Runnables (Classic)

langchain-classic documentation

These docs cover the langchain-classic package. This package will be maintained for security vulnerabilities until December 2026. Users are encouraged to migrate to the langchain package for the latest features and improvements. See docs for langchain

langchain_classic.runnables.hub

HubRunnable

Bases: RunnableBindingBase[Input, Output]

An instance of a runnable stored in the LangChain Hub.

METHOD	DESCRIPTION
__init__	Initialize the HubRunnable.
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.
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]).

__init__

__init__(
    owner_repo_commit: str,
    *,
    api_url: str | None = None,
    api_key: str | None = None,
    **kwargs: Any,
) -> None

Initialize the HubRunnable.

PARAMETER	DESCRIPTION
owner_repo_commit	The full name of the prompt to pull from in the format of owner/prompt_name:commit_hash or owner/prompt_name or just prompt_name if it's your own prompt. TYPE: str
api_url	The URL of the LangChain Hub API. Defaults to the hosted API service if you have an api key set, or a localhost instance if not. TYPE: str | None DEFAULT: None
api_key	The API key to use to authenticate with the LangChain Hub API. TYPE: str | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the parent class. TYPE: Any DEFAULT: {}

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.

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")

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

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

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

langchain_classic.runnables.openai_functions

OpenAIFunction

Bases: TypedDict

A function description for ChatOpenAI.

name instance-attribute

name: str

The name of the function.

description instance-attribute

description: str

The description of the function.

parameters instance-attribute

parameters: dict

The parameters to the function.

OpenAIFunctionsRouter

Bases: RunnableBindingBase[BaseMessage, Any]

A runnable that routes to the selected function.

METHOD	DESCRIPTION
__init__	Initialize the OpenAIFunctionsRouter.
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.
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]).

__init__

__init__(
    runnables: Mapping[str, Runnable[dict, Any] | Callable[[dict], Any]],
    functions: list[OpenAIFunction] | None = None,
)

Initialize the OpenAIFunctionsRouter.

PARAMETER	DESCRIPTION
runnables	A mapping of function names to runnables. TYPE: Mapping[str, Runnable[dict, Any] | Callable[[dict], Any]]
functions	Optional list of functions to check against the runnables. TYPE: list[OpenAIFunction] | None DEFAULT: None

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.

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")

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

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

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