BaseTool #

classlangchain_core.tools.base.BaseTool[source]#

Bases:RunnableSerializable[Union[str,dict,ToolCall],Any]

Base class for all LangChain tools.

This abstract class defines the interface that all LangChain tools must implement.Tools are components that can be called by agents to perform specific actions.

Initialize the tool.

Note

BaseTool implements the standardRunnableInterface. 🏃

TheRunnableInterface has additional methods that are available on runnables, such aswith_config,with_types,with_retry,assign,bind,get_graph, and more.

paramargs_schema:Annotated[ArgsSchema|None,SkipValidation()]=None#

Pydantic model class to validate and parse the tool’s input arguments.

Args schema should be either:

A subclass of pydantic.BaseModel.

or- A subclass of pydantic.v1.BaseModel if accessing v1 namespace in pydantic 2or- a JSON schema dict

The tool schema.

paramcallback_manager:BaseCallbackManager|None=None#: Deprecated since version 0.1.7:Usecallbacks() instead. It will be removed in pydantic==1.0.
Callback manager to add to the run trace.

paramcallbacks:Callbacks=None#: Callbacks to be called during tool execution.

paramdescription:str[Required]#

Used to tell the model how/when/why to use the tool.

You can provide few-shot examples as a part of the description.

paramhandle_tool_error:bool|str|Callable[[ToolException],str]|None=False#: Handle the content of the ToolException thrown.

paramhandle_validation_error:bool|str|Callable[[ValidationError|ValidationErrorV1],str]|None=False#: Handle the content of the ValidationError thrown.

parammetadata:dict[str,Any]|None=None#: Optional metadata associated with the tool. Defaults to None.This metadata will be associated with each call to this tool,and passed as arguments to the handlers defined incallbacks.You can use these to eg identify a specific instance of a tool with its use case.

paramresponse_format:Literal['content','content_and_artifact']='content'#

The tool response format. Defaults to ‘content’.

If “content” then the output of the tool is interpreted as the contents of aToolMessage. If “content_and_artifact” then the output is expected to be atwo-tuple corresponding to the (content, artifact) of a ToolMessage.

paramreturn_direct:bool=False#

Whether to return the tool’s output directly.

Setting this to True meansthat after the tool is called, the AgentExecutor will stop looping.

paramtags:list[str]|None=None#: Optional list of tags associated with the tool. Defaults to None.These tags will be associated with each call to this tool,and passed as arguments to the handlers defined incallbacks.You can use these to eg identify a specific instance of a tool with its use case.

paramverbose:bool=False#: Whether to log the tool’s progress.

__call__( tool_input:str, callbacks:list[BaseCallbackHandler]|BaseCallbackManager|None=None, )→str[source]#

Deprecated since version 0.1.47:Useinvoke() instead. It will not be removed until langchain-core==1.0.

Make tool callable (deprecated).

Parameters:

tool_input (str) – The input to the tool.
callbacks (list[BaseCallbackHandler]|BaseCallbackManager |None) – Callbacks to use during execution.

Returns:

The tool’s output.

Return type:

str

asyncabatch(
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 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 tracingpurposes, ‘max_concurrency’ for controlling how much work to doin parallel, and other keys. Please refer to the RunnableConfigfor 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]

asyncabatch_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.

Parameters:

inputs (Sequence[Input]) – A list of inputs to the Runnable.
config (RunnableConfig |Sequence[RunnableConfig]|None) – A config to use when invoking the Runnable.The config supports standard keys like ‘tags’, ‘metadata’ for tracingpurposes, ‘max_concurrency’ for controlling how much work to doin parallel, and other keys. Please refer to the RunnableConfigfor more details. Defaults to None. 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.

Yields:

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

Return type:

AsyncIterator[tuple[int,Output | Exception]]

asyncainvoke(
input:str|dict|ToolCall,
config:RunnableConfig|None=None,
**kwargs:Any,
)→Any[source]#

Default implementation of ainvoke, calls invoke from a thread.

The default implementation allows usage of async code even ifthe Runnable did not implement a native async version of invoke.

Subclasses should override this method if they can run asynchronously.

Parameters:

input (str |dict |ToolCall)
config (RunnableConfig |None)
kwargs (Any)

Return type:

Any

Run the tool asynchronously.

Parameters:

tool_input (Union[str,dict]) – The input to the tool.
verbose (Optional[bool]) – Whether to log the tool’s progress. Defaults to None.
start_color (Optional[str]) – The color to use when starting the tool. Defaults to ‘green’.
color (Optional[str]) – The color to use when ending the tool. Defaults to ‘green’.
callbacks (Callbacks) – Callbacks to be called during tool execution. Defaults to None.
tags (Optional[list[str]]) – Optional list of tags associated with the tool. Defaults to None.
metadata (Optional[dict[str,Any]]) – Optional metadata associated with the tool. Defaults to None.
run_name (Optional[str]) – The name of the run. Defaults to None.
run_id (Optional[uuid.UUID]) – The id of the run. Defaults to None.
config (Optional[RunnableConfig]) – The configuration for the tool. Defaults to None.
tool_call_id (Optional[str]) – The id of the tool call. Defaults to None.
kwargs (Any) – Keyword arguments to be passed to tool callbacks

Returns:

The output of the tool.

Raises:

ToolException – If an error occurs during tool execution.

Return type:

Any

asyncastream(
input:Input,
config:RunnableConfig|None=None,
**kwargs:Any|None,
)→AsyncIterator[Output]#

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]

asyncastream_events(
input:Any,
config:RunnableConfig|None=None,
*,
version:Literal['v1','v2']='v2',
include_names:Sequence[str]|None=None,
include_types:Sequence[str]|None=None,
include_tags:Sequence[str]|None=None,
exclude_names:Sequence[str]|None=None,
exclude_types:Sequence[str]|None=None,
exclude_tags:Sequence[str]|None=None,
**kwargs:Any,
)→AsyncIterator[StreamEvent]#

Generate a stream of events.

Use to create an iterator over StreamEvents that provide real-time informationabout the progress of the Runnable, including StreamEvents from intermediateresults.

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 givenexecution of the Runnable that emitted the event. A child Runnable that getsinvoked as part of the execution of a parent Runnable is assigned its ownunique ID.
parent_ids:list[str] - The IDs of the parent runnables that generatedthe event. The root Runnable will have an empty list. The order of the parentIDs is from the root to the immediate parent. Only available for v2 version ofthe API. The v1 version of the API will return an empty list.
tags:Optional[list[str]] - The tags of the Runnable that generatedthe event.
metadata:Optional[dict[str, Any]] - The metadata of the Runnable thatgenerated the event.
data:dict[str, Any]

Below is a table that illustrates some events that might be emitted by variouschains. 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 thev2 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:

defformat_docs(docs:list[Document])->str:'''Format the docs.'''return", ".join([doc.page_contentfordocindocs])format_docs=RunnableLambda(format_docs)

some_tool:

@tooldefsome_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:

fromlangchain_core.runnablesimportRunnableLambdaasyncdefreverse(s:str)->str:returns[::-1]chain=RunnableLambda(func=reverse)events=[eventasyncforeventinchain.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

fromlangchain_core.callbacks.managerimport(adispatch_custom_event,)fromlangchain_core.runnablesimportRunnableLambda,RunnableConfigimportasyncioasyncdefslow_thing(some_input:str,config:RunnableConfig)->str:"""Do something that takes a long time."""awaitasyncio.sleep(1)# Placeholder for some slow operationawaitadispatch_custom_event("progress_event",{"message":"Finished step 1 of 3"},config=config# Must be included for python < 3.10)awaitasyncio.sleep(1)# Placeholder for some slow operationawaitadispatch_custom_event("progress_event",{"message":"Finished step 2 of 3"},config=config# Must be included for python < 3.10)awaitasyncio.sleep(1)# Placeholder for some slow operationreturn"Done"slow_thing=RunnableLambda(slow_thing)asyncforeventinslow_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 eitherv2 orv1.Users should usev2.v1 is for backwards compatibility and will be deprecatedin 0.4.0.No default will be assigned until the API is stabilized.custom events will only be surfaced inv2.
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 implementationof astream_events is built on top of astream_log.

Yields:

An async stream of StreamEvents.

Raises:

NotImplementedError – If the version is notv1 orv2.

Return type:

AsyncIterator[StreamEvent]

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 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])
config (RunnableConfig |list[RunnableConfig]|None)
return_exceptions (bool)
kwargs (Any |None)

Return type:

list[Output]

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.

Parameters:

inputs (Sequence[Input])
config (RunnableConfig |Sequence[RunnableConfig]|None)
return_exceptions (bool)
kwargs (Any |None)

Return type:

Iterator[tuple[int,Output | Exception]]

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 notin 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:

fromlangchain_ollamaimportChatOllamafromlangchain_core.output_parsersimportStrOutputParserllm=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'

configurable_alternatives(
which:ConfigurableField,
*,
default_key:str='default',
prefix_keys:bool=False,
**kwargs:Runnable[Input,Output]|Callable[[],Runnable[Input,Output]],
)→RunnableSerializable#

Configure alternatives for Runnables that can be set at runtime.

Parameters:

which (ConfigurableField) – The ConfigurableField instance that will be used to select thealternative.
default_key (str) – The default key to use if no alternative is selected.Defaults to “default”.
prefix_keys (bool) – Whether to prefix the keys with the ConfigurableField id.Defaults to False.
**kwargs (Runnable[Input,Output]|Callable[[],Runnable[Input,Output]]) – A dictionary of keys to Runnable instances or callables thatreturn Runnable instances.

Returns:

A new Runnable with the alternatives configured.

Return type:

RunnableSerializable

fromlangchain_anthropicimportChatAnthropicfromlangchain_core.runnables.utilsimportConfigurableFieldfromlangchain_openaiimportChatOpenAImodel=ChatAnthropic(model_name="claude-3-sonnet-20240229").configurable_alternatives(ConfigurableField(id="llm"),default_key="anthropic",openai=ChatOpenAI())# uses the default model ChatAnthropicprint(model.invoke("which organization created you?").content)# uses ChatOpenAIprint(model.with_config(configurable={"llm":"openai"}).invoke("which organization created you?").content)

configurable_fields(
**kwargs:ConfigurableField|ConfigurableFieldSingleOption|ConfigurableFieldMultiOption,
)→RunnableSerializable#

Configure particular Runnable fields at runtime.

Parameters:: **kwargs (ConfigurableField |ConfigurableFieldSingleOption |ConfigurableFieldMultiOption) – A dictionary of ConfigurableField instances to configure.
Returns:: A new Runnable with the fields configured.
Return type:: RunnableSerializable

fromlangchain_core.runnablesimportConfigurableFieldfromlangchain_openaiimportChatOpenAImodel=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 = 20print("max_tokens_20: ",model.invoke("tell me something about chess").content)# max_tokens = 200print("max_tokens_200: ",model.with_config(configurable={"output_token_number":200}).invoke("tell me something about chess").content)

invoke(
input:str|dict|ToolCall,
config:RunnableConfig|None=None,
**kwargs:Any,
)→Any[source]#

Transform a single input into an output.

Parameters:

input (str |dict |ToolCall) – 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 tracingpurposes, ‘max_concurrency’ for controlling how much work to doin parallel, and other keys. Please refer to the RunnableConfigfor more details.
kwargs (Any)

Returns:

The output of the Runnable.

Return type:

Any

Run the tool.

Parameters:

tool_input (Union[str,dict[str,Any]]) – The input to the tool.
verbose (Optional[bool]) – Whether to log the tool’s progress. Defaults to None.
start_color (Optional[str]) – The color to use when starting the tool. Defaults to ‘green’.
color (Optional[str]) – The color to use when ending the tool. Defaults to ‘green’.
callbacks (Callbacks) – Callbacks to be called during tool execution. Defaults to None.
tags (Optional[list[str]]) – Optional list of tags associated with the tool. Defaults to None.
metadata (Optional[dict[str,Any]]) – Optional metadata associated with the tool. Defaults to None.
run_name (Optional[str]) – The name of the run. Defaults to None.
run_id (Optional[uuid.UUID]) – The id of the run. Defaults to None.
config (Optional[RunnableConfig]) – The configuration for the tool. Defaults to None.
tool_call_id (Optional[str]) – The id of the tool call. Defaults to None.
kwargs (Any) – Keyword arguments to be passed to tool callbacks (event handler)

Returns:

The output of the tool.

Raises:

ToolException – If an error occurs during tool execution.

Return type:

Any

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

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]

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, returning a new Runnable.

on_start: Asynchronously called before the Runnable starts running.on_end: Asynchronously called after the Runnable finishes running.on_error: Asynchronously called if the Runnable throws an error.

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

Parameters:

on_start (Optional[AsyncListener]) – Asynchronously called before the Runnable starts running.Defaults to None.
on_end (Optional[AsyncListener]) – Asynchronously called after the Runnable finishes running.Defaults to None.
on_error (Optional[AsyncListener]) – Asynchronously called if the Runnable throws an error.Defaults to None.

Returns:

A new Runnable with the listeners bound.

Return type:

Runnable[Input, Output]

Example:

fromlangchain_core.runnablesimportRunnableLambda,Runnablefromdatetimeimportdatetime,timezoneimporttimeimportasynciodefformat_t(timestamp:float)->str:returndatetime.fromtimestamp(timestamp,tz=timezone.utc).isoformat()asyncdeftest_runnable(time_to_sleep:int):print(f"Runnable[{time_to_sleep}s]: starts at{format_t(time.time())}")awaitasyncio.sleep(time_to_sleep)print(f"Runnable[{time_to_sleep}s]: ends at{format_t(time.time())}")asyncdeffn_start(run_obj:Runnable):print(f"on start callback starts at{format_t(time.time())}")awaitasyncio.sleep(3)print(f"on start callback ends at{format_t(time.time())}")asyncdeffn_end(run_obj:Runnable):print(f"on end callback starts at{format_t(time.time())}")awaitasyncio.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)asyncdefconcurrent_runs():awaitasyncio.gather(runnable.ainvoke(2),runnable.ainvoke(3))asyncio.run(concurrent_runs())Result:onstartcallbackstartsat2025-03-01T07:05:22.875378+00:00onstartcallbackstartsat2025-03-01T07:05:22.875495+00:00onstartcallbackendsat2025-03-01T07:05:25.878862+00:00onstartcallbackendsat2025-03-01T07:05:25.878947+00:00Runnable[2s]:startsat2025-03-01T07:05:25.879392+00:00Runnable[3s]:startsat2025-03-01T07:05:25.879804+00:00Runnable[2s]:endsat2025-03-01T07:05:27.881998+00:00onendcallbackstartsat2025-03-01T07:05:27.882360+00:00Runnable[3s]:endsat2025-03-01T07:05:28.881737+00:00onendcallbackstartsat2025-03-01T07:05:28.882428+00:00onendcallbackendsat2025-03-01T07:05:29.883893+00:00onendcallbackendsat2025-03-01T07:05:30.884831+00:00

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

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]#

Add fallbacks to a Runnable, returning a new Runnable.

The new Runnable will try the original Runnable, and then each fallbackin 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 passedto fallbacks as part of the input under the specified key. If None,exceptions will not be passed to fallbacks. If used, the base Runnableand its fallbacks must accept a dictionary as input. Defaults to None.

Returns:

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

Return type:

RunnableWithFallbacksT[Input, Output]

Example

fromtypingimportIteratorfromlangchain_core.runnablesimportRunnableGeneratordef_generate_immediate_error(input:Iterator)->Iterator[str]:raiseValueError()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 passedto fallbacks as part of the input under the specified key. If None,exceptions will not be passed to fallbacks. If used, the base Runnableand its fallbacks must accept a dictionary as input.

Returns:

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

Return type:

RunnableWithFallbacksT[Input, Output]

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

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

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

Parameters:

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

Returns:

A new Runnable with the listeners bound.

Return type:

Runnable[Input, Output]

Example:

fromlangchain_core.runnablesimportRunnableLambdafromlangchain_core.tracers.schemasimportRunimporttimedeftest_runnable(time_to_sleep:int):time.sleep(time_to_sleep)deffn_start(run_obj:Run):print("start_time:",run_obj.start_time)deffn_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]#

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 waittime between retries. Defaults to True.
stop_after_attempt (int) – The maximum number of attempts to make beforegiving up. Defaults to 3.
exponential_jitter_params (Optional[ExponentialJitterParams]) – Parameters fortenacity.wait_exponential_jitter. Namely:initial,max,exp_base, andjitter (all float values).

Returns:

A new Runnable that retries the original Runnable on exceptions.

Return type:

Runnable[Input, Output]

Example:

fromlangchain_core.runnablesimportRunnableLambdacount=0def_lambda(x:int)->None:globalcountcount=count+1ifx==1:raiseValueError("x is 1")else:passrunnable=RunnableLambda(_lambda)try:runnable.with_retry(stop_after_attempt=2,retry_if_exception_type=(ValueError,),).invoke(1)exceptValueError:passassert(count==2)

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.

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]

propertyargs:dict#

Get the tool’s input arguments schema.

Returns:: Dictionary containing the tool’s argument properties.

propertyis_single_input:bool#

Check if the tool accepts only a single input argument.

Returns:: True if the tool has only one input argument, False otherwise.

propertytool_call_schema:type[BaseModel]|dict[str,Any]#

Get the schema for tool calls, excluding injected arguments.

Returns:: The schema that should be used for tool calls from language models.

Examples using BaseTool

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BaseTool#

BaseTool #