Runnable#

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

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

Key Methods#

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

Built-in optimizations:

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

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

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

LCEL and Composition#

The LangChain Expression Language (LCEL) is a declarative way to compose ``Runnables``into chains. Any chain constructed this way will automatically have sync, async, batch, and streaming support.

The main composition primitives are RunnableSequence and RunnableParallel.

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

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

For example,

from langchain_core.runnables import RunnableLambda

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


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

Standard Methods#

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

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

For example,

from langchain_core.runnables import RunnableLambda

import random

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


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

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

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

Debugging and tracing#

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

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

from langchain_core.globals import set_debug

set_debug(True)

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

from langchain_core.tracers import ConsoleCallbackHandler

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

For a UI (and much more) checkout LangSmith.

Attributes

`InputType`	Input type.
`OutputType`	Output Type.
`config_specs`	List configurable fields for this `Runnable`.
`input_schema`	The type of input this `Runnable` accepts specified as a pydantic model.
`output_schema`	Output schema.

Methods

`abatch`(inputs[, config, return_exceptions])	Default implementation runs `ainvoke` in parallel using `asyncio.gather`.
`abatch_as_completed`()	Run `ainvoke` in parallel on a list of inputs.
`ainvoke`(input[, config])	Transform a single input into an output.
`as_tool`([args_schema, name, description, ...])
`assign`(**kwargs)	Assigns new fields to the dict output of this `Runnable`.
`astream`(input[, config])	Default implementation of `astream`, which calls `ainvoke`.
`astream_events`(input[, config, version, ...])	Generate a stream of events.
`astream_log`()	Stream all output from a `Runnable`, as reported to the callback system.
`atransform`(input[, config])	Transform inputs to outputs.
`batch`(inputs[, config, return_exceptions])	Default implementation runs invoke in parallel using a thread pool executor.
`batch_as_completed`()	Run `invoke` in parallel on a list of inputs.
`bind`(**kwargs)	Bind arguments to a `Runnable`, returning a new `Runnable`.
`config_schema`(*[, include])	The type of config this `Runnable` accepts specified as a pydantic model.
`get_config_jsonschema`(*[, include])	Get a JSON schema that represents the config of the `Runnable`.
`get_graph`([config])	Return a graph representation of this `Runnable`.
`get_input_jsonschema`([config])	Get a JSON schema that represents the input to the `Runnable`.
`get_input_schema`([config])	Get a pydantic model that can be used to validate input to the Runnable.
`get_name`([suffix, name])	Get the name of the `Runnable`.
`get_output_jsonschema`([config])	Get a JSON schema that represents the output of the `Runnable`.
`get_output_schema`([config])	Get a pydantic model that can be used to validate output to the `Runnable`.
`get_prompts`([config])	Return a list of prompts used by this `Runnable`.
`invoke`(input[, config])	Transform a single input into an output.
`map`()	Return a new `Runnable` that maps a list of inputs to a list of outputs.
`pick`(keys)	Pick keys from the output dict of this `Runnable`.
`pipe`(*others[, name])	Pipe runnables.
`stream`(input[, config])	Default implementation of `stream`, which calls `invoke`.
`transform`(input[, config])	Transform inputs to outputs.
`with_alisteners`(*[, on_start, on_end, on_error])	Bind async lifecycle listeners to a `Runnable`.
`with_config`([config])	Bind config to a `Runnable`, returning a new `Runnable`.
`with_fallbacks`(fallbacks, *[, ...])	Add fallbacks to a `Runnable`, returning a new `Runnable`.
`with_listeners`(*[, on_start, on_end, on_error])	Bind lifecycle listeners to a `Runnable`, returning a new `Runnable`.
`with_retry`(*[, retry_if_exception_type, ...])	Create a new Runnable that retries the original Runnable on exceptions.
`with_types`(*[, input_type, output_type])	Bind input and output types to a `Runnable`, returning a new `Runnable`.

async abatch(

inputs: list[Input],

config: RunnableConfig | list[RunnableConfig] | None = None,

*,

return_exceptions: bool = False,

**kwargs: Any | None,

) → list[Output][source]#

Default implementation runs ainvoke in parallel using asyncio.gather.

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

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

Parameters:

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

Returns:

A list of outputs from the Runnable.

Return type:

list[Output]

async abatch_as_completed(

inputs: Sequence[Input],

config: RunnableConfig | Sequence[RunnableConfig] | None = None,

*,

return_exceptions: Literal[False] = False,

**kwargs: Any | None,

) → AsyncIterator[tuple[int, Output]][source]#

async abatch_as_completed(

inputs: Sequence[Input],

config: RunnableConfig | Sequence[RunnableConfig] | None = None,

*,

return_exceptions: Literal[True],

**kwargs: Any | None,

) → AsyncIterator[tuple[int, Output | Exception]]

Run ainvoke in parallel on a list of inputs.

Yields results as they complete.

Parameters:

inputs – A list of inputs to the Runnable.
config – A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.
return_exceptions – Whether to return exceptions instead of raising them. Defaults to False.
kwargs – Additional keyword arguments to pass to the Runnable.

Yields:

A tuple of the index of the input and the output from the Runnable.

async ainvoke(

input: Input,

config: RunnableConfig | None = None,

**kwargs: Any,

) → Output[source]#

Transform a single input into an output.

Parameters:

input (Input) – The input to the Runnable.
config (RunnableConfig | None) – A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.
kwargs (Any)

Returns:

The output of the Runnable.

Return type:

Output

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

Beta

This API is in beta and may change in the future.

Create a BaseTool from a Runnable.

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

Parameters:

args_schema (Optional[type[BaseModel]]) – The schema for the tool. Defaults to None.
name (Optional[str]) – The name of the tool. Defaults to None.
description (Optional[str]) – The description of the tool. Defaults to None.
arg_types (Optional[dict[str, type]]) – A dictionary of argument names to types. Defaults to None.

Returns:

A BaseTool instance.

Return type:

BaseTool

Typed dict input:

from typing_extensions import TypedDict
from langchain_core.runnables import RunnableLambda


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


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


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

dict input, specifying schema via args_schema:

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

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

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

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

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

dict input, specifying schema via arg_types:

from typing import Any
from langchain_core.runnables import RunnableLambda


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


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

String input:

from langchain_core.runnables import RunnableLambda

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

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

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

Added in version 0.2.14.

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][source]#

Assigns new fields to the dict output of this Runnable.

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

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

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

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

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

Parameters:: **kwargs (Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any] | Mapping[str, Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any]]) – A mapping of keys to Runnable or Runnable-like objects that will be invoked with the entire output dict of this Runnable.
Returns:: A new Runnable.
Return type:: RunnableSerializable[Any, Any]

async astream(

input: Input,

config: RunnableConfig | None = None,

**kwargs: Any | None,

) → AsyncIterator[Output][source]#

Default implementation of astream, which calls ainvoke.

Subclasses should override this method if they support streaming output.

Parameters:

input (Input) – The input to the Runnable.
config (RunnableConfig | None) – The config to use for the Runnable. Defaults to None.
kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.

Yields:

The output of the Runnable.

Return type:

AsyncIterator[Output]

async astream_events(

input: Any,

config: RunnableConfig | None = None,

*,

version: Literal['v1', 'v2'] = 'v2',

include_names: Sequence[str] | None = None,

include_types: Sequence[str] | None = None,

include_tags: Sequence[str] | None = None,

exclude_names: Sequence[str] | None = None,

exclude_types: Sequence[str] | None = None,

exclude_tags: Sequence[str] | None = None,

**kwargs: Any,

) → AsyncIterator[StreamEvent][source]#

Generate a stream of events.

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

A StreamEvent is a dictionary with the following schema:

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

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

Note

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

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

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

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

A custom event has following format:

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

Here are declarations associated with the standard events shown above:

format_docs:

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


format_docs = RunnableLambda(format_docs)

some_tool:

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

prompt:

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

Example:

from langchain_core.runnables import RunnableLambda


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


chain = RunnableLambda(func=reverse)

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

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

Example: Dispatch Custom Event

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


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

slow_thing = RunnableLambda(slow_thing)

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

Parameters:

input (Any) – The input to the Runnable.
config (Optional[RunnableConfig]) – The config to use for the Runnable.
version (Literal['v1', 'v2']) – The version of the schema to use either 'v2' or 'v1'. Users should use 'v2'. 'v1' is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in 'v2'.
include_names (Optional[Sequence[str]]) – Only include events from Runnables with matching names.
include_types (Optional[Sequence[str]]) – Only include events from Runnables with matching types.
include_tags (Optional[Sequence[str]]) – Only include events from Runnables with matching tags.
exclude_names (Optional[Sequence[str]]) – Exclude events from Runnables with matching names.
exclude_types (Optional[Sequence[str]]) – Exclude events from Runnables with matching types.
exclude_tags (Optional[Sequence[str]]) – Exclude events from Runnables with matching tags.
kwargs (Any) – Additional keyword arguments to pass to the Runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.

Yields:

An async stream of StreamEvents.

Raises:

NotImplementedError – If the version is not 'v1' or 'v2'.

Return type:

AsyncIterator[StreamEvent]

async astream_log(

input: Any,

config: RunnableConfig | None = None,

*,

diff: Literal[True] = 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][source]#

async astream_log(

input: Any,

config: RunnableConfig | None = None,

*,

diff: Literal[False],

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[RunLog]

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

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

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

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

Parameters:

input – The input to the Runnable.
config – The config to use for the Runnable.
diff – Whether to yield diffs between each step or the current state.
with_streamed_output_list – Whether to yield the streamed_output list.
include_names – Only include logs with these names.
include_types – Only include logs with these types.
include_tags – Only include logs with these tags.
exclude_names – Exclude logs with these names.
exclude_types – Exclude logs with these types.
exclude_tags – Exclude logs with these tags.
kwargs – Additional keyword arguments to pass to the Runnable.

Yields:

A RunLogPatch or RunLog object.

async atransform(

input: AsyncIterator[Input],

config: RunnableConfig | None = None,

**kwargs: Any | None,

) → AsyncIterator[Output][source]#

Transform inputs to outputs.

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

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

Parameters:

input (AsyncIterator[Input]) – An async iterator of inputs to the Runnable.
config (RunnableConfig | None) – The config to use for the Runnable. Defaults to None.
kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.

Yields:

The output of the Runnable.

Return type:

AsyncIterator[Output]

batch(

inputs: list[Input],

config: RunnableConfig | list[RunnableConfig] | None = None,

*,

return_exceptions: bool = False,

**kwargs: Any | None,

) → list[Output][source]#

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

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

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

Parameters:

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

Returns:

A list of outputs from the Runnable.

Return type:

list[Output]

batch_as_completed(

inputs: Sequence[Input],

config: RunnableConfig | Sequence[RunnableConfig] | None = None,

*,

return_exceptions: Literal[False] = False,

**kwargs: Any,

) → Iterator[tuple[int, Output]][source]#

batch_as_completed(

inputs: Sequence[Input],

config: RunnableConfig | Sequence[RunnableConfig] | None = None,

*,

return_exceptions: Literal[True],

**kwargs: Any,

) → Iterator[tuple[int, Output | Exception]]

Run invoke in parallel on a list of inputs.

Yields results as they complete.

Parameters:

inputs – A list of inputs to the Runnable.
config – A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.
return_exceptions – Whether to return exceptions instead of raising them. Defaults to False.
**kwargs – Additional keyword arguments to pass to the Runnable.

Yields:

Tuples of the index of the input and the output from the Runnable.

bind(

**kwargs: Any,

) → Runnable[Input, Output][source]#

Bind arguments to a Runnable, returning a new Runnable.

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

Parameters:: kwargs (Any) – The arguments to bind to the Runnable.
Returns:: A new Runnable with the arguments bound.
Return type:: Runnable[Input, Output]

Example:

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

llm = ChatOllama(model="llama2")

# Without bind.
chain = llm | StrOutputParser()

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

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

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

config_schema( *, include: Sequence[str] | None = None, ) → type[BaseModel][source]#

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

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

Parameters:: include (Sequence[str] | None) – A list of fields to include in the config schema.
Returns:: A pydantic model that can be used to validate config.
Return type:: type[BaseModel]

get_config_jsonschema( *, include: Sequence[str] | None = None, ) → dict[str, Any][source]#

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

Parameters:: include (Sequence[str] | None) – A list of fields to include in the config schema.
Returns:: A JSON schema that represents the config of the Runnable.
Return type:: dict[str, Any]

Added in version 0.3.0.

get_graph( config: RunnableConfig | None = None, ) → Graph[source]#

Return a graph representation of this Runnable.

Parameters:: config (Optional[RunnableConfig])
Return type:: Graph

get_input_jsonschema( config: RunnableConfig | None = None, ) → dict[str, Any][source]#

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

Parameters:: config (RunnableConfig | None) – A config to use when generating the schema.
Returns:: A JSON schema that represents the input to the Runnable.
Return type:: dict[str, Any]

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_input_schema( config: RunnableConfig | None = None, ) → type[BaseModel][source]#

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

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

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

Parameters:: config (RunnableConfig | None) – A config to use when generating the schema.
Returns:: A pydantic model that can be used to validate input.
Return type:: type[BaseModel]

get_name( suffix: str | None = None, *, name: str | None = None, ) → str[source]#

Get the name of the Runnable.

Parameters:

suffix (str | None) – An optional suffix to append to the name.
name (str | None) – An optional name to use instead of the Runnable’s name.

Returns:

The name of the Runnable.

Return type:

str

get_output_jsonschema( config: RunnableConfig | None = None, ) → dict[str, Any][source]#

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

Parameters:: config (RunnableConfig | None) – A config to use when generating the schema.
Returns:: A JSON schema that represents the output of the Runnable.
Return type:: dict[str, Any]

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.

get_output_schema( config: RunnableConfig | None = None, ) → type[BaseModel][source]#

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

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

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

Parameters:: config (RunnableConfig | None) – A config to use when generating the schema.
Returns:: A pydantic model that can be used to validate output.
Return type:: type[BaseModel]

get_prompts( config: RunnableConfig | None = None, ) → list[BasePromptTemplate][source]#

Return a list of prompts used by this Runnable.

Parameters:: config (Optional[RunnableConfig])
Return type:: list[BasePromptTemplate]

abstractmethod invoke(

input: Input,

config: RunnableConfig | None = None,

**kwargs: Any,

) → Output[source]#

Transform a single input into an output.

Parameters:

input (Input) – The input to the Runnable.
config (RunnableConfig | None) – A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.
kwargs (Any)

Returns:

The output of the Runnable.

Return type:

Output

map() → Runnable[list[Input], list[Output]][source]#

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

Calls invoke with each input.

Returns:: A new Runnable that maps a list of inputs to a list of outputs.
Return type:: Runnable[list[Input], list[Output]]

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]

pick( keys: str | list[str], ) → RunnableSerializable[Any, Any][source]#

Pick keys from the output dict of this Runnable.

Pick 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 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]"}

Parameters:: keys (str | list[str]) – A key or list of keys to pick from the output dict.
Returns:: a new Runnable.
Return type:: RunnableSerializable[Any, Any]

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

Pipe runnables.

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

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

Example

from langchain_core.runnables import RunnableLambda


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


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


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

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

Parameters:

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

Returns:

A new Runnable.

Return type:

RunnableSerializable[-Input, ~Other]

stream(

input: Input,

config: RunnableConfig | None = None,

**kwargs: Any | None,

) → Iterator[Output][source]#

Default implementation of stream, which calls invoke.

Subclasses should override this method if they support streaming output.

Parameters:

input (Input) – The input to the Runnable.
config (RunnableConfig | None) – The config to use for the Runnable. Defaults to None.
kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.

Yields:

The output of the Runnable.

Return type:

Iterator[Output]

transform(

input: Iterator[Input],

config: RunnableConfig | None = None,

**kwargs: Any | None,

) → Iterator[Output][source]#

Transform inputs to outputs.

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

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

Parameters:

input (Iterator[Input]) – An iterator of inputs to the Runnable.
config (RunnableConfig | None) – The config to use for the Runnable. Defaults to None.
kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.

Yields:

The output of the Runnable.

Return type:

Iterator[Output]

with_alisteners( *, on_start: AsyncListener | None = None, on_end: AsyncListener | None = None, on_error: AsyncListener | None = None, ) → Runnable[Input, Output][source]#

Bind async lifecycle listeners to a Runnable.

Returns a new Runnable.

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

Parameters:

on_start (Optional[AsyncListener]) – Called asynchronously before the Runnable starts running, with the Run object. Defaults to None.
on_end (Optional[AsyncListener]) – Called asynchronously after the Runnable finishes running, with the Run object. Defaults to None.
on_error (Optional[AsyncListener]) – Called asynchronously if the Runnable throws an error, with the Run object. Defaults to None.

Returns:

A new Runnable with the listeners bound.

Return type:

Runnable[Input, Output]

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_config(

config: RunnableConfig | None = None,

**kwargs: Any,

) → Runnable[Input, Output][source]#

Bind config to a Runnable, returning a new Runnable.

Parameters:

config (RunnableConfig | None) – The config to bind to the Runnable.
kwargs (Any) – Additional keyword arguments to pass to the Runnable.

Returns:

A new Runnable with the config bound.

Return type:

Runnable[Input, Output]

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

Add fallbacks to a Runnable, returning a new Runnable.

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

Parameters:

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

Returns:

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

Return type:

RunnableWithFallbacksT[Input, Output]

Example

from typing import Iterator

from langchain_core.runnables import RunnableGenerator


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


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


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

Parameters:

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

Returns:

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

Return type:

RunnableWithFallbacksT[Input, Output]

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][source]#

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

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

Parameters:

on_start (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) – Called before the Runnable starts running, with the Run object. Defaults to None.
on_end (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) – Called after the Runnable finishes running, with the Run object. Defaults to None.
on_error (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) – Called if the Runnable throws an error, with the Run object. Defaults to None.

Returns:

A new Runnable with the listeners bound.

Return type:

Runnable[Input, Output]

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_retry(*, retry_if_exception_type: tuple[type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, exponential_jitter_params: Optional[ExponentialJitterParams] = None, stop_after_attempt: int = 3) → Runnable[Input, Output][source]#

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

Parameters:

retry_if_exception_type (tuple[type[BaseException], ...]) – A tuple of exception types to retry on. Defaults to (Exception,).
wait_exponential_jitter (bool) – Whether to add jitter to the wait time between retries. Defaults to True.
stop_after_attempt (int) – The maximum number of attempts to make before giving up. Defaults to 3.
exponential_jitter_params (Optional[ExponentialJitterParams]) – Parameters for tenacity.wait_exponential_jitter. Namely: initial, max, exp_base, and jitter (all float values).

Returns:

A new Runnable that retries the original Runnable on exceptions.

Return type:

Runnable[Input, Output]

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

with_types( *, input_type: type[Input] | None = None, output_type: type[Output] | None = None, ) → Runnable[Input, Output][source]#

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

Parameters:

input_type (type[Input] | None) – The input type to bind to the Runnable. Defaults to None.
output_type (type[Output] | None) – The output type to bind to the Runnable. Defaults to None.

Returns:

A new Runnable with the types bound.

Return type:

Runnable[Input, Output]

Examples using Runnable