langchain_core.runnables.base.Runnable

class langchain_core.runnables.base.Runnable

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

• 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.

The LangChain Expression Language (LCEL) is a declarative way to compose Runnables into chains. 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}

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

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: https://docs.smith.langchain.com/

Attributes

InputType	The type of input this runnable accepts specified as a type annotation.
OutputType	The type of output this runnable produces specified as a type annotation.
config_specs	List configurable fields for this runnable.
input_schema	The type of input this runnable accepts specified as a pydantic model.
name	The name of the runnable.
output_schema	The type of output this runnable produces specified as a pydantic model.

Methods

__init__()
abatch(inputs[, config, return_exceptions])	Default implementation runs ainvoke in parallel using asyncio.gather.
ainvoke(input[, config])	Default implementation of ainvoke, calls invoke from a thread.
assign(**kwargs)	Assigns new fields to the dict output of this runnable.
astream(input[, config])	Default implementation of astream, which calls ainvoke.
astream_log()	Stream all output from a runnable, as reported to the callback system.
atransform(input[, config])	Default implementation of atransform, which buffers input and calls astream.
batch(inputs[, config, return_exceptions])	Default implementation runs invoke in parallel using a thread pool executor.
bind(**kwargs)	Bind arguments to a Runnable, returning a new Runnable.
config_schema(*[, include])	The type of config this runnable accepts specified as a pydantic model.
get_graph([config])	Return a graph representation of this runnable.
get_input_schema([config])	Get a pydantic model that can be used to validate input to the runnable.
get_name([suffix, name])	Get the name of the runnable.
get_output_schema([config])	Get a pydantic model that can be used to validate output to the runnable.
get_prompts([config])
invoke(input[, config])	Transform a single input into an output.
map()	Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
pick(keys)	Pick keys from the dict output of this runnable.
pipe(*others[, name])	Compose this runnable with another object to create a RunnableSequence.
stream(input[, config])	Default implementation of stream, which calls invoke.
transform(input[, config])	Default implementation of transform, which buffers input and then calls stream.
with_config([config])	Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks, *[, ...])	Add fallbacks to a runnable, returning a new Runnable.
with_listeners(*[, on_start, on_end, on_error])	Bind lifecycle listeners to a Runnable, returning a new Runnable.
with_retry(*[, retry_if_exception_type, ...])	Create a new Runnable that retries the original runnable on exceptions.
with_types(*[, input_type, output_type])	Bind input and output types to a Runnable, returning a new Runnable.

__init__()

async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]

Default implementation runs ainvoke in parallel using asyncio.gather.

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

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

async ainvoke(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any) → Output

Default implementation of ainvoke, calls invoke from a thread.

The default implementation allows usage of async code even if the runnable did not implement a native async version of invoke.

Subclasses should override this method if they can run asynchronously.

assign(**kwargs: Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) → RunnableSerializable[Any, Any]

Assigns new fields to the dict output of this runnable. Returns a new runnable.

async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]

Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.

async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: Literal[True] = 'True', with_streamed_output_list: bool = 'True', include_names: Optional[Sequence[str]] = 'None', include_types: Optional[Sequence[str]] = 'None', include_tags: Optional[Sequence[str]] = 'None', exclude_names: Optional[Sequence[str]] = 'None', exclude_types: Optional[Sequence[str]] = 'None', exclude_tags: Optional[Sequence[str]] = 'None', **kwargs: Optional[Any]) → AsyncIterator[RunLogPatch]

async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: Literal[False], with_streamed_output_list: bool = 'True', include_names: Optional[Sequence[str]] = 'None', include_types: Optional[Sequence[str]] = 'None', include_tags: Optional[Sequence[str]] = 'None', exclude_names: Optional[Sequence[str]] = 'None', exclude_types: Optional[Sequence[str]] = 'None', exclude_tags: Optional[Sequence[str]] = 'None', **kwargs: Optional[Any]) → AsyncIterator[RunLog]

Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc.

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

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

Parameters

• input – The input to the runnable.

• config – The config to use for the runnable.

• diff – Whether to yield diffs between each step, or the current state.

• with_streamed_output_list – Whether to yield the streamed_output list.

• include_names – Only include logs with these names.

• include_types – Only include logs with these types.

• include_tags – Only include logs with these tags.

• exclude_names – Exclude logs with these names.

• exclude_types – Exclude logs with these types.

• exclude_tags – Exclude logs with these tags.

async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]

Default implementation of atransform, which buffers input and calls astream. Subclasses should override this method if they can start producing output while input is still being generated.

batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]

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

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

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

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

Bind arguments to a Runnable, returning a new Runnable.

config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]

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

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

Parameters

include – A list of fields to include in the config schema.

Returns

A pydantic model that can be used to validate config.

get_graph(config: Optional[RunnableConfig] = None) → Graph

Return a graph representation of this runnable.

get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]

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

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

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

Parameters

config – A config to use when generating the schema.

Returns

A pydantic model that can be used to validate input.

get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) → str

Get the name of the runnable.

get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]

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

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

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

Parameters

config – A config to use when generating the schema.

Returns

A pydantic model that can be used to validate output.

get_prompts(config: Optional[RunnableConfig] = None) → List[BasePromptTemplate]

abstract invoke(input: Input, config: Optional[RunnableConfig] = None) → Output

Transform a single input into an output. Override to implement.

Parameters

• input – The input to the runnable.

• 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.

Returns

The output of the runnable.

map() → Runnable[List[Input], List[Output]]

Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.

pick(keys: Union[str, List[str]]) → RunnableSerializable[Any, Any]

Pick keys from the dict output of this runnable. Returns a new runnable.

pipe(*others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None) → RunnableSerializable[Input, Other]

Compose this runnable with another object to create a RunnableSequence.

stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]

Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.

transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]

Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.

with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]

Bind config to a Runnable, returning a new Runnable.

with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacksT[Input, Output]

Add fallbacks to a runnable, returning a new Runnable.

Parameters

• fallbacks – A sequence of runnables to try if the original runnable fails.

• exceptions_to_handle – A tuple of exception types to handle.

Returns

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

with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]

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

on_start: Called before the runnable starts running, with the Run object. on_end: Called after the runnable finishes running, with the Run object. on_error: Called if the runnable throws an error, with the Run object.

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.

with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]

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

Parameters

• retry_if_exception_type – A tuple of exception types to retry on

• wait_exponential_jitter – Whether to add jitter to the wait time between retries

• stop_after_attempt – The maximum number of attempts to make before giving up

Returns

A new Runnable that retries the original runnable on exceptions.

with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]

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