`langchain_core.runnables.base`.Runnable¶

class langchain_core.runnables.base.Runnable[source]¶

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.

LCEL and Composition¶

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}

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!')
        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!!)

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: 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_events`(input[, config, ...])	[Beta] Generate a stream of events.
`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][source]¶

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[source]¶

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][source]¶: 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][source]¶: Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.

astream_events(input: Any, config: Optional[RunnableConfig] = None, *, version: Literal['v1'], 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: Any) → AsyncIterator[StreamEvent][source]¶

[Beta] Generate a stream of events.

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

A StreamEvent is a dictionary with the following schema:

event: str - Event names are of the
format: on_[runnable_type]_(start|stream|end).
name: str - The name of the runnable that generated the event.
run_id: str - randomly generated ID associated with the given execution of
the runnable that emitted the event. A child runnable that gets invoked as part of the execution of a parent runnable is assigned its own unique ID.
tags: Optional[List[str]] - The tags of the runnable that generated
the event.
metadata: Optional[Dict[str, Any]] - The metadata of the runnable
that generated the event.
data: Dict[str, Any]

Below is a table that illustrates some evens 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.

|----------------------|——————|---------------------------------|———————————————–|-------------------------------------------------| | 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]]} | {“generations”: […], “llm_output”: None, …} | | 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_stream | some_tool | {“x”: 1, “y”: “2”} | | | | on_tool_end | some_tool | | | {“x”: 1, “y”: “2”} | | on_retriever_start | [retriever name] | | {“query”: “hello”} | | | on_retriever_chunk | [retriever name] | {documents: […]} | | | | on_retriever_end | [retriever name] | | {“query”: “hello”} | {documents: […]} | | on_prompt_start | [template_name] | | {“question”: “hello”} | | | on_prompt_end | [template_name] | | {“question”: “hello”} | ChatPromptValue(messages: [SystemMessage, …]) |

Here are declarations associated with the events shown above:

format_docs:

```python 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:

```python @tool def some_tool(x: int, y: str) -> dict:

‘’’Some_tool.’’’ return {“x”: x, “y”: y}

```

prompt:

```python template = ChatPromptTemplate.from_messages(

[(“system”, “You are Cat Agent 007”), (“human”, “{question}”)]

).with_config({“run_name”: “my_template”, “tags”: [“my_template”]}) ```

Example:

from langchain_core.runnables import RunnableLambda

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

chain = RunnableLambda(func=reverse)

events = [
    event async for event in chain.astream_events("hello", version="v1")
]

# will produce the following events (run_id 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": [],
    },
]

Parameters

input – The input to the runnable.
config – The config to use for the runnable.
version – The version of the schema to use. Currently only version 1 is available. No default will be assigned until the API is stabilized.
include_names – Only include events from runnables with matching names.
include_types – Only include events from runnables with matching types.
include_tags – Only include events from runnables with matching tags.
exclude_names – Exclude events from runnables with matching names.
exclude_types – Exclude events from runnables with matching types.
exclude_tags – Exclude events from runnables with matching tags.
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.

Returns

An async stream of StreamEvents.[Beta] Generate a stream of events.

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

A StreamEvent is a dictionary with the following schema:

event: str - Event names are of the
format: on_[runnable_type]_(start|stream|end).
name: str - The name of the runnable that generated the event.
run_id: str - randomly generated ID associated with the given execution of
the runnable that emitted the event. A child runnable that gets invoked as part of the execution of a parent runnable is assigned its own unique ID.
tags: Optional[List[str]] - The tags of the runnable that generated
the event.
metadata: Optional[Dict[str, Any]] - The metadata of the runnable
that generated the event.
data: Dict[str, Any]

Below is a table that illustrates some evens 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.

|----------------------|——————|---------------------------------|———————————————–|-------------------------------------------------| | 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]]} | {“generations”: […], “llm_output”: None, …} | | 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_stream | some_tool | {“x”: 1, “y”: “2”} | | | | on_tool_end | some_tool | | | {“x”: 1, “y”: “2”} | | on_retriever_start | [retriever name] | | {“query”: “hello”} | | | on_retriever_chunk | [retriever name] | {documents: […]} | | | | on_retriever_end | [retriever name] | | {“query”: “hello”} | {documents: […]} | | on_prompt_start | [template_name] | | {“question”: “hello”} | | | on_prompt_end | [template_name] | | {“question”: “hello”} | ChatPromptValue(messages: [SystemMessage, …]) |

Here are declarations associated with the events shown above:

format_docs:

```python 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:

```python @tool def some_tool(x: int, y: str) -> dict:

‘’’Some_tool.’’’ return {“x”: x, “y”: y}

```

prompt:

```python template = ChatPromptTemplate.from_messages(

[(“system”, “You are Cat Agent 007”), (“human”, “{question}”)]

).with_config({“run_name”: “my_template”, “tags”: [“my_template”]}) ```

Example:

from langchain_core.runnables import RunnableLambda

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

chain = RunnableLambda(func=reverse)

events = [
    event async for event in chain.astream_events("hello", version="v1")
]

# will produce the following events (run_id 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": [],
    },
]

Parameters

input – The input to the runnable.
config – The config to use for the runnable.
version – The version of the schema to use. Currently only version 1 is available. No default will be assigned until the API is stabilized.
include_names – Only include events from runnables with matching names.
include_types – Only include events from runnables with matching types.
include_tags – Only include events from runnables with matching tags.
exclude_names – Exclude events from runnables with matching names.
exclude_types – Exclude events from runnables with matching types.
exclude_tags – Exclude events from runnables with matching tags.
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.

Returns

An async stream of StreamEvents.

Notes

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: Any) → AsyncIterator[RunLogPatch][source]¶

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: 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][source]¶: 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][source]¶

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][source]¶: Bind arguments to a Runnable, returning a new Runnable.

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

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[source]¶: Return a graph representation of this runnable.

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

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[source]¶: Get the name of the runnable.

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

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][source]¶

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

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]][source]¶: 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][source]¶: 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][source]¶: Compose this runnable with another object to create a RunnableSequence.

stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output][source]¶: 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][source]¶: 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][source]¶: Bind config to a Runnable, returning a new Runnable.

with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,), exception_key: Optional[str] = None) → RunnableWithFallbacksT[Input, Output][source]¶

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.
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.

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][source]¶

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][source]¶

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][source]¶: Bind input and output types to a Runnable, returning a new Runnable.

langchain_core.runnables.base.Runnable¶

LCEL and Composition¶

Standard Methods¶

Debugging and tracing¶

`langchain_core.runnables.base`.Runnable¶