Ensure that a config is a dict with all keys present.

Generate a UUID from a Unix timestamp in nanoseconds and random bits.

UUIDv7 objects feature monotonicity within a millisecond.

Base async callback handler.

Base callback manager.

Message chunk from an AI (yielded when streaming).

Base abstract message class.

Messages are the inputs and outputs of a chat model.

Examples include HumanMessage, AIMessage, and SystemMessage.

Message chunk, which can be concatenated with other Message chunks.

ChatGeneration chunk.

ChatGeneration chunks can be concatenated with other ChatGeneration chunks.

GenerationChunk, which can be concatenated with other Generation chunks.

A container for results of an LLM call.

Both chat models and LLMs generate an LLMResult object. This object contains the generated outputs and any additional information that the model provider wants to return.

Custom stream event created by the user.

Data associated with a streaming event.

A standard stream event that follows LangChain convention for event data.

Tracer that streams run logs to a stream.

Run log.

Class for storing a piece of text and associated metadata.

A single entry in the run log.

Information about a run.

This is used to keep track of the metadata associated with a run.

Async context manager to wrap an AsyncGenerator that has a aclose() method.

Code like this:

async with aclosing(<module>.fetch(<arguments>)) as agen:
    <block>

...is equivalent to this:

agen = <module>.fetch(<arguments>)
try:
    <block>
finally:
    await agen.aclose()

A unit of work that can be invoked, batched, streamed, transformed and composed.

Key Methods

• invoke/ainvoke: Transforms a single input into an output.
• batch/abatch: Efficiently transforms multiple inputs into outputs.
• stream/astream: Streams output from a single input as it's produced.
• astream_log: Streams output and selected intermediate results from an input.

Built-in optimizations:

• Batch: By default, batch runs invoke() in parallel using a thread pool executor. Override to optimize batching.

• Async: Methods with 'a' prefix are asynchronous. By default, they execute the sync counterpart using asyncio's thread pool. Override for native async.

All methods accept an optional config argument, which can be used to configure execution, add tags and metadata for tracing and debugging etc.

Runnables expose schematic information about their input, output and config via the input_schema property, the output_schema property and config_schema method.

Composition

Runnable objects can be composed together to create chains in a declarative way.

Any chain constructed this way will automatically have sync, async, batch, and streaming support.

The main composition primitives are RunnableSequence and RunnableParallel.

RunnableSequence invokes a series of runnables sequentially, with one Runnable's output serving as the next's input. Construct using the | operator or by passing a list of runnables to RunnableSequence.

RunnableParallel invokes runnables concurrently, providing the same input to each. Construct it using a dict literal within a sequence or by passing a dict to RunnableParallel.

For example,

from langchain_core.runnables import RunnableLambda

# A RunnableSequence constructed using the `|` operator
sequence = RunnableLambda(lambda x: x + 1) | RunnableLambda(lambda x: x * 2)
sequence.invoke(1)  # 4
sequence.batch([1, 2, 3])  # [4, 6, 8]

# A sequence that contains a RunnableParallel constructed using a dict literal
sequence = RunnableLambda(lambda x: x + 1) | {
    "mul_2": RunnableLambda(lambda x: x * 2),
    "mul_5": RunnableLambda(lambda x: x * 5),
}
sequence.invoke(1)  # {'mul_2': 4, 'mul_5': 10}

Standard Methods

All Runnables expose additional methods that can be used to modify their behavior (e.g., add a retry policy, add lifecycle listeners, make them configurable, etc.).

These methods will work on any Runnable, including Runnable chains constructed by composing other Runnables. See the individual methods for details.

For example,

from langchain_core.runnables import RunnableLambda

import random

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

def buggy_double(y: int) -> int:
    """Buggy code that will fail 70% of the time"""
    if random.random() > 0.3:
        print('This code failed, and will probably be retried!')  # noqa: T201
        raise ValueError('Triggered buggy code')
    return y * 2

sequence = (
    RunnableLambda(add_one) |
    RunnableLambda(buggy_double).with_retry( # Retry on failure
        stop_after_attempt=10,
        wait_exponential_jitter=False
    )
)

print(sequence.input_schema.model_json_schema()) # Show inferred input schema
print(sequence.output_schema.model_json_schema()) # Show inferred output schema
print(sequence.invoke(2)) # invoke the sequence (note the retry above!!)

Debugging and tracing

As the chains get longer, it can be useful to be able to see intermediate results to debug and trace the chain.

You can set the global debug flag to True to enable debug output for all chains:

from langchain_core.globals import set_debug

set_debug(True)

Alternatively, you can pass existing or custom callbacks to any given chain:

from langchain_core.tracers import ConsoleCallbackHandler

chain.invoke(..., config={"callbacks": [ConsoleCallbackHandler()]})

For a UI (and much more) checkout LangSmith.

Configuration for a Runnable.

# Parent sets tags
chain.invoke(input, config={"tags": ["parent"]})
# Child automatically inherits and can add:
# ensure_config({"tags": ["child"]}) -> {"tags": ["parent", "child"]}