Prebuilt

create_react_agent

create_react_agent(
    model: Union[str, LanguageModelLike],
    tools: Union[
        Sequence[Union[BaseTool, Callable]], ToolNode
    ],
    *,
    prompt: Optional[Prompt] = None,
    response_format: Optional[
        Union[
            StructuredResponseSchema,
            tuple[str, StructuredResponseSchema],
        ]
    ] = None,
    pre_model_hook: Optional[RunnableLike] = None,
    state_schema: Optional[StateSchemaType] = None,
    config_schema: Optional[Type[Any]] = None,
    checkpointer: Optional[Checkpointer] = None,
    store: Optional[BaseStore] = None,
    interrupt_before: Optional[list[str]] = None,
    interrupt_after: Optional[list[str]] = None,
    debug: bool = False,
    version: Literal["v1", "v2"] = "v1",
    name: Optional[str] = None
) -> CompiledGraph

Creates a graph that works with a chat model that utilizes tool calling.

Parameters:

• model (Union[str, LanguageModelLike]) –

  The LangChain chat model that supports tool calling.

• tools (Union[Sequence[Union[BaseTool, Callable]], ToolNode]) –

  A list of tools or a ToolNode instance. If an empty list is provided, the agent will consist of a single LLM node without tool calling.

• prompt (Optional[Prompt], default: None ) –

  An optional prompt for the LLM. Can take a few different forms:

  • str: This is converted to a SystemMessage and added to the beginning of the list of messages in state["messages"].
  • SystemMessage: this is added to the beginning of the list of messages in state["messages"].
  • Callable: This function should take in full graph state and the output is then passed to the language model.
  • Runnable: This runnable should take in full graph state and the output is then passed to the language model.
• response_format (Optional[Union[StructuredResponseSchema, tuple[str, StructuredResponseSchema]]], default: None ) –

  An optional schema for the final agent output.

  If provided, output will be formatted to match the given schema and returned in the 'structured_response' state key. If not provided, structured_response will not be present in the output state. Can be passed in as:

  - an OpenAI function/tool schema,
  - a JSON Schema,
  - a TypedDict class,
  - or a Pydantic class.
  - a tuple (prompt, schema), where schema is one of the above.
      The prompt will be used together with the model that is being used to generate the structured response.
  

  Important

  response_format requires the model to support .with_structured_output

  Note

  The graph will make a separate call to the LLM to generate the structured response after the agent loop is finished. This is not the only strategy to get structured responses, see more options in this guide.

• pre_model_hook (Optional[RunnableLike], default: None ) –

  An optional node to add before the agent node (i.e., the node that calls the LLM). Useful for managing long message histories (e.g., message trimming, summarization, etc.). Pre-model hook must be a callable or a runnable that takes in current graph state and returns a state update in the form of

  # At least one of `messages` or `llm_input_messages` MUST be provided
  {
      # If provided, will UPDATE the `messages` in the state
      "messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES), ...],
      # If provided, will be used as the input to the LLM,
      # and will NOT UPDATE `messages` in the state
      "llm_input_messages": [...],
      # Any other state keys that need to be propagated
      ...
  }
  

  Important

  At least one of messages or llm_input_messages MUST be provided and will be used as an input to the agent node. The rest of the keys will be added to the graph state.

  Warning

  If you are returning messages in the pre-model hook, you should OVERWRITE the messages key by doing the following:

  {
      "messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES), *new_messages]
      ...
  }
  

• state_schema (Optional[StateSchemaType], default: None ) –

  An optional state schema that defines graph state. Must have messages and remaining_steps keys. Defaults to AgentState that defines those two keys.

• config_schema (Optional[Type[Any]], default: None ) –

  An optional schema for configuration. Use this to expose configurable parameters via agent.config_specs.

• checkpointer (Optional[Checkpointer], default: None ) –

  An optional checkpoint saver object. This is used for persisting the state of the graph (e.g., as chat memory) for a single thread (e.g., a single conversation).

• store (Optional[BaseStore], default: None ) –

  An optional store object. This is used for persisting data across multiple threads (e.g., multiple conversations / users).

• interrupt_before (Optional[list[str]], default: None ) –

  An optional list of node names to interrupt before. Should be one of the following: "agent", "tools". This is useful if you want to add a user confirmation or other interrupt before taking an action.

• interrupt_after (Optional[list[str]], default: None ) –

  An optional list of node names to interrupt after. Should be one of the following: "agent", "tools". This is useful if you want to return directly or run additional processing on an output.

• debug (bool, default: False ) –

  A flag indicating whether to enable debug mode.

• version (Literal['v1', 'v2'], default: 'v1' ) –

  Determines the version of the graph to create. Can be one of:

  • "v1": The tool node processes a single message. All tool calls in the message are executed in parallel within the tool node.
  • "v2": The tool node processes a tool call. Tool calls are distributed across multiple instances of the tool node using the Send API.
• name (Optional[str], default: None ) –

  An optional name for the CompiledStateGraph. This name will be automatically used when adding ReAct agent graph to another graph as a subgraph node - particularly useful for building multi-agent systems.

Returns:

• CompiledGraph –

  A compiled LangChain runnable that can be used for chat interactions.

The resulting graph looks like this:

stateDiagram-v2
    [*] --> Start
    Start --> Agent
    Agent --> Tools : continue
    Tools --> Agent
    Agent --> End : end
    End --> [*]

    classDef startClass fill:#ffdfba;
    classDef endClass fill:#baffc9;
    classDef otherClass fill:#fad7de;

    class Start startClass
    class End endClass
    class Agent,Tools otherClass

The "agent" node calls the language model with the messages list (after applying the messages modifier). If the resulting AIMessage contains tool_calls, the graph will then call the "tools". The "tools" node executes the tools (1 tool per tool_call) and adds the responses to the messages list as ToolMessage objects. The agent node then calls the language model again. The process repeats until no more tool_calls are present in the response. The agent then returns the full list of messages as a dictionary containing the key "messages".

    sequenceDiagram
        participant U as User
        participant A as Agent (LLM)
        participant T as Tools
        U->>A: Initial input
        Note over A: Messages modifier + LLM
        loop while tool_calls present
            A->>T: Execute tools
            T-->>A: ToolMessage for each tool_calls
        end
        A->>U: Return final state

Examples:

Use with a simple tool:

>>> from langchain_openai import ChatOpenAI
>>> from langgraph.prebuilt import create_react_agent


... def check_weather(location: str) -> str:
...     '''Return the weather forecast for the specified location.'''
...     return f"It's always sunny in {location}"
>>>
>>> tools = [check_weather]
>>> model = ChatOpenAI(model="gpt-4o")
>>> graph = create_react_agent(model, tools=tools)
>>> inputs = {"messages": [("user", "what is the weather in sf")]}
>>> for s in graph.stream(inputs, stream_mode="values"):
...     message = s["messages"][-1]
...     if isinstance(message, tuple):
...         print(message)
...     else:
...         message.pretty_print()
('user', 'what is the weather in sf')
================================== Ai Message ==================================
Tool Calls:
check_weather (call_LUzFvKJRuaWQPeXvBOzwhQOu)
Call ID: call_LUzFvKJRuaWQPeXvBOzwhQOu
Args:
    location: San Francisco
================================= Tool Message =================================
Name: check_weather
It's always sunny in San Francisco
================================== Ai Message ==================================
The weather in San Francisco is sunny.

Add a system prompt for the LLM:

>>> system_prompt = "You are a helpful bot named Fred."
>>> graph = create_react_agent(model, tools, prompt=system_prompt)
>>> inputs = {"messages": [("user", "What's your name? And what's the weather in SF?")]}
>>> for s in graph.stream(inputs, stream_mode="values"):
...     message = s["messages"][-1]
...     if isinstance(message, tuple):
...         print(message)
...     else:
...         message.pretty_print()
('user', "What's your name? And what's the weather in SF?")
================================== Ai Message ==================================
Hi, my name is Fred. Let me check the weather in San Francisco for you.
Tool Calls:
check_weather (call_lqhj4O0hXYkW9eknB4S41EXk)
Call ID: call_lqhj4O0hXYkW9eknB4S41EXk
Args:
    location: San Francisco
================================= Tool Message =================================
Name: check_weather
It's always sunny in San Francisco
================================== Ai Message ==================================
The weather in San Francisco is currently sunny. If you need any more details or have other questions, feel free to ask!

Add a more complex prompt for the LLM:

>>> from langchain_core.prompts import ChatPromptTemplate
>>> prompt = ChatPromptTemplate.from_messages([
...     ("system", "You are a helpful bot named Fred."),
...     ("placeholder", "{messages}"),
...     ("user", "Remember, always be polite!"),
... ])
>>>
>>> graph = create_react_agent(model, tools, prompt=prompt)
>>> inputs = {"messages": [("user", "What's your name? And what's the weather in SF?")]}
>>> for s in graph.stream(inputs, stream_mode="values"):
...     message = s["messages"][-1]
...     if isinstance(message, tuple):
...         print(message)
...     else:
...         message.pretty_print()

Add complex prompt with custom graph state:

>>> from typing_extensions import TypedDict
>>>
>>> from langgraph.managed import IsLastStep
>>> prompt = ChatPromptTemplate.from_messages(
...     [
...         ("system", "Today is {today}"),
...         ("placeholder", "{messages}"),
...     ]
... )
>>>
>>> class CustomState(TypedDict):
...     today: str
...     messages: Annotated[list[BaseMessage], add_messages]
...     is_last_step: IsLastStep
>>>
>>> graph = create_react_agent(model, tools, state_schema=CustomState, prompt=prompt)
>>> inputs = {"messages": [("user", "What's today's date? And what's the weather in SF?")], "today": "July 16, 2004"}
>>> for s in graph.stream(inputs, stream_mode="values"):
...     message = s["messages"][-1]
...     if isinstance(message, tuple):
...         print(message)
...     else:
...         message.pretty_print()

Add thread-level "chat memory" to the graph:

>>> from langgraph.checkpoint.memory import MemorySaver
>>> graph = create_react_agent(model, tools, checkpointer=MemorySaver())
>>> config = {"configurable": {"thread_id": "thread-1"}}
>>> def print_stream(graph, inputs, config):
...     for s in graph.stream(inputs, config, stream_mode="values"):
...         message = s["messages"][-1]
...         if isinstance(message, tuple):
...             print(message)
...         else:
...             message.pretty_print()
>>> inputs = {"messages": [("user", "What's the weather in SF?")]}
>>> print_stream(graph, inputs, config)
>>> inputs2 = {"messages": [("user", "Cool, so then should i go biking today?")]}
>>> print_stream(graph, inputs2, config)
('user', "What's the weather in SF?")
================================== Ai Message ==================================
Tool Calls:
check_weather (call_ChndaktJxpr6EMPEB5JfOFYc)
Call ID: call_ChndaktJxpr6EMPEB5JfOFYc
Args:
    location: San Francisco
================================= Tool Message =================================
Name: check_weather
It's always sunny in San Francisco
================================== Ai Message ==================================
The weather in San Francisco is sunny. Enjoy your day!
================================ Human Message =================================
Cool, so then should i go biking today?
================================== Ai Message ==================================
Since the weather in San Francisco is sunny, it sounds like a great day for biking! Enjoy your ride!

Add an interrupt to let the user confirm before taking an action:

>>> graph = create_react_agent(
...     model, tools, interrupt_before=["tools"], checkpointer=MemorySaver()
>>> )
>>> config = {"configurable": {"thread_id": "thread-1"}}

>>> inputs = {"messages": [("user", "What's the weather in SF?")]}
>>> print_stream(graph, inputs, config)
>>> snapshot = graph.get_state(config)
>>> print("Next step: ", snapshot.next)
>>> print_stream(graph, None, config)

Add cross-thread memory to the graph:

>>> from langgraph.prebuilt import InjectedStore
>>> from langgraph.store.base import BaseStore

>>> def save_memory(memory: str, *, config: RunnableConfig, store: Annotated[BaseStore, InjectedStore()]) -> str:
...     '''Save the given memory for the current user.'''
...     # This is a **tool** the model can use to save memories to storage
...     user_id = config.get("configurable", {}).get("user_id")
...     namespace = ("memories", user_id)
...     store.put(namespace, f"memory_{len(store.search(namespace))}", {"data": memory})
...     return f"Saved memory: {memory}"

>>> def prepare_model_inputs(state: AgentState, config: RunnableConfig, store: BaseStore):
...     # Retrieve user memories and add them to the system message
...     # This function is called **every time** the model is prompted. It converts the state to a prompt
...     user_id = config.get("configurable", {}).get("user_id")
...     namespace = ("memories", user_id)
...     memories = [m.value["data"] for m in store.search(namespace)]
...     system_msg = f"User memories: {', '.join(memories)}"
...     return [{"role": "system", "content": system_msg)] + state["messages"]

>>> from langgraph.checkpoint.memory import MemorySaver
>>> from langgraph.store.memory import InMemoryStore
>>> store = InMemoryStore()
>>> graph = create_react_agent(model, [save_memory], prompt=prepare_model_inputs, store=store, checkpointer=MemorySaver())
>>> config = {"configurable": {"thread_id": "thread-1", "user_id": "1"}}

>>> inputs = {"messages": [("user", "Hey I'm Will, how's it going?")]}
>>> print_stream(graph, inputs, config)
('user', "Hey I'm Will, how's it going?")
================================== Ai Message ==================================
Hello Will! It's nice to meet you. I'm doing well, thank you for asking. How are you doing today?

>>> inputs2 = {"messages": [("user", "I like to bike")]}
>>> print_stream(graph, inputs2, config)
================================ Human Message =================================
I like to bike
================================== Ai Message ==================================
That's great to hear, Will! Biking is an excellent hobby and form of exercise. It's a fun way to stay active and explore your surroundings. Do you have any favorite biking routes or trails you enjoy? Or perhaps you're into a specific type of biking, like mountain biking or road cycling?

>>> config = {"configurable": {"thread_id": "thread-2", "user_id": "1"}}
>>> inputs3 = {"messages": [("user", "Hi there! Remember me?")]}
>>> print_stream(graph, inputs3, config)
================================ Human Message =================================
Hi there! Remember me?
================================== Ai Message ==================================
User memories:
Hello! Of course, I remember you, Will! You mentioned earlier that you like to bike. It's great to hear from you again. How have you been? Have you been on any interesting bike rides lately?

Add a timeout for a given step:

>>> import time
... def check_weather(location: str) -> str:
...     '''Return the weather forecast for the specified location.'''
...     time.sleep(2)
...     return f"It's always sunny in {location}"
>>>
>>> tools = [check_weather]
>>> graph = create_react_agent(model, tools)
>>> graph.step_timeout = 1 # Seconds
>>> for s in graph.stream({"messages": [("user", "what is the weather in sf")]}):
...     print(s)
TimeoutError: Timed out at step 2

ToolNode

Bases: RunnableCallable

A node that runs the tools called in the last AIMessage.

It can be used either in StateGraph with a "messages" state key (or a custom key passed via ToolNode's 'messages_key'). If multiple tool calls are requested, they will be run in parallel. The output will be a list of ToolMessages, one for each tool call.

Tool calls can also be passed directly as a list of ToolCall dicts.

Parameters:

• tools (Sequence[Union[BaseTool, Callable]]) –

  A sequence of tools that can be invoked by the ToolNode.

• name (str, default: 'tools' ) –

  The name of the ToolNode in the graph. Defaults to "tools".

• tags (Optional[list[str]], default: None ) –

  Optional tags to associate with the node. Defaults to None.

• handle_tool_errors (Union[bool, str, Callable[..., str], tuple[type[Exception], ...]], default: True ) –

  How to handle tool errors raised by tools inside the node. Defaults to True. Must be one of the following:

  • True: all errors will be caught and a ToolMessage with a default error message (TOOL_CALL_ERROR_TEMPLATE) will be returned.
  • str: all errors will be caught and a ToolMessage with the string value of 'handle_tool_errors' will be returned.
  • tuple[type[Exception], ...]: exceptions in the tuple will be caught and a ToolMessage with a default error message (TOOL_CALL_ERROR_TEMPLATE) will be returned.
  • Callable[..., str]: exceptions from the signature of the callable will be caught and a ToolMessage with the string value of the result of the 'handle_tool_errors' callable will be returned.
  • False: none of the errors raised by the tools will be caught
• messages_key (str, default: 'messages' ) –

  The state key in the input that contains the list of messages. The same key will be used for the output from the ToolNode. Defaults to "messages".

The ToolNode is roughly analogous to:

tools_by_name = {tool.name: tool for tool in tools}
def tool_node(state: dict):
    result = []
    for tool_call in state["messages"][-1].tool_calls:
        tool = tools_by_name[tool_call["name"]]
        observation = tool.invoke(tool_call["args"])
        result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"]))
    return {"messages": result}

Tool calls can also be passed directly to a ToolNode. This can be useful when using the Send API, e.g., in a conditional edge:

def example_conditional_edge(state: dict) -> List[Send]:
    tool_calls = state["messages"][-1].tool_calls
    # If tools rely on state or store variables (whose values are not generated
    # directly by a model), you can inject them into the tool calls.
    tool_calls = [
        tool_node.inject_tool_args(call, state, store)
        for call in last_message.tool_calls
    ]
    return [Send("tools", [tool_call]) for tool_call in tool_calls]

Important

• The input state can be one of the following:
  • A dict with a messages key containing a list of messages.
  • A list of messages.
  • A list of tool calls.
• If operating on a message list, the last message must be an AIMessage with tool_calls populated.

InputType property

InputType: type[Input]

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

OutputType property

OutputType: type[Output]

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

input_schema property

input_schema: type[BaseModel]

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

output_schema property

output_schema: type[BaseModel]

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

config_specs property

config_specs: list[ConfigurableFieldSpec]

List configurable fields for this Runnable.

get_name

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

Get the name of the Runnable.

get_input_schema

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], default: None ) –

  A config to use when generating the schema.

Returns:

• type[BaseModel] –

  A pydantic model that can be used to validate input.

get_input_jsonschema

get_input_jsonschema(
    config: Optional[RunnableConfig] = None,
) -> dict[str, Any]

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

Parameters:

• config (Optional[RunnableConfig], default: None ) –

  A config to use when generating the schema.

Returns:

• dict[str, Any] –

  A JSON schema that represents the input to the Runnable.

Example:

.. code-block:: python

    from langchain_core.runnables import RunnableLambda

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

    runnable = RunnableLambda(add_one)

    print(runnable.get_input_jsonschema())

.. versionadded:: 0.3.0

get_output_schema

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], default: None ) –

  A config to use when generating the schema.

Returns:

• type[BaseModel] –

  A pydantic model that can be used to validate output.

get_output_jsonschema

get_output_jsonschema(
    config: Optional[RunnableConfig] = None,
) -> dict[str, Any]

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

Parameters:

• config (Optional[RunnableConfig], default: None ) –

  A config to use when generating the schema.

Returns:

• dict[str, Any] –

  A JSON schema that represents the output of the Runnable.

Example:

.. code-block:: python

    from langchain_core.runnables import RunnableLambda

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

    runnable = RunnableLambda(add_one)

    print(runnable.get_output_jsonschema())

.. versionadded:: 0.3.0

config_schema

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]], default: None ) –

  A list of fields to include in the config schema.

Returns:

• type[BaseModel] –

  A pydantic model that can be used to validate config.

get_config_jsonschema

get_config_jsonschema(
    *, include: Optional[Sequence[str]] = None
) -> dict[str, Any]

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

Parameters:

• include (Optional[Sequence[str]], default: None ) –

  A list of fields to include in the config schema.

Returns:

• dict[str, Any] –

  A JSON schema that represents the config of the Runnable.

.. versionadded:: 0.3.0

get_graph

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

Return a graph representation of this Runnable.

get_prompts

get_prompts(
    config: Optional[RunnableConfig] = None,
) -> list[BasePromptTemplate]

Return a list of prompts used by this Runnable.

__or__

__or__(
    other: Union[
        Runnable[Any, Other],
        Callable[[Any], Other],
        Callable[[Iterator[Any]], Iterator[Other]],
        Mapping[
            str,
            Union[
                Runnable[Any, Other],
                Callable[[Any], Other],
                Any,
            ],
        ],
    ],
) -> RunnableSerializable[Input, Other]

Compose this Runnable with another object to create a RunnableSequence.

__ror__

__ror__(
    other: Union[
        Runnable[Other, Any],
        Callable[[Other], Any],
        Callable[[Iterator[Other]], Iterator[Any]],
        Mapping[
            str,
            Union[
                Runnable[Other, Any],
                Callable[[Other], Any],
                Any,
            ],
        ],
    ],
) -> RunnableSerializable[Other, Output]

Compose this Runnable with another object to create a RunnableSequence.

pipe

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

.. code-block:: python

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]

pick

pick(
    keys: Union[str, list[str]],
) -> RunnableSerializable[Any, Any]

Pick keys from the output dict of this Runnable.

Pick single key

.. code-block:: python

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

.. code-block:: python

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]"}

assign

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.

.. code-block:: python

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'}}}

batch

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.

batch_as_completed

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

Run invoke in parallel on a list of inputs.

Yields results as they complete.

abatch 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]) –

  A list of inputs to the Runnable.

• config (Optional[Union[RunnableConfig, list[RunnableConfig]]], default: 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, default: False ) –

  Whether to return exceptions instead of raising them. Defaults to False.

• kwargs (Optional[Any], default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Returns:

• list[Output] –

  A list of outputs from the Runnable.

abatch_as_completed async

abatch_as_completed(
    inputs: Sequence[Input],
    config: Optional[
        Union[RunnableConfig, Sequence[RunnableConfig]]
    ] = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Optional[Any]
) -> AsyncIterator[tuple[int, Union[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 (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]], default: 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. Defaults to None.

• return_exceptions (bool, default: False ) –

  Whether to return exceptions instead of raising them. Defaults to False.

• kwargs (Optional[Any], default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Yields:

• AsyncIterator[tuple[int, Union[Output, Exception]]] –

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

stream

stream(
    input: Input,
    config: Optional[RunnableConfig] = None,
    **kwargs: Optional[Any]
) -> 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 (Optional[RunnableConfig], default: None ) –

  The config to use for the Runnable. Defaults to None.

• kwargs (Optional[Any], default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Yields:

• Output –

  The output of the Runnable.

astream async

astream(
    input: Input,
    config: Optional[RunnableConfig] = None,
    **kwargs: Optional[Any]
) -> 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 (Optional[RunnableConfig], default: None ) –

  The config to use for the Runnable. Defaults to None.

• kwargs (Optional[Any], default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Yields:

• AsyncIterator[Output] –

  The output of the Runnable.

astream_log 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], default: None ) –

  The config to use for the Runnable.

• diff (bool, default: True ) –

  Whether to yield diffs between each step or the current state.

• with_streamed_output_list (bool, default: True ) –

  Whether to yield the streamed_output list.

• include_names (Optional[Sequence[str]], default: None ) –

  Only include logs with these names.

• include_types (Optional[Sequence[str]], default: None ) –

  Only include logs with these types.

• include_tags (Optional[Sequence[str]], default: None ) –

  Only include logs with these tags.

• exclude_names (Optional[Sequence[str]], default: None ) –

  Exclude logs with these names.

• exclude_types (Optional[Sequence[str]], default: None ) –

  Exclude logs with these types.

• exclude_tags (Optional[Sequence[str]], default: None ) –

  Exclude logs with these tags.

• kwargs (Any, default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Yields:

• Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]] –

  A RunLogPatch or RunLog object.

astream_events async

astream_events(
    input: Any,
    config: Optional[RunnableConfig] = None,
    *,
    version: Literal["v1", "v2"] = "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]

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.

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, ...]) | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+

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:

.. code-block:: python

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

format_docs = RunnableLambda(format_docs)

some_tool:

.. code-block:: python

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

prompt:

.. code-block:: python

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

Example:

.. code-block:: python

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

.. code-block:: python

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], default: None ) –

  The config to use for the Runnable.

• version (Literal['v1', 'v2'], default: '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]], default: None ) –

  Only include events from runnables with matching names.

• include_types (Optional[Sequence[str]], default: None ) –

  Only include events from runnables with matching types.

• include_tags (Optional[Sequence[str]], default: None ) –

  Only include events from runnables with matching tags.

• exclude_names (Optional[Sequence[str]], default: None ) –

  Exclude events from runnables with matching names.

• exclude_types (Optional[Sequence[str]], default: None ) –

  Exclude events from runnables with matching types.

• exclude_tags (Optional[Sequence[str]], default: None ) –

  Exclude events from runnables with matching tags.

• kwargs (Any, default: {} ) –

  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:

• AsyncIterator[StreamEvent] –

  An async stream of StreamEvents.

Raises:

• NotImplementedError –

  If the version is not v1 or v2.

transform

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

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 (Optional[RunnableConfig], default: None ) –

  The config to use for the Runnable. Defaults to None.

• kwargs (Optional[Any], default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Yields:

• Output –

  The output of the Runnable.

atransform 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]) –

  An async iterator of inputs to the Runnable.

• config (Optional[RunnableConfig], default: None ) –

  The config to use for the Runnable. Defaults to None.

• kwargs (Optional[Any], default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Yields:

• AsyncIterator[Output] –

  The output of the Runnable.

bind

bind(**kwargs: Any) -> Runnable[Input, Output]

Bind arguments to a Runnable, returning a new Runnable.

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

Parameters:

• kwargs (Any, default: {} ) –

  The arguments to bind to the Runnable.

Returns:

• Runnable[Input, Output] –

  A new Runnable with the arguments bound.

Example:

.. code-block:: python

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'

with_config

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

Bind config to a Runnable, returning a new Runnable.

Parameters:

• config (Optional[RunnableConfig], default: None ) –

  The config to bind to the Runnable.

• kwargs (Any, default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Returns:

• Runnable[Input, Output] –

  A new Runnable with the config bound.

with_listeners

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.

Parameters:

• on_start (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]], default: None ) –

  Called before the Runnable starts running. Defaults to None.

• on_end (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]], default: None ) –

  Called after the Runnable finishes running. Defaults to None.

• on_error (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]], default: None ) –

  Called if the Runnable throws an error. Defaults to None.

Returns:

• Runnable[Input, Output] –

  A new Runnable with the listeners bound.

Example:

.. code-block:: python

from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run

import time

def test_runnable(time_to_sleep : int):
    time.sleep(time_to_sleep)

def fn_start(run_obj: Run):
    print("start_time:", run_obj.start_time)

def fn_end(run_obj: Run):
    print("end_time:", run_obj.end_time)

chain = RunnableLambda(test_runnable).with_listeners(
    on_start=fn_start,
    on_end=fn_end
)
chain.invoke(2)

with_alisteners

with_alisteners(
    *,
    on_start: Optional[AsyncListener] = None,
    on_end: Optional[AsyncListener] = None,
    on_error: Optional[AsyncListener] = 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 metadata added to the run.

Parameters:

• on_start (Optional[AsyncListener], default: None ) –

  Asynchronously called before the Runnable starts running. Defaults to None.

• on_end (Optional[AsyncListener], default: None ) –

  Asynchronously called after the Runnable finishes running. Defaults to None.

• on_error (Optional[AsyncListener], default: None ) –

  Asynchronously called if the Runnable throws an error. Defaults to None.

Returns:

• Runnable[Input, Output] –

  A new Runnable with the listeners bound.

Example:

.. code-block:: python

from langchain_core.runnables import RunnableLambda, Runnable
from datetime import datetime, timezone
import time
import asyncio

def format_t(timestamp: float) -> str:
    return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()

async def test_runnable(time_to_sleep : int):
    print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
    await asyncio.sleep(time_to_sleep)
    print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")

async def fn_start(run_obj : Runnable):
    print(f"on start callback starts at {format_t(time.time())}")
    await asyncio.sleep(3)
    print(f"on start callback ends at {format_t(time.time())}")

async def fn_end(run_obj : Runnable):
    print(f"on end callback starts at {format_t(time.time())}")
    await asyncio.sleep(2)
    print(f"on end callback ends at {format_t(time.time())}")

runnable = RunnableLambda(test_runnable).with_alisteners(
    on_start=fn_start,
    on_end=fn_end
)
async def concurrent_runs():
    await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))

asyncio.run(concurrent_runs())
Result:
on start callback starts at 2025-03-01T07:05:22.875378+00:00
on start callback starts at 2025-03-01T07:05:22.875495+00:00
on start callback ends at 2025-03-01T07:05:25.878862+00:00
on start callback ends at 2025-03-01T07:05:25.878947+00:00
Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
on end callback starts at 2025-03-01T07:05:27.882360+00:00
Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
on end callback starts at 2025-03-01T07:05:28.882428+00:00
on end callback ends at 2025-03-01T07:05:29.883893+00:00
on end callback ends at 2025-03-01T07:05:30.884831+00:00

with_types

with_types(
    *,
    input_type: 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]], default: None ) –

  The input type to bind to the Runnable. Defaults to None.

• output_type (Optional[type[Output]], default: None ) –

  The output type to bind to the Runnable. Defaults to None.

Returns:

• Runnable[Input, Output] –

  A new Runnable with the types bound.

with_retry

with_retry(
    *,
    retry_if_exception_type: tuple[
        type[BaseException], ...
    ] = (Exception,),
    wait_exponential_jitter: bool = True,
    exponential_jitter_params: 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], ...], default: (Exception,) ) –

  A tuple of exception types to retry on. Defaults to (Exception,).

• wait_exponential_jitter (bool, default: True ) –

  Whether to add jitter to the wait time between retries. Defaults to True.

• stop_after_attempt (int, default: 3 ) –

  The maximum number of attempts to make before giving up. Defaults to 3.

• exponential_jitter_params (Optional[ExponentialJitterParams], default: None ) –

  Parameters for tenacity.wait_exponential_jitter. Namely: initial, max, exp_base, and jitter (all float values).

Returns:

• Runnable[Input, Output] –

  A new Runnable that retries the original Runnable on exceptions.

Example:

.. code-block:: python

from langchain_core.runnables import RunnableLambda

count = 0


def _lambda(x: int) -> None:
    global count
    count = count + 1
    if x == 1:
        raise ValueError("x is 1")
    else:
         pass


runnable = RunnableLambda(_lambda)
try:
    runnable.with_retry(
        stop_after_attempt=2,
        retry_if_exception_type=(ValueError,),
    ).invoke(1)
except ValueError:
    pass

assert (count == 2)

map

map() -> Runnable[list[Input], list[Output]]

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

Calls invoke() with each input.

Returns:

• Runnable[list[Input], list[Output]] –

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

Example:

.. code-block:: python

        from langchain_core.runnables import RunnableLambda

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

        runnable = RunnableLambda(_lambda)
        print(runnable.map().invoke([1, 2, 3])) # [2, 3, 4]

with_fallbacks

with_fallbacks(
    fallbacks: Sequence[Runnable[Input, Output]],
    *,
    exceptions_to_handle: tuple[
        type[BaseException], ...
    ] = (Exception,),
    exception_key: Optional[str] = None
) -> RunnableWithFallbacks[Input, Output]

Add fallbacks to a Runnable, returning a new Runnable.

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

Parameters:

• fallbacks (Sequence[Runnable[Input, Output]]) –

  A sequence of runnables to try if the original Runnable fails.

• exceptions_to_handle (tuple[type[BaseException], ...], default: (Exception,) ) –

  A tuple of exception types to handle. Defaults to (Exception,).

• exception_key (Optional[str], default: None ) –

  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:

• RunnableWithFallbacks[Input, Output] –

  A new Runnable that will try the original Runnable, and then each

• RunnableWithFallbacks[Input, Output] –

  fallback in order, upon failures.

Example:

.. code-block:: python

    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], ...], default: (Exception,) ) –

  A tuple of exception types to handle.

• exception_key (Optional[str], default: None ) –

  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:

• RunnableWithFallbacks[Input, Output] –

  A new Runnable that will try the original Runnable, and then each

• RunnableWithFallbacks[Input, Output] –

  fallback in order, upon failures.

as_tool

as_tool(
    args_schema: Optional[type[BaseModel]] = None,
    *,
    name: Optional[str] = None,
    description: Optional[str] = None,
    arg_types: Optional[dict[str, type]] = None
) -> BaseTool

Create a BaseTool from a Runnable.

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

Parameters:

• args_schema (Optional[type[BaseModel]], default: None ) –

  The schema for the tool. Defaults to None.

• name (Optional[str], default: None ) –

  The name of the tool. Defaults to None.

• description (Optional[str], default: None ) –

  The description of the tool. Defaults to None.

• arg_types (Optional[dict[str, type]], default: None ) –

  A dictionary of argument names to types. Defaults to None.

Returns:

• BaseTool –

  A BaseTool instance.

Typed dict input:

.. code-block:: python

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:

.. code-block:: python

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:

.. code-block:: python

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:

.. code-block:: python

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")

.. versionadded:: 0.2.14

inject_tool_args

inject_tool_args(
    tool_call: ToolCall,
    input: Union[
        list[AnyMessage], dict[str, Any], BaseModel
    ],
    store: Optional[BaseStore],
) -> ToolCall

Injects the state and store into the tool call.

Tool arguments with types annotated as InjectedState and InjectedStore are ignored in tool schemas for generation purposes. This method injects them into tool calls for tool invocation.

Parameters:

• tool_call (ToolCall) –

  The tool call to inject state and store into.

• input (Union[list[AnyMessage], dict[str, Any], BaseModel]) –

  The input state to inject.

• store (Optional[BaseStore]) –

  The store to inject.

Returns:

• ToolCall ( ToolCall ) –

  The tool call with injected state and store.

InjectedState

Bases: InjectedToolArg

Annotation for a Tool arg that is meant to be populated with the graph state.

Any Tool argument annotated with InjectedState will be hidden from a tool-calling model, so that the model doesn't attempt to generate the argument. If using ToolNode, the appropriate graph state field will be automatically injected into the model-generated tool args.

Parameters:

• field (Optional[str], default: None ) –

  The key from state to insert. If None, the entire state is expected to be passed in.

Example

from typing import List
from typing_extensions import Annotated, TypedDict

from langchain_core.messages import BaseMessage, AIMessage
from langchain_core.tools import tool

from langgraph.prebuilt import InjectedState, ToolNode


class AgentState(TypedDict):
    messages: List[BaseMessage]
    foo: str

@tool
def state_tool(x: int, state: Annotated[dict, InjectedState]) -> str:
    '''Do something with state.'''
    if len(state["messages"]) > 2:
        return state["foo"] + str(x)
    else:
        return "not enough messages"

@tool
def foo_tool(x: int, foo: Annotated[str, InjectedState("foo")]) -> str:
    '''Do something else with state.'''
    return foo + str(x + 1)

node = ToolNode([state_tool, foo_tool])

tool_call1 = {"name": "state_tool", "args": {"x": 1}, "id": "1", "type": "tool_call"}
tool_call2 = {"name": "foo_tool", "args": {"x": 1}, "id": "2", "type": "tool_call"}
state = {
    "messages": [AIMessage("", tool_calls=[tool_call1, tool_call2])],
    "foo": "bar",
}
node.invoke(state)

[
    ToolMessage(content='not enough messages', name='state_tool', tool_call_id='1'),
    ToolMessage(content='bar2', name='foo_tool', tool_call_id='2')
]

InjectedStore

Bases: InjectedToolArg

Annotation for a Tool arg that is meant to be populated with LangGraph store.

Any Tool argument annotated with InjectedStore will be hidden from a tool-calling model, so that the model doesn't attempt to generate the argument. If using ToolNode, the appropriate store field will be automatically injected into the model-generated tool args. Note: if a graph is compiled with a store object, the store will be automatically propagated to the tools with InjectedStore args when using ToolNode.

Warning

InjectedStore annotation requires langchain-core >= 0.3.8

Example

from typing import Any
from typing_extensions import Annotated

from langchain_core.messages import AIMessage
from langchain_core.tools import tool

from langgraph.store.memory import InMemoryStore
from langgraph.prebuilt import InjectedStore, ToolNode

store = InMemoryStore()
store.put(("values",), "foo", {"bar": 2})

@tool
def store_tool(x: int, my_store: Annotated[Any, InjectedStore()]) -> str:
    '''Do something with store.'''
    stored_value = my_store.get(("values",), "foo").value["bar"]
    return stored_value + x

node = ToolNode([store_tool])

tool_call = {"name": "store_tool", "args": {"x": 1}, "id": "1", "type": "tool_call"}
state = {
    "messages": [AIMessage("", tool_calls=[tool_call])],
}

node.invoke(state, store=store)

{
    "messages": [
        ToolMessage(content='3', name='store_tool', tool_call_id='1'),
    ]
}

tools_condition

tools_condition(
    state: Union[
        list[AnyMessage], dict[str, Any], BaseModel
    ],
    messages_key: str = "messages",
) -> Literal["tools", "__end__"]

Use in the conditional_edge to route to the ToolNode if the last message

has tool calls. Otherwise, route to the end.

Parameters:

• state (Union[list[AnyMessage], dict[str, Any], BaseModel]) –

  The state to check for tool calls. Must have a list of messages (MessageGraph) or have the "messages" key (StateGraph).

Returns:

• Literal['tools', '__end__'] –

  The next node to route to.

Examples:

Create a custom ReAct-style agent with tools.

>>> from langchain_anthropic import ChatAnthropic
>>> from langchain_core.tools import tool
...
>>> from langgraph.graph import StateGraph
>>> from langgraph.prebuilt import ToolNode, tools_condition
>>> from langgraph.graph.message import add_messages
...
>>> from typing import Annotated
>>> from typing_extensions import TypedDict
...
>>> @tool
>>> def divide(a: float, b: float) -> int:
...     """Return a / b."""
...     return a / b
...
>>> llm = ChatAnthropic(model="claude-3-haiku-20240307")
>>> tools = [divide]
...
>>> class State(TypedDict):
...     messages: Annotated[list, add_messages]
>>>
>>> graph_builder = StateGraph(State)
>>> graph_builder.add_node("tools", ToolNode(tools))
>>> graph_builder.add_node("chatbot", lambda state: {"messages":llm.bind_tools(tools).invoke(state['messages'])})
>>> graph_builder.add_edge("tools", "chatbot")
>>> graph_builder.add_conditional_edges(
...     "chatbot", tools_condition
... )
>>> graph_builder.set_entry_point("chatbot")
>>> graph = graph_builder.compile()
>>> graph.invoke({"messages": {"role": "user", "content": "What's 329993 divided by 13662?"}})

This module provides a ValidationNode class that can be used to validate tool calls in a langchain graph. It applies a pydantic schema to tool_calls in the models' outputs, and returns a ToolMessage with the validated content. If the schema is not valid, it returns a ToolMessage with the error message. The ValidationNode can be used in a StateGraph with a "messages" key or in a MessageGraph. If multiple tool calls are requested, they will be run in parallel.

ValidationNode

Bases: RunnableCallable

A node that validates all tools requests from the last AIMessage.

It can be used either in StateGraph with a "messages" key or in MessageGraph.

Note

This node does not actually run the tools, it only validates the tool calls, which is useful for extraction and other use cases where you need to generate structured output that conforms to a complex schema without losing the original messages and tool IDs (for use in multi-turn conversations).

Parameters:

• schemas (Sequence[Union[BaseTool, Type[BaseModel], Callable]]) –

  A list of schemas to validate the tool calls with. These can be any of the following: - A pydantic BaseModel class - A BaseTool instance (the args_schema will be used) - A function (a schema will be created from the function signature)

• format_error (Optional[Callable[[BaseException, ToolCall, Type[BaseModel]], str]], default: None ) –

  A function that takes an exception, a ToolCall, and a schema and returns a formatted error string. By default, it returns the exception repr and a message to respond after fixing validation errors.

• name (str, default: 'validation' ) –

  The name of the node.

• tags (Optional[list[str]], default: None ) –

  A list of tags to add to the node.

Returns:

• Union[Dict[str, List[ToolMessage]], Sequence[ToolMessage]] –

  A list of ToolMessages with the validated content or error messages.

Examples:

Example usage for re-prompting the model to generate a valid response:

>>> from typing import Literal, Annotated
>>> from typing_extensions import TypedDict
...
>>> from langchain_anthropic import ChatAnthropic
>>> from pydantic import BaseModel, field_validator
...
>>> from langgraph.graph import END, START, StateGraph
>>> from langgraph.prebuilt import ValidationNode
>>> from langgraph.graph.message import add_messages
...
...
>>> class SelectNumber(BaseModel):
...     a: int
...
...     @field_validator("a")
...     def a_must_be_meaningful(cls, v):
...         if v != 37:
...             raise ValueError("Only 37 is allowed")
...         return v
...
...
>>> builder = StateGraph(Annotated[list, add_messages])
>>> llm = ChatAnthropic(model="claude-3-5-haiku-latest").bind_tools([SelectNumber])
>>> builder.add_node("model", llm)
>>> builder.add_node("validation", ValidationNode([SelectNumber]))
>>> builder.add_edge(START, "model")
...
...
>>> def should_validate(state: list) -> Literal["validation", "__end__"]:
...     if state[-1].tool_calls:
...         return "validation"
...     return END
...
...
>>> builder.add_conditional_edges("model", should_validate)
...
...
>>> def should_reprompt(state: list) -> Literal["model", "__end__"]:
...     for msg in state[::-1]:
...         # None of the tool calls were errors
...         if msg.type == "ai":
...             return END
...         if msg.additional_kwargs.get("is_error"):
...             return "model"
...     return END
...
...
>>> builder.add_conditional_edges("validation", should_reprompt)
...
...
>>> graph = builder.compile()
>>> res = graph.invoke(("user", "Select a number, any number"))
>>> # Show the retry logic
>>> for msg in res:
...     msg.pretty_print()
================================ Human Message =================================
Select a number, any number
================================== Ai Message ==================================
[{'id': 'toolu_01JSjT9Pq8hGmTgmMPc6KnvM', 'input': {'a': 42}, 'name': 'SelectNumber', 'type': 'tool_use'}]
Tool Calls:
SelectNumber (toolu_01JSjT9Pq8hGmTgmMPc6KnvM)
Call ID: toolu_01JSjT9Pq8hGmTgmMPc6KnvM
Args:
    a: 42
================================= Tool Message =================================
Name: SelectNumber
ValidationError(model='SelectNumber', errors=[{'loc': ('a',), 'msg': 'Only 37 is allowed', 'type': 'value_error'}])
Respond after fixing all validation errors.
================================== Ai Message ==================================
[{'id': 'toolu_01PkxSVxNxc5wqwCPW1FiSmV', 'input': {'a': 37}, 'name': 'SelectNumber', 'type': 'tool_use'}]
Tool Calls:
SelectNumber (toolu_01PkxSVxNxc5wqwCPW1FiSmV)
Call ID: toolu_01PkxSVxNxc5wqwCPW1FiSmV
Args:
    a: 37
================================= Tool Message =================================
Name: SelectNumber
{"a": 37}

InputType property

InputType: type[Input]

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

OutputType property

OutputType: type[Output]

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

input_schema property

input_schema: type[BaseModel]

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

output_schema property

output_schema: type[BaseModel]

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

config_specs property

config_specs: list[ConfigurableFieldSpec]

List configurable fields for this Runnable.

get_name

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

Get the name of the Runnable.

get_input_schema

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], default: None ) –

  A config to use when generating the schema.

Returns:

• type[BaseModel] –

  A pydantic model that can be used to validate input.

get_input_jsonschema

get_input_jsonschema(
    config: Optional[RunnableConfig] = None,
) -> dict[str, Any]

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

Parameters:

• config (Optional[RunnableConfig], default: None ) –

  A config to use when generating the schema.

Returns:

• dict[str, Any] –

  A JSON schema that represents the input to the Runnable.

Example:

.. code-block:: python

    from langchain_core.runnables import RunnableLambda

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

    runnable = RunnableLambda(add_one)

    print(runnable.get_input_jsonschema())

.. versionadded:: 0.3.0

get_output_schema

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], default: None ) –

  A config to use when generating the schema.

Returns:

• type[BaseModel] –

  A pydantic model that can be used to validate output.

get_output_jsonschema

get_output_jsonschema(
    config: Optional[RunnableConfig] = None,
) -> dict[str, Any]

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

Parameters:

• config (Optional[RunnableConfig], default: None ) –

  A config to use when generating the schema.

Returns:

• dict[str, Any] –

  A JSON schema that represents the output of the Runnable.

Example:

.. code-block:: python

    from langchain_core.runnables import RunnableLambda

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

    runnable = RunnableLambda(add_one)

    print(runnable.get_output_jsonschema())

.. versionadded:: 0.3.0

config_schema

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]], default: None ) –

  A list of fields to include in the config schema.

Returns:

• type[BaseModel] –

  A pydantic model that can be used to validate config.

get_config_jsonschema

get_config_jsonschema(
    *, include: Optional[Sequence[str]] = None
) -> dict[str, Any]

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

Parameters:

• include (Optional[Sequence[str]], default: None ) –

  A list of fields to include in the config schema.

Returns:

• dict[str, Any] –

  A JSON schema that represents the config of the Runnable.

.. versionadded:: 0.3.0

get_graph

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

Return a graph representation of this Runnable.

get_prompts

get_prompts(
    config: Optional[RunnableConfig] = None,
) -> list[BasePromptTemplate]

Return a list of prompts used by this Runnable.

__or__

__or__(
    other: Union[
        Runnable[Any, Other],
        Callable[[Any], Other],
        Callable[[Iterator[Any]], Iterator[Other]],
        Mapping[
            str,
            Union[
                Runnable[Any, Other],
                Callable[[Any], Other],
                Any,
            ],
        ],
    ],
) -> RunnableSerializable[Input, Other]

Compose this Runnable with another object to create a RunnableSequence.

__ror__

__ror__(
    other: Union[
        Runnable[Other, Any],
        Callable[[Other], Any],
        Callable[[Iterator[Other]], Iterator[Any]],
        Mapping[
            str,
            Union[
                Runnable[Other, Any],
                Callable[[Other], Any],
                Any,
            ],
        ],
    ],
) -> RunnableSerializable[Other, Output]

Compose this Runnable with another object to create a RunnableSequence.

pipe

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

.. code-block:: python

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]

pick

pick(
    keys: Union[str, list[str]],
) -> RunnableSerializable[Any, Any]

Pick keys from the output dict of this Runnable.

Pick single key

.. code-block:: python

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

.. code-block:: python

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]"}

assign

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.

.. code-block:: python

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'}}}

batch

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.

batch_as_completed

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

Run invoke in parallel on a list of inputs.

Yields results as they complete.

abatch 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]) –

  A list of inputs to the Runnable.

• config (Optional[Union[RunnableConfig, list[RunnableConfig]]], default: 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, default: False ) –

  Whether to return exceptions instead of raising them. Defaults to False.

• kwargs (Optional[Any], default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Returns:

• list[Output] –

  A list of outputs from the Runnable.

abatch_as_completed async

abatch_as_completed(
    inputs: Sequence[Input],
    config: Optional[
        Union[RunnableConfig, Sequence[RunnableConfig]]
    ] = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Optional[Any]
) -> AsyncIterator[tuple[int, Union[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 (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]], default: 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. Defaults to None.

• return_exceptions (bool, default: False ) –

  Whether to return exceptions instead of raising them. Defaults to False.

• kwargs (Optional[Any], default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Yields:

• AsyncIterator[tuple[int, Union[Output, Exception]]] –

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

stream

stream(
    input: Input,
    config: Optional[RunnableConfig] = None,
    **kwargs: Optional[Any]
) -> 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 (Optional[RunnableConfig], default: None ) –

  The config to use for the Runnable. Defaults to None.

• kwargs (Optional[Any], default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Yields:

• Output –

  The output of the Runnable.

astream async

astream(
    input: Input,
    config: Optional[RunnableConfig] = None,
    **kwargs: Optional[Any]
) -> 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 (Optional[RunnableConfig], default: None ) –

  The config to use for the Runnable. Defaults to None.

• kwargs (Optional[Any], default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Yields:

• AsyncIterator[Output] –

  The output of the Runnable.

astream_log 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], default: None ) –

  The config to use for the Runnable.

• diff (bool, default: True ) –

  Whether to yield diffs between each step or the current state.

• with_streamed_output_list (bool, default: True ) –

  Whether to yield the streamed_output list.

• include_names (Optional[Sequence[str]], default: None ) –

  Only include logs with these names.

• include_types (Optional[Sequence[str]], default: None ) –

  Only include logs with these types.

• include_tags (Optional[Sequence[str]], default: None ) –

  Only include logs with these tags.

• exclude_names (Optional[Sequence[str]], default: None ) –

  Exclude logs with these names.

• exclude_types (Optional[Sequence[str]], default: None ) –

  Exclude logs with these types.

• exclude_tags (Optional[Sequence[str]], default: None ) –

  Exclude logs with these tags.

• kwargs (Any, default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Yields:

• Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]] –

  A RunLogPatch or RunLog object.

astream_events async

astream_events(
    input: Any,
    config: Optional[RunnableConfig] = None,
    *,
    version: Literal["v1", "v2"] = "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]

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.

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, ...]) | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+

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:

.. code-block:: python

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

format_docs = RunnableLambda(format_docs)

some_tool:

.. code-block:: python

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

prompt:

.. code-block:: python

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

Example:

.. code-block:: python

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

.. code-block:: python

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], default: None ) –

  The config to use for the Runnable.

• version (Literal['v1', 'v2'], default: '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]], default: None ) –

  Only include events from runnables with matching names.

• include_types (Optional[Sequence[str]], default: None ) –

  Only include events from runnables with matching types.

• include_tags (Optional[Sequence[str]], default: None ) –

  Only include events from runnables with matching tags.

• exclude_names (Optional[Sequence[str]], default: None ) –

  Exclude events from runnables with matching names.

• exclude_types (Optional[Sequence[str]], default: None ) –

  Exclude events from runnables with matching types.

• exclude_tags (Optional[Sequence[str]], default: None ) –

  Exclude events from runnables with matching tags.

• kwargs (Any, default: {} ) –

  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:

• AsyncIterator[StreamEvent] –

  An async stream of StreamEvents.

Raises:

• NotImplementedError –

  If the version is not v1 or v2.

transform

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

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 (Optional[RunnableConfig], default: None ) –

  The config to use for the Runnable. Defaults to None.

• kwargs (Optional[Any], default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Yields:

• Output –

  The output of the Runnable.

atransform 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]) –

  An async iterator of inputs to the Runnable.

• config (Optional[RunnableConfig], default: None ) –

  The config to use for the Runnable. Defaults to None.

• kwargs (Optional[Any], default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Yields:

• AsyncIterator[Output] –

  The output of the Runnable.

bind

bind(**kwargs: Any) -> Runnable[Input, Output]

Bind arguments to a Runnable, returning a new Runnable.

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

Parameters:

• kwargs (Any, default: {} ) –

  The arguments to bind to the Runnable.

Returns:

• Runnable[Input, Output] –

  A new Runnable with the arguments bound.

Example:

.. code-block:: python

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'

with_config

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

Bind config to a Runnable, returning a new Runnable.

Parameters:

• config (Optional[RunnableConfig], default: None ) –

  The config to bind to the Runnable.

• kwargs (Any, default: {} ) –

  Additional keyword arguments to pass to the Runnable.

Returns:

• Runnable[Input, Output] –

  A new Runnable with the config bound.

with_listeners

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.

Parameters:

• on_start (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]], default: None ) –

  Called before the Runnable starts running. Defaults to None.

• on_end (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]], default: None ) –

  Called after the Runnable finishes running. Defaults to None.

• on_error (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]], default: None ) –

  Called if the Runnable throws an error. Defaults to None.

Returns:

• Runnable[Input, Output] –

  A new Runnable with the listeners bound.

Example:

.. code-block:: python

from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run

import time

def test_runnable(time_to_sleep : int):
    time.sleep(time_to_sleep)

def fn_start(run_obj: Run):
    print("start_time:", run_obj.start_time)

def fn_end(run_obj: Run):
    print("end_time:", run_obj.end_time)

chain = RunnableLambda(test_runnable).with_listeners(
    on_start=fn_start,
    on_end=fn_end
)
chain.invoke(2)

with_alisteners

with_alisteners(
    *,
    on_start: Optional[AsyncListener] = None,
    on_end: Optional[AsyncListener] = None,
    on_error: Optional[AsyncListener] = 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 metadata added to the run.

Parameters:

• on_start (Optional[AsyncListener], default: None ) –

  Asynchronously called before the Runnable starts running. Defaults to None.

• on_end (Optional[AsyncListener], default: None ) –

  Asynchronously called after the Runnable finishes running. Defaults to None.

• on_error (Optional[AsyncListener], default: None ) –

  Asynchronously called if the Runnable throws an error. Defaults to None.

Returns:

• Runnable[Input, Output] –

  A new Runnable with the listeners bound.

Example:

.. code-block:: python

from langchain_core.runnables import RunnableLambda, Runnable
from datetime import datetime, timezone
import time
import asyncio

def format_t(timestamp: float) -> str:
    return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()

async def test_runnable(time_to_sleep : int):
    print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
    await asyncio.sleep(time_to_sleep)
    print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")

async def fn_start(run_obj : Runnable):
    print(f"on start callback starts at {format_t(time.time())}")
    await asyncio.sleep(3)
    print(f"on start callback ends at {format_t(time.time())}")

async def fn_end(run_obj : Runnable):
    print(f"on end callback starts at {format_t(time.time())}")
    await asyncio.sleep(2)
    print(f"on end callback ends at {format_t(time.time())}")

runnable = RunnableLambda(test_runnable).with_alisteners(
    on_start=fn_start,
    on_end=fn_end
)
async def concurrent_runs():
    await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))

asyncio.run(concurrent_runs())
Result:
on start callback starts at 2025-03-01T07:05:22.875378+00:00
on start callback starts at 2025-03-01T07:05:22.875495+00:00
on start callback ends at 2025-03-01T07:05:25.878862+00:00
on start callback ends at 2025-03-01T07:05:25.878947+00:00
Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
on end callback starts at 2025-03-01T07:05:27.882360+00:00
Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
on end callback starts at 2025-03-01T07:05:28.882428+00:00
on end callback ends at 2025-03-01T07:05:29.883893+00:00
on end callback ends at 2025-03-01T07:05:30.884831+00:00

with_types

with_types(
    *,
    input_type: 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]], default: None ) –

  The input type to bind to the Runnable. Defaults to None.

• output_type (Optional[type[Output]], default: None ) –

  The output type to bind to the Runnable. Defaults to None.

Returns:

• Runnable[Input, Output] –

  A new Runnable with the types bound.

with_retry

with_retry(
    *,
    retry_if_exception_type: tuple[
        type[BaseException], ...
    ] = (Exception,),
    wait_exponential_jitter: bool = True,
    exponential_jitter_params: 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], ...], default: (Exception,) ) –

  A tuple of exception types to retry on. Defaults to (Exception,).

• wait_exponential_jitter (bool, default: True ) –

  Whether to add jitter to the wait time between retries. Defaults to True.

• stop_after_attempt (int, default: 3 ) –

  The maximum number of attempts to make before giving up. Defaults to 3.

• exponential_jitter_params (Optional[ExponentialJitterParams], default: None ) –

  Parameters for tenacity.wait_exponential_jitter. Namely: initial, max, exp_base, and jitter (all float values).

Returns:

• Runnable[Input, Output] –

  A new Runnable that retries the original Runnable on exceptions.

Example:

.. code-block:: python

from langchain_core.runnables import RunnableLambda

count = 0


def _lambda(x: int) -> None:
    global count
    count = count + 1
    if x == 1:
        raise ValueError("x is 1")
    else:
         pass


runnable = RunnableLambda(_lambda)
try:
    runnable.with_retry(
        stop_after_attempt=2,
        retry_if_exception_type=(ValueError,),
    ).invoke(1)
except ValueError:
    pass

assert (count == 2)

map

map() -> Runnable[list[Input], list[Output]]

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

Calls invoke() with each input.

Returns:

• Runnable[list[Input], list[Output]] –

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

Example:

.. code-block:: python

        from langchain_core.runnables import RunnableLambda

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

        runnable = RunnableLambda(_lambda)
        print(runnable.map().invoke([1, 2, 3])) # [2, 3, 4]

with_fallbacks

with_fallbacks(
    fallbacks: Sequence[Runnable[Input, Output]],
    *,
    exceptions_to_handle: tuple[
        type[BaseException], ...
    ] = (Exception,),
    exception_key: Optional[str] = None
) -> RunnableWithFallbacks[Input, Output]

Add fallbacks to a Runnable, returning a new Runnable.

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

Parameters:

• fallbacks (Sequence[Runnable[Input, Output]]) –

  A sequence of runnables to try if the original Runnable fails.

• exceptions_to_handle (tuple[type[BaseException], ...], default: (Exception,) ) –

  A tuple of exception types to handle. Defaults to (Exception,).

• exception_key (Optional[str], default: None ) –

  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:

• RunnableWithFallbacks[Input, Output] –

  A new Runnable that will try the original Runnable, and then each

• RunnableWithFallbacks[Input, Output] –

  fallback in order, upon failures.

Example:

.. code-block:: python

    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], ...], default: (Exception,) ) –

  A tuple of exception types to handle.

• exception_key (Optional[str], default: None ) –

  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:

• RunnableWithFallbacks[Input, Output] –

  A new Runnable that will try the original Runnable, and then each

• RunnableWithFallbacks[Input, Output] –

  fallback in order, upon failures.

as_tool

as_tool(
    args_schema: Optional[type[BaseModel]] = None,
    *,
    name: Optional[str] = None,
    description: Optional[str] = None,
    arg_types: Optional[dict[str, type]] = None
) -> BaseTool

Create a BaseTool from a Runnable.

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

Parameters:

• args_schema (Optional[type[BaseModel]], default: None ) –

  The schema for the tool. Defaults to None.

• name (Optional[str], default: None ) –

  The name of the tool. Defaults to None.

• description (Optional[str], default: None ) –

  The description of the tool. Defaults to None.

• arg_types (Optional[dict[str, type]], default: None ) –

  A dictionary of argument names to types. Defaults to None.

Returns:

• BaseTool –

  A BaseTool instance.

Typed dict input:

.. code-block:: python

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:

.. code-block:: python

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:

.. code-block:: python

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:

.. code-block:: python

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")

.. versionadded:: 0.2.14

Comments