`langchain-groq`¶

Groq integration for LangChain.

Modules:

Name	Description
`chat_models`	Groq Chat wrapper.
`version`	Main entrypoint into package.

Classes:

Name	Description
`ChatGroq`	Groq Chat large language models API.

ChatGroq ¶

Bases: BaseChatModel

Groq Chat large language models API.

To use, you should have the environment variable GROQ_API_KEY set with your API key.

Any parameters that are valid to be passed to the groq.create call can be passed in, even if not explicitly saved on this class.

Setup

Install langchain-groq and set environment variable GROQ_API_KEY.

.. code-block:: bash

pip install -U langchain-groq
export GROQ_API_KEY="your-api-key"

Key init args — completion params: model: str Name of Groq model to use, e.g. llama-3.1-8b-instant. temperature: float Sampling temperature. Ranges from 0.0 to 1.0. max_tokens: Optional[int] Max number of tokens to generate. reasoning_format: Optional[Literal["parsed", "raw", "hidden]] The format for reasoning output. Groq will default to raw if left undefined.

    - ``'parsed'``: Separates reasoning into a dedicated field while keeping the
      response concise. Reasoning will be returned in the
      ``additional_kwargs.reasoning_content`` field of the response.
    - ``'raw'``: Includes reasoning within think tags (e.g.
      ``<think>{reasoning_content}</think>``).
    - ``'hidden'``: Returns only the final answer content. Note: this only
      supresses reasoning content in the response; the model will still perform
      reasoning unless overridden in ``reasoning_effort``.

    See the `Groq documentation
    <https://console.groq.com/docs/reasoning#reasoning>`__ for more
    details and a list of supported models.
model_kwargs: Dict[str, Any]
    Holds any model parameters valid for create call not
    explicitly specified.

Key init args — client params: timeout: Union[float, Tuple[float, float], Any, None] Timeout for requests. max_retries: int Max number of retries. api_key: Optional[str] Groq API key. If not passed in will be read from env var GROQ_API_KEY. base_url: Optional[str] Base URL path for API requests, leave blank if not using a proxy or service emulator. custom_get_token_ids: Optional[Callable[[str], List[int]]] Optional encoder to use for counting tokens.

See full list of supported init args and their descriptions in the params section.

Instantiate

.. code-block:: python

from langchain_groq import ChatGroq

llm = ChatGroq(
    model="llama-3.1-8b-instant",
    temperature=0.0,
    max_retries=2,
    # other params...
)

Invoke

.. code-block:: python

messages = [
    ("system", "You are a helpful translator. Translate the user sentence to French."),
    ("human", "I love programming."),
]
llm.invoke(messages)

.. code-block:: python

AIMessage(content='The English sentence "I love programming" can
be translated to French as "J\'aime programmer". The word
"programming" is translated as "programmer" in French.',
response_metadata={'token_usage': {'completion_tokens': 38,
'prompt_tokens': 28, 'total_tokens': 66, 'completion_time':
0.057975474, 'prompt_time': 0.005366091, 'queue_time': None,
'total_time': 0.063341565}, 'model_name': 'llama-3.1-8b-instant',
'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop',
'logprobs': None}, id='run-ecc71d70-e10c-4b69-8b8c-b8027d95d4b8-0')

Stream

.. code-block:: python

# Streaming `text` for each content chunk received
for chunk in llm.stream(messages):
    print(chunk.text, end="")

.. code-block:: python

content='' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
content='The' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
content=' English' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
content=' sentence' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
...
content=' program' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
content='".' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
content='' response_metadata={'finish_reason': 'stop'}
id='run-4e9f926b-73f5-483b-8ef5-09533d925853

.. code-block:: python

# Reconstructing a full response
stream = llm.stream(messages)
full = next(stream)
for chunk in stream:
    full += chunk
full

.. code-block:: python

AIMessageChunk(content='The English sentence "I love programming"
can be translated to French as "J\'aime programmer". Here\'s the
breakdown of the sentence: "J\'aime" is the French equivalent of "
I love", and "programmer" is the French infinitive for "to program".
So, the literal translation is "I love to program". However, in
English we often omit the "to" when talking about activities we
love, and the same applies to French. Therefore, "J\'aime
programmer" is the correct and natural way to express "I love
programming" in French.', response_metadata={'finish_reason':
'stop'}, id='run-a3c35ac4-0750-4d08-ac55-bfc63805de76')

Async

.. code-block:: python

await llm.ainvoke(messages)

.. code-block:: python

AIMessage(content='The English sentence "I love programming" can be translated to French as "J\'aime programmer". The word "programming" is translated as "programmer" in French. I hope this helps! Let me know if you have any other questions.', response_metadata={'token_usage': {'completion_tokens': 53, 'prompt_tokens': 28, 'total_tokens': 81, 'completion_time': 0.083623752, 'prompt_time': 0.007365126, 'queue_time': None, 'total_time': 0.090988878}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop', 'logprobs': None}, id='run-897f3391-1bea-42e2-82e0-686e2367bcf8-0')

Tool calling

.. code-block:: python

from pydantic import BaseModel, Field


class GetWeather(BaseModel):
    '''Get the current weather in a given location'''

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")


class GetPopulation(BaseModel):
    '''Get the current population in a given location'''

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")


model_with_tools = llm.bind_tools([GetWeather, GetPopulation])
ai_msg = model_with_tools.invoke("What is the population of NY?")
ai_msg.tool_calls

.. code-block:: python

[
    {
        "name": "GetPopulation",
        "args": {"location": "NY"},
        "id": "call_bb8d",
    }
]

See ChatGroq.bind_tools() method for more.

Structured output

.. code-block:: python

from typing import Optional

from pydantic import BaseModel, Field


class Joke(BaseModel):
    '''Joke to tell user.'''

    setup: str = Field(description="The setup of the joke")
    punchline: str = Field(description="The punchline to the joke")
    rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")


structured_model = llm.with_structured_output(Joke)
structured_model.invoke("Tell me a joke about cats")

.. code-block:: python

Joke(
    setup="Why don't cats play poker in the jungle?",
    punchline="Too many cheetahs!",
    rating=None,
)

See ChatGroq.with_structured_output() for more.

Response metadata

.. code-block:: python

ai_msg = llm.invoke(messages)
ai_msg.response_metadata

.. code-block:: python

{
    "token_usage": {
        "completion_tokens": 70,
        "prompt_tokens": 28,
        "total_tokens": 98,
        "completion_time": 0.111956391,
        "prompt_time": 0.007518279,
        "queue_time": None,
        "total_time": 0.11947467,
    },
    "model_name": "llama-3.1-8b-instant",
    "system_fingerprint": "fp_c5f20b5bb1",
    "finish_reason": "stop",
    "logprobs": None,
}

Methods:

Name	Description
`get_name`	Get the name of the `Runnable`.
`get_input_schema`	Get a pydantic model that can be used to validate input to the Runnable.
`get_input_jsonschema`	Get a JSON schema that represents the input to the `Runnable`.
`get_output_schema`	Get a pydantic model that can be used to validate output to the `Runnable`.
`get_output_jsonschema`	Get a JSON schema that represents the output of the `Runnable`.
`config_schema`	The type of config this `Runnable` accepts specified as a pydantic model.
`get_config_jsonschema`	Get a JSON schema that represents the config of the `Runnable`.
`get_graph`	Return a graph representation of this `Runnable`.
`get_prompts`	Return a list of prompts used by this `Runnable`.
`__or__`	Runnable "or" operator.
`__ror__`	Runnable "reverse-or" operator.
`pipe`	Pipe runnables.
`pick`	Pick keys from the output dict of this `Runnable`.
`assign`	Assigns new fields to the dict output of this `Runnable`.
`batch`	Default implementation runs invoke in parallel using a thread pool executor.
`batch_as_completed`	Run `invoke` in parallel on a list of inputs.
`abatch`	Default implementation runs `ainvoke` in parallel using `asyncio.gather`.
`abatch_as_completed`	Run `ainvoke` in parallel on a list of inputs.
`astream_log`	Stream all output from a `Runnable`, as reported to the callback system.
`astream_events`	Generate a stream of events.
`transform`	Transform inputs to outputs.
`atransform`	Transform inputs to outputs.
`bind`	Bind arguments to a `Runnable`, returning a new `Runnable`.
`with_config`	Bind config to a `Runnable`, returning a new `Runnable`.
`with_listeners`	Bind lifecycle listeners to a `Runnable`, returning a new `Runnable`.
`with_alisteners`	Bind async lifecycle listeners to a `Runnable`.
`with_types`	Bind input and output types to a `Runnable`, returning a new `Runnable`.
`with_retry`	Create a new Runnable that retries the original Runnable on exceptions.
`map`	Return a new `Runnable` that maps a list of inputs to a list of outputs.
`with_fallbacks`	Add fallbacks to a `Runnable`, returning a new `Runnable`.
`as_tool`	Create a `BaseTool` from a `Runnable`.
`__init__`
`get_lc_namespace`	Get the namespace of the langchain object.
`lc_id`	Return a unique identifier for this class for serialization purposes.
`to_json`	Serialize the `Runnable` to JSON.
`to_json_not_implemented`	Serialize a "not implemented" object.
`configurable_fields`	Configure particular `Runnable` fields at runtime.
`configurable_alternatives`	Configure alternatives for `Runnables` that can be set at runtime.
`set_verbose`	If verbose is None, set it.
`get_token_ids`	Return the ordered ids of the tokens in a text.
`get_num_tokens`	Get the number of tokens present in the text.
`get_num_tokens_from_messages`	Get the number of tokens in the messages.
`generate`	Pass a sequence of prompts to the model and return model generations.
`agenerate`	Asynchronously pass a sequence of prompts to a model and return generations.
`dict`	Return a dictionary of the LLM.
`build_extra`	Build extra kwargs from additional params that were passed in.
`validate_environment`	Validate that api key and python package exists in environment.
`is_lc_serializable`	Return whether this model can be serialized by LangChain.
`bind_tools`	Bind tool-like objects to this chat model.
`with_structured_output`	Model wrapper that returns outputs formatted to match the given schema.

Attributes:

Name	Type	Description
`InputType`	`TypeAlias`	Get the input type for this runnable.
`OutputType`	`Any`	Get the output type for this runnable.
`input_schema`	`type[BaseModel]`	The type of input this `Runnable` accepts specified as a pydantic model.
`output_schema`	`type[BaseModel]`	Output schema.
`config_specs`	`list[ConfigurableFieldSpec]`	List configurable fields for this `Runnable`.
`lc_attributes`	`dict`	List of attribute names that should be included in the serialized kwargs.
`cache`	`BaseCache \| bool \| None`	Whether to cache the response.
`verbose`	`bool`	Whether to print out response text.
`callbacks`	`Callbacks`	Callbacks to add to the run trace.
`tags`	`list[str] \| None`	Tags to add to the run trace.
`metadata`	`dict[str, Any] \| None`	Metadata to add to the run trace.
`custom_get_token_ids`	`Callable[[str], list[int]] \| None`	Optional encoder to use for counting tokens.
`rate_limiter`	`BaseRateLimiter \| None`	An optional rate limiter to use for limiting the number of requests.
`disable_streaming`	`bool \| Literal['tool_calling']`	Whether to disable streaming for this model.
`output_version`	`str \| None`	Version of `AIMessage` output format to store in message content.
`model_name`	`str`	Model name to use.
`temperature`	`float`	What sampling temperature to use.
`stop`	`Optional[Union[list[str], str]]`	Default stop sequences.
`reasoning_format`	`Optional[Literal['parsed', 'raw', 'hidden']]`	The format for reasoning output. Groq will default to raw if left undefined.
`reasoning_effort`	`Optional[str]`	The level of effort the model will put into reasoning. Groq will default to
`model_kwargs`	`dict[str, Any]`	Holds any model parameters valid for `create` call not explicitly specified.
`groq_api_key`	`Optional[SecretStr]`	Automatically inferred from env var `GROQ_API_KEY` if not provided.
`groq_api_base`	`Optional[str]`	Base URL path for API requests. Leave blank if not using a proxy or service
`request_timeout`	`Union[float, tuple[float, float], Any, None]`	Timeout for requests to Groq completion API. Can be float, `httpx.Timeout` or
`max_retries`	`int`	Maximum number of retries to make when generating.
`streaming`	`bool`	Whether to stream the results or not.
`n`	`int`	Number of chat completions to generate for each prompt.
`max_tokens`	`Optional[int]`	Maximum number of tokens to generate.
`service_tier`	`Literal['on_demand', 'flex', 'auto']`	Optional parameter that you can include to specify the service tier you'd like to
`http_client`	`Union[Any, None]`	Optional `httpx.Client`.
`http_async_client`	`Union[Any, None]`	Optional `httpx.AsyncClient`. Only used for async invocations. Must specify
`lc_secrets`	`dict[str, str]`	Mapping of secret environment variables.

InputType `property` ¶

InputType: TypeAlias

Get the input type for this runnable.

OutputType `property` ¶

OutputType: Any

Get the output type for this runnable.

input_schema `property` ¶

input_schema: type[BaseModel]

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

output_schema `property` ¶

output_schema: type[BaseModel]

Output schema.

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

config_specs `property` ¶

config_specs: list[ConfigurableFieldSpec]

List configurable fields for this Runnable.

lc_attributes `property` ¶

lc_attributes: dict

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

These attributes must be accepted by the constructor. Default is an empty dictionary.

cache `class-attribute` `instance-attribute` ¶

cache: BaseCache | bool | None = Field(
    default=None, exclude=True
)

Whether to cache the response.

If true, will use the global cache.
If false, will not use a cache
If None, will use the global cache if it's set, otherwise no cache.
If instance of BaseCache, will use the provided cache.

Caching is not currently supported for streaming methods of models.

verbose `class-attribute` `instance-attribute` ¶

verbose: bool = Field(
    default_factory=_get_verbosity, exclude=True, repr=False
)

Whether to print out response text.

callbacks `class-attribute` `instance-attribute` ¶

callbacks: Callbacks = Field(default=None, exclude=True)

Callbacks to add to the run trace.

tags `class-attribute` `instance-attribute` ¶

tags: list[str] | None = Field(default=None, exclude=True)

Tags to add to the run trace.

metadata `class-attribute` `instance-attribute` ¶

metadata: dict[str, Any] | None = Field(
    default=None, exclude=True
)

Metadata to add to the run trace.

custom_get_token_ids `class-attribute` `instance-attribute` ¶

custom_get_token_ids: Callable[[str], list[int]] | None = (
    Field(default=None, exclude=True)
)

Optional encoder to use for counting tokens.

rate_limiter `class-attribute` `instance-attribute` ¶

rate_limiter: BaseRateLimiter | None = Field(
    default=None, exclude=True
)

An optional rate limiter to use for limiting the number of requests.

disable_streaming `class-attribute` `instance-attribute` ¶

disable_streaming: bool | Literal['tool_calling'] = False

Whether to disable streaming for this model.

If streaming is bypassed, then stream()/astream()/astream_events() will defer to invoke()/ainvoke().

If True, will always bypass streaming case.
If 'tool_calling', will bypass streaming case only when the model is called with a tools keyword argument. In other words, LangChain will automatically switch to non-streaming behavior (invoke()) only when the tools argument is provided. This offers the best of both worlds.
If False (default), will always use streaming case if available.

The main reason for this flag is that code might be written using stream() and a user may want to swap out a given model for another model whose the implementation does not properly support streaming.

output_version `class-attribute` `instance-attribute` ¶

output_version: str | None = Field(
    default_factory=from_env(
        "LC_OUTPUT_VERSION", default=None
    )
)

Version of AIMessage output format to store in message content.

AIMessage.content_blocks will lazily parse the contents of content into a standard format. This flag can be used to additionally store the standard format in message content, e.g., for serialization purposes.

Supported values:

"v0": provider-specific format in content (can lazily-parse with .content_blocks)
"v1": standardized format in content (consistent with .content_blocks)

Partner packages (e.g., langchain-openai) can also use this field to roll out new content formats in a backward-compatible way.

Added in version 1.0

model_name `class-attribute` `instance-attribute` ¶

model_name: str = Field(alias='model')

Model name to use.

temperature `class-attribute` `instance-attribute` ¶

temperature: float = 0.7

What sampling temperature to use.

stop `class-attribute` `instance-attribute` ¶

stop: Optional[Union[list[str], str]] = Field(
    default=None, alias="stop_sequences"
)

Default stop sequences.

reasoning_format `class-attribute` `instance-attribute` ¶

reasoning_format: Optional[
    Literal["parsed", "raw", "hidden"]
] = Field(default=None)

The format for reasoning output. Groq will default to raw if left undefined.

'parsed': Separates reasoning into a dedicated field while keeping the response concise. Reasoning will be returned in the additional_kwargs.reasoning_content field of the response.
'raw': Includes reasoning within think tags (e.g. <think>{reasoning_content}</think>).
'hidden': Returns only the final answer content. Note: this only supresses reasoning content in the response; the model will still perform reasoning unless overridden in reasoning_effort.

See the Groq documentation <https://console.groq.com/docs/reasoning#reasoning>__ for more details and a list of supported models.

reasoning_effort `class-attribute` `instance-attribute` ¶

reasoning_effort: Optional[str] = Field(default=None)

The level of effort the model will put into reasoning. Groq will default to enabling reasoning if left undefined.

See the Groq documentation <https://console.groq.com/docs/reasoning#options-for-reasoning-effort>__ for more details and a list of options and models that support setting a reasoning effort.

model_kwargs `class-attribute` `instance-attribute` ¶

model_kwargs: dict[str, Any] = Field(default_factory=dict)

Holds any model parameters valid for create call not explicitly specified.

groq_api_key `class-attribute` `instance-attribute` ¶

groq_api_key: Optional[SecretStr] = Field(
    alias="api_key",
    default_factory=secret_from_env(
        "GROQ_API_KEY", default=None
    ),
)

Automatically inferred from env var GROQ_API_KEY if not provided.

groq_api_base `class-attribute` `instance-attribute` ¶

groq_api_base: Optional[str] = Field(
    alias="base_url",
    default_factory=from_env("GROQ_API_BASE", default=None),
)

Base URL path for API requests. Leave blank if not using a proxy or service emulator.

request_timeout `class-attribute` `instance-attribute` ¶

request_timeout: Union[
    float, tuple[float, float], Any, None
] = Field(default=None, alias="timeout")

Timeout for requests to Groq completion API. Can be float, httpx.Timeout or None.

max_retries `class-attribute` `instance-attribute` ¶

max_retries: int = 2

Maximum number of retries to make when generating.

streaming `class-attribute` `instance-attribute` ¶

streaming: bool = False

Whether to stream the results or not.

n `class-attribute` `instance-attribute` ¶

n: int = 1

Number of chat completions to generate for each prompt.

max_tokens `class-attribute` `instance-attribute` ¶

max_tokens: Optional[int] = None

Maximum number of tokens to generate.

service_tier `class-attribute` `instance-attribute` ¶

service_tier: Literal["on_demand", "flex", "auto"] = Field(
    default="on_demand"
)

Optional parameter that you can include to specify the service tier you'd like to use for requests.

'on_demand': Default.
'flex': On-demand processing when capacity is available, with rapid timeouts if resources are constrained. Provides balance between performance and reliability for workloads that don't require guaranteed processing.
'auto': Uses on-demand rate limits, then falls back to 'flex' if those limits are exceeded

See the Groq documentation <https://console.groq.com/docs/flex-processing>__ for more details and a list of service tiers and descriptions.

http_client `class-attribute` `instance-attribute` ¶

http_client: Union[Any, None] = None

Optional httpx.Client.

http_async_client `class-attribute` `instance-attribute` ¶

http_async_client: Union[Any, None] = None

Optional httpx.AsyncClient. Only used for async invocations. Must specify http_client as well if you'd like a custom client for sync invocations.

lc_secrets `property` ¶

lc_secrets: dict[str, str]

Mapping of secret environment variables.

get_name ¶

get_name(
    suffix: str | None = None, *, name: str | None = None
) -> str

Get the name of the Runnable.

Parameters:

Name	Type	Description	Default
`suffix`	`str \| None`	An optional suffix to append to the name.	`None`
`name`	`str \| None`	An optional name to use instead of the `Runnable`'s name.	`None`

Returns:

Type	Description
`str`	The name of the `Runnable`.

get_input_schema ¶

get_input_schema(
    config: RunnableConfig | None = None,
) -> type[BaseModel]

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

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:

Name	Type	Description	Default
`config`	`RunnableConfig \| None`	A config to use when generating the schema.	`None`

Returns:

Type	Description
`type[BaseModel]`	A pydantic model that can be used to validate input.

get_input_jsonschema ¶

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

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

Parameters:

Name	Type	Description	Default
`config`	`RunnableConfig \| None`	A config to use when generating the schema.	`None`

Returns:

Type	Description
`dict[str, Any]`	A JSON schema that represents the input to the `Runnable`.

Example

from langchain_core.runnables import RunnableLambda


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


runnable = RunnableLambda(add_one)

print(runnable.get_input_jsonschema())

Added in version 0.3.0

get_output_schema ¶

get_output_schema(
    config: RunnableConfig | None = None,
) -> type[BaseModel]

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

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:

Name	Type	Description	Default
`config`	`RunnableConfig \| None`	A config to use when generating the schema.	`None`

Returns:

Type	Description
`type[BaseModel]`	A pydantic model that can be used to validate output.

get_output_jsonschema ¶

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

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

Parameters:

Name	Type	Description	Default
`config`	`RunnableConfig \| None`	A config to use when generating the schema.	`None`

Returns:

Type	Description
`dict[str, Any]`	A JSON schema that represents the output of the `Runnable`.

Example

from langchain_core.runnables import RunnableLambda


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


runnable = RunnableLambda(add_one)

print(runnable.get_output_jsonschema())

Added in version 0.3.0

config_schema ¶

config_schema(
    *, include: Sequence[str] | None = None
) -> type[BaseModel]

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

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

Parameters:

Name	Type	Description	Default
`include`	`Sequence[str] \| None`	A list of fields to include in the config schema.	`None`

Returns:

Type	Description
`type[BaseModel]`	A pydantic model that can be used to validate config.

get_config_jsonschema ¶

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

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

Parameters:

Name	Type	Description	Default
`include`	`Sequence[str] \| None`	A list of fields to include in the config schema.	`None`

Returns:

Type	Description
`dict[str, Any]`	A JSON schema that represents the config of the `Runnable`.

Added in version 0.3.0

get_graph ¶

get_graph(config: RunnableConfig | None = None) -> Graph

Return a graph representation of this Runnable.

get_prompts ¶

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

Return a list of prompts used by this Runnable.

or ¶

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

Runnable "or" operator.

Compose this Runnable with another object to create a RunnableSequence.

Parameters:

Name	Type	Description	Default
`other`	`Runnable[Any, Other] \| Callable[[Iterator[Any]], Iterator[Other]] \| Callable[[AsyncIterator[Any]], AsyncIterator[Other]] \| Callable[[Any], Other] \| Mapping[str, Runnable[Any, Other] \| Callable[[Any], Other] \| Any]`	Another `Runnable` or a `Runnable`-like object.	required

Returns:

Type	Description
`RunnableSerializable[Input, Other]`	A new `Runnable`.

ror ¶

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

Runnable "reverse-or" operator.

Compose this Runnable with another object to create a RunnableSequence.

Parameters:

Name	Type	Description	Default
`other`	`Runnable[Other, Any] \| Callable[[Iterator[Other]], Iterator[Any]] \| Callable[[AsyncIterator[Other]], AsyncIterator[Any]] \| Callable[[Other], Any] \| Mapping[str, Runnable[Other, Any] \| Callable[[Other], Any] \| Any]`	Another `Runnable` or a `Runnable`-like object.	required

Returns:

Type	Description
`RunnableSerializable[Other, Output]`	A new `Runnable`.

pipe ¶

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

Pipe runnables.

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

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

Example

from langchain_core.runnables import RunnableLambda


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


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


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

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

Parameters:

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

Returns:

Type	Description
`RunnableSerializable[Input, Other]`	A new `Runnable`.

pick ¶

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

Pick keys from the output dict of this Runnable.

Pick single key:

```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:

```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]"}
```

Parameters:

Name	Type	Description	Default
`keys`	`str \| list[str]`	A key or list of keys to pick from the output dict.	required

Returns:

Type	Description
`RunnableSerializable[Any, Any]`	a new `Runnable`.

assign ¶

assign(
    **kwargs: (
        Runnable[dict[str, Any], Any]
        | Callable[[dict[str, Any]], Any]
        | Mapping[
            str,
            Runnable[dict[str, Any], Any]
            | Callable[[dict[str, Any]], Any],
        ]
    ),
) -> RunnableSerializable[Any, Any]

Assigns new fields to the dict output of this Runnable.

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

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

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

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

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

Parameters:

Name	Type	Description	Default
`**kwargs`	`Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any] \| Mapping[str, Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any]]`	A mapping of keys to `Runnable` or `Runnable`-like objects that will be invoked with the entire output dict of this `Runnable`.	`{}`

Returns:

Type	Description
`RunnableSerializable[Any, Any]`	A new `Runnable`.

batch ¶

batch(
    inputs: list[Input],
    config: (
        RunnableConfig | list[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> list[Output]

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

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

Subclasses 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:

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

Returns:

Type	Description
`list[Output]`	A list of outputs from the `Runnable`.

batch_as_completed ¶

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

Run invoke in parallel on a list of inputs.

Yields results as they complete.

Parameters:

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

Yields:

Type	Description
`tuple[int, Output \| Exception]`	Tuples of the index of the input and the output from the `Runnable`.

abatch `async` ¶

abatch(
    inputs: list[Input],
    config: (
        RunnableConfig | list[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> list[Output]

Default implementation runs ainvoke in parallel using asyncio.gather.

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

Subclasses 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:

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

Returns:

Type	Description
`list[Output]`	A list of outputs from the `Runnable`.

abatch_as_completed `async` ¶

abatch_as_completed(
    inputs: Sequence[Input],
    config: (
        RunnableConfig | Sequence[RunnableConfig] | None
    ) = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None
) -> AsyncIterator[tuple[int, Output | Exception]]

Run ainvoke in parallel on a list of inputs.

Yields results as they complete.

Parameters:

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

Yields:

Type	Description
`AsyncIterator[tuple[int, Output \| Exception]]`	A tuple of the index of the input and the output from the `Runnable`.

astream_log `async` ¶

astream_log(
    input: Any,
    config: RunnableConfig | None = None,
    *,
    diff: bool = True,
    with_streamed_output_list: bool = True,
    include_names: Sequence[str] | None = None,
    include_types: Sequence[str] | None = None,
    include_tags: Sequence[str] | None = None,
    exclude_names: Sequence[str] | None = None,
    exclude_types: Sequence[str] | None = None,
    exclude_tags: Sequence[str] | None = None,
    **kwargs: Any
) -> AsyncIterator[RunLogPatch] | AsyncIterator[RunLog]

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

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

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

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

Parameters:

Name	Type	Description	Default
`input`	`Any`	The input to the `Runnable`.	required
`config`	`RunnableConfig \| None`	The config to use for the `Runnable`.	`None`
`diff`	`bool`	Whether to yield diffs between each step or the current state.	`True`
`with_streamed_output_list`	`bool`	Whether to yield the `streamed_output` list.	`True`
`include_names`	`Sequence[str] \| None`	Only include logs with these names.	`None`
`include_types`	`Sequence[str] \| None`	Only include logs with these types.	`None`
`include_tags`	`Sequence[str] \| None`	Only include logs with these tags.	`None`
`exclude_names`	`Sequence[str] \| None`	Exclude logs with these names.	`None`
`exclude_types`	`Sequence[str] \| None`	Exclude logs with these types.	`None`
`exclude_tags`	`Sequence[str] \| None`	Exclude logs with these tags.	`None`
`kwargs`	`Any`	Additional keyword arguments to pass to the `Runnable`.	`{}`

Yields:

Type	Description
`AsyncIterator[RunLogPatch] \| AsyncIterator[RunLog]`	A `RunLogPatch` or `RunLog` object.

astream_events `async` ¶

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

Generate a stream of events.

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

A StreamEvent is a dictionary with the following schema:

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

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

Note

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

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

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

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

A custom event has following format:

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

Here are declarations associated with the standard events shown above:

format_docs:

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


format_docs = RunnableLambda(format_docs)

some_tool:

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

prompt:

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

Example:

from langchain_core.runnables import RunnableLambda


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


chain = RunnableLambda(func=reverse)

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

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

Example: Dispatch Custom Event

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


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

slow_thing = RunnableLambda(slow_thing)

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

Parameters:

Name	Type	Description	Default
`input`	`Any`	The input to the `Runnable`.	required
`config`	`RunnableConfig \| None`	The config to use for the `Runnable`.	`None`
`version`	`Literal['v1', 'v2']`	The version of the schema to use either `'v2'` or `'v1'`. Users should use `'v2'`. `'v1'` is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in `'v2'`.	`'v2'`
`include_names`	`Sequence[str] \| None`	Only include events from `Runnables` with matching names.	`None`
`include_types`	`Sequence[str] \| None`	Only include events from `Runnables` with matching types.	`None`
`include_tags`	`Sequence[str] \| None`	Only include events from `Runnables` with matching tags.	`None`
`exclude_names`	`Sequence[str] \| None`	Exclude events from `Runnables` with matching names.	`None`
`exclude_types`	`Sequence[str] \| None`	Exclude events from `Runnables` with matching types.	`None`
`exclude_tags`	`Sequence[str] \| None`	Exclude events from `Runnables` with matching tags.	`None`
`kwargs`	`Any`	Additional keyword arguments to pass to the `Runnable`. These will be passed to `astream_log` as this implementation of `astream_events` is built on top of `astream_log`.	`{}`

Yields:

Type	Description
`AsyncIterator[StreamEvent]`	An async stream of `StreamEvents`.

Raises:

Type	Description
`NotImplementedError`	If the version is not `'v1'` or `'v2'`.

transform ¶

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

Transform inputs to outputs.

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

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

Parameters:

Name	Type	Description	Default
`input`	`Iterator[Input]`	An iterator of inputs to the `Runnable`.	required
`config`	`RunnableConfig \| None`	The config to use for the `Runnable`. Defaults to None.	`None`
`kwargs`	`Any \| None`	Additional keyword arguments to pass to the `Runnable`.	`{}`

Yields:

Type	Description
`Output`	The output of the `Runnable`.

atransform `async` ¶

atransform(
    input: AsyncIterator[Input],
    config: RunnableConfig | None = None,
    **kwargs: Any | None
) -> AsyncIterator[Output]

Transform inputs to outputs.

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

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

Parameters:

Name	Type	Description	Default
`input`	`AsyncIterator[Input]`	An async iterator of inputs to the `Runnable`.	required
`config`	`RunnableConfig \| None`	The config to use for the `Runnable`. Defaults to None.	`None`
`kwargs`	`Any \| None`	Additional keyword arguments to pass to the `Runnable`.	`{}`

Yields:

Type	Description
`AsyncIterator[Output]`	The output of the `Runnable`.

bind ¶

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

Bind arguments to a Runnable, returning a new Runnable.

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

Parameters:

Name	Type	Description	Default
`kwargs`	`Any`	The arguments to bind to the `Runnable`.	`{}`

Returns:

Type	Description
`Runnable[Input, Output]`	A new `Runnable` with the arguments bound.

Example

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

llm = ChatOllama(model="llama3.1")

# 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: RunnableConfig | None = None, **kwargs: Any
) -> Runnable[Input, Output]

Bind config to a Runnable, returning a new Runnable.

Parameters:

Name	Type	Description	Default
`config`	`RunnableConfig \| None`	The config to bind to the `Runnable`.	`None`
`kwargs`	`Any`	Additional keyword arguments to pass to the `Runnable`.	`{}`

Returns:

Type	Description
`Runnable[Input, Output]`	A new `Runnable` with the config bound.

with_listeners ¶

with_listeners(
    *,
    on_start: (
        Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None
    ) = None,
    on_end: (
        Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None
    ) = None,
    on_error: (
        Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None
    ) = None
) -> Runnable[Input, Output]

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

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

Parameters:

Name	Type	Description	Default
`on_start`	`Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None`	Called before the `Runnable` starts running, with the `Run` object. Defaults to None.	`None`
`on_end`	`Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None`	Called after the `Runnable` finishes running, with the `Run` object. Defaults to None.	`None`
`on_error`	`Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None`	Called if the `Runnable` throws an error, with the `Run` object. Defaults to None.	`None`

Returns:

Type	Description
`Runnable[Input, Output]`	A new `Runnable` with the listeners bound.

Example

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

import time


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


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


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


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

with_alisteners ¶

with_alisteners(
    *,
    on_start: AsyncListener | None = None,
    on_end: AsyncListener | None = None,
    on_error: AsyncListener | None = None
) -> Runnable[Input, Output]

Bind async lifecycle listeners to a Runnable.

Returns a new Runnable.

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

Parameters:

Name	Type	Description	Default
`on_start`	`AsyncListener \| None`	Called asynchronously before the `Runnable` starts running, with the `Run` object. Defaults to None.	`None`
`on_end`	`AsyncListener \| None`	Called asynchronously after the `Runnable` finishes running, with the `Run` object. Defaults to None.	`None`
`on_error`	`AsyncListener \| None`	Called asynchronously if the `Runnable` throws an error, with the `Run` object. Defaults to None.	`None`

Returns:

Type	Description
`Runnable[Input, Output]`	A new `Runnable` with the listeners bound.

Example

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

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

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

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

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

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

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

with_types ¶

with_types(
    *,
    input_type: type[Input] | None = None,
    output_type: type[Output] | None = None
) -> Runnable[Input, Output]

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

Parameters:

Name	Type	Description	Default
`input_type`	`type[Input] \| None`	The input type to bind to the `Runnable`. Defaults to None.	`None`
`output_type`	`type[Output] \| None`	The output type to bind to the `Runnable`. Defaults to None.	`None`

Returns:

Type	Description
`Runnable[Input, Output]`	A new Runnable with the types bound.

with_retry ¶

with_retry(
    *,
    retry_if_exception_type: tuple[
        type[BaseException], ...
    ] = (Exception,),
    wait_exponential_jitter: bool = True,
    exponential_jitter_params: (
        ExponentialJitterParams | None
    ) = None,
    stop_after_attempt: int = 3
) -> Runnable[Input, Output]

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

Parameters:

Name	Type	Description	Default
`retry_if_exception_type`	`tuple[type[BaseException], ...]`	A tuple of exception types to retry on. Defaults to (Exception,).	`(Exception,)`
`wait_exponential_jitter`	`bool`	Whether to add jitter to the wait time between retries. Defaults to True.	`True`
`stop_after_attempt`	`int`	The maximum number of attempts to make before giving up. Defaults to 3.	`3`
`exponential_jitter_params`	`ExponentialJitterParams \| None`	Parameters for `tenacity.wait_exponential_jitter`. Namely: `initial`, `max`, `exp_base`, and `jitter` (all float values).	`None`

Returns:

Type	Description
`Runnable[Input, Output]`	A new Runnable that retries the original Runnable on exceptions.

Example

from langchain_core.runnables import RunnableLambda

count = 0


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


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

assert count == 2

map ¶

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

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

Calls invoke with each input.

Returns:

Type	Description
`Runnable[list[Input], list[Output]]`	A new `Runnable` that maps a list of inputs to a list of outputs.

Example

from langchain_core.runnables import RunnableLambda


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


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

with_fallbacks ¶

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

Add fallbacks to a Runnable, returning a new Runnable.

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

Parameters:

Name	Type	Description	Default
`fallbacks`	`Sequence[Runnable[Input, Output]]`	A sequence of runnables to try if the original `Runnable` fails.	required
`exceptions_to_handle`	`tuple[type[BaseException], ...]`	A tuple of exception types to handle. Defaults to `(Exception,)`.	`(Exception,)`
`exception_key`	`str \| 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.	`None`

Returns:

Type	Description
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each
`RunnableWithFallbacks[Input, Output]`	fallback in order, upon failures.

Example

from typing import Iterator

from langchain_core.runnables import RunnableGenerator


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


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


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

Parameters:

Name	Type	Description	Default
`fallbacks`	`Sequence[Runnable[Input, Output]]`	A sequence of runnables to try if the original `Runnable` fails.	required
`exceptions_to_handle`	`tuple[type[BaseException], ...]`	A tuple of exception types to handle.	`(Exception,)`
`exception_key`	`str \| 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.	`None`

Returns:

Type	Description
`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: type[BaseModel] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    arg_types: dict[str, type] | None = None
) -> BaseTool

Create a BaseTool from a Runnable.

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

Parameters:

Name	Type	Description	Default
`args_schema`	`type[BaseModel] \| None`	The schema for the tool. Defaults to None.	`None`
`name`	`str \| None`	The name of the tool. Defaults to None.	`None`
`description`	`str \| None`	The description of the tool. Defaults to None.	`None`
`arg_types`	`dict[str, type] \| None`	A dictionary of argument names to types. Defaults to None.	`None`

Returns:

Type	Description
`BaseTool`	A `BaseTool` instance.

Typed dict input:

from typing_extensions import TypedDict
from langchain_core.runnables import RunnableLambda


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


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


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

dict input, specifying schema via args_schema:

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

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

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

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

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

dict input, specifying schema via arg_types:

from typing import Any
from langchain_core.runnables import RunnableLambda


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


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

String input:

from langchain_core.runnables import RunnableLambda


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


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


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

Added in version 0.2.14

init ¶

__init__(*args: Any, **kwargs: Any) -> None

get_lc_namespace `classmethod` ¶

get_lc_namespace() -> list[str]

Get the namespace of the langchain object.

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

Returns:

Type	Description
`list[str]`	The namespace as a list of strings.

lc_id `classmethod` ¶

lc_id() -> list[str]

Return a unique identifier for this class for serialization purposes.

The unique identifier is a list of strings that describes the path to the object. For example, for the class langchain.llms.openai.OpenAI, the id is ["langchain", "llms", "openai", "OpenAI"].

to_json ¶

to_json() -> (
    SerializedConstructor | SerializedNotImplemented
)

Serialize the Runnable to JSON.

Returns:

Type	Description
`SerializedConstructor \| SerializedNotImplemented`	A JSON-serializable representation of the `Runnable`.

to_json_not_implemented ¶

to_json_not_implemented() -> SerializedNotImplemented

Serialize a "not implemented" object.

Returns:

Type	Description
`SerializedNotImplemented`	SerializedNotImplemented.

configurable_fields ¶

configurable_fields(
    **kwargs: AnyConfigurableField,
) -> RunnableSerializable[Input, Output]

Configure particular Runnable fields at runtime.

Parameters:

Name	Type	Description	Default
`**kwargs`	`AnyConfigurableField`	A dictionary of `ConfigurableField` instances to configure.	`{}`

Raises:

Type	Description
`ValueError`	If a configuration key is not found in the `Runnable`.

Returns:

Type	Description
`RunnableSerializable[Input, Output]`	A new `Runnable` with the fields configured.

from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatOpenAI(max_tokens=20).configurable_fields(
    max_tokens=ConfigurableField(
        id="output_token_number",
        name="Max tokens in the output",
        description="The maximum number of tokens in the output",
    )
)

# max_tokens = 20
print("max_tokens_20: ", model.invoke("tell me something about chess").content)

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

configurable_alternatives ¶

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

Configure alternatives for Runnables that can be set at runtime.

Parameters:

Name	Type	Description	Default
`which`	`ConfigurableField`	The `ConfigurableField` instance that will be used to select the alternative.	required
`default_key`	`str`	The default key to use if no alternative is selected. Defaults to `'default'`.	`'default'`
`prefix_keys`	`bool`	Whether to prefix the keys with the `ConfigurableField` id. Defaults to False.	`False`
`**kwargs`	`Runnable[Input, Output] \| Callable[[], Runnable[Input, Output]]`	A dictionary of keys to `Runnable` instances or callables that return `Runnable` instances.	`{}`

Returns:

Type	Description
`RunnableSerializable[Input, Output]`	A new `Runnable` with the alternatives configured.

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

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

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

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

set_verbose ¶

set_verbose(verbose: bool | None) -> bool

If verbose is None, set it.

This allows users to pass in None as verbose to access the global setting.

Parameters:

Name	Type	Description	Default
`verbose`	`bool \| None`	The verbosity setting to use.	required

Returns:

Type	Description
`bool`	The verbosity setting to use.

get_token_ids ¶

get_token_ids(text: str) -> list[int]

Return the ordered ids of the tokens in a text.

Parameters:

Name	Type	Description	Default
`text`	`str`	The string input to tokenize.	required

Returns:

Type	Description
`list[int]`	A list of ids corresponding to the tokens in the text, in order they occur
`list[int]`	in the text.

get_num_tokens ¶

get_num_tokens(text: str) -> int

Get the number of tokens present in the text.

Useful for checking if an input fits in a model's context window.

Parameters:

Name	Type	Description	Default
`text`	`str`	The string input to tokenize.	required

Returns:

Type	Description
`int`	The integer number of tokens in the text.

get_num_tokens_from_messages ¶

get_num_tokens_from_messages(
    messages: list[BaseMessage],
    tools: Sequence | None = None,
) -> int

Get the number of tokens in the messages.

Useful for checking if an input fits in a model's context window.

Note

The base implementation of get_num_tokens_from_messages ignores tool schemas.

Parameters:

Name	Type	Description	Default
`messages`	`list[BaseMessage]`	The message inputs to tokenize.	required
`tools`	`Sequence \| None`	If provided, sequence of dict, `BaseModel`, function, or `BaseTools` to be converted to tool schemas.	`None`

Returns:

Type	Description
`int`	The sum of the number of tokens across the messages.

generate ¶

generate(
    messages: list[list[BaseMessage]],
    stop: list[str] | None = None,
    callbacks: Callbacks = None,
    *,
    tags: list[str] | None = None,
    metadata: dict[str, Any] | None = None,
    run_name: str | None = None,
    run_id: UUID | None = None,
    **kwargs: Any
) -> LLMResult

Pass a sequence of prompts to the model and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:

Take advantage of batched calls,
Need more output from the model than just the top generated value,
Are building chains that are agnostic to the underlying language model type (e.g., pure text completion models vs chat models).

Parameters:

Name	Type	Description	Default
`messages`	`list[list[BaseMessage]]`	List of list of messages.	required
`stop`	`list[str] \| None`	Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.	`None`
`callbacks`	`Callbacks`	Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.	`None`
`tags`	`list[str] \| None`	The tags to apply.	`None`
`metadata`	`dict[str, Any] \| None`	The metadata to apply.	`None`
`run_name`	`str \| None`	The name of the run.	`None`
`run_id`	`UUID \| None`	The ID of the run.	`None`
`**kwargs`	`Any`	Arbitrary additional keyword arguments. These are usually passed to the model provider API call.	`{}`

Returns:

Type	Description
`LLMResult`	An LLMResult, which contains a list of candidate Generations for each input
`LLMResult`	prompt and additional model provider-specific output.

agenerate `async` ¶

agenerate(
    messages: list[list[BaseMessage]],
    stop: list[str] | None = None,
    callbacks: Callbacks = None,
    *,
    tags: list[str] | None = None,
    metadata: dict[str, Any] | None = None,
    run_name: str | None = None,
    run_id: UUID | None = None,
    **kwargs: Any
) -> LLMResult

Asynchronously pass a sequence of prompts to a model and return generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:

Take advantage of batched calls,
Need more output from the model than just the top generated value,
Are building chains that are agnostic to the underlying language model type (e.g., pure text completion models vs chat models).

Parameters:

Name	Type	Description	Default
`messages`	`list[list[BaseMessage]]`	List of list of messages.	required
`stop`	`list[str] \| None`	Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.	`None`
`callbacks`	`Callbacks`	Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.	`None`
`tags`	`list[str] \| None`	The tags to apply.	`None`
`metadata`	`dict[str, Any] \| None`	The metadata to apply.	`None`
`run_name`	`str \| None`	The name of the run.	`None`
`run_id`	`UUID \| None`	The ID of the run.	`None`
`**kwargs`	`Any`	Arbitrary additional keyword arguments. These are usually passed to the model provider API call.	`{}`

Returns:

Type	Description
`LLMResult`	An LLMResult, which contains a list of candidate Generations for each input
`LLMResult`	prompt and additional model provider-specific output.

dict ¶

dict(**kwargs: Any) -> dict

Return a dictionary of the LLM.

build_extra `classmethod` ¶

build_extra(values: dict[str, Any]) -> Any

Build extra kwargs from additional params that were passed in.

validate_environment ¶

validate_environment() -> Self

Validate that api key and python package exists in environment.

is_lc_serializable `classmethod` ¶

is_lc_serializable() -> bool

Return whether this model can be serialized by LangChain.

bind_tools ¶

bind_tools(
    tools: Sequence[
        Union[
            dict[str, Any],
            type[BaseModel],
            Callable,
            BaseTool,
        ]
    ],
    *,
    tool_choice: Optional[
        Union[
            dict, str, Literal["auto", "any", "none"], bool
        ]
    ] = None,
    **kwargs: Any
) -> Runnable[LanguageModelInput, AIMessage]

Bind tool-like objects to this chat model.

Parameters:

Name	Type	Description	Default
`tools`	`Sequence[Union[dict[str, Any], type[BaseModel], Callable, BaseTool]]`	A list of tool definitions to bind to this chat model. Supports any tool definition handled by `langchain_core.utils.function_calling.convert_to_openai_tool`.	required
`tool_choice`	`Optional[Union[dict, str, Literal['auto', 'any', 'none'], bool]]`	Which tool to require the model to call. Must be the name of the single provided function, "auto" to automatically determine which function to call with the option to not call any function, "any" to enforce that some function is called, or a dict of the form: `{"type": "function", "function": {"name": <<tool_name>>}}`.	`None`
`**kwargs`	`Any`	Any additional parameters to pass to the `langchain.runnable.Runnable` constructor.	`{}`

with_structured_output ¶

with_structured_output(
    schema: Optional[Union[dict, type[BaseModel]]] = None,
    *,
    method: Literal[
        "function_calling", "json_mode", "json_schema"
    ] = "function_calling",
    include_raw: bool = False,
    **kwargs: Any
) -> Runnable[LanguageModelInput, dict | BaseModel]

Model wrapper that returns outputs formatted to match the given schema.

Parameters:

Name	Type	Description	Default
`schema`	`Optional[Union[dict, type[BaseModel]]]`	The output schema. Can be passed in as: an OpenAI function/tool schema, a JSON Schema, a TypedDict class (supported added in 0.1.9), or a Pydantic class. If `schema` is a Pydantic class then the model output will be a Pydantic instance of that class, and the model-generated fields will be validated by the Pydantic class. Otherwise the model output will be a dict and will not be validated. See `langchain_core.utils.function_calling.convert_to_openai_tool` for more on how to properly specify types and descriptions of schema fields when specifying a Pydantic or TypedDict class. Behavior changed in 0.1.9 Added support for TypedDict class. Behavior changed in 0.3.8 Added support for Groq's dedicated structured output feature via `method="json_schema"`.	`None`
`method`	`Literal['function_calling', 'json_mode', 'json_schema']`	The method for steering model generation, one of: `'function_calling'`: Uses Groq's tool-calling `API <https://console.groq.com/docs/tool-use>`__ `'json_schema'`: Uses Groq's `Structured Output API <https://console.groq.com/docs/structured-outputs>`. Supported for a subset of models, including `openai/gpt-oss`, `moonshotai/kimi-k2-instruct-0905`, and some `meta-llama/llama-4` models. See `docs <https://console.groq.com/docs/structured-outputs>` for details. `'json_mode'`: Uses Groq's `JSON mode <https://console.groq.com/docs/structured-outputs#json-object-mode>`__. Note that if using JSON mode then you must include instructions for formatting the output into the desired schema into the model call Learn more about the differences between the methods and which models support which methods `here <https://console.groq.com/docs/structured-outputs>`__.	`'function_calling'`
`method`	`Literal['function_calling', 'json_mode', 'json_schema']`	The method for steering model generation, either `'function_calling'` or `'json_mode'`. If `'function_calling'` then the schema will be converted to an OpenAI function and the returned model will make use of the function-calling API. If `'json_mode'` then JSON mode will be used. Note If using `'json_mode'` then you must include instructions for formatting the output into the desired schema into the model call. (either via the prompt itself or in the system message/prompt/instructions). Warning `'json_mode'` does not support streaming responses stop sequences.	`'function_calling'`
`include_raw`	`bool`	If False then only the parsed structured output is returned. If an error occurs during model output parsing it will be raised. If True then both the raw model response (a BaseMessage) and the parsed model response will be returned. If an error occurs during output parsing it will be caught and returned as well. The final output is always a dict with keys `'raw'`, `'parsed'`, and `'parsing_error'`.	`False`
`kwargs`	`Any`	Any additional parameters to pass to the `langchain.runnable.Runnable` constructor.	`{}`

Returns:

Type	Description
`Runnable[LanguageModelInput, dict \| BaseModel]`	A Runnable that takes same inputs as a `langchain_core.language_models.chat.BaseChatModel`.
`Runnable[LanguageModelInput, dict \| BaseModel]`	If `include_raw` is False and `schema` is a Pydantic class, Runnable outputs
`Runnable[LanguageModelInput, dict \| BaseModel]`	an instance of `schema` (i.e., a Pydantic object).
`Runnable[LanguageModelInput, dict \| BaseModel]`	Otherwise, if `include_raw` is False then Runnable outputs a dict.
`Runnable[LanguageModelInput, dict \| BaseModel]`	If `include_raw` is True, then Runnable outputs a dict with keys:
`Runnable[LanguageModelInput, dict \| BaseModel]`	`'raw'`: BaseMessage
`Runnable[LanguageModelInput, dict \| BaseModel]`	`'parsed'`: None if there was a parsing error, otherwise the type depends on the `schema` as described above.
`Runnable[LanguageModelInput, dict \| BaseModel]`	`'parsing_error'`: Optional[BaseException]

Example: schema=Pydantic class, method="function_calling", include_raw=False:

.. code-block:: python

    from typing import Optional

    from langchain_groq import ChatGroq
    from pydantic import BaseModel, Field


    class AnswerWithJustification(BaseModel):
        '''An answer to the user question along with justification for the answer.'''

        answer: str
        # If we provide default values and/or descriptions for fields, these will be passed
        # to the model. This is an important part of improving a model's ability to
        # correctly return structured outputs.
        justification: Optional[str] = Field(
            default=None, description="A justification for the answer."
        )


    llm = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
    structured_llm = llm.with_structured_output(AnswerWithJustification)

    structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")

    # -> AnswerWithJustification(
    #     answer='They weigh the same',
    #     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
    # )

schema=Pydantic class, method="function_calling", include_raw=True:

.. code-block:: python

from langchain_groq import ChatGroq
from pydantic import BaseModel


class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    justification: str


llm = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_llm = llm.with_structured_output(
    AnswerWithJustification,
    include_raw=True,
)

structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
#     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
#     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
#     'parsing_error': None
# }

schema=TypedDict class, method="function_calling", include_raw=False:

.. code-block:: python

# IMPORTANT: If you are using Python <=3.8, you need to import Annotated
# from typing_extensions, not from typing.
from typing_extensions import Annotated, TypedDict

from langchain_groq import ChatGroq


class AnswerWithJustification(TypedDict):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    justification: Annotated[Optional[str], None, "A justification for the answer."]


llm = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)

structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
#     'answer': 'They weigh the same',
#     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }

schema=OpenAI function schema, method="function_calling", include_raw=False:

.. code-block:: python

from langchain_groq import ChatGroq

oai_schema = {
    'name': 'AnswerWithJustification',
    'description': 'An answer to the user question along with justification for the answer.',
    'parameters': {
        'type': 'object',
        'properties': {
            'answer': {'type': 'string'},
            'justification': {'description': 'A justification for the answer.', 'type': 'string'}
        },
       'required': ['answer']
   }

}

llm = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_llm = llm.with_structured_output(oai_schema)

structured_llm.invoke(
    "What weighs more a pound of bricks or a pound of feathers"
)
# -> {
#     'answer': 'They weigh the same',
#     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }

schema=Pydantic class, method="json_schema", include_raw=False:

.. code-block:: python

from typing import Optional

from langchain_groq import ChatGroq
from pydantic import BaseModel, Field


class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    # If we provide default values and/or descriptions for fields, these will be passed
    # to the model. This is an important part of improving a model's ability to
    # correctly return structured outputs.
    justification: Optional[str] = Field(
        default=None, description="A justification for the answer."
    )


llm = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_llm = llm.with_structured_output(
    AnswerWithJustification,
    method="json_schema",
)

structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")

# -> AnswerWithJustification(
#     answer='They weigh the same',
#     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )

schema=Pydantic class, method="json_mode", include_raw=True:

.. code-block::

from langchain_groq import ChatGroq
from pydantic import BaseModel

class AnswerWithJustification(BaseModel):
    answer: str
    justification: str

llm = ChatGroq(model="openai/gpt-oss-120b", temperature=0)
structured_llm = llm.with_structured_output(
    AnswerWithJustification,
    method="json_mode",
    include_raw=True
)

structured_llm.invoke(
    "Answer the following question. "
    "Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
    "What's heavier a pound of bricks or a pound of feathers?"
)
# -> {
#     'raw': AIMessage(content='{\n    "answer": "They are both the same weight.",\n    "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
#     'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
#     'parsing_error': None
# }

langchain-groq¶

ChatGroq ¶

InputType property ¶

OutputType property ¶

input_schema property ¶

output_schema property ¶

config_specs property ¶

lc_attributes property ¶

cache class-attribute instance-attribute ¶

verbose class-attribute instance-attribute ¶

callbacks class-attribute instance-attribute ¶

tags class-attribute instance-attribute ¶

metadata class-attribute instance-attribute ¶

custom_get_token_ids class-attribute instance-attribute ¶

rate_limiter class-attribute instance-attribute ¶

disable_streaming class-attribute instance-attribute ¶

output_version class-attribute instance-attribute ¶

model_name class-attribute instance-attribute ¶

temperature class-attribute instance-attribute ¶

stop class-attribute instance-attribute ¶

reasoning_format class-attribute instance-attribute ¶

reasoning_effort class-attribute instance-attribute ¶

model_kwargs class-attribute instance-attribute ¶

groq_api_key class-attribute instance-attribute ¶

groq_api_base class-attribute instance-attribute ¶

request_timeout class-attribute instance-attribute ¶

max_retries class-attribute instance-attribute ¶

streaming class-attribute instance-attribute ¶

n class-attribute instance-attribute ¶

max_tokens class-attribute instance-attribute ¶

service_tier class-attribute instance-attribute ¶

http_client class-attribute instance-attribute ¶

http_async_client class-attribute instance-attribute ¶

lc_secrets property ¶

get_name ¶

get_input_schema ¶

get_input_jsonschema ¶

get_output_schema ¶

get_output_jsonschema ¶

config_schema ¶

get_config_jsonschema ¶

get_graph ¶

get_prompts ¶

__or__ ¶

__ror__ ¶

pipe ¶

pick ¶

assign ¶

batch ¶

batch_as_completed ¶

abatch async ¶

abatch_as_completed async ¶

astream_log async ¶

astream_events async ¶

transform ¶

atransform async ¶

bind ¶

with_config ¶

with_listeners ¶

with_alisteners ¶

with_types ¶

with_retry ¶

map ¶

with_fallbacks ¶

as_tool ¶

__init__ ¶

get_lc_namespace classmethod ¶

lc_id classmethod ¶

to_json ¶

to_json_not_implemented ¶

configurable_fields ¶

configurable_alternatives ¶

set_verbose ¶

get_token_ids ¶

get_num_tokens ¶

get_num_tokens_from_messages ¶

generate ¶

agenerate async ¶

dict ¶

build_extra classmethod ¶

`langchain-groq`¶

InputType `property` ¶

OutputType `property` ¶

input_schema `property` ¶

output_schema `property` ¶

config_specs `property` ¶

lc_attributes `property` ¶

cache `class-attribute` `instance-attribute` ¶

verbose `class-attribute` `instance-attribute` ¶

callbacks `class-attribute` `instance-attribute` ¶

tags `class-attribute` `instance-attribute` ¶

metadata `class-attribute` `instance-attribute` ¶

custom_get_token_ids `class-attribute` `instance-attribute` ¶

rate_limiter `class-attribute` `instance-attribute` ¶

disable_streaming `class-attribute` `instance-attribute` ¶

output_version `class-attribute` `instance-attribute` ¶

model_name `class-attribute` `instance-attribute` ¶

temperature `class-attribute` `instance-attribute` ¶

stop `class-attribute` `instance-attribute` ¶

reasoning_format `class-attribute` `instance-attribute` ¶

reasoning_effort `class-attribute` `instance-attribute` ¶

model_kwargs `class-attribute` `instance-attribute` ¶

groq_api_key `class-attribute` `instance-attribute` ¶

groq_api_base `class-attribute` `instance-attribute` ¶

request_timeout `class-attribute` `instance-attribute` ¶

max_retries `class-attribute` `instance-attribute` ¶

streaming `class-attribute` `instance-attribute` ¶

n `class-attribute` `instance-attribute` ¶

max_tokens `class-attribute` `instance-attribute` ¶

service_tier `class-attribute` `instance-attribute` ¶

http_client `class-attribute` `instance-attribute` ¶

http_async_client `class-attribute` `instance-attribute` ¶

lc_secrets `property` ¶

or ¶

ror ¶

abatch `async` ¶

abatch_as_completed `async` ¶

astream_log `async` ¶

astream_events `async` ¶

atransform `async` ¶

init ¶

get_lc_namespace `classmethod` ¶

lc_id `classmethod` ¶

agenerate `async` ¶

build_extra `classmethod` ¶

is_lc_serializable `classmethod` ¶