`langchain-groq`¶

Reference docs

This page contains reference documentation for Groq. See the docs for conceptual guides, tutorials, and examples on using Groq modules.

langchain_groq ¶

Groq integration for LangChain.

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.

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

Key init args — completion params: model: Name of Groq model to use, e.g. llama-3.1-8b-instant. temperature: Sampling temperature. Ranges from 0.0 to 1.0. max_tokens: Max number of tokens to generate. reasoning_format: 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 suppresses 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: Holds any model parameters valid for create call not explicitly specified.

Key init args — client params: timeout: Timeout for requests. max_retries: Max number of retries. api_key: Groq API key. If not passed in will be read from env var GROQ_API_KEY. base_url: Base URL path for API requests, leave blank if not using a proxy or service emulator. custom_get_token_ids: Optional encoder to use for counting tokens.

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

Instantiate

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

Invoke

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

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

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

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

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

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

await model.ainvoke(messages)

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

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

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

See ChatGroq.bind_tools() method for more.

Structured output

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: int | None = Field(description="How funny the joke is, from 1 to 10")   structured_model = model.with_structured_output(Joke) structured_model.invoke("Tell me a joke about cats")

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

ai_msg = model.invoke(messages) ai_msg.response_metadata

{  "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, }

METHOD	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 `Runnable` objects.
`pick`	Pick keys from the output `dict` of this `Runnable`.
`assign`	Assigns new fields to the `dict` output of this `Runnable`.
`invoke`	Transform a single input into an output.
`ainvoke`	Transform a single input into an output.
`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.
`stream`	Default implementation of `stream`, which calls `invoke`.
`astream`	Default implementation of `astream`, which calls `ainvoke`.
`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 `Runnable` objects that can be set at runtime.
`set_verbose`	If verbose is `None`, set it.
`generate_prompt`	Pass a sequence of prompts to the model and return model generations.
`agenerate_prompt`	Asynchronously pass a sequence of prompts and return model generations.
`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.

name `class-attribute` `instance-attribute` ¶

name: str | None = None

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

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 langchain-core 1.0.0

profile `class-attribute` `instance-attribute` ¶

profile: ModelProfile | None = Field(default=None, exclude=True)

Profile detailing model capabilities.

Beta feature

This is a beta feature. The format of model profiles is subject to change.

If not specified, automatically loaded from the provider package on initialization if data is available.

Example profile data includes context window sizes, supported modalities, or support for tool calling, structured output, and other features.

Added in langchain-core 1.1.0

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: list[str] | str | None = Field(default=None, alias='stop_sequences')

Default stop sequences.

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

reasoning_format: Literal['parsed', 'raw', 'hidden'] | None = 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 suppresses reasoning content in the response; the model will still perform reasoning unless overridden in reasoning_effort.

See the Groq documentation for more details and a list of supported models.

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

reasoning_effort: str | None = 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 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: SecretStr | None = 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: str | None = 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: 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: int | None = 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 for more details and a list of service tiers and descriptions.

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

http_client: Any | None = None

Optional httpx.Client.

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

http_async_client: 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.

PARAMETER	DESCRIPTION
`suffix`	An optional suffix to append to the name. TYPE: `str \| None` DEFAULT: `None`
`name`	An optional name to use instead of the `Runnable`'s name. TYPE: `str \| None` DEFAULT: `None`

RETURNS	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.

Runnable objects 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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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 langchain-core 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.

Runnable objects 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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`config`	A config to use when generating the schema. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	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 langchain-core 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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`include`	A list of fields to include in the config schema. TYPE: `Sequence[str] \| None` DEFAULT: `None`

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

Added in langchain-core 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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	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.

PARAMETER	DESCRIPTION
`other`	Another `Runnable` or a `Runnable`-like object. TYPE: `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]`

RETURNS	DESCRIPTION
`RunnableSerializable[Other, Output]`	A new `Runnable`.

pipe ¶

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

Pipe Runnable objects.

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]

PARAMETER	DESCRIPTION
`*others`	Other `Runnable` or `Runnable`-like objects to compose TYPE: `Runnable[Any, Other] \| Callable[[Any], Other]` DEFAULT: `()`
`name`	An optional name for the resulting `RunnableSequence`. TYPE: `str \| None` DEFAULT: `None`

RETURNS	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 a single key

import json  from langchain_core.runnables import RunnableLambda, RunnableMap  as_str = RunnableLambda(str) as_json = RunnableLambda(json.loads) chain = RunnableMap(str=as_str, json=as_json)  chain.invoke("[1, 2, 3]") # -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}  json_only_chain = chain.pick("json") json_only_chain.invoke("[1, 2, 3]") # -> [1, 2, 3]

Pick a list of keys

from typing import Any  import json  from langchain_core.runnables import RunnableLambda, RunnableMap  as_str = RunnableLambda(str) as_json = RunnableLambda(json.loads)   def as_bytes(x: Any) -> bytes:  return bytes(x, "utf-8")   chain = RunnableMap(  str=as_str, json=as_json, bytes=RunnableLambda(as_bytes) )  chain.invoke("[1, 2, 3]") # -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}  json_and_bytes_chain = chain.pick(["json", "bytes"]) json_and_bytes_chain.invoke("[1, 2, 3]") # -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}

PARAMETER	DESCRIPTION
`keys`	A key or list of keys to pick from the output dict. TYPE: `str \| list[str]`

RETURNS	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_core.language_models.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}" ) model = FakeStreamingListLLM(responses=["foo-lish"])  chain: Runnable = prompt | model | {"str": StrOutputParser()}  chain_with_assign = chain.assign(hello=itemgetter("str") | model)  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'}}}

PARAMETER	DESCRIPTION
`**kwargs`	A mapping of keys to `Runnable` or `Runnable`-like objects that will be invoked with the entire output dict of this `Runnable`. TYPE: `Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any] \| Mapping[str, Runnable[dict[str, Any], Any] \| Callable[[dict[str, Any]], Any]]` DEFAULT: `{}`

RETURNS	DESCRIPTION
`RunnableSerializable[Any, Any]`	A new `Runnable`.

invoke ¶

invoke(  input: LanguageModelInput,  config: RunnableConfig | None = None,  *,  stop: list[str] | None = None,  **kwargs: Any, ) -> AIMessage

Transform a single input into an output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	A config to use when invoking the `Runnable`. The config supports standard keys like `'tags'`, `'metadata'` for tracing purposes, `'max_concurrency'` for controlling how much work to do in parallel, and other keys. Please refer to `RunnableConfig` for more details. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Output`	The output of the `Runnable`.

ainvoke `async` ¶

ainvoke(  input: LanguageModelInput,  config: RunnableConfig | None = None,  *,  stop: list[str] | None = None,  **kwargs: Any, ) -> AIMessage

Transform a single input into an output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	A config to use when invoking the `Runnable`. The config supports standard keys like `'tags'`, `'metadata'` for tracing purposes, `'max_concurrency'` for controlling how much work to do in parallel, and other keys. Please refer to `RunnableConfig` for more details. TYPE: `RunnableConfig \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`Output`	The output of the `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 must override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

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

RETURNS	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.

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

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

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

RETURNS	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.

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

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

stream ¶

stream(  input: LanguageModelInput,  config: RunnableConfig | None = None,  *,  stop: list[str] | None = None,  **kwargs: Any, ) -> Iterator[AIMessageChunk]

Default implementation of stream, which calls invoke.

Subclasses must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`Output`	The output of the `Runnable`.

astream `async` ¶

astream(  input: LanguageModelInput,  config: RunnableConfig | None = None,  *,  stop: list[str] | None = None,  **kwargs: Any, ) -> AsyncIterator[AIMessageChunk]

Default implementation of astream, which calls ainvoke.

Subclasses must override this method if they support streaming output.

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Input`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[Output]`	The output of 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.

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

YIELDS	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 StreamEvent that provide real-time information about the progress of the Runnable, including StreamEvent from intermediate results.

A StreamEvent is a dictionary with the following schema:

event: Event names are of the format: on_[runnable_type]_(start|stream|end).
name: The name of the Runnable that generated the event.
run_id: 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: 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: The tags of the Runnable that generated the event.
metadata: The metadata of the Runnable that generated the event.
data: The data associated with the event. The contents of this field depend on the type of event. See the table below for more details.

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": [],  }, ]

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)

PARAMETER	DESCRIPTION
`input`	The input to the `Runnable`. TYPE: `Any`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`version`	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'`. TYPE: `Literal['v1', 'v2']` DEFAULT: `'v2'`
`include_names`	Only include events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_types`	Only include events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`include_tags`	Only include events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_names`	Exclude events from `Runnable` objects with matching names. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_types`	Exclude events from `Runnable` objects with matching types. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`exclude_tags`	Exclude events from `Runnable` objects with matching tags. TYPE: `Sequence[str] \| None` DEFAULT: `None`
`**kwargs`	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`. TYPE: `Any` DEFAULT: `{}`

YIELDS	DESCRIPTION
`AsyncIterator[StreamEvent]`	An async stream of `StreamEvent`.

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

PARAMETER	DESCRIPTION
`input`	An iterator of inputs to the `Runnable`. TYPE: `Iterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

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

PARAMETER	DESCRIPTION
`input`	An async iterator of inputs to the `Runnable`. TYPE: `AsyncIterator[Input]`
`config`	The config to use for the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any \| None` DEFAULT: `{}`

YIELDS	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.

PARAMETER	DESCRIPTION
`**kwargs`	The arguments to bind to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`Runnable[Input, Output]`	A new `Runnable` with the arguments bound.

Example

from langchain_ollama import ChatOllama from langchain_core.output_parsers import StrOutputParser  model = ChatOllama(model="llama3.1")  # Without bind chain = model | StrOutputParser()  chain.invoke("Repeat quoted words exactly: 'One two three four five.'") # Output is 'One two three four five.'  # With bind chain = model.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.

PARAMETER	DESCRIPTION
`config`	The config to bind to the `Runnable`. TYPE: `RunnableConfig \| None` DEFAULT: `None`
`**kwargs`	Additional keyword arguments to pass to the `Runnable`. TYPE: `Any` DEFAULT: `{}`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called before the `Runnable` starts running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_end`	Called after the `Runnable` finishes running, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`
`on_error`	Called if the `Runnable` throws an error, with the `Run` object. TYPE: `Callable[[Run], None] \| Callable[[Run, RunnableConfig], None] \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`on_start`	Called asynchronously before the `Runnable` starts running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_end`	Called asynchronously after the `Runnable` finishes running, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`
`on_error`	Called asynchronously if the `Runnable` throws an error, with the `Run` object. TYPE: `AsyncListener \| None` DEFAULT: `None`

RETURNS	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.

PARAMETER	DESCRIPTION
`input_type`	The input type to bind to the `Runnable`. TYPE: `type[Input] \| None` DEFAULT: `None`
`output_type`	The output type to bind to the `Runnable`. TYPE: `type[Output] \| None` DEFAULT: `None`

RETURNS	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.

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

RETURNS	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	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.

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	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. TYPE: `str \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`RunnableWithFallbacks[Input, Output]`	A new `Runnable` that will try the original `Runnable`, and then each 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.

PARAMETER	DESCRIPTION
`args_schema`	The schema for the tool. TYPE: `type[BaseModel] \| None` DEFAULT: `None`
`name`	The name of the tool. TYPE: `str \| None` DEFAULT: `None`
`description`	The description of the tool. TYPE: `str \| None` DEFAULT: `None`
`arg_types`	A dictionary of argument names to types. TYPE: `dict[str, type] \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`BaseTool`	A `BaseTool` instance.

TypedDict 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]})

str 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")

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	DESCRIPTION
`list[str]`	The namespace.

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	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	DESCRIPTION
`SerializedNotImplemented`	`SerializedNotImplemented`.

configurable_fields ¶

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

Configure particular Runnable fields at runtime.

PARAMETER	DESCRIPTION
`**kwargs`	A dictionary of `ConfigurableField` instances to configure. TYPE: `AnyConfigurableField` DEFAULT: `{}`

RAISES	DESCRIPTION
`ValueError`	If a configuration key is not found in the `Runnable`.

RETURNS	DESCRIPTION
`RunnableSerializable[Input, Output]`	A new `Runnable` with the fields configured.

Example

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 Runnable objects that can be set at runtime.

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

RETURNS	DESCRIPTION
`RunnableSerializable[Input, Output]`	A new `Runnable` with the alternatives configured.

Example

from langchain_anthropic import ChatAnthropic from langchain_core.runnables.utils import ConfigurableField from langchain_openai import ChatOpenAI  model = ChatAnthropic(  model_name="claude-sonnet-4-5-20250929" ).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.

PARAMETER	DESCRIPTION
`verbose`	The verbosity setting to use. TYPE: `bool \| None`

RETURNS	DESCRIPTION
`bool`	The verbosity setting to use.

generate_prompt ¶

generate_prompt(  prompts: list[PromptValue],  stop: list[str] | None = None,  callbacks: Callbacks = 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).

PARAMETER	DESCRIPTION
`prompts`	List of `PromptValue` objects. A `PromptValue` is an object that can be converted to match the format of any language model (string for pure text generation models and `BaseMessage` objects for chat models). TYPE: `list[PromptValue]`
`stop`	Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. TYPE: `list[str] \| None` DEFAULT: `None`
`callbacks`	`Callbacks` to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. TYPE: `Callbacks` DEFAULT: `None`
`**kwargs`	Arbitrary additional keyword arguments. These are usually passed to the model provider API call. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`LLMResult`	An `LLMResult`, which contains a list of candidate `Generation` objects for each input prompt and additional model provider-specific output.

agenerate_prompt `async` ¶

agenerate_prompt(  prompts: list[PromptValue],  stop: list[str] | None = None,  callbacks: Callbacks = None,  **kwargs: Any, ) -> LLMResult

Asynchronously pass a sequence of prompts 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).

PARAMETER	DESCRIPTION
`prompts`	List of `PromptValue` objects. A `PromptValue` is an object that can be converted to match the format of any language model (string for pure text generation models and `BaseMessage` objects for chat models). TYPE: `list[PromptValue]`
`stop`	Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. TYPE: `list[str] \| None` DEFAULT: `None`
`callbacks`	`Callbacks` to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. TYPE: `Callbacks` DEFAULT: `None`
`**kwargs`	Arbitrary additional keyword arguments. These are usually passed to the model provider API call. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`LLMResult`	An `LLMResult`, which contains a list of candidate `Generation` objects for each input prompt and additional model provider-specific output.

get_token_ids ¶

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

Return the ordered IDs of the tokens in a text.

PARAMETER	DESCRIPTION
`text`	The string input to tokenize. TYPE: `str`

RETURNS	DESCRIPTION
`list[int]`	A list of IDs corresponding to the tokens in the text, in order they occur 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.

This should be overridden by model-specific implementations to provide accurate token counts via model-specific tokenizers.

PARAMETER	DESCRIPTION
`text`	The string input to tokenize. TYPE: `str`

RETURNS	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.

This should be overridden by model-specific implementations to provide accurate token counts via model-specific tokenizers.

Note

The base implementation of get_num_tokens_from_messages ignores tool schemas.
The base implementation of get_num_tokens_from_messages adds additional prefixes to messages in represent user roles, which will add to the overall token count. Model-specific implementations may choose to handle this differently.

PARAMETER	DESCRIPTION
`messages`	The message inputs to tokenize. TYPE: `list[BaseMessage]`
`tools`	If provided, sequence of dict, `BaseModel`, function, or `BaseTool` objects to be converted to tool schemas. TYPE: `Sequence \| None` DEFAULT: `None`

RETURNS	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).

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

RETURNS	DESCRIPTION
`LLMResult`	An `LLMResult`, which contains a list of candidate `Generations` for each input 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).

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

RETURNS	DESCRIPTION
`LLMResult`	An `LLMResult`, which contains a list of candidate `Generations` for each input 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[dict[str, Any] | type[BaseModel] | Callable | BaseTool],  *,  tool_choice: dict | str | bool | None = None,  **kwargs: Any, ) -> Runnable[LanguageModelInput, AIMessage]

Bind tool-like objects to this chat model.

PARAMETER	DESCRIPTION
`tools`	A list of tool definitions to bind to this chat model. Supports any tool definition handled by `convert_to_openai_tool`. TYPE: `Sequence[dict[str, Any] \| type[BaseModel] \| Callable \| BaseTool]`
`tool_choice`	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>>}}`. TYPE: `dict \| str \| bool \| None` DEFAULT: `None`
`**kwargs`	Any additional parameters to pass to the `langchain.runnable.Runnable` constructor. TYPE: `Any` DEFAULT: `{}`

with_structured_output ¶

with_structured_output(  schema: dict | type[BaseModel] | None = 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.

PARAMETER	DESCRIPTION
`schema`	The output schema. Can be passed in as: An OpenAI function/tool schema, A JSON Schema, A `TypedDict` class, 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 `langchain-groq` 0.3.8 Added support for Groq's dedicated structured output feature via `method="json_schema"`. TYPE: `dict \| type[BaseModel] \| None` DEFAULT: `None`
`method`	The method for steering model generation, one of: `'function_calling'`: Uses Groq's tool-calling API `'json_schema'`: Uses Groq's Structured Output API. Supported for a subset of models, including `openai/gpt-oss`, `moonshotai/kimi-k2-instruct-0905`, and some `meta-llama/llama-4` models. See docs for details. `'json_mode'`: Uses Groq's JSON 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. TYPE: `Literal['function_calling', 'json_mode', 'json_schema']` DEFAULT: `'function_calling'`
`method`	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. TYPE: `Literal['function_calling', 'json_mode', 'json_schema']` DEFAULT: `'function_calling'`
`include_raw`	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'`. TYPE: `bool` DEFAULT: `False`
`kwargs`	Any additional parameters to pass to the `langchain.runnable.Runnable` constructor. TYPE: `Any` DEFAULT: `{}`

RETURNS DESCRIPTION

Runnable[LanguageModelInput, dict | BaseModel]

A Runnable that takes same inputs as a langchain_core.language_models.chat.BaseChatModel. If include_raw is False and schema is a Pydantic class, Runnable outputs an instance of schema (i.e., a Pydantic object). Otherwise, if include_raw is False then Runnable outputs a dict.

If include_raw is True, then Runnable outputs a dict with keys:

'raw': BaseMessage
'parsed': None if there was a parsing error, otherwise the type depends on the schema as described above.
'parsing_error': BaseException | None

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

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: str | None = Field(default=None, description="A justification for the answer.")   model = ChatGroq(model="openai/gpt-oss-120b", temperature=0) structured_model = model.with_structured_output(AnswerWithJustification)  structured_model.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.' # )

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

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

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

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[str | None, None, "A justification for the answer."]   model = ChatGroq(model="openai/gpt-oss-120b", temperature=0) structured_model = model.with_structured_output(AnswerWithJustification)  structured_model.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.' # }

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

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']  }   model = ChatGroq(model="openai/gpt-oss-120b", temperature=0)  structured_model = model.with_structured_output(oai_schema)   structured_model.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.'  # }

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

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: str | None = Field(default=None, description="A justification for the answer.")   model = ChatGroq(model="openai/gpt-oss-120b", temperature=0) structured_model = model.with_structured_output(  AnswerWithJustification,  method="json_schema", )  structured_model.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.' # )

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

from langchain_groq import ChatGroq from pydantic import BaseModel   class AnswerWithJustification(BaseModel):  answer: str  justification: str   model = ChatGroq(model="openai/gpt-oss-120b", temperature=0) structured_model = model.with_structured_output(  AnswerWithJustification, method="json_mode", include_raw=True )  structured_model.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¶

langchain_groq ¶

ChatGroq ¶

name class-attribute instance-attribute ¶

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 ¶

profile 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 ¶

invoke ¶

ainvoke async ¶

batch ¶

batch_as_completed ¶

abatch async ¶

abatch_as_completed async ¶

stream ¶

astream 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 ¶

`langchain-groq`¶

name `class-attribute` `instance-attribute` ¶

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` ¶

profile `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 ¶

ainvoke `async` ¶

abatch `async` ¶

abatch_as_completed `async` ¶

astream `async` ¶

astream_log `async` ¶

astream_events `async` ¶

atransform `async` ¶

init ¶

get_lc_namespace `classmethod` ¶

lc_id `classmethod` ¶

agenerate_prompt `async` ¶

agenerate `async` ¶

build_extra `classmethod` ¶

is_lc_serializable `classmethod` ¶