `Runnable` starts running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_end`	Called after the `Runnable` finishes running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_error`	Called if the `Runnable` throws an error, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called asynchronously before the `Runnable` starts running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_end`	Called asynchronously after the `Runnable` finishes running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_error`	Called asynchronously if the `Runnable` throws an error, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`input_type`	The input type to bind to the `Runnable`. TYPE: `type[Input] \| None` DEFAULT: `None`
`output_type`	The output type to bind to the `Runnable`. TYPE: `type[Output] \| None` DEFAULT: `None`

RETURNS	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.

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

RETURNS	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	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.

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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.

PARAMETER	DESCRIPTION
`args_schema`	The schema for the tool. TYPE: `type[BaseModel] \| None` DEFAULT: `None`
`name`	The name of the tool. TYPE: `str \| None` DEFAULT: `None`
`description`	The description of the tool. TYPE: `str \| None` DEFAULT: `None`
`arg_types`	A dictionary of argument names to types. TYPE: `dict[str, type] \| None` DEFAULT: `None`

RETURNS	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")

langchain_core.runnables.base.RunnableBinding ¶

Bases: RunnableBindingBase[Input, Output]

Wrap a Runnable with additional functionality.

A RunnableBinding can be thought of as a "runnable decorator" that preserves the essential features of Runnable; i.e., batching, streaming, and async support, while adding additional functionality.

Any class that inherits from Runnable can be bound to a RunnableBinding. Runnables expose a standard set of methods for creating RunnableBindings or sub-classes of RunnableBindings (e.g., RunnableRetry, RunnableWithFallbacks) that add additional functionality.

These methods include:

bind: Bind kwargs to pass to the underlying Runnable when running it.
with_config: Bind config to pass to the underlying Runnable when running it.
with_listeners: Bind lifecycle listeners to the underlying Runnable.
with_types: Override the input and output types of the underlying Runnable.
with_retry: Bind a retry policy to the underlying Runnable.
with_fallbacks: Bind a fallback policy to the underlying Runnable.

Example: bind: Bind kwargs to pass to the underlying Runnable when running it.

```python
# Create a Runnable binding that invokes the chat model with the
# additional kwarg `stop=['-']` when running it.
from langchain_community.chat_models import ChatOpenAI

model = ChatOpenAI()
model.invoke('Say "Parrot-MAGIC"', stop=["-"])  # Should return `Parrot`
# Using it the easy way via `bind` method which returns a new
# RunnableBinding
runnable_binding = model.bind(stop=["-"])
runnable_binding.invoke('Say "Parrot-MAGIC"')  # Should return `Parrot`
```
Can also be done by instantiating a `RunnableBinding` directly (not
recommended):

```python
from langchain_core.runnables import RunnableBinding

runnable_binding = RunnableBinding(
    bound=model,
    kwargs={"stop": ["-"]},  # <-- Note the additional kwargs
)
runnable_binding.invoke('Say "Parrot-MAGIC"')  # Should return `Parrot`
```

METHOD	DESCRIPTION
`bind`	Bind additional kwargs 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_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.
`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 `Runnable` objects.
`pick`	Pick keys from the output `dict` of this `Runnable`.
`assign`	Assigns new fields to the `dict` output of this `Runnable`.
`invoke`	Transform a single input into an output.
`ainvoke`	Transform a single input into an output.
`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.
`with_alisteners`	Bind async lifecycle listeners to a `Runnable`.
`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__`	Create a `RunnableBinding` from a `Runnable` and kwargs.
`is_lc_serializable`	Return True as this class is 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 `Runnable` objects that can be set at runtime.

name `class-attribute` `instance-attribute` ¶

name: str | None = None

The name of the Runnable. Used for debugging and tracing.

InputType `property` ¶

InputType: type[Input]

Input type.

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

RAISES	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	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, {"openai_api_key": "OPENAI_API_KEY"}

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.

bound `instance-attribute` ¶

bound: Runnable[Input, Output]

The underlying Runnable that this Runnable delegates to.

kwargs `class-attribute` `instance-attribute` ¶

kwargs: Mapping[str, Any] = Field(default_factory=dict)

kwargs to pass to the underlying Runnable when running.

For example, when the Runnable binding is invoked the underlying Runnable will be invoked with the same input but with these additional kwargs.

config `class-attribute` `instance-attribute` ¶

config: RunnableConfig = config or {}

The config to bind to the underlying Runnable.

config_factories `class-attribute` `instance-attribute` ¶

config_factories: list[Callable[[RunnableConfig], RunnableConfig]] = Field(
    default_factory=list
)

The config factories to bind to the underlying Runnable.

custom_input_type `class-attribute` `instance-attribute` ¶

custom_input_type: Any | None = None

Override the input type of the underlying Runnable with a custom type.

The type can be a Pydantic model, or a type annotation (e.g., list[str]).

custom_output_type `class-attribute` `instance-attribute` ¶

custom_output_type: Any | None = None

Override the output type of the underlying Runnable with a custom type.

The type can be a Pydantic model, or a type annotation (e.g., list[str]).

bind ¶

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

Bind additional kwargs to a Runnable, returning a new Runnable.

PARAMETER	DESCRIPTION
`**kwargs`	The kwargs to bind to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`Runnable[Input, Output]`	A new `Runnable` with the same type and config as the original,
`Runnable[Input, Output]`	but with the additional kwargs bound.

with_config ¶

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

Bind config to a Runnable, returning a new Runnable.

PARAMETER	DESCRIPTION
`config`	The config to bind to the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called before the `Runnable` starts running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_end`	Called after the `Runnable` finishes running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_error`	Called if the `Runnable` throws an error, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Runnable[Input, Output]`	A new `Runnable` with the listeners bound.

with_types ¶

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

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

PARAMETER	DESCRIPTION
`input_type`	The input type to bind to the `Runnable`. TYPE: `type[Input] \| None` DEFAULT: `None`
`output_type`	The output type to bind to the `Runnable`. TYPE: `type[Output] \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Runnable[Input, Output]`	A new `Runnable` with the types bound.

with_retry ¶

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

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

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

RETURNS	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

get_name ¶

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

Get the name of the Runnable.

PARAMETER	DESCRIPTION
`suffix`	An optional suffix to append to the name. TYPE: `str \| None` DEFAULT: `None`
`name`	An optional name to use instead of the `Runnable`'s name. TYPE: `str \| None` DEFAULT: `None`

RETURNS	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.

Runnable objects 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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

Runnable objects 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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	DESCRIPTION
`RunnableSerializable[Other, Output]`	A new `Runnable`.

pipe ¶

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

Pipe Runnable objects.

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]

PARAMETER	DESCRIPTION
`*others`	Other `Runnable` or `Runnable`-like objects to compose TYPE: `Runnable[Any, Other] \| Callable[[Any], Other]` DEFAULT: `()`
`name`	An optional name for the resulting `RunnableSequence`. TYPE: `str \| None` DEFAULT: `None`

RETURNS	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 a single key:

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 a list of keys:

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]"}

PARAMETER	DESCRIPTION
`keys`	A key or list of keys to pick from the output dict. TYPE: `str \| list[str]`

RETURNS	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}"
)
model = FakeStreamingListLLM(responses=["foo-lish"])

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

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

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'}}}

PARAMETER	DESCRIPTION
`**kwargs`	A mapping of keys to `Runnable` or `Runnable`-like objects that will be invoked with the entire output dict of this `Runnable`. TYPE: `Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any] \| Mapping[str, Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any]]` DEFAULT: `{}`

RETURNS	DESCRIPTION
`RunnableSerializable[Any, Any]`	A new `Runnable`.

invoke ¶

invoke(
    input: Input, config: RunnableConfig | None = None, **kwargs: Any | None
) -> Output

Transform a single input into an output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	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. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Output`	The output of the `Runnable`.

ainvoke `async` ¶

ainvoke(
    input: Input, config: RunnableConfig | None = None, **kwargs: Any | None
) -> Output

Transform a single input into an output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	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. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Output`	The output of the `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 must override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `list[Input]`
`config`	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. TYPE: `RunnableConfig \| list[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `Sequence[Input]`
`config`	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. TYPE: `RunnableConfig \| Sequence[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

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

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `list[Input]`
`config`	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. TYPE: `RunnableConfig \| list[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `Sequence[Input]`
`config`	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. TYPE: `RunnableConfig \| Sequence[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	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 must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	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 must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	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.

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

YIELDS	DESCRIPTION
`AsyncIterator[RunLogPatch] \| AsyncIterator[RunLog]`	A `RunLogPatch` or `RunLog` object.

astream_events `async` ¶

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

Generate a stream of events.

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

A StreamEvent is a dictionary with the following schema:

event: Event names are of the format: on_[runnable_type]_(start|stream|end).
name: The name of the Runnable that generated the event.
run_id: 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: 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: The tags of the Runnable that generated the event.
metadata: The metadata of the Runnable that generated the event.
data: The data associated with the event. The contents of this field depend on the type of event. See the table below for more details.

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"]})

For instance:

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)

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Any`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`version`	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'`. TYPE: `Literal['v1', 'v2']` DEFAULT: `'v2'`
`include_names`	Only include events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_types`	Only include events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_tags`	Only include events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_names`	Exclude events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_types`	Exclude events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_tags`	Exclude events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`**kwargs`	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`. TYPE: `Any` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[StreamEvent]`	An async stream of `StreamEvent`.

RAISES	DESCRIPTION
`NotImplementedError`	If the version is not `'v1'` or `'v2'`.

transform ¶

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

Transform inputs to outputs.

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

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

PARAMETER	DESCRIPTION
`input`	An iterator of inputs to the `Runnable`. TYPE: `Iterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`Output`	The output of the `Runnable`.

atransform `async` ¶

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

Transform inputs to outputs.

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

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

PARAMETER	DESCRIPTION
`input`	An async iterator of inputs to the `Runnable`. TYPE: `AsyncIterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[Output]`	The output of the `Runnable`.

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.

PARAMETER	DESCRIPTION
`on_start`	Called asynchronously before the `Runnable` starts running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_end`	Called asynchronously after the `Runnable` finishes running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_error`	Called asynchronously if the `Runnable` throws an error, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`

RETURNS	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

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	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.

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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.

PARAMETER	DESCRIPTION
`args_schema`	The schema for the tool. TYPE: `type[BaseModel] \| None` DEFAULT: `None`
`name`	The name of the tool. TYPE: `str \| None` DEFAULT: `None`
`description`	The description of the tool. TYPE: `str \| None` DEFAULT: `None`
`arg_types`	A dictionary of argument names to types. TYPE: `dict[str, type] \| None` DEFAULT: `None`

RETURNS	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")

init ¶

__init__(
    *,
    bound: Runnable[Input, Output],
    kwargs: Mapping[str, Any] | None = None,
    config: RunnableConfig | None = None,
    config_factories: list[Callable[[RunnableConfig], RunnableConfig]] | None = None,
    custom_input_type: type[Input] | BaseModel | None = None,
    custom_output_type: type[Output] | BaseModel | None = None,
    **other_kwargs: Any,
) -> None

Create a RunnableBinding from a Runnable and kwargs.

PARAMETER	DESCRIPTION
`bound`	The underlying `Runnable` that this `Runnable` delegates calls to. TYPE: `Runnable[Input, Output]`
`kwargs`	optional kwargs to pass to the underlying `Runnable`, when running the underlying `Runnable` (e.g., via `invoke`, `batch`, `transform`, or `stream` or async variants) TYPE: `Mapping[str, Any] \| None` DEFAULT: `None`
`config`	optional config to bind to the underlying `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`config_factories`	optional list of config factories to apply to the config before binding to the underlying `Runnable`. TYPE: `list[Callable[[RunnableConfig], RunnableConfig]] \| None` DEFAULT: `None`
`custom_input_type`	Specify to override the input type of the underlying `Runnable` with a custom type. TYPE: `type[Input] \| BaseModel \| None` DEFAULT: `None`
`custom_output_type`	Specify to override the output type of the underlying `Runnable` with a custom type. TYPE: `type[Output] \| BaseModel \| None` DEFAULT: `None`
`**other_kwargs`	Unpacked into the base class. TYPE: `Any` DEFAULT: `{}`

is_lc_serializable `classmethod` ¶

is_lc_serializable() -> bool

Return True as this class is serializable.

get_lc_namespace `classmethod` ¶

get_lc_namespace() -> list[str]

Get the namespace of the LangChain object.

RETURNS	DESCRIPTION
`list[str]`	`["langchain", "schema", "runnable"]`

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	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	DESCRIPTION
`SerializedNotImplemented`	`SerializedNotImplemented`.

configurable_fields ¶

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

Configure particular Runnable fields at runtime.

PARAMETER	DESCRIPTION
`**kwargs`	A dictionary of `ConfigurableField` instances to configure. TYPE: `AnyConfigurableField` DEFAULT: `{}`

RAISES	DESCRIPTION
`ValueError`	If a configuration key is not found in the `Runnable`.

RETURNS	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 Runnable objects that can be set at runtime.

PARAMETER	DESCRIPTION
`which`	The `ConfigurableField` instance that will be used to select the alternative. TYPE: `ConfigurableField`
`default_key`	The default key to use if no alternative is selected. TYPE: `str` DEFAULT: `'default'`
`prefix_keys`	Whether to prefix the keys with the `ConfigurableField` id. TYPE: `bool` DEFAULT: `False`
`**kwargs`	A dictionary of keys to `Runnable` instances or callables that return `Runnable` instances. TYPE: `Runnable[Input, Output] \| Callable[[], Runnable[Input, Output]]` DEFAULT: `{}`

RETURNS	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-sonnet-4-5-20250929"
).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
)

langchain_core.runnables.base.RunnableGenerator ¶

Bases: Runnable[Input, Output]

Runnable that runs a generator function.

RunnableGenerators can be instantiated directly or by using a generator within a sequence.

RunnableGenerators can be used to implement custom behavior, such as custom output parsers, while preserving streaming capabilities. Given a generator function with a signature Iterator[A] -> Iterator[B], wrapping it in a RunnableGenerator allows it to emit output chunks as soon as they are streamed in from the previous step.

Note

If a generator function has a signature A -> Iterator[B], such that it requires its input from the previous step to be completed before emitting chunks (e.g., most LLMs need the entire prompt available to start generating), it can instead be wrapped in a RunnableLambda.

Here is an example to show the basic mechanics of a RunnableGenerator:

```python
from typing import Any, AsyncIterator, Iterator

from langchain_core.runnables import RunnableGenerator


def gen(input: Iterator[Any]) -> Iterator[str]:
    for token in ["Have", " a", " nice", " day"]:
        yield token


runnable = RunnableGenerator(gen)
runnable.invoke(None)  # "Have a nice day"
list(runnable.stream(None))  # ["Have", " a", " nice", " day"]
runnable.batch([None, None])  # ["Have a nice day", "Have a nice day"]


# Async version:
async def agen(input: AsyncIterator[Any]) -> AsyncIterator[str]:
    for token in ["Have", " a", " nice", " day"]:
        yield token


runnable = RunnableGenerator(agen)
await runnable.ainvoke(None)  # "Have a nice day"
[p async for p in runnable.astream(None)]  # ["Have", " a", " nice", " day"]
```

RunnableGenerator makes it easy to implement custom behavior within a streaming context. Below we show an example:

```python
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableGenerator, RunnableLambda
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser


model = ChatOpenAI()
chant_chain = (
    ChatPromptTemplate.from_template("Give me a 3 word chant about {topic}")
    | model
    | StrOutputParser()
)


def character_generator(input: Iterator[str]) -> Iterator[str]:
    for token in input:
        if "," in token or "." in token:
            yield "👏" + token
        else:
            yield token


runnable = chant_chain | character_generator
assert type(runnable.last) is RunnableGenerator
"".join(runnable.stream({"topic": "waste"}))  # Reduce👏, Reuse👏, Recycle👏.


# Note that RunnableLambda can be used to delay streaming of one step in a
# sequence until the previous step is finished:
def reverse_generator(input: str) -> Iterator[str]:
    # Yield characters of input in reverse order.
    for character in input[::-1]:
        yield character


runnable = chant_chain | RunnableLambda(reverse_generator)
"".join(runnable.stream({"topic": "waste"}))  # ".elcycer ,esuer ,ecudeR"
```

METHOD	DESCRIPTION
`__init__`	Initialize a `RunnableGenerator`.
`get_input_schema`	Get a Pydantic model that can be used to validate input to the `Runnable`.
`get_output_schema`	Get a Pydantic model that can be used to validate output to the `Runnable`.
`transform`	Transform inputs to outputs.
`stream`	Default implementation of `stream`, which calls `invoke`.
`invoke`	Transform a single input into an output.
`atransform`	Transform inputs to outputs.
`astream`	Default implementation of `astream`, which calls `ainvoke`.
`ainvoke`	Transform a single input into an output.
`get_name`	Get the name of the `Runnable`.
`get_input_jsonschema`	Get a JSON schema that represents the input 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 `Runnable` objects.
`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.
`astream_log`	Stream all output from a `Runnable`, as reported to the callback system.
`astream_events`	Generate a stream of events.
`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`.

name `instance-attribute` ¶

name = name or __name__

The name of the Runnable. Used for debugging and tracing.

InputType `property` ¶

InputType: Any

Input type.

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

RAISES	DESCRIPTION
`TypeError`	If the input type cannot be inferred.

OutputType `property` ¶

OutputType: Any

Output Type.

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

RAISES	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.

init ¶

__init__(
    transform: Callable[[Iterator[Input]], Iterator[Output]]
    | Callable[[AsyncIterator[Input]], AsyncIterator[Output]],
    atransform: Callable[[AsyncIterator[Input]], AsyncIterator[Output]] | None = None,
    *,
    name: str | None = None,
) -> None

Initialize a RunnableGenerator.

PARAMETER	DESCRIPTION
`transform`	The transform function. TYPE: `Callable[[Iterator[Input]], Iterator[Output]] \| Callable[[AsyncIterator[Input]], AsyncIterator[Output]]`
`atransform`	The async transform function. TYPE: `Callable[[AsyncIterator[Input]], AsyncIterator[Output]] \| None` DEFAULT: `None`
`name`	The name of the `Runnable`. TYPE: `str \| None` DEFAULT: `None`

RAISES	DESCRIPTION
`TypeError`	If the transform is not a generator function.

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.

Runnable objects 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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`type[BaseModel]`	A Pydantic model that can be used to validate input.

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.

Runnable objects 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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`type[BaseModel]`	A Pydantic model that can be used to validate output.

transform ¶

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

Transform inputs to outputs.

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

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

PARAMETER	DESCRIPTION
`input`	An iterator of inputs to the `Runnable`. TYPE: `Iterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`Output`	The output of the `Runnable`.

stream ¶

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

Default implementation of stream, which calls invoke.

Subclasses must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`Output`	The output of the `Runnable`.

invoke ¶

invoke(input: Input, config: RunnableConfig | None = None, **kwargs: Any) -> Output

Transform a single input into an output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	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. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Output`	The output of the `Runnable`.

atransform ¶

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

Transform inputs to outputs.

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

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

PARAMETER	DESCRIPTION
`input`	An async iterator of inputs to the `Runnable`. TYPE: `AsyncIterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[Output]`	The output of the `Runnable`.

astream ¶

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

Default implementation of astream, which calls ainvoke.

Subclasses must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[Output]`	The output of the `Runnable`.

ainvoke `async` ¶

ainvoke(input: Input, config: RunnableConfig | None = None, **kwargs: Any) -> Output

Transform a single input into an output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	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. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Output`	The output of the `Runnable`.

get_name ¶

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

Get the name of the Runnable.

PARAMETER	DESCRIPTION
`suffix`	An optional suffix to append to the name. TYPE: `str \| None` DEFAULT: `None`
`name`	An optional name to use instead of the `Runnable`'s name. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`str`	The name of the `Runnable`.

get_input_jsonschema ¶

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

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

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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_jsonschema ¶

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

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

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	DESCRIPTION
`RunnableSerializable[Other, Output]`	A new `Runnable`.

pipe ¶

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

Pipe Runnable objects.

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]

PARAMETER	DESCRIPTION
`*others`	Other `Runnable` or `Runnable`-like objects to compose TYPE: `Runnable[Any, Other] \| Callable[[Any], Other]` DEFAULT: `()`
`name`	An optional name for the resulting `RunnableSequence`. TYPE: `str \| None` DEFAULT: `None`

RETURNS	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 a single key:

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 a list of keys:

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]"}

PARAMETER	DESCRIPTION
`keys`	A key or list of keys to pick from the output dict. TYPE: `str \| list[str]`

RETURNS	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}"
)
model = FakeStreamingListLLM(responses=["foo-lish"])

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

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

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'}}}

PARAMETER	DESCRIPTION
`**kwargs`	A mapping of keys to `Runnable` or `Runnable`-like objects that will be invoked with the entire output dict of this `Runnable`. TYPE: `Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any] \| Mapping[str, Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any]]` DEFAULT: `{}`

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

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `list[Input]`
`config`	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. TYPE: `RunnableConfig \| list[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `Sequence[Input]`
`config`	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. TYPE: `RunnableConfig \| Sequence[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

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

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `list[Input]`
`config`	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. TYPE: `RunnableConfig \| list[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `Sequence[Input]`
`config`	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. TYPE: `RunnableConfig \| Sequence[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[tuple[int, Output \| Exception]]`	A tuple of the index of the input and the output from 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.

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

YIELDS	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 StreamEvent that provide real-time information about the progress of the Runnable, including StreamEvent from intermediate results.

A StreamEvent is a dictionary with the following schema:

event: Event names are of the format: on_[runnable_type]_(start|stream|end).
name: The name of the Runnable that generated the event.
run_id: 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: 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: The tags of the Runnable that generated the event.
metadata: The metadata of the Runnable that generated the event.
data: The data associated with the event. The contents of this field depend on the type of event. See the table below for more details.

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"]})

For instance:

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)

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Any`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`version`	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'`. TYPE: `Literal['v1', 'v2']` DEFAULT: `'v2'`
`include_names`	Only include events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_types`	Only include events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_tags`	Only include events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_names`	Exclude events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_types`	Exclude events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_tags`	Exclude events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`**kwargs`	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`. TYPE: `Any` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[StreamEvent]`	An async stream of `StreamEvent`.

RAISES	DESCRIPTION
`NotImplementedError`	If the version is not `'v1'` or `'v2'`.

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.

PARAMETER	DESCRIPTION
`**kwargs`	The arguments to bind to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`Runnable[Input, Output]`	A new `Runnable` with the arguments bound.

Example

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

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

# Without bind
chain = model | StrOutputParser()

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

# With bind
chain = model.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.

PARAMETER	DESCRIPTION
`config`	The config to bind to the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called before the `Runnable` starts running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_end`	Called after the `Runnable` finishes running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_error`	Called if the `Runnable` throws an error, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called asynchronously before the `Runnable` starts running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_end`	Called asynchronously after the `Runnable` finishes running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_error`	Called asynchronously if the `Runnable` throws an error, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`input_type`	The input type to bind to the `Runnable`. TYPE: `type[Input] \| None` DEFAULT: `None`
`output_type`	The output type to bind to the `Runnable`. TYPE: `type[Output] \| None` DEFAULT: `None`

RETURNS	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.

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

RETURNS	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	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.

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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.

PARAMETER	DESCRIPTION
`args_schema`	The schema for the tool. TYPE: `type[BaseModel] \| None` DEFAULT: `None`
`name`	The name of the tool. TYPE: `str \| None` DEFAULT: `None`
`description`	The description of the tool. TYPE: `str \| None` DEFAULT: `None`
`arg_types`	A dictionary of argument names to types. TYPE: `dict[str, type] \| None` DEFAULT: `None`

RETURNS	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")

langchain_core.runnables.base.RunnableLambda ¶

Bases: Runnable[Input, Output]

RunnableLambda converts a python callable into a Runnable.

Wrapping a callable in a RunnableLambda makes the callable usable within either a sync or async context.

RunnableLambda can be composed as any other Runnable and provides seamless integration with LangChain tracing.

RunnableLambda is best suited for code that does not need to support streaming. If you need to support streaming (i.e., be able to operate on chunks of inputs and yield chunks of outputs), use RunnableGenerator instead.

Note that if a RunnableLambda returns an instance of Runnable, that instance is invoked (or streamed) during execution.

Examples:

# This is a RunnableLambda
from langchain_core.runnables import RunnableLambda


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


runnable = RunnableLambda(add_one)

runnable.invoke(1)  # returns 2
runnable.batch([1, 2, 3])  # returns [2, 3, 4]

# Async is supported by default by delegating to the sync implementation
await runnable.ainvoke(1)  # returns 2
await runnable.abatch([1, 2, 3])  # returns [2, 3, 4]


# Alternatively, can provide both synd and sync implementations
async def add_one_async(x: int) -> int:
    return x + 1


runnable = RunnableLambda(add_one, afunc=add_one_async)
runnable.invoke(1)  # Uses add_one
await runnable.ainvoke(1)  # Uses add_one_async

METHOD	DESCRIPTION
`__init__`	Create a `RunnableLambda` from a callable, and async callable or both.
`get_input_schema`	The Pydantic schema for the input to this `Runnable`.
`get_output_schema`	Get a Pydantic model that can be used to validate output to the `Runnable`.
`get_graph`	Return a graph representation of this `Runnable`.
`__repr__`	Return a string representation of this `Runnable`.
`invoke`	Invoke this `Runnable` synchronously.
`ainvoke`	Invoke this `Runnable` asynchronously.
`transform`	Transform inputs to outputs.
`stream`	Default implementation of `stream`, which calls `invoke`.
`atransform`	Transform inputs to outputs.
`astream`	Default implementation of `astream`, which calls `ainvoke`.
`get_name`	Get the name of the `Runnable`.
`get_input_jsonschema`	Get a JSON schema that represents the input 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_prompts`	Return a list of prompts used by this `Runnable`.
`__or__`	Runnable "or" operator.
`__ror__`	Runnable "reverse-or" operator.
`pipe`	Pipe `Runnable` objects.
`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.
`astream_log`	Stream all output from a `Runnable`, as reported to the callback system.
`astream_events`	Generate a stream of events.
`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`.

name `instance-attribute` ¶

name = name

The name of the Runnable. Used for debugging and tracing.

InputType `property` ¶

InputType: Any

The type of the input to this Runnable.

OutputType `property` ¶

OutputType: Any

The type of the output of this Runnable as a type annotation.

RETURNS	DESCRIPTION
`Any`	The type of the output of this `Runnable`.

deps `cached` `property` ¶

deps: list[Runnable]

The dependencies of this Runnable.

RETURNS	DESCRIPTION
`list[Runnable]`	The dependencies of this `Runnable`. If the function has nonlocal
`list[Runnable]`	variables that are `Runnable`s, they are considered dependencies.

config_specs `property` ¶

config_specs: list[ConfigurableFieldSpec]

List configurable fields for this Runnable.

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.

init ¶

__init__(
    func: Callable[[Input], Iterator[Output]]
    | Callable[[Input], Runnable[Input, Output]]
    | Callable[[Input], Output]
    | Callable[[Input, RunnableConfig], Output]
    | Callable[[Input, CallbackManagerForChainRun], Output]
    | Callable[[Input, CallbackManagerForChainRun, RunnableConfig], Output]
    | Callable[[Input], Awaitable[Output]]
    | Callable[[Input], AsyncIterator[Output]]
    | Callable[[Input, RunnableConfig], Awaitable[Output]]
    | Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]]
    | Callable[
        [Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]
    ],
    afunc: Callable[[Input], Awaitable[Output]]
    | Callable[[Input], AsyncIterator[Output]]
    | Callable[[Input, RunnableConfig], Awaitable[Output]]
    | Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]]
    | Callable[
        [Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]
    ]
    | None = None,
    name: str | None = None,
) -> None

Create a RunnableLambda from a callable, and async callable or both.

Accepts both sync and async variants to allow providing efficient implementations for sync and async execution.

PARAMETER	DESCRIPTION
`func`	Either sync or async callable TYPE: Callable[[Input], Iterator[Output]] \| Callable[[Input], Runnable[Input, Output]] \| Callable[[Input], Output] \| Callable[[Input, RunnableConfig], Output] \| Callable[[Input, CallbackManagerForChainRun], Output] \| Callable[[Input, CallbackManagerForChainRun, RunnableConfig], Output] \| Callable[[Input], Awaitable[Output]] \| Callable[[Input], AsyncIterator[Output]] \| Callable[[Input, RunnableConfig], Awaitable[Output]] \| Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]] \| Callable[[Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]]
`afunc`	An async callable that takes an input and returns an output. TYPE: `Callable[[Input], Awaitable[Output]] \| Callable[[Input], AsyncIterator[Output]] \| Callable[[Input, RunnableConfig], Awaitable[Output]] \| Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]] \| Callable[[Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]] \| None` DEFAULT: `None`
`name`	The name of the `Runnable`. TYPE: `str \| None` DEFAULT: `None`

RAISES	DESCRIPTION
`TypeError`	If the `func` is not a callable type.
`TypeError`	If both `func` and `afunc` are provided.

get_input_schema ¶

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

The Pydantic schema for the input to this Runnable.

PARAMETER	DESCRIPTION
`config`	The config to use. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`type[BaseModel]`	The input schema for this `Runnable`.

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.

Runnable objects 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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`type[BaseModel]`	A Pydantic model that can be used to validate output.

get_graph ¶

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

Return a graph representation of this Runnable.

repr ¶

__repr__() -> str

Return a string representation of this Runnable.

invoke ¶

invoke(
    input: Input, config: RunnableConfig | None = None, **kwargs: Any | None
) -> Output

Invoke this Runnable synchronously.

PARAMETER	DESCRIPTION
`input`	The input to this `Runnable`. TYPE: `Input`
`config`	The config to use. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	DESCRIPTION
`Output`	The output of this `Runnable`.

RAISES	DESCRIPTION
`TypeError`	If the `Runnable` is a coroutine function.

ainvoke `async` ¶

ainvoke(
    input: Input, config: RunnableConfig | None = None, **kwargs: Any | None
) -> Output

Invoke this Runnable asynchronously.

PARAMETER	DESCRIPTION
`input`	The input to this `Runnable`. TYPE: `Input`
`config`	The config to use. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	DESCRIPTION
`Output`	The output of this `Runnable`.

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

PARAMETER	DESCRIPTION
`input`	An iterator of inputs to the `Runnable`. TYPE: `Iterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`Output`	The output of the `Runnable`.

stream ¶

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

Default implementation of stream, which calls invoke.

Subclasses must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

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

PARAMETER	DESCRIPTION
`input`	An async iterator of inputs to the `Runnable`. TYPE: `AsyncIterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[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 must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[Output]`	The output of the `Runnable`.

get_name ¶

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

Get the name of the Runnable.

PARAMETER	DESCRIPTION
`suffix`	An optional suffix to append to the name. TYPE: `str \| None` DEFAULT: `None`
`name`	An optional name to use instead of the `Runnable`'s name. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`str`	The name of the `Runnable`.

get_input_jsonschema ¶

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

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

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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_jsonschema ¶

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

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

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`dict[str, Any]`	A JSON schema that represents the config of the `Runnable`.

Added in version 0.3.0

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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	DESCRIPTION
`RunnableSerializable[Other, Output]`	A new `Runnable`.

pipe ¶

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

Pipe Runnable objects.

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]

PARAMETER	DESCRIPTION
`*others`	Other `Runnable` or `Runnable`-like objects to compose TYPE: `Runnable[Any, Other] \| Callable[[Any], Other]` DEFAULT: `()`
`name`	An optional name for the resulting `RunnableSequence`. TYPE: `str \| None` DEFAULT: `None`

RETURNS	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 a single key:

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 a list of keys:

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]"}

PARAMETER	DESCRIPTION
`keys`	A key or list of keys to pick from the output dict. TYPE: `str \| list[str]`

RETURNS	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}"
)
model = FakeStreamingListLLM(responses=["foo-lish"])

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

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

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'}}}

PARAMETER	DESCRIPTION
`**kwargs`	A mapping of keys to `Runnable` or `Runnable`-like objects that will be invoked with the entire output dict of this `Runnable`. TYPE: `Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any] \| Mapping[str, Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any]]` DEFAULT: `{}`

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

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `list[Input]`
`config`	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. TYPE: `RunnableConfig \| list[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `Sequence[Input]`
`config`	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. TYPE: `RunnableConfig \| Sequence[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

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

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `list[Input]`
`config`	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. TYPE: `RunnableConfig \| list[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `Sequence[Input]`
`config`	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. TYPE: `RunnableConfig \| Sequence[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[tuple[int, Output \| Exception]]`	A tuple of the index of the input and the output from 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.

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

YIELDS	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 StreamEvent that provide real-time information about the progress of the Runnable, including StreamEvent from intermediate results.

A StreamEvent is a dictionary with the following schema:

event: Event names are of the format: on_[runnable_type]_(start|stream|end).
name: The name of the Runnable that generated the event.
run_id: 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: 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: The tags of the Runnable that generated the event.
metadata: The metadata of the Runnable that generated the event.
data: The data associated with the event. The contents of this field depend on the type of event. See the table below for more details.

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"]})

For instance:

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)

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Any`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`version`	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'`. TYPE: `Literal['v1', 'v2']` DEFAULT: `'v2'`
`include_names`	Only include events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_types`	Only include events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_tags`	Only include events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_names`	Exclude events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_types`	Exclude events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_tags`	Exclude events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`**kwargs`	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`. TYPE: `Any` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[StreamEvent]`	An async stream of `StreamEvent`.

RAISES	DESCRIPTION
`NotImplementedError`	If the version is not `'v1'` or `'v2'`.

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.

PARAMETER	DESCRIPTION
`**kwargs`	The arguments to bind to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`Runnable[Input, Output]`	A new `Runnable` with the arguments bound.

Example

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

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

# Without bind
chain = model | StrOutputParser()

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

# With bind
chain = model.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.

PARAMETER	DESCRIPTION
`config`	The config to bind to the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called before the `Runnable` starts running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_end`	Called after the `Runnable` finishes running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_error`	Called if the `Runnable` throws an error, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called asynchronously before the `Runnable` starts running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_end`	Called asynchronously after the `Runnable` finishes running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_error`	Called asynchronously if the `Runnable` throws an error, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`input_type`	The input type to bind to the `Runnable`. TYPE: `type[Input] \| None` DEFAULT: `None`
`output_type`	The output type to bind to the `Runnable`. TYPE: `type[Output] \| None` DEFAULT: `None`

RETURNS	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.

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

RETURNS	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	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.

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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.

PARAMETER	DESCRIPTION
`args_schema`	The schema for the tool. TYPE: `type[BaseModel] \| None` DEFAULT: `None`
`name`	The name of the tool. TYPE: `str \| None` DEFAULT: `None`
`description`	The description of the tool. TYPE: `str \| None` DEFAULT: `None`
`arg_types`	A dictionary of argument names to types. TYPE: `dict[str, type] \| None` DEFAULT: `None`

RETURNS	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")

langchain_core.runnables.base.RunnableParallel ¶

Bases: RunnableSerializable[Input, dict[str, Any]]

Runnable that runs a mapping of Runnables in parallel.

Returns a mapping of their outputs.

RunnableParallel is one of the two main composition primitives, alongside RunnableSequence. It invokes Runnables concurrently, providing the same input to each.

A RunnableParallel can be instantiated directly or by using a dict literal within a sequence.

Here is a simple example that uses functions to illustrate the use of RunnableParallel:

```python
from langchain_core.runnables import RunnableLambda


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


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


def mul_three(x: int) -> int:
    return x * 3


runnable_1 = RunnableLambda(add_one)
runnable_2 = RunnableLambda(mul_two)
runnable_3 = RunnableLambda(mul_three)

sequence = runnable_1 | {  # this dict is coerced to a RunnableParallel
    "mul_two": runnable_2,
    "mul_three": runnable_3,
}
# Or equivalently:
# sequence = runnable_1 | RunnableParallel(
#     {"mul_two": runnable_2, "mul_three": runnable_3}
# )
# Also equivalently:
# sequence = runnable_1 | RunnableParallel(
#     mul_two=runnable_2,
#     mul_three=runnable_3,
# )

sequence.invoke(1)
await sequence.ainvoke(1)

sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
```

RunnableParallel makes it easy to run Runnables in parallel. In the below example, we simultaneously stream output from two different Runnable objects:

```python
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableParallel
from langchain_openai import ChatOpenAI

model = ChatOpenAI()
joke_chain = (
    ChatPromptTemplate.from_template("tell me a joke about {topic}") | model
)
poem_chain = (
    ChatPromptTemplate.from_template("write a 2-line poem about {topic}")
    | model
)

runnable = RunnableParallel(joke=joke_chain, poem=poem_chain)

# Display stream
output = {key: "" for key, _ in runnable.output_schema()}
for chunk in runnable.stream({"topic": "bear"}):
    for key in chunk:
        output[key] = output[key] + chunk[key].content
    print(output)  # noqa: T201
```

METHOD	DESCRIPTION
`__init__`	Create a `RunnableParallel`.
`is_lc_serializable`	Return True as this class is serializable.
`get_lc_namespace`	Get the namespace of the LangChain object.
`get_name`	Get the name of the `Runnable`.
`get_input_schema`	Get the input schema of the `Runnable`.
`get_output_schema`	Get the output schema of the `Runnable`.
`get_graph`	Get the graph representation of the `Runnable`.
`invoke`	Transform a single input into an output.
`ainvoke`	Transform a single input into an output.
`transform`	Transform inputs to outputs.
`stream`	Default implementation of `stream`, which calls `invoke`.
`atransform`	Transform inputs to outputs.
`astream`	Default implementation of `astream`, which calls `ainvoke`.
`get_input_jsonschema`	Get a JSON schema that represents the input 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_prompts`	Return a list of prompts used by this `Runnable`.
`__or__`	Runnable "or" operator.
`__ror__`	Runnable "reverse-or" operator.
`pipe`	Pipe `Runnable` objects.
`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.
`astream_log`	Stream all output from a `Runnable`, as reported to the callback system.
`astream_events`	Generate a stream of events.
`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`.
`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 `Runnable` objects that can be set at runtime.

InputType `property` ¶

InputType: Any

The type of the input to the Runnable.

config_specs `property` ¶

config_specs: list[ConfigurableFieldSpec]

Get the config specs of the Runnable.

RETURNS	DESCRIPTION
`list[ConfigurableFieldSpec]`	The config specs of the `Runnable`.

name `class-attribute` `instance-attribute` ¶

name: str | None = None

The name of the Runnable. Used for debugging and tracing.

OutputType `property` ¶

OutputType: type[Output]

Output Type.

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

RAISES	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.

lc_secrets `property` ¶

lc_secrets: dict[str, str]

A map of constructor argument names to secret ids.

For example, {"openai_api_key": "OPENAI_API_KEY"}

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.

init ¶

__init__(
    steps__: Mapping[
        str,
        Runnable[Input, Any]
        | Callable[[Input], Any]
        | Mapping[str, Runnable[Input, Any] | Callable[[Input], Any]],
    ]
    | None = None,
    **kwargs: Runnable[Input, Any]
    | Callable[[Input], Any]
    | Mapping[str, Runnable[Input, Any] | Callable[[Input], Any]],
) -> None

Create a RunnableParallel.

PARAMETER	DESCRIPTION
`steps__`	The steps to include. TYPE: `Mapping[str, Runnable[Input, Any] \| Callable[[Input], Any] \| Mapping[str, Runnable[Input, Any] \| Callable[[Input], Any]]] \| None` DEFAULT: `None`
`**kwargs`	Additional steps to include. TYPE: `Runnable[Input, Any] \| Callable[[Input], Any] \| Mapping[str, Runnable[Input, Any] \| Callable[[Input], Any]]` DEFAULT: `{}`

is_lc_serializable `classmethod` ¶

is_lc_serializable() -> bool

Return True as this class is serializable.

get_lc_namespace `classmethod` ¶

get_lc_namespace() -> list[str]

Get the namespace of the LangChain object.

RETURNS	DESCRIPTION
`list[str]`	`["langchain", "schema", "runnable"]`

get_name ¶

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

Get the name of the Runnable.

PARAMETER	DESCRIPTION
`suffix`	The suffix to use. TYPE: `str \| None` DEFAULT: `None`
`name`	The name to use. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`str`	The name of the `Runnable`.

get_input_schema ¶

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

Get the input schema of the Runnable.

PARAMETER	DESCRIPTION
`config`	The config to use. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`type[BaseModel]`	The input schema of the `Runnable`.

get_output_schema ¶

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

Get the output schema of the Runnable.

PARAMETER	DESCRIPTION
`config`	The config to use. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`type[BaseModel]`	The output schema of the `Runnable`.

get_graph ¶

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

Get the graph representation of the Runnable.

PARAMETER	DESCRIPTION
`config`	The config to use. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Graph`	The graph representation of the `Runnable`.

RAISES	DESCRIPTION
`ValueError`	If a `Runnable` has no first or last node.

invoke ¶

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

Transform a single input into an output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	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. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Output`	The output of the `Runnable`.

ainvoke `async` ¶

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

Transform a single input into an output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	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. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Output`	The output of the `Runnable`.

transform ¶

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

Transform inputs to outputs.

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

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

PARAMETER	DESCRIPTION
`input`	An iterator of inputs to the `Runnable`. TYPE: `Iterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`Output`	The output of the `Runnable`.

stream ¶

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

Default implementation of stream, which calls invoke.

Subclasses must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`Output`	The output of the `Runnable`.

atransform `async` ¶

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

Transform inputs to outputs.

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

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

PARAMETER	DESCRIPTION
`input`	An async iterator of inputs to the `Runnable`. TYPE: `AsyncIterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[Output]`	The output of the `Runnable`.

astream `async` ¶

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

Default implementation of astream, which calls ainvoke.

Subclasses must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[Output]`	The output of the `Runnable`.

get_input_jsonschema ¶

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

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

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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_jsonschema ¶

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

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

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`dict[str, Any]`	A JSON schema that represents the config of the `Runnable`.

Added in version 0.3.0

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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	DESCRIPTION
`RunnableSerializable[Other, Output]`	A new `Runnable`.

pipe ¶

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

Pipe Runnable objects.

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]

PARAMETER	DESCRIPTION
`*others`	Other `Runnable` or `Runnable`-like objects to compose TYPE: `Runnable[Any, Other] \| Callable[[Any], Other]` DEFAULT: `()`
`name`	An optional name for the resulting `RunnableSequence`. TYPE: `str \| None` DEFAULT: `None`

RETURNS	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 a single key:

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 a list of keys:

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]"}

PARAMETER	DESCRIPTION
`keys`	A key or list of keys to pick from the output dict. TYPE: `str \| list[str]`

RETURNS	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}"
)
model = FakeStreamingListLLM(responses=["foo-lish"])

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

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

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'}}}

PARAMETER	DESCRIPTION
`**kwargs`	A mapping of keys to `Runnable` or `Runnable`-like objects that will be invoked with the entire output dict of this `Runnable`. TYPE: `Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any] \| Mapping[str, Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any]]` DEFAULT: `{}`

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

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `list[Input]`
`config`	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. TYPE: `RunnableConfig \| list[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `Sequence[Input]`
`config`	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. TYPE: `RunnableConfig \| Sequence[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

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

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `list[Input]`
`config`	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. TYPE: `RunnableConfig \| list[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `Sequence[Input]`
`config`	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. TYPE: `RunnableConfig \| Sequence[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[tuple[int, Output \| Exception]]`	A tuple of the index of the input and the output from 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.

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

YIELDS	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 StreamEvent that provide real-time information about the progress of the Runnable, including StreamEvent from intermediate results.

A StreamEvent is a dictionary with the following schema:

event: Event names are of the format: on_[runnable_type]_(start|stream|end).
name: The name of the Runnable that generated the event.
run_id: 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: 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: The tags of the Runnable that generated the event.
metadata: The metadata of the Runnable that generated the event.
data: The data associated with the event. The contents of this field depend on the type of event. See the table below for more details.

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"]})

For instance:

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)

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Any`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`version`	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'`. TYPE: `Literal['v1', 'v2']` DEFAULT: `'v2'`
`include_names`	Only include events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_types`	Only include events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_tags`	Only include events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_names`	Exclude events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_types`	Exclude events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_tags`	Exclude events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`**kwargs`	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`. TYPE: `Any` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[StreamEvent]`	An async stream of `StreamEvent`.

RAISES	DESCRIPTION
`NotImplementedError`	If the version is not `'v1'` or `'v2'`.

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.

PARAMETER	DESCRIPTION
`**kwargs`	The arguments to bind to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`Runnable[Input, Output]`	A new `Runnable` with the arguments bound.

Example

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

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

# Without bind
chain = model | StrOutputParser()

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

# With bind
chain = model.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.

PARAMETER	DESCRIPTION
`config`	The config to bind to the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called before the `Runnable` starts running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_end`	Called after the `Runnable` finishes running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_error`	Called if the `Runnable` throws an error, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called asynchronously before the `Runnable` starts running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_end`	Called asynchronously after the `Runnable` finishes running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_error`	Called asynchronously if the `Runnable` throws an error, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`input_type`	The input type to bind to the `Runnable`. TYPE: `type[Input] \| None` DEFAULT: `None`
`output_type`	The output type to bind to the `Runnable`. TYPE: `type[Output] \| None` DEFAULT: `None`

RETURNS	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.

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

RETURNS	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	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.

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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.

PARAMETER	DESCRIPTION
`args_schema`	The schema for the tool. TYPE: `type[BaseModel] \| None` DEFAULT: `None`
`name`	The name of the tool. TYPE: `str \| None` DEFAULT: `None`
`description`	The description of the tool. TYPE: `str \| None` DEFAULT: `None`
`arg_types`	A dictionary of argument names to types. TYPE: `dict[str, type] \| None` DEFAULT: `None`

RETURNS	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")

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	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	DESCRIPTION
`SerializedNotImplemented`	`SerializedNotImplemented`.

configurable_fields ¶

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

Configure particular Runnable fields at runtime.

PARAMETER	DESCRIPTION
`**kwargs`	A dictionary of `ConfigurableField` instances to configure. TYPE: `AnyConfigurableField` DEFAULT: `{}`

RAISES	DESCRIPTION
`ValueError`	If a configuration key is not found in the `Runnable`.

RETURNS	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 Runnable objects that can be set at runtime.

PARAMETER	DESCRIPTION
`which`	The `ConfigurableField` instance that will be used to select the alternative. TYPE: `ConfigurableField`
`default_key`	The default key to use if no alternative is selected. TYPE: `str` DEFAULT: `'default'`
`prefix_keys`	Whether to prefix the keys with the `ConfigurableField` id. TYPE: `bool` DEFAULT: `False`
`**kwargs`	A dictionary of keys to `Runnable` instances or callables that return `Runnable` instances. TYPE: `Runnable[Input, Output] \| Callable[[], Runnable[Input, Output]]` DEFAULT: `{}`

RETURNS	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-sonnet-4-5-20250929"
).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
)

langchain_core.runnables.base.RunnableSequence ¶

Bases: RunnableSerializable[Input, Output]

Sequence of Runnable objects, where the output of one is the input of the next.

RunnableSequence is the most important composition operator in LangChain as it is used in virtually every chain.

A RunnableSequence can be instantiated directly or more commonly by using the | operator where either the left or right operands (or both) must be a Runnable.

Any RunnableSequence automatically supports sync, async, batch.

The default implementations of batch and abatch utilize threadpools and asyncio gather and will be faster than naive invocation of invoke or ainvoke for IO bound Runnables.

Batching is implemented by invoking the batch method on each component of the RunnableSequence in order.

A RunnableSequence preserves the streaming properties of its components, so if all components of the sequence implement a transform method -- which is the method that implements the logic to map a streaming input to a streaming output -- then the sequence will be able to stream input to output!

If any component of the sequence does not implement transform then the streaming will only begin after this component is run. If there are multiple blocking components, streaming begins after the last one.

Note

RunnableLambdas do not support transform by default! So if you need to use a RunnableLambdas be careful about where you place them in a RunnableSequence (if you need to use the stream/astream methods).

If you need arbitrary logic and need streaming, you can subclass Runnable, and implement transform for whatever logic you need.

Here is a simple example that uses simple functions to illustrate the use of RunnableSequence:

```python
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 | runnable_2
# Or equivalently:
# sequence = RunnableSequence(first=runnable_1, last=runnable_2)
sequence.invoke(1)
await sequence.ainvoke(1)

sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
```

Here's an example that uses streams JSON output generated by an LLM:

```python
from langchain_core.output_parsers.json import SimpleJsonOutputParser
from langchain_openai import ChatOpenAI

prompt = PromptTemplate.from_template(
    "In JSON format, give me a list of {topic} and their "
    "corresponding names in French, Spanish and in a "
    "Cat Language."
)

model = ChatOpenAI()
chain = prompt | model | SimpleJsonOutputParser()

async for chunk in chain.astream({"topic": "colors"}):
    print("-")  # noqa: T201
    print(chunk, sep="", flush=True)  # noqa: T201
```

METHOD	DESCRIPTION
`__init__`	Create a new `RunnableSequence`.
`get_lc_namespace`	Get the namespace of the LangChain object.
`is_lc_serializable`	Return True as this class is serializable.
`get_input_schema`	Get the input schema of the `Runnable`.
`get_output_schema`	Get the output schema of the `Runnable`.
`get_graph`	Get the graph representation of the `Runnable`.
`__or__`	Runnable "or" operator.
`__ror__`	Runnable "reverse-or" operator.
`invoke`	Transform a single input into an output.
`ainvoke`	Transform a single input into an output.
`batch`	Default implementation runs invoke in parallel using a thread pool executor.
`abatch`	Default implementation runs `ainvoke` in parallel using `asyncio.gather`.
`transform`	Transform inputs to outputs.
`stream`	Default implementation of `stream`, which calls `invoke`.
`atransform`	Transform inputs to outputs.
`astream`	Default implementation of `astream`, which calls `ainvoke`.
`get_name`	Get the name of the `Runnable`.
`get_input_jsonschema`	Get a JSON schema that represents the input 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_prompts`	Return a list of prompts used by this `Runnable`.
`pipe`	Pipe `Runnable` objects.
`pick`	Pick keys from the output `dict` of this `Runnable`.
`assign`	Assigns new fields to the `dict` output of this `Runnable`.
`batch_as_completed`	Run `invoke` in parallel on a list of inputs.
`abatch_as_completed`	Run `ainvoke` in parallel on a list of inputs.
`astream_log`	Stream all output from a `Runnable`, as reported to the callback system.
`astream_events`	Generate a stream of events.
`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`.
`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 `Runnable` objects that can be set at runtime.

first `instance-attribute` ¶

first: Runnable[Input, Any]

The first Runnable in the sequence.

middle `class-attribute` `instance-attribute` ¶

middle: list[Runnable[Any, Any]] = Field(default_factory=list)

The middle Runnable in the sequence.

last `instance-attribute` ¶

last: Runnable[Any, Output]

The last Runnable in the sequence.

steps `property` ¶

steps: list[Runnable[Any, Any]]

All the Runnables that make up the sequence in order.

RETURNS	DESCRIPTION
`list[Runnable[Any, Any]]`	A list of `Runnable`s.

InputType `property` ¶

InputType: type[Input]

The type of the input to the Runnable.

OutputType `property` ¶

OutputType: type[Output]

The type of the output of the Runnable.

config_specs `property` ¶

config_specs: list[ConfigurableFieldSpec]

Get the config specs of the Runnable.

RETURNS	DESCRIPTION
`list[ConfigurableFieldSpec]`	The config specs of the `Runnable`.

name `class-attribute` `instance-attribute` ¶

name: str | None = None

The name of the Runnable. Used for debugging and tracing.

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.

lc_secrets `property` ¶

lc_secrets: dict[str, str]

A map of constructor argument names to secret ids.

For example, {"openai_api_key": "OPENAI_API_KEY"}

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.

init ¶

__init__(
    *steps: RunnableLike,
    name: str | None = None,
    first: Runnable[Any, Any] | None = None,
    middle: list[Runnable[Any, Any]] | None = None,
    last: Runnable[Any, Any] | None = None,
) -> None

Create a new RunnableSequence.

PARAMETER	DESCRIPTION
`steps`	The steps to include in the sequence. TYPE: `RunnableLike` DEFAULT: `()`
`name`	The name of the `Runnable`. TYPE: `str \| None` DEFAULT: `None`
`first`	The first `Runnable` in the sequence. TYPE: `Runnable[Any, Any] \| None` DEFAULT: `None`
`middle`	The middle `Runnable` objects in the sequence. TYPE: `list[Runnable[Any, Any]] \| None` DEFAULT: `None`
`last`	The last Runnable in the sequence. TYPE: `Runnable[Any, Any] \| None` DEFAULT: `None`

RAISES	DESCRIPTION
`ValueError`	If the sequence has less than 2 steps.

get_lc_namespace `classmethod` ¶

get_lc_namespace() -> list[str]

Get the namespace of the LangChain object.

RETURNS	DESCRIPTION
`list[str]`	`["langchain", "schema", "runnable"]`

is_lc_serializable `classmethod` ¶

is_lc_serializable() -> bool

Return True as this class is serializable.

get_input_schema ¶

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

Get the input schema of the Runnable.

PARAMETER	DESCRIPTION
`config`	The config to use. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`type[BaseModel]`	The input schema of the `Runnable`.

get_output_schema ¶

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

Get the output schema of the Runnable.

PARAMETER	DESCRIPTION
`config`	The config to use. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`type[BaseModel]`	The output schema of the `Runnable`.

get_graph ¶

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

Get the graph representation of the Runnable.

PARAMETER	DESCRIPTION
`config`	The config to use. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Graph`	The graph representation of the `Runnable`.

RAISES	DESCRIPTION
`ValueError`	If a `Runnable` has no first or last node.

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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	DESCRIPTION
`RunnableSerializable[Other, Output]`	A new `Runnable`.

invoke ¶

invoke(input: Input, config: RunnableConfig | None = None, **kwargs: Any) -> Output

Transform a single input into an output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	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. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Output`	The output of the `Runnable`.

ainvoke `async` ¶

ainvoke(
    input: Input, config: RunnableConfig | None = None, **kwargs: Any | None
) -> Output

Transform a single input into an output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	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. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Output`	The output of the `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 must override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `list[Input]`
`config`	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. TYPE: `RunnableConfig \| list[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[Output]`	A list of outputs 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 must override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `list[Input]`
`config`	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. TYPE: `RunnableConfig \| list[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[Output]`	A list of outputs from the `Runnable`.

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

PARAMETER	DESCRIPTION
`input`	An iterator of inputs to the `Runnable`. TYPE: `Iterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`Output`	The output of the `Runnable`.

stream ¶

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

Default implementation of stream, which calls invoke.

Subclasses must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

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

PARAMETER	DESCRIPTION
`input`	An async iterator of inputs to the `Runnable`. TYPE: `AsyncIterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[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 must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[Output]`	The output of the `Runnable`.

get_name ¶

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

Get the name of the Runnable.

PARAMETER	DESCRIPTION
`suffix`	An optional suffix to append to the name. TYPE: `str \| None` DEFAULT: `None`
`name`	An optional name to use instead of the `Runnable`'s name. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`str`	The name of the `Runnable`.

get_input_jsonschema ¶

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

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

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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_jsonschema ¶

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

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

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`dict[str, Any]`	A JSON schema that represents the config of the `Runnable`.

Added in version 0.3.0

get_prompts ¶

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

Return a list of prompts used by this Runnable.

pipe ¶

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

Pipe Runnable objects.

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]

PARAMETER	DESCRIPTION
`*others`	Other `Runnable` or `Runnable`-like objects to compose TYPE: `Runnable[Any, Other] \| Callable[[Any], Other]` DEFAULT: `()`
`name`	An optional name for the resulting `RunnableSequence`. TYPE: `str \| None` DEFAULT: `None`

RETURNS	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 a single key:

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 a list of keys:

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]"}

PARAMETER	DESCRIPTION
`keys`	A key or list of keys to pick from the output dict. TYPE: `str \| list[str]`

RETURNS	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}"
)
model = FakeStreamingListLLM(responses=["foo-lish"])

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

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

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'}}}

PARAMETER	DESCRIPTION
`**kwargs`	A mapping of keys to `Runnable` or `Runnable`-like objects that will be invoked with the entire output dict of this `Runnable`. TYPE: `Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any] \| Mapping[str, Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any]]` DEFAULT: `{}`

RETURNS	DESCRIPTION
`RunnableSerializable[Any, Any]`	A new `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.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `Sequence[Input]`
`config`	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. TYPE: `RunnableConfig \| Sequence[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`tuple[int, Output \| Exception]`	Tuples of the index of the input and the output 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.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `Sequence[Input]`
`config`	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. TYPE: `RunnableConfig \| Sequence[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[tuple[int, Output \| Exception]]`	A tuple of the index of the input and the output from 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.

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

YIELDS	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 StreamEvent that provide real-time information about the progress of the Runnable, including StreamEvent from intermediate results.

A StreamEvent is a dictionary with the following schema:

event: Event names are of the format: on_[runnable_type]_(start|stream|end).
name: The name of the Runnable that generated the event.
run_id: 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: 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: The tags of the Runnable that generated the event.
metadata: The metadata of the Runnable that generated the event.
data: The data associated with the event. The contents of this field depend on the type of event. See the table below for more details.

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"]})

For instance:

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)

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Any`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`version`	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'`. TYPE: `Literal['v1', 'v2']` DEFAULT: `'v2'`
`include_names`	Only include events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_types`	Only include events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_tags`	Only include events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_names`	Exclude events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_types`	Exclude events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_tags`	Exclude events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`**kwargs`	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`. TYPE: `Any` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[StreamEvent]`	An async stream of `StreamEvent`.

RAISES	DESCRIPTION
`NotImplementedError`	If the version is not `'v1'` or `'v2'`.

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.

PARAMETER	DESCRIPTION
`**kwargs`	The arguments to bind to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`Runnable[Input, Output]`	A new `Runnable` with the arguments bound.

Example

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

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

# Without bind
chain = model | StrOutputParser()

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

# With bind
chain = model.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.

PARAMETER	DESCRIPTION
`config`	The config to bind to the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called before the `Runnable` starts running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_end`	Called after the `Runnable` finishes running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_error`	Called if the `Runnable` throws an error, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called asynchronously before the `Runnable` starts running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_end`	Called asynchronously after the `Runnable` finishes running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_error`	Called asynchronously if the `Runnable` throws an error, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`input_type`	The input type to bind to the `Runnable`. TYPE: `type[Input] \| None` DEFAULT: `None`
`output_type`	The output type to bind to the `Runnable`. TYPE: `type[Output] \| None` DEFAULT: `None`

RETURNS	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.

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

RETURNS	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	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.

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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.

PARAMETER	DESCRIPTION
`args_schema`	The schema for the tool. TYPE: `type[BaseModel] \| None` DEFAULT: `None`
`name`	The name of the tool. TYPE: `str \| None` DEFAULT: `None`
`description`	The description of the tool. TYPE: `str \| None` DEFAULT: `None`
`arg_types`	A dictionary of argument names to types. TYPE: `dict[str, type] \| None` DEFAULT: `None`

RETURNS	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")

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	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	DESCRIPTION
`SerializedNotImplemented`	`SerializedNotImplemented`.

configurable_fields ¶

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

Configure particular Runnable fields at runtime.

PARAMETER	DESCRIPTION
`**kwargs`	A dictionary of `ConfigurableField` instances to configure. TYPE: `AnyConfigurableField` DEFAULT: `{}`

RAISES	DESCRIPTION
`ValueError`	If a configuration key is not found in the `Runnable`.

RETURNS	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 Runnable objects that can be set at runtime.

PARAMETER	DESCRIPTION
`which`	The `ConfigurableField` instance that will be used to select the alternative. TYPE: `ConfigurableField`
`default_key`	The default key to use if no alternative is selected. TYPE: `str` DEFAULT: `'default'`
`prefix_keys`	Whether to prefix the keys with the `ConfigurableField` id. TYPE: `bool` DEFAULT: `False`
`**kwargs`	A dictionary of keys to `Runnable` instances or callables that return `Runnable` instances. TYPE: `Runnable[Input, Output] \| Callable[[], Runnable[Input, Output]]` DEFAULT: `{}`

RETURNS	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-sonnet-4-5-20250929"
).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
)

langchain_core.runnables.base.RunnableSerializable ¶

Bases: Serializable, Runnable[Input, Output]

Runnable that can be serialized to JSON.

METHOD	DESCRIPTION
`to_json`	Serialize the `Runnable` to JSON.
`configurable_fields`	Configure particular `Runnable` fields at runtime.
`configurable_alternatives`	Configure alternatives for `Runnable` objects that can be set at runtime.
`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 `Runnable` objects.
`pick`	Pick keys from the output `dict` of this `Runnable`.
`assign`	Assigns new fields to the `dict` output of this `Runnable`.
`invoke`	Transform a single input into an output.
`ainvoke`	Transform a single input into an output.
`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_not_implemented`	Serialize a "not implemented" object.

name `class-attribute` `instance-attribute` ¶

name: str | None = None

The name of the Runnable. Used for debugging and tracing.

InputType `property` ¶

InputType: type[Input]

Input type.

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

RAISES	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	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, {"openai_api_key": "OPENAI_API_KEY"}

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.

to_json ¶

to_json() -> SerializedConstructor | SerializedNotImplemented

Serialize the Runnable to JSON.

RETURNS	DESCRIPTION
`SerializedConstructor \| SerializedNotImplemented`	A JSON-serializable representation of the `Runnable`.

configurable_fields ¶

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

Configure particular Runnable fields at runtime.

PARAMETER	DESCRIPTION
`**kwargs`	A dictionary of `ConfigurableField` instances to configure. TYPE: `AnyConfigurableField` DEFAULT: `{}`

RAISES	DESCRIPTION
`ValueError`	If a configuration key is not found in the `Runnable`.

RETURNS	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 Runnable objects that can be set at runtime.

PARAMETER	DESCRIPTION
`which`	The `ConfigurableField` instance that will be used to select the alternative. TYPE: `ConfigurableField`
`default_key`	The default key to use if no alternative is selected. TYPE: `str` DEFAULT: `'default'`
`prefix_keys`	Whether to prefix the keys with the `ConfigurableField` id. TYPE: `bool` DEFAULT: `False`
`**kwargs`	A dictionary of keys to `Runnable` instances or callables that return `Runnable` instances. TYPE: `Runnable[Input, Output] \| Callable[[], Runnable[Input, Output]]` DEFAULT: `{}`

RETURNS	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-sonnet-4-5-20250929"
).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_name ¶

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

Get the name of the Runnable.

PARAMETER	DESCRIPTION
`suffix`	An optional suffix to append to the name. TYPE: `str \| None` DEFAULT: `None`
`name`	An optional name to use instead of the `Runnable`'s name. TYPE: `str \| None` DEFAULT: `None`

RETURNS	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.

Runnable objects 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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

Runnable objects 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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	DESCRIPTION
`RunnableSerializable[Other, Output]`	A new `Runnable`.

pipe ¶

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

Pipe Runnable objects.

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]

PARAMETER	DESCRIPTION
`*others`	Other `Runnable` or `Runnable`-like objects to compose TYPE: `Runnable[Any, Other] \| Callable[[Any], Other]` DEFAULT: `()`
`name`	An optional name for the resulting `RunnableSequence`. TYPE: `str \| None` DEFAULT: `None`

RETURNS	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 a single key:

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 a list of keys:

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]"}

PARAMETER	DESCRIPTION
`keys`	A key or list of keys to pick from the output dict. TYPE: `str \| list[str]`

RETURNS	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}"
)
model = FakeStreamingListLLM(responses=["foo-lish"])

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

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

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'}}}

PARAMETER	DESCRIPTION
`**kwargs`	A mapping of keys to `Runnable` or `Runnable`-like objects that will be invoked with the entire output dict of this `Runnable`. TYPE: `Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any] \| Mapping[str, Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any]]` DEFAULT: `{}`

RETURNS	DESCRIPTION
`RunnableSerializable[Any, Any]`	A new `Runnable`.

invoke `abstractmethod` ¶

invoke(input: Input, config: RunnableConfig | None = None, **kwargs: Any) -> Output

Transform a single input into an output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	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. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Output`	The output of the `Runnable`.

ainvoke `async` ¶

ainvoke(input: Input, config: RunnableConfig | None = None, **kwargs: Any) -> Output

Transform a single input into an output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	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. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Output`	The output of the `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 must override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `list[Input]`
`config`	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. TYPE: `RunnableConfig \| list[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `Sequence[Input]`
`config`	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. TYPE: `RunnableConfig \| Sequence[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

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

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `list[Input]`
`config`	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. TYPE: `RunnableConfig \| list[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`inputs`	A list of inputs to the `Runnable`. TYPE: `Sequence[Input]`
`config`	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. TYPE: `RunnableConfig \| Sequence[RunnableConfig] \| None` DEFAULT: `None`
`return_exceptions`	Whether to return exceptions instead of raising them. TYPE: `bool` DEFAULT: `False`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	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 must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	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 must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	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.

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

YIELDS	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 StreamEvent that provide real-time information about the progress of the Runnable, including StreamEvent from intermediate results.

A StreamEvent is a dictionary with the following schema:

event: Event names are of the format: on_[runnable_type]_(start|stream|end).
name: The name of the Runnable that generated the event.
run_id: 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: 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: The tags of the Runnable that generated the event.
metadata: The metadata of the Runnable that generated the event.
data: The data associated with the event. The contents of this field depend on the type of event. See the table below for more details.

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"]})

For instance:

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)

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Any`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`version`	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'`. TYPE: `Literal['v1', 'v2']` DEFAULT: `'v2'`
`include_names`	Only include events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_types`	Only include events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_tags`	Only include events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_names`	Exclude events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_types`	Exclude events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_tags`	Exclude events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`**kwargs`	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`. TYPE: `Any` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[StreamEvent]`	An async stream of `StreamEvent`.

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

PARAMETER	DESCRIPTION
`input`	An iterator of inputs to the `Runnable`. TYPE: `Iterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

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

PARAMETER	DESCRIPTION
`input`	An async iterator of inputs to the `Runnable`. TYPE: `AsyncIterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	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.

PARAMETER	DESCRIPTION
`**kwargs`	The arguments to bind to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`Runnable[Input, Output]`	A new `Runnable` with the arguments bound.

Example

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

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

# Without bind
chain = model | StrOutputParser()

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

# With bind
chain = model.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.

PARAMETER	DESCRIPTION
`config`	The config to bind to the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called before the `Runnable` starts running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_end`	Called after the `Runnable` finishes running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_error`	Called if the `Runnable` throws an error, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called asynchronously before the `Runnable` starts running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_end`	Called asynchronously after the `Runnable` finishes running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_error`	Called asynchronously if the `Runnable` throws an error, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`input_type`	The input type to bind to the `Runnable`. TYPE: `type[Input] \| None` DEFAULT: `None`
`output_type`	The output type to bind to the `Runnable`. TYPE: `type[Output] \| None` DEFAULT: `None`

RETURNS	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.

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

RETURNS	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	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.

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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.

PARAMETER	DESCRIPTION
`args_schema`	The schema for the tool. TYPE: `type[BaseModel] \| None` DEFAULT: `None`
`name`	The name of the tool. TYPE: `str \| None` DEFAULT: `None`
`description`	The description of the tool. TYPE: `str \| None` DEFAULT: `None`
`arg_types`	A dictionary of argument names to types. TYPE: `dict[str, type] \| None` DEFAULT: `None`

RETURNS	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")

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	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	DESCRIPTION
`list[str]`	The namespace.

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_not_implemented ¶

to_json_not_implemented() -> SerializedNotImplemented

Serialize a "not implemented" object.

RETURNS	DESCRIPTION
`SerializedNotImplemented`	`SerializedNotImplemented`.

langchain_core.runnables.RunnableConfig ¶

Bases: TypedDict

Configuration for a Runnable.

tags `instance-attribute` ¶

tags: list[str]

Tags for this call and any sub-calls (eg. a Chain calling an LLM). You can use these to filter calls.

metadata `instance-attribute` ¶

metadata: dict[str, Any]

Metadata for this call and any sub-calls (eg. a Chain calling an LLM). Keys should be strings, values should be JSON-serializable.

callbacks `instance-attribute` ¶

callbacks: Callbacks

Callbacks for this call and any sub-calls (eg. a Chain calling an LLM). Tags are passed to all callbacks, metadata is passed to handle*Start callbacks.

run_name `instance-attribute` ¶

run_name: str

Name for the tracer run for this call. Defaults to the name of the class.

max_concurrency `instance-attribute` ¶

max_concurrency: int | None

Maximum number of parallel calls to make. If not provided, defaults to ThreadPoolExecutor's default.

recursion_limit `instance-attribute` ¶

recursion_limit: int

Maximum number of times a call can recurse. If not provided, defaults to 25.

configurable `instance-attribute` ¶

configurable: dict[str, Any]

Runtime values for attributes previously made configurable on this Runnable, or sub-Runnables, through configurable_fields or configurable_alternatives. Check output_schema for a description of the attributes that have been made configurable.

run_id `instance-attribute` ¶

run_id: UUID | None

Unique identifier for the tracer run for this call. If not provided, a new UUID will be generated.

Runnables

langchain_core.runnables.base.Runnable ¶

Key Methods¶

Composition¶

Standard Methods¶

Debugging and tracing¶

name instance-attribute ¶

InputType property ¶

OutputType property ¶

input_schema property ¶

output_schema property ¶

config_specs property ¶

get_name ¶

get_input_schema ¶

get_input_jsonschema ¶

get_output_schema ¶

get_output_jsonschema ¶

config_schema ¶

get_config_jsonschema ¶

get_graph ¶

get_prompts ¶

__or__ ¶

__ror__ ¶

pipe ¶

pick ¶

assign ¶

invoke abstractmethod ¶

ainvoke async ¶

batch ¶

batch_as_completed ¶

abatch async ¶

abatch_as_completed async ¶

stream ¶

astream async ¶

astream_log async ¶

astream_events async ¶

transform ¶

atransform async ¶

bind ¶

with_config ¶

with_listeners ¶

with_alisteners ¶

with_types ¶

with_retry ¶

map ¶

with_fallbacks ¶

as_tool ¶

langchain_core.runnables.base.RunnableBinding ¶

name class-attribute instance-attribute ¶

InputType property ¶

OutputType property ¶

input_schema property ¶

output_schema property ¶

config_specs property ¶

lc_secrets property ¶

lc_attributes property ¶

bound instance-attribute ¶

kwargs class-attribute instance-attribute ¶

config class-attribute instance-attribute ¶

config_factories class-attribute instance-attribute ¶

custom_input_type class-attribute instance-attribute ¶

custom_output_type class-attribute instance-attribute ¶

bind ¶

with_config ¶

with_listeners ¶

with_types ¶

with_retry ¶

get_name ¶

get_input_schema ¶

get_input_jsonschema ¶

get_output_schema ¶

get_output_jsonschema ¶

config_schema ¶

get_config_jsonschema ¶

get_graph ¶

get_prompts ¶

__or__ ¶

__ror__ ¶

pipe ¶

pick ¶

name `instance-attribute` ¶

InputType `property` ¶

OutputType `property` ¶

input_schema `property` ¶

output_schema `property` ¶

config_specs `property` ¶

or ¶

ror ¶

invoke `abstractmethod` ¶

ainvoke `async` ¶

abatch `async` ¶

abatch_as_completed `async` ¶

astream `async` ¶

astream_log `async` ¶

astream_events `async` ¶

atransform `async` ¶

name `class-attribute` `instance-attribute` ¶

InputType `property` ¶

OutputType `property` ¶

input_schema `property` ¶

output_schema `property` ¶

config_specs `property` ¶

lc_secrets `property` ¶

lc_attributes `property` ¶

bound `instance-attribute` ¶

kwargs `class-attribute` `instance-attribute` ¶

config `class-attribute` `instance-attribute` ¶

config_factories `class-attribute` `instance-attribute` ¶

custom_input_type `class-attribute` `instance-attribute` ¶

custom_output_type `class-attribute` `instance-attribute` ¶

or ¶

ror ¶

ainvoke `async` ¶

abatch `async` ¶

abatch_as_completed `async` ¶

astream `async` ¶

astream_log `async` ¶

astream_events `async` ¶

atransform `async` ¶

init ¶

is_lc_serializable `classmethod` ¶

get_lc_namespace `classmethod` ¶

lc_id `classmethod` ¶

name `instance-attribute` ¶

InputType `property` ¶

OutputType `property` ¶

input_schema `property` ¶

output_schema `property` ¶

config_specs `property` ¶

init ¶

ainvoke `async` ¶

or ¶

ror ¶

abatch `async` ¶

abatch_as_completed `async` ¶

astream_log `async` ¶

astream_events `async` ¶

name `instance-attribute` ¶