langchain.agents.agent.AgentExecutor

class langchain.agents.agent.AgentExecutor[source]

Bases: Chain

Agent that is using tools.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] [Required]

The agent to run for creating a plan and determining actions to take at each step of the execution loop.

param callback_manager: Optional[BaseCallbackManager] = None

[DEPRECATED] Use callbacks instead.

param callbacks: Callbacks = None

Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details.

param early_stopping_method: str = 'force'

The method to use for early stopping if the agent never returns AgentFinish. Either ‘force’ or ‘generate’.

“force” returns a string saying that it stopped because it met a

time or iteration limit.

“generate” calls the agent’s LLM Chain one final time to generate

a final answer based on the previous steps.

param handle_parsing_errors: Union[bool, str, Callable[[OutputParserException], str]] = False

How to handle errors raised by the agent’s output parser. Defaults to False, which raises the error. If true, the error will be sent back to the LLM as an observation. If a string, the string itself will be sent to the LLM as an observation. If a callable function, the function will be called with the exception

as an argument, and the result of that function will be passed to the agent

as an observation.

param max_execution_time: Optional[float] = None

The maximum amount of wall clock time to spend in the execution loop.

param max_iterations: Optional[int] = 15

The maximum number of steps to take before ending the execution loop.

Setting to ‘None’ could lead to an infinite loop.

param memory: Optional[BaseMemory] = None

Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog.

param metadata: Optional[Dict[str, Any]] = None

Optional metadata associated with the chain. Defaults to None. This metadata will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case.

param return_intermediate_steps: bool = False

Whether to return the agent’s trajectory of intermediate steps at the end in addition to the final output.

param tags: Optional[List[str]] = None

Optional list of tags associated with the chain. Defaults to None. These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case.

param tools: Sequence[BaseTool] [Required]

The valid tools the agent can call.

param trim_intermediate_steps: Union[int, Callable[[List[Tuple[AgentAction, str]]], List[Tuple[AgentAction, str]]]] = -1
param verbose: bool [Optional]

Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to the global verbose value, accessible via langchain.globals.get_verbose().

__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, include_run_info: bool = False) Dict[str, Any]

[Deprecated] Execute the chain.

Parameters
  • inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects only one param. Should contain all inputs specified in Chain.input_keys except for inputs that will be set by the chain’s memory.

  • return_only_outputs (bool) – Whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False.

  • callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects.

  • tags (Optional[List[str]]) – List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects.

  • metadata (Optional[Dict[str, Any]]) – Optional metadata associated with the chain. Defaults to None

  • include_run_info (bool) – Whether to include run info in the response. Defaults to False.

  • run_name (Optional[str]) –

Returns

A dict of named outputs. Should contain all outputs specified in

Chain.output_keys.

Return type

Dict[str, Any]

Notes

Deprecated since version langchain==0.1.0: Use invoke instead.

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

Default implementation runs ainvoke in parallel using asyncio.gather.

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

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

Parameters
  • inputs (List[Input]) –

  • config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –

  • return_exceptions (bool) –

  • kwargs (Optional[Any]) –

Return type

List[Output]

async abatch_as_completed(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) AsyncIterator[Tuple[int, Union[Output, Exception]]]

Run ainvoke in parallel on a list of inputs, yielding results as they complete.

Parameters
  • inputs (List[Input]) –

  • config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –

  • return_exceptions (bool) –

  • kwargs (Optional[Any]) –

Return type

AsyncIterator[Tuple[int, Union[Output, Exception]]]

async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, include_run_info: bool = False) Dict[str, Any]

[Deprecated] Asynchronously execute the chain.

Parameters
  • inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects only one param. Should contain all inputs specified in Chain.input_keys except for inputs that will be set by the chain’s memory.

  • return_only_outputs (bool) – Whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False.

  • callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects.

  • tags (Optional[List[str]]) – List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects.

  • metadata (Optional[Dict[str, Any]]) – Optional metadata associated with the chain. Defaults to None

  • include_run_info (bool) – Whether to include run info in the response. Defaults to False.

  • run_name (Optional[str]) –

Returns

A dict of named outputs. Should contain all outputs specified in

Chain.output_keys.

Return type

Dict[str, Any]

Notes

Deprecated since version langchain==0.1.0: Use ainvoke instead.

async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) Dict[str, Any]

Default implementation of ainvoke, calls invoke from a thread.

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

Subclasses should override this method if they can run asynchronously.

Parameters
  • input (Dict[str, Any]) –

  • config (Optional[RunnableConfig]) –

  • kwargs (Any) –

Return type

Dict[str, Any]

apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) List[Dict[str, str]]

[Deprecated] Call the chain on all inputs in the list.

Notes

Deprecated since version langchain==0.1.0: Use batch instead.

Parameters
Return type

List[Dict[str, str]]

async aprep_inputs(inputs: Union[Dict[str, Any], Any]) Dict[str, str]

Prepare chain inputs, including adding inputs from memory.

Parameters

inputs (Union[Dict[str, Any], Any]) – Dictionary of raw inputs, or single input if chain expects only one param. Should contain all inputs specified in Chain.input_keys except for inputs that will be set by the chain’s memory.

Returns

A dictionary of all inputs, including those added by the chain’s memory.

Return type

Dict[str, str]

async aprep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) Dict[str, str]

Validate and prepare chain outputs, and save info about this run to memory.

Parameters
  • inputs (Dict[str, str]) – Dictionary of chain inputs, including any inputs added by chain memory.

  • outputs (Dict[str, str]) – Dictionary of initial chain outputs.

  • return_only_outputs (bool) – Whether to only return the chain outputs. If False, inputs are also added to the final outputs.

Returns

A dict of the final chain outputs.

Return type

Dict[str, str]

async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) Any

[Deprecated] Convenience method for executing chain.

The main difference between this method and Chain.__call__ is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas Chain.__call__ expects a single input dictionary with all the inputs

Parameters
  • *args (Any) – If the chain expects a single input, it can be passed in as the sole positional argument.

  • callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects.

  • tags (Optional[List[str]]) – List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects.

  • **kwargs (Any) – If the chain expects multiple inputs, they can be passed in directly as keyword arguments.

  • metadata (Optional[Dict[str, Any]]) –

  • **kwargs

Returns

The chain output.

Return type

Any

Example

# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."

# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in Boise is..."

Notes

Deprecated since version langchain==0.1.0: Use ainvoke instead.

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

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

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

kwargs (Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) –

Return type

RunnableSerializable[Any, Any]

async astream(input: Union[Dict[str, Any], Any], config: Optional[RunnableConfig] = None, **kwargs: Any) AsyncIterator[AddableDict][source]

Enables streaming over steps taken to reach final output.

Parameters
  • input (Union[Dict[str, Any], Any]) –

  • config (Optional[RunnableConfig]) –

  • kwargs (Any) –

Return type

AsyncIterator[AddableDict]

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

[Beta] 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.

  • tags: Optional[List[str]] - The tags of the runnable that generated

    the event.

  • metadata: Optional[Dict[str, Any]] - The metadata of the runnable

    that generated the event.

  • data: Dict[str, Any]

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

ATTENTION 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, …])

Here are declarations associated with the 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 has been omitted for brevity):
[
    {
        "data": {"input": "hello"},
        "event": "on_chain_start",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"chunk": "olleh"},
        "event": "on_chain_stream",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"output": "olleh"},
        "event": "on_chain_end",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
]
Parameters
  • input (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.

  • 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.

Returns

An async stream of StreamEvents.

Return type

AsyncIterator[StreamEvent]

Notes

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

Parameters
  • input (Any) – The input to the runnable.

  • config (Optional[RunnableConfig]) – The config to use for the runnable.

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

  • with_streamed_output_list (bool) – Whether to yield the streamed_output list.

  • include_names (Optional[Sequence[str]]) – Only include logs with these names.

  • include_types (Optional[Sequence[str]]) – Only include logs with these types.

  • include_tags (Optional[Sequence[str]]) – Only include logs with these tags.

  • exclude_names (Optional[Sequence[str]]) – Exclude logs with these names.

  • exclude_types (Optional[Sequence[str]]) – Exclude logs with these types.

  • exclude_tags (Optional[Sequence[str]]) – Exclude logs with these tags.

  • kwargs (Any) –

Return type

Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]

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

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

Parameters
  • input (AsyncIterator[Input]) –

  • config (Optional[RunnableConfig]) –

  • kwargs (Optional[Any]) –

Return type

AsyncIterator[Output]

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

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

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

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

Parameters
  • inputs (List[Input]) –

  • config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –

  • return_exceptions (bool) –

  • kwargs (Optional[Any]) –

Return type

List[Output]

batch_as_completed(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) Iterator[Tuple[int, Union[Output, Exception]]]

Run invoke in parallel on a list of inputs, yielding results as they complete.

Parameters
  • inputs (List[Input]) –

  • config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –

  • return_exceptions (bool) –

  • kwargs (Optional[Any]) –

Return type

Iterator[Tuple[int, Union[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 not in the output of the previous runnable or included in the user input.

Example:

from langchain_community.chat_models 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'
Parameters

kwargs (Any) –

Return type

Runnable[Input, Output]

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

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

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

Parameters

include (Optional[Sequence[str]]) – 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]

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

Configure alternatives for runnables that can be set at runtime.

from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatAnthropic(
    model_name="claude-3-sonnet-20240229"
).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
)
Parameters
Return type

RunnableSerializable[Input, Output]

configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) RunnableSerializable[Input, Output]

Configure particular runnable fields at runtime.

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
)
Parameters

kwargs (Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) –

Return type

RunnableSerializable[Input, Output]

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Parameters
  • _fields_set (Optional[SetStr]) –

  • values (Any) –

Return type

Model

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters
  • include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include

  • update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep (bool) – set to True to make a deep copy of the model

  • self (Model) –

Returns

new model instance

Return type

Model

dict(**kwargs: Any) Dict

Dictionary representation of chain.

Expects Chain._chain_type property to be implemented and for memory to be

null.

Parameters

**kwargs (Any) – Keyword arguments passed to default pydantic.BaseModel.dict method.

Returns

A dictionary representation of the chain.

Return type

Dict

Example

chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
classmethod from_agent_and_tools(agent: Union[BaseSingleActionAgent, BaseMultiActionAgent], tools: Sequence[BaseTool], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) AgentExecutor[source]

Create from agent and tools.

Parameters
Return type

AgentExecutor

classmethod from_orm(obj: Any) Model
Parameters

obj (Any) –

Return type

Model

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

Return a graph representation of this runnable.

Parameters

config (Optional[RunnableConfig]) –

Return type

Graph

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

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

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

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

Parameters

config (Optional[RunnableConfig]) – A config to use when generating the schema.

Returns

A pydantic model that can be used to validate input.

Return type

Type[BaseModel]

classmethod get_lc_namespace() List[str]

Get the namespace of the langchain object.

For example, if the class is langchain.llms.openai.OpenAI, then the namespace is [“langchain”, “llms”, “openai”]

Return type

List[str]

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

Get the name of the runnable.

Parameters
  • suffix (Optional[str]) –

  • name (Optional[str]) –

Return type

str

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

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

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

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

Parameters

config (Optional[RunnableConfig]) – 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: Optional[RunnableConfig] = None) List[BasePromptTemplate]
Parameters

config (Optional[RunnableConfig]) –

Return type

List[BasePromptTemplate]

invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) Dict[str, Any]

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

Parameters
  • input (Dict[str, Any]) – The input to the runnable.

  • config (Optional[RunnableConfig]) – 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.

  • kwargs (Any) –

Returns

The output of the runnable.

Return type

Dict[str, Any]

classmethod is_lc_serializable() bool

Is this class serializable?

Return type

bool

iter(inputs: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, include_run_info: bool = False, async_: bool = False) AgentExecutorIterator[source]

Enables iteration over steps taken to reach final output.

Parameters
Return type

AgentExecutorIterator

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

Parameters
  • include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • by_alias (bool) –

  • skip_defaults (Optional[bool]) –

  • exclude_unset (bool) –

  • exclude_defaults (bool) –

  • exclude_none (bool) –

  • encoder (Optional[Callable[[Any], Any]]) –

  • models_as_dict (bool) –

  • dumps_kwargs (Any) –

Return type

unicode

classmethod lc_id() List[str]

A unique identifier for this class for serialization purposes.

The unique identifier is a list of strings that describes the path to the object.

Return type

List[str]

lookup_tool(name: str) BaseTool[source]

Lookup tool by name.

Parameters

name (str) –

Return type

BaseTool

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

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

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]
Return type

Runnable[List[Input], List[Output]]

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model
Parameters
  • path (Union[str, Path]) –

  • content_type (unicode) –

  • encoding (unicode) –

  • proto (Protocol) –

  • allow_pickle (bool) –

Return type

Model

classmethod parse_obj(obj: Any) Model
Parameters

obj (Any) –

Return type

Model

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model
Parameters
  • b (Union[str, bytes]) –

  • content_type (unicode) –

  • encoding (unicode) –

  • proto (Protocol) –

  • allow_pickle (bool) –

Return type

Model

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

Pick keys from the dict output 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 (Union[str, List[str]]) –

Return type

RunnableSerializable[Any, Any]

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

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 (Union[Runnable[Any, Other], Callable[[Any], Other]]) –

  • name (Optional[str]) –

Return type

RunnableSerializable[Input, Other]

prep_inputs(inputs: Union[Dict[str, Any], Any]) Dict[str, str]

Prepare chain inputs, including adding inputs from memory.

Parameters

inputs (Union[Dict[str, Any], Any]) – Dictionary of raw inputs, or single input if chain expects only one param. Should contain all inputs specified in Chain.input_keys except for inputs that will be set by the chain’s memory.

Returns

A dictionary of all inputs, including those added by the chain’s memory.

Return type

Dict[str, str]

prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) Dict[str, str]

Validate and prepare chain outputs, and save info about this run to memory.

Parameters
  • inputs (Dict[str, str]) – Dictionary of chain inputs, including any inputs added by chain memory.

  • outputs (Dict[str, str]) – Dictionary of initial chain outputs.

  • return_only_outputs (bool) – Whether to only return the chain outputs. If False, inputs are also added to the final outputs.

Returns

A dict of the final chain outputs.

Return type

Dict[str, str]

run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) Any

[Deprecated] Convenience method for executing chain.

The main difference between this method and Chain.__call__ is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas Chain.__call__ expects a single input dictionary with all the inputs

Parameters
  • *args (Any) – If the chain expects a single input, it can be passed in as the sole positional argument.

  • callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects.

  • tags (Optional[List[str]]) – List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects.

  • **kwargs (Any) – If the chain expects multiple inputs, they can be passed in directly as keyword arguments.

  • metadata (Optional[Dict[str, Any]]) –

  • **kwargs

Returns

The chain output.

Return type

Any

Example

# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."

# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
chain.run(question=question, context=context)
# -> "The temperature in Boise is..."

Notes

Deprecated since version langchain==0.1.0: Use invoke instead.

save(file_path: Union[Path, str]) None[source]

Raise error - saving not supported for Agent Executors.

Parameters

file_path (Union[Path, str]) –

Return type

None

save_agent(file_path: Union[Path, str]) None[source]

Save the underlying agent.

Parameters

file_path (Union[Path, str]) –

Return type

None

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny
Parameters
  • by_alias (bool) –

  • ref_template (unicode) –

Return type

DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode
Parameters
  • by_alias (bool) –

  • ref_template (unicode) –

  • dumps_kwargs (Any) –

Return type

unicode

stream(input: Union[Dict[str, Any], Any], config: Optional[RunnableConfig] = None, **kwargs: Any) Iterator[AddableDict][source]

Enables streaming over steps taken to reach final output.

Parameters
  • input (Union[Dict[str, Any], Any]) –

  • config (Optional[RunnableConfig]) –

  • kwargs (Any) –

Return type

Iterator[AddableDict]

to_json() Union[SerializedConstructor, SerializedNotImplemented]

Serialize the runnable to JSON.

Return type

Union[SerializedConstructor, SerializedNotImplemented]

to_json_not_implemented() SerializedNotImplemented
Return type

SerializedNotImplemented

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

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

Parameters
  • input (Iterator[Input]) –

  • config (Optional[RunnableConfig]) –

  • kwargs (Optional[Any]) –

Return type

Iterator[Output]

classmethod update_forward_refs(**localns: Any) None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

Parameters

localns (Any) –

Return type

None

classmethod validate(value: Any) Model
Parameters

value (Any) –

Return type

Model

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

Bind config to a Runnable, returning a new Runnable.

Parameters
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.

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: Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]] = None, on_end: Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]] = None, on_error: Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]] = None) Runnable[Input, Output]

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

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

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

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)
Parameters
Return type

Runnable[Input, Output]

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

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

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)
Parameters
  • retry_if_exception_type (Tuple[Type[BaseException], ...]) – A tuple of exception types to retry on

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

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

Returns

A new Runnable that retries the original runnable on exceptions.

Return type

Runnable[Input, Output]

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

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

Parameters
  • input_type (Optional[Type[Input]]) –

  • output_type (Optional[Type[Output]]) –

Return type

Runnable[Input, Output]

property InputType: Type[Input]

The type of input this runnable accepts specified as a type annotation.

property OutputType: Type[Output]

The type of output this runnable produces specified as a type annotation.

property config_specs: List[ConfigurableFieldSpec]

List configurable fields for this runnable.

property input_schema: Type[BaseModel]

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

property lc_attributes: Dict

List of attribute names that should be included in the serialized kwargs.

These attributes must be accepted by the constructor.

property lc_secrets: Dict[str, str]

A map of constructor argument names to secret ids.

For example,

{“openai_api_key”: “OPENAI_API_KEY”}

name: Optional[str] = None

The name of the runnable. Used for debugging and tracing.

property output_schema: Type[BaseModel]

The type of output this runnable produces specified as a pydantic model.

Examples using AgentExecutor