langchain-core: 0.3.28

langchain-core defines the base abstractions for the LangChain ecosystem.

The interfaces for core components like chat models, LLMs, vector stores, retrievers, and more are defined here. The universal invocation protocol (Runnables) along with a syntax for combining components (LangChain Expression Language) are also defined here.

No third-party integrations are defined here. The dependencies are kept purposefully very lightweight.

agents

Classes

agents.AgentAction	Represents a request to execute an action by an agent.
agents.AgentActionMessageLog	Representation of an action to be executed by an agent.
agents.AgentFinish	Final return value of an ActionAgent.
agents.AgentStep	Result of running an AgentAction.

beta

Classes

beta.runnables.context.Context()	Context for a runnable.
beta.runnables.context.ContextGet
beta.runnables.context.ContextSet
beta.runnables.context.PrefixContext([prefix])	Context for a runnable with a prefix.

Functions

beta.runnables.context.aconfig_with_context(...)	Asynchronously patch a runnable config with context getters and setters.
beta.runnables.context.config_with_context(...)	Patch a runnable config with context getters and setters.

caches

Classes

caches.BaseCache()	Interface for a caching layer for LLMs and Chat models.
caches.InMemoryCache(*[, maxsize])	Cache that stores things in memory.

callbacks

Classes

callbacks.base.AsyncCallbackHandler()	Async callback handler for LangChain.
callbacks.base.BaseCallbackHandler()	Base callback handler for LangChain.
callbacks.base.BaseCallbackManager(handlers)	Base callback manager for LangChain.
callbacks.base.CallbackManagerMixin()	Mixin for callback manager.
callbacks.base.ChainManagerMixin()	Mixin for chain callbacks.
callbacks.base.LLMManagerMixin()	Mixin for LLM callbacks.
callbacks.base.RetrieverManagerMixin()	Mixin for Retriever callbacks.
callbacks.base.RunManagerMixin()	Mixin for run manager.
callbacks.base.ToolManagerMixin()	Mixin for tool callbacks.
callbacks.file.FileCallbackHandler(filename)	Callback Handler that writes to a file.
callbacks.manager.AsyncCallbackManager(handlers)	Async callback manager that handles callbacks from LangChain.
callbacks.manager.AsyncCallbackManagerForChainGroup(...)	Async callback manager for the chain group.
callbacks.manager.AsyncCallbackManagerForChainRun(*, ...)	Async callback manager for chain run.
callbacks.manager.AsyncCallbackManagerForLLMRun(*, ...)	Async callback manager for LLM run.
callbacks.manager.AsyncCallbackManagerForRetrieverRun(*, ...)	Async callback manager for retriever run.
callbacks.manager.AsyncCallbackManagerForToolRun(*, ...)	Async callback manager for tool run.
callbacks.manager.AsyncParentRunManager(*, ...)	Async Parent Run Manager.
callbacks.manager.AsyncRunManager(*, run_id, ...)	Async Run Manager.
callbacks.manager.BaseRunManager(*, run_id, ...)	Base class for run manager (a bound callback manager).
callbacks.manager.CallbackManager(handlers)	Callback manager for LangChain.
callbacks.manager.CallbackManagerForChainGroup(...)	Callback manager for the chain group.
callbacks.manager.CallbackManagerForChainRun(*, ...)	Callback manager for chain run.
callbacks.manager.CallbackManagerForLLMRun(*, ...)	Callback manager for LLM run.
callbacks.manager.CallbackManagerForRetrieverRun(*, ...)	Callback manager for retriever run.
callbacks.manager.CallbackManagerForToolRun(*, ...)	Callback manager for tool run.
callbacks.manager.ParentRunManager(*, ...[, ...])	Sync Parent Run Manager.
callbacks.manager.RunManager(*, run_id, ...)	Sync Run Manager.
callbacks.stdout.StdOutCallbackHandler([color])	Callback Handler that prints to std out.
callbacks.streaming_stdout.StreamingStdOutCallbackHandler()	Callback handler for streaming.

Functions

callbacks.manager.adispatch_custom_event(...)	Dispatch an adhoc event to the handlers.
callbacks.manager.ahandle_event(handlers, ...)	Async generic event handler for AsyncCallbackManager.
callbacks.manager.atrace_as_chain_group(...)	Get an async callback manager for a chain group in a context manager.
callbacks.manager.dispatch_custom_event(...)	Dispatch an adhoc event.
callbacks.manager.handle_event(handlers, ...)	Generic event handler for CallbackManager.
callbacks.manager.shielded(func)	Makes so an awaitable method is always shielded from cancellation.
callbacks.manager.trace_as_chain_group(...)	Get a callback manager for a chain group in a context manager.

chat_history

Classes

chat_history.BaseChatMessageHistory()	Abstract base class for storing chat message history.
chat_history.InMemoryChatMessageHistory	In memory implementation of chat message history.

chat_loaders

Classes

chat_loaders.BaseChatLoader()

Base class for chat loaders.

chat_sessions

Classes

chat_sessions.ChatSession

Chat Session represents a single conversation, channel, or other group of messages.

document_loaders

Classes

document_loaders.base.BaseBlobParser()	Abstract interface for blob parsers.
document_loaders.base.BaseLoader()	Interface for Document Loader.
document_loaders.blob_loaders.BlobLoader()	Abstract interface for blob loaders implementation.
document_loaders.langsmith.LangSmithLoader(*)	Load LangSmith Dataset examples as Documents.

documents

Classes

documents.base.BaseMedia	Use to represent media content.
documents.base.Blob	Blob represents raw data by either reference or value.
documents.base.Document	Class for storing a piece of text and associated metadata.
documents.compressor.BaseDocumentCompressor	Base class for document compressors.
documents.transformers.BaseDocumentTransformer()	Abstract base class for document transformation.

embeddings

Classes

embeddings.embeddings.Embeddings()	Interface for embedding models.
embeddings.fake.DeterministicFakeEmbedding	Deterministic fake embedding model for unit testing purposes.
embeddings.fake.FakeEmbeddings	Fake embedding model for unit testing purposes.

example_selectors

Classes

example_selectors.base.BaseExampleSelector()	Interface for selecting examples to include in prompts.
example_selectors.length_based.LengthBasedExampleSelector	Select examples based on length.
example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector	Select examples based on Max Marginal Relevance.
example_selectors.semantic_similarity.SemanticSimilarityExampleSelector	Select examples based on semantic similarity.

Functions

example_selectors.semantic_similarity.sorted_values(values)

Return a list of values in dict sorted by key.

exceptions

Classes

exceptions.ErrorCode(value[, names, module, ...])
exceptions.LangChainException	General LangChain exception.
exceptions.OutputParserException(error[, ...])	Exception that output parsers should raise to signify a parsing error.
exceptions.TracerException	Base class for exceptions in tracers module.

Functions

exceptions.create_message(*, message, error_code)

globals

Functions

globals.get_debug()	Get the value of the debug global setting.
globals.get_llm_cache()	Get the value of the llm_cache global setting.
globals.get_verbose()	Get the value of the verbose global setting.
globals.set_debug(value)	Set a new value for the debug global setting.
globals.set_llm_cache(value)	Set a new LLM cache, overwriting the previous value, if any.
globals.set_verbose(value)	Set a new value for the verbose global setting.

indexing

Classes

indexing.api.IndexingException	Raised when an indexing operation fails.
indexing.api.IndexingResult	Return a detailed a breakdown of the result of the indexing operation.
indexing.base.DeleteResponse	A generic response for delete operation.
indexing.base.DocumentIndex
indexing.base.InMemoryRecordManager(namespace)	An in-memory record manager for testing purposes.
indexing.base.RecordManager(namespace)	Abstract base class representing the interface for a record manager.
indexing.base.UpsertResponse	A generic response for upsert operations.
indexing.in_memory.InMemoryDocumentIndex

Functions

indexing.api.aindex(docs_source, ...[, ...])	Async index data from the loader into the vector store.
indexing.api.index(docs_source, ...[, ...])	Index data from the loader into the vector store.

language_models

Classes

language_models.base.BaseLanguageModel	Abstract base class for interfacing with language models.
language_models.base.BaseLanguageModel[BaseMessage]	Abstract base class for interfacing with language models.
language_models.base.BaseLanguageModel[str]	Abstract base class for interfacing with language models.
language_models.base.LangSmithParams	LangSmith parameters for tracing.
language_models.chat_models.BaseChatModel	Base class for chat models.
language_models.chat_models.SimpleChatModel	Simplified implementation for a chat model to inherit from.
language_models.fake.FakeListLLM	Fake LLM for testing purposes.
language_models.fake.FakeListLLMError	Fake error for testing purposes.
language_models.fake.FakeStreamingListLLM	Fake streaming list LLM for testing purposes.
language_models.fake_chat_models.FakeChatModel	Fake Chat Model wrapper for testing purposes.
language_models.fake_chat_models.FakeListChatModel	Fake ChatModel for testing purposes.
language_models.fake_chat_models.FakeListChatModelError
language_models.fake_chat_models.FakeMessagesListChatModel	Fake ChatModel for testing purposes.
language_models.fake_chat_models.GenericFakeChatModel	Generic fake chat model that can be used to test the chat model interface.
language_models.fake_chat_models.ParrotFakeChatModel	Generic fake chat model that can be used to test the chat model interface.
language_models.llms.BaseLLM	Base LLM abstract interface.
language_models.llms.LLM	Simple interface for implementing a custom LLM.

Functions

language_models.chat_models.agenerate_from_stream(stream)	Async generate from a stream.
language_models.chat_models.generate_from_stream(stream)	Generate from a stream.
language_models.llms.aget_prompts(params, ...)	Get prompts that are already cached.
language_models.llms.aupdate_cache(cache, ...)	Update the cache and get the LLM output.
language_models.llms.create_base_retry_decorator(...)	Create a retry decorator for a given LLM and provided
language_models.llms.get_prompts(params, prompts)	Get prompts that are already cached.
language_models.llms.update_cache(cache, ...)	Update the cache and get the LLM output.

load

Classes

load.load.Reviver([secrets_map, ...])	Reviver for JSON objects.
load.serializable.BaseSerialized	Base class for serialized objects.
load.serializable.Serializable	Serializable base class.
load.serializable.SerializedConstructor	Serialized constructor.
load.serializable.SerializedNotImplemented	Serialized not implemented.
load.serializable.SerializedSecret	Serialized secret.

Functions

load.dump.default(obj)	Return a default value for a Serializable object or a SerializedNotImplemented object.
load.dump.dumpd(obj)	Return a dict representation of an object.
load.dump.dumps(obj, *[, pretty])	Return a json string representation of an object.
load.load.load(obj, *[, secrets_map, ...])
load.load.loads(text, *[, secrets_map, ...])
load.serializable.to_json_not_implemented(obj)	Serialize a "not implemented" object.
load.serializable.try_neq_default(value, ...)	Try to determine if a value is different from the default.

messages

Classes

messages.ai.AIMessage	Message from an AI.
messages.ai.AIMessageChunk	Message chunk from an AI.
messages.ai.InputTokenDetails	Breakdown of input token counts.
messages.ai.OutputTokenDetails	Breakdown of output token counts.
messages.ai.UsageMetadata	Usage metadata for a message, such as token counts.
messages.base.BaseMessage	Base abstract message class.
messages.base.BaseMessageChunk	Message chunk, which can be concatenated with other Message chunks.
messages.chat.ChatMessage	Message that can be assigned an arbitrary speaker (i.e. role).
messages.chat.ChatMessageChunk	Chat Message chunk.
messages.function.FunctionMessage	Message for passing the result of executing a tool back to a model.
messages.function.FunctionMessageChunk	Function Message chunk.
messages.human.HumanMessage	Message from a human.
messages.human.HumanMessageChunk	Human Message chunk.
messages.modifier.RemoveMessage	Message responsible for deleting other messages.
messages.system.SystemMessage	Message for priming AI behavior.
messages.system.SystemMessageChunk	System Message chunk.
messages.tool.InvalidToolCall	Allowance for errors made by LLM.
messages.tool.ToolCall	Represents a request to call a tool.
messages.tool.ToolCallChunk	A chunk of a tool call (e.g., as part of a stream).
messages.tool.ToolMessage	Message for passing the result of executing a tool back to a model.
messages.tool.ToolMessageChunk	Tool Message chunk.
messages.tool.ToolOutputMixin()	Mixin for objects that tools can return directly.

Functions

messages.ai.add_ai_message_chunks(left, *others)	Add multiple AIMessageChunks together.
messages.ai.add_usage(left, right)	Recursively add two UsageMetadata objects.
messages.ai.subtract_usage(left, right)	Recursively subtract two UsageMetadata objects.
messages.base.get_msg_title_repr(title, *[, ...])	Get a title representation for a message.
messages.base.merge_content(first_content, ...)	Merge two message contents.
messages.base.message_to_dict(message)	Convert a Message to a dictionary.
messages.base.messages_to_dict(messages)	Convert a sequence of Messages to a list of dictionaries.
messages.tool.default_tool_chunk_parser(...)	Best-effort parsing of tool chunks.
messages.tool.default_tool_parser(raw_tool_calls)	Best-effort parsing of tools.
messages.tool.invalid_tool_call(*[, name, ...])
messages.tool.tool_call(*, name, args, id)
messages.tool.tool_call_chunk(*[, name, ...])
messages.utils.convert_to_messages(messages)	Convert a sequence of messages to a list of messages.
messages.utils.convert_to_openai_messages(...)	Convert LangChain messages into OpenAI message dicts.
messages.utils.filter_messages([messages])	Filter messages based on name, type or id.
messages.utils.get_buffer_string(messages[, ...])	Convert a sequence of Messages to strings and concatenate them into one string.
messages.utils.merge_message_runs([messages])	Merge consecutive Messages of the same type.
messages.utils.message_chunk_to_message(chunk)	Convert a message chunk to a message.
messages.utils.messages_from_dict(messages)	Convert a sequence of messages from dicts to Message objects.
messages.utils.trim_messages([messages])	Trim messages to be below a token count.

output_parsers

Classes

output_parsers.base.BaseGenerationOutputParser	Base class to parse the output of an LLM call.
output_parsers.base.BaseGenerationOutputParser[Any]	Base class to parse the output of an LLM call.
output_parsers.base.BaseLLMOutputParser()	Abstract base class for parsing the outputs of a model.
output_parsers.base.BaseOutputParser	Base class to parse the output of an LLM call.
output_parsers.base.BaseOutputParser[Enum]	Base class to parse the output of an LLM call.
output_parsers.base.BaseOutputParser[List[str]]	Base class to parse the output of an LLM call.
output_parsers.base.BaseOutputParser[StructuredQuery]	Base class to parse the output of an LLM call.
output_parsers.base.BaseOutputParser[bool]	Base class to parse the output of an LLM call.
output_parsers.base.BaseOutputParser[datetime]	Base class to parse the output of an LLM call.
output_parsers.base.BaseOutputParser[dict]	Base class to parse the output of an LLM call.
output_parsers.base.BaseOutputParser[str]	Base class to parse the output of an LLM call.
output_parsers.base.BaseOutputParser[~T]	Base class to parse the output of an LLM call.
output_parsers.base.BaseOutputParser[~T]_
output_parsers.base.BaseOutputParser[~T]__
output_parsers.json.JsonOutputParser	Parse the output of an LLM call to a JSON object.
output_parsers.json.SimpleJsonOutputParser	alias of JsonOutputParser
output_parsers.list.CommaSeparatedListOutputParser	Parse the output of an LLM call to a comma-separated list.
output_parsers.list.ListOutputParser	Parse the output of an LLM call to a list.
output_parsers.list.MarkdownListOutputParser	Parse a Markdown list.
output_parsers.list.NumberedListOutputParser	Parse a numbered list.
output_parsers.openai_functions.JsonKeyOutputFunctionsParser	Parse an output as the element of the Json object.
output_parsers.openai_functions.JsonOutputFunctionsParser	Parse an output as the Json object.
output_parsers.openai_functions.OutputFunctionsParser	Parse an output that is one of sets of values.
output_parsers.openai_functions.PydanticAttrOutputFunctionsParser	Parse an output as an attribute of a pydantic object.
output_parsers.openai_functions.PydanticOutputFunctionsParser	Parse an output as a pydantic object.
output_parsers.openai_tools.JsonOutputKeyToolsParser	Parse tools from OpenAI response.
output_parsers.openai_tools.JsonOutputToolsParser	Parse tools from OpenAI response.
output_parsers.openai_tools.PydanticToolsParser	Parse tools from OpenAI response.
output_parsers.pydantic.PydanticOutputParser	Parse an output using a pydantic model.
output_parsers.string.StrOutputParser	OutputParser that parses LLMResult into the top likely string.
output_parsers.transform.BaseCumulativeTransformOutputParser	Base class for an output parser that can handle streaming input.
output_parsers.transform.BaseCumulativeTransformOutputParser[Any]	Base class for an output parser that can handle streaming input.
output_parsers.transform.BaseTransformOutputParser	Base class for an output parser that can handle streaming input.
output_parsers.transform.BaseTransformOutputParser[list[str]]	Base class for an output parser that can handle streaming input.
output_parsers.transform.BaseTransformOutputParser[str]	Base class for an output parser that can handle streaming input.
output_parsers.xml.XMLOutputParser	Parse an output using xml format.

Functions

output_parsers.list.droplastn(iter, n)	Drop the last n elements of an iterator.
output_parsers.openai_tools.make_invalid_tool_call(...)	Create an InvalidToolCall from a raw tool call.
output_parsers.openai_tools.parse_tool_call(...)	Parse a single tool call.
output_parsers.openai_tools.parse_tool_calls(...)	Parse a list of tool calls.
output_parsers.xml.nested_element(path, elem)	Get nested element from path.

outputs

Classes

outputs.chat_generation.ChatGeneration	A single chat generation output.
outputs.chat_generation.ChatGenerationChunk	ChatGeneration chunk, which can be concatenated with other ChatGeneration chunks.
outputs.chat_result.ChatResult	Use to represent the result of a chat model call with a single prompt.
outputs.generation.Generation	A single text generation output.
outputs.generation.GenerationChunk	Generation chunk, which can be concatenated with other Generation chunks.
outputs.llm_result.LLMResult	A container for results of an LLM call.
outputs.run_info.RunInfo	Class that contains metadata for a single execution of a Chain or model.

prompt_values

Classes

prompt_values.ChatPromptValue	Chat prompt value.
prompt_values.ChatPromptValueConcrete	Chat prompt value which explicitly lists out the message types it accepts.
prompt_values.ImagePromptValue	Image prompt value.
prompt_values.ImageURL	Image URL.
prompt_values.PromptValue	Base abstract class for inputs to any language model.
prompt_values.StringPromptValue	String prompt value.

prompts

Classes

prompts.base.BasePromptTemplate	Base class for all prompt templates, returning a prompt.
prompts.base.BasePromptTemplate[ImageURL]	Base class for all prompt templates, returning a prompt.
prompts.chat.AIMessagePromptTemplate	AI message prompt template.
prompts.chat.BaseChatPromptTemplate	Base class for chat prompt templates.
prompts.chat.BaseMessagePromptTemplate	Base class for message prompt templates.
prompts.chat.BaseStringMessagePromptTemplate	Base class for message prompt templates that use a string prompt template.
prompts.chat.ChatMessagePromptTemplate	Chat message prompt template.
prompts.chat.ChatPromptTemplate	Prompt template for chat models.
prompts.chat.HumanMessagePromptTemplate	Human message prompt template.
prompts.chat.MessagesPlaceholder	Prompt template that assumes variable is already list of messages.
prompts.chat.SystemMessagePromptTemplate	System message prompt template.
prompts.few_shot.FewShotChatMessagePromptTemplate	Chat prompt template that supports few-shot examples.
prompts.few_shot.FewShotPromptTemplate	Prompt template that contains few shot examples.
prompts.few_shot_with_templates.FewShotPromptWithTemplates	Prompt template that contains few shot examples.
prompts.image.ImagePromptTemplate	Image prompt template for a multimodal model.
prompts.prompt.PromptTemplate	Prompt template for a language model.
prompts.string.StringPromptTemplate	String prompt that exposes the format method, returning a prompt.
prompts.structured.StructuredPrompt

Functions

prompts.base.aformat_document(doc, prompt)	Async format a document into a string based on a prompt template.
prompts.base.format_document(doc, prompt)	Format a document into a string based on a prompt template.
prompts.loading.load_prompt(path[, encoding])	Unified method for loading a prompt from LangChainHub or local fs.
prompts.loading.load_prompt_from_config(config)	Load prompt from Config Dict.
prompts.string.check_valid_template(...)	Check that template string is valid.
prompts.string.get_template_variables(...)	Get the variables from the template.
prompts.string.jinja2_formatter(template, /, ...)	Format a template using jinja2.
prompts.string.mustache_formatter(template, ...)	Format a template using mustache.
prompts.string.mustache_schema(template)	Get the variables from a mustache template.
prompts.string.mustache_template_vars(template)	Get the variables from a mustache template.
prompts.string.validate_jinja2(template, ...)	Validate that the input variables are valid for the template.

Deprecated classes

prompts.pipeline.PipelinePromptTemplate

rate_limiters

Classes

rate_limiters.BaseRateLimiter(*args, **kwargs)
rate_limiters.InMemoryRateLimiter(*[, ...])

retrievers

Classes

retrievers.BaseRetriever	Abstract base class for a Document retrieval system.
retrievers.LangSmithRetrieverParams	LangSmith parameters for tracing.

runnables

Classes

runnables.base.Runnable()	A unit of work that can be invoked, batched, streamed, transformed and composed.
runnables.base.RunnableBinding	Wrap a Runnable with additional functionality.
runnables.base.RunnableBindingBase	Runnable that delegates calls to another Runnable with a set of kwargs.
runnables.base.RunnableEach	Runnable that delegates calls to another Runnable with each element of the input sequence.
runnables.base.RunnableEachBase	Runnable that delegates calls to another Runnable with each element of the input sequence.
runnables.base.RunnableGenerator(transform)	Runnable that runs a generator function.
runnables.base.RunnableLambda(func[, afunc, ...])	RunnableLambda converts a python callable into a Runnable.
runnables.base.RunnableMap	alias of RunnableParallel
runnables.base.RunnableParallel	Runnable that runs a mapping of Runnables in parallel, and returns a mapping of their outputs.
runnables.base.RunnableSequence	Sequence of Runnables, where the output of each is the input of the next.
runnables.base.RunnableSerializable	Runnable that can be serialized to JSON.
runnables.branch.RunnableBranch	Runnable that selects which branch to run based on a condition.
runnables.config.ContextThreadPoolExecutor([...])	ThreadPoolExecutor that copies the context to the child thread.
runnables.config.EmptyDict	Empty dict type.
runnables.config.RunnableConfig	Configuration for a Runnable.
runnables.configurable.DynamicRunnable	Serializable Runnable that can be dynamically configured.
runnables.configurable.RunnableConfigurableAlternatives	Runnable that can be dynamically configured.
runnables.configurable.RunnableConfigurableFields	Runnable that can be dynamically configured.
runnables.configurable.StrEnum(value[, ...])	String enum.
runnables.fallbacks.RunnableWithFallbacks	Runnable that can fallback to other Runnables if it fails.
runnables.graph.Branch(condition, ends)	Branch in a graph.
runnables.graph.CurveStyle(value[, names, ...])	Enum for different curve styles supported by Mermaid
runnables.graph.Edge(source, target[, data, ...])	Edge in a graph.
runnables.graph.Graph(nodes, ...)	Graph of nodes and edges.
runnables.graph.LabelsDict	Dictionary of labels for nodes and edges in a graph.
runnables.graph.MermaidDrawMethod(value[, ...])	Enum for different draw methods supported by Mermaid
runnables.graph.Node(id, name, data, metadata)	Node in a graph.
runnables.graph.NodeStyles([default, first, ...])	Schema for Hexadecimal color codes for different node types.
runnables.graph.Stringifiable(*args, **kwargs)
runnables.graph_ascii.AsciiCanvas(cols, lines)	Class for drawing in ASCII.
runnables.graph_ascii.VertexViewer(name)	Class to define vertex box boundaries that will be accounted for during graph building by grandalf.
runnables.graph_png.PngDrawer([fontname, labels])	Helper class to draw a state graph into a PNG file.
runnables.history.RunnableWithMessageHistory	Runnable that manages chat message history for another Runnable.
runnables.passthrough.RunnableAssign	Runnable that assigns key-value pairs to Dict[str, Any] inputs.
runnables.passthrough.RunnablePassthrough	Runnable to passthrough inputs unchanged or with additional keys.
runnables.passthrough.RunnablePick	Runnable that picks keys from Dict[str, Any] inputs.
runnables.retry.RunnableRetry	Retry a Runnable if it fails.
runnables.router.RouterInput	Router input.
runnables.router.RouterRunnable	Runnable that routes to a set of Runnables based on Input['key'].
runnables.schema.BaseStreamEvent	Streaming event.
runnables.schema.CustomStreamEvent	Custom stream event created by the user.
runnables.schema.EventData	Data associated with a streaming event.
runnables.schema.StandardStreamEvent	A standard stream event that follows LangChain convention for event data.
runnables.utils.AddableDict	Dictionary that can be added to another dictionary.
runnables.utils.ConfigurableField(id[, ...])	Field that can be configured by the user.
runnables.utils.ConfigurableFieldMultiOption(id, ...)	Field that can be configured by the user with multiple default values.
runnables.utils.ConfigurableFieldSingleOption(id, ...)	Field that can be configured by the user with a default value.
runnables.utils.ConfigurableFieldSpec(id, ...)	Field that can be configured by the user.
runnables.utils.FunctionNonLocals()	Get the nonlocal variables accessed of a function.
runnables.utils.GetLambdaSource()	Get the source code of a lambda function.
runnables.utils.IsFunctionArgDict()	Check if the first argument of a function is a dict.
runnables.utils.IsLocalDict(name, keys)	Check if a name is a local dict.
runnables.utils.NonLocals()	Get nonlocal variables accessed.
runnables.utils.SupportsAdd(*args, **kwargs)	Protocol for objects that support addition.

Functions

runnables.base.chain()	Decorate a function to make it a Runnable.
runnables.base.coerce_to_runnable(thing)	Coerce a Runnable-like object into a Runnable.
runnables.config.acall_func_with_variable_args(...)	Async call function that may optionally accept a run_manager and/or config.
runnables.config.call_func_with_variable_args(...)	Call function that may optionally accept a run_manager and/or config.
runnables.config.ensure_config([config])	Ensure that a config is a dict with all keys present.
runnables.config.get_async_callback_manager_for_config(config)	Get an async callback manager for a config.
runnables.config.get_callback_manager_for_config(config)	Get a callback manager for a config.
runnables.config.get_config_list(config, length)	Get a list of configs from a single config or a list of configs.
runnables.config.get_executor_for_config(config)	Get an executor for a config.
runnables.config.merge_configs(*configs)	Merge multiple configs into one.
runnables.config.patch_config(config, *[, ...])	Patch a config with new values.
runnables.config.run_in_executor(...)	Run a function in an executor.
runnables.configurable.make_options_spec(...)	Make a ConfigurableFieldSpec for a ConfigurableFieldSingleOption or ConfigurableFieldMultiOption.
runnables.configurable.prefix_config_spec(...)	Prefix the id of a ConfigurableFieldSpec.
runnables.graph.is_uuid(value)	Check if a string is a valid UUID.
runnables.graph.node_data_json(node, *[, ...])	Convert the data of a node to a JSON-serializable format.
runnables.graph.node_data_str(id, data)	Convert the data of a node to a string.
runnables.graph_ascii.draw_ascii(vertices, edges)	Build a DAG and draw it in ASCII.
runnables.graph_mermaid.draw_mermaid(nodes, ...)	Draws a Mermaid graph using the provided graph data.
runnables.graph_mermaid.draw_mermaid_png(...)	Draws a Mermaid graph as PNG using provided syntax.
runnables.passthrough.aidentity(x)	Async identity function.
runnables.passthrough.identity(x)	Identity function.
runnables.utils.aadd(addables)	Asynchronously add a sequence of addable objects together.
runnables.utils.accepts_config(callable)	Check if a callable accepts a config argument.
runnables.utils.accepts_context(callable)	Check if a callable accepts a context argument.
runnables.utils.accepts_run_manager(callable)	Check if a callable accepts a run_manager argument.
runnables.utils.add(addables)	Add a sequence of addable objects together.
runnables.utils.gated_coro(semaphore, coro)	Run a coroutine with a semaphore.
runnables.utils.gather_with_concurrency(n, ...)	Gather coroutines with a limit on the number of concurrent coroutines.
runnables.utils.get_function_first_arg_dict_keys(func)	Get the keys of the first argument of a function if it is a dict.
runnables.utils.get_lambda_source(func)	Get the source code of a lambda function.
runnables.utils.get_unique_config_specs(specs)	Get the unique config specs from a sequence of config specs.
runnables.utils.indent_lines_after_first(...)	Indent all lines of text after the first line.
runnables.utils.is_async_callable(func)	Check if a function is async.
runnables.utils.is_async_generator(func)	Check if a function is an async generator.

stores

Classes

stores.BaseStore()	Abstract interface for a key-value store.
stores.InMemoryBaseStore()	In-memory implementation of the BaseStore using a dictionary.
stores.InMemoryByteStore()	In-memory store for bytes.
stores.InMemoryStore()	In-memory store for any type of data.
stores.InvalidKeyException	Raised when a key is invalid; e.g., uses incorrect characters.

structured_query

Classes

structured_query.Comparator(value[, names, ...])	Enumerator of the comparison operators.
structured_query.Comparison	Comparison to a value.
structured_query.Expr	Base class for all expressions.
structured_query.FilterDirective	Filtering expression.
structured_query.Operation	Logical operation over other directives.
structured_query.Operator(value[, names, ...])	Enumerator of the operations.
structured_query.StructuredQuery	Structured query.
structured_query.Visitor()	Defines interface for IR translation using a visitor pattern.

sys_info

Functions

sys_info.print_sys_info(*[, additional_pkgs])

Print information about the environment for debugging purposes.

tools

Classes

tools.base.BaseTool	Interface LangChain tools must implement.
tools.base.BaseToolkit	Base Toolkit representing a collection of related tools.
tools.base.InjectedToolArg()	Annotation for a Tool arg that is not meant to be generated by a model.
tools.base.InjectedToolCallId()	Annotation for injecting the tool_call_id.
tools.base.SchemaAnnotationError	Raised when 'args_schema' is missing or has an incorrect type annotation.
tools.base.ToolException	Optional exception that tool throws when execution error occurs.
tools.retriever.RetrieverInput	Input to the retriever.
tools.simple.Tool	Tool that takes in function or coroutine directly.
tools.structured.StructuredTool	Tool that can operate on any number of inputs.

Functions

tools.base.create_schema_from_function(...)	Create a pydantic schema from a function's signature.
tools.base.get_all_basemodel_annotations(cls, *)
tools.convert.convert_runnable_to_tool(runnable)	Convert a Runnable into a BaseTool.
tools.convert.tool()	Make tools out of functions, can be used with or without arguments.
tools.render.render_text_description(tools)	Render the tool name and description in plain text.
tools.render.render_text_description_and_args(tools)	Render the tool name, description, and args in plain text.
tools.retriever.create_retriever_tool(...[, ...])	Create a tool to do retrieval of documents.

tracers

Classes

tracers.base.AsyncBaseTracer(*[, _schema_format])	Async Base interface for tracers.
tracers.base.BaseTracer(*[, _schema_format])	Base interface for tracers.
tracers.evaluation.EvaluatorCallbackHandler(...)	Tracer that runs a run evaluator whenever a run is persisted.
tracers.event_stream.RunInfo	Information about a run.
tracers.langchain.LangChainTracer([...])	Implementation of the SharedTracer that POSTS to the LangChain endpoint.
tracers.log_stream.LogEntry	A single entry in the run log.
tracers.log_stream.LogStreamCallbackHandler(*)	Tracer that streams run logs to a stream.
tracers.log_stream.RunLog(*ops, state)	Run log.
tracers.log_stream.RunLogPatch(*ops)	Patch to the run log.
tracers.log_stream.RunState	State of the run.
tracers.root_listeners.AsyncRootListenersTracer(*, ...)	Async Tracer that calls listeners on run start, end, and error.
tracers.root_listeners.RootListenersTracer(*, ...)	Tracer that calls listeners on run start, end, and error.
tracers.run_collector.RunCollectorCallbackHandler([...])	Tracer that collects all nested runs in a list.
tracers.stdout.ConsoleCallbackHandler(**kwargs)	Tracer that prints to the console.
tracers.stdout.FunctionCallbackHandler(...)	Tracer that calls a function with a single str parameter.

Functions

tracers.context.collect_runs()	Collect all run traces in context.
tracers.context.register_configure_hook(...)	Register a configure hook.
tracers.context.tracing_enabled([session_name])	Throw an error because this has been replaced by tracing_v2_enabled.
tracers.context.tracing_v2_enabled([...])	Instruct LangChain to log all runs in context to LangSmith.
tracers.evaluation.wait_for_all_evaluators()	Wait for all tracers to finish.
tracers.langchain.get_client()	Get the client.
tracers.langchain.log_error_once(method, ...)	Log an error once.
tracers.langchain.wait_for_all_tracers()	Wait for all tracers to finish.
tracers.langchain_v1.LangChainTracerV1(...)	Throw an error because this has been replaced by LangChainTracer.
tracers.langchain_v1.get_headers(*args, **kwargs)	Throw an error because this has been replaced by get_headers.
tracers.stdout.elapsed(run)	Get the elapsed time of a run.
tracers.stdout.try_json_stringify(obj, fallback)	Try to stringify an object to JSON.

Deprecated classes

tracers.schemas.BaseRun(*, uuid[, ...])
tracers.schemas.ChainRun(*, uuid[, ...])
tracers.schemas.LLMRun(*, uuid[, ...])
tracers.schemas.ToolRun(*, uuid[, ...])
tracers.schemas.TracerSession(*[, ...])
tracers.schemas.TracerSessionBase(*[, ...])
tracers.schemas.TracerSessionV1(*[, ...])
tracers.schemas.TracerSessionV1Base(*[, ...])
tracers.schemas.TracerSessionV1Create(*[, ...])

Deprecated functions

tracers.schemas.RunTypeEnum()

utils

Classes

utils.aiter.NoLock()	Dummy lock that provides the proper interface but no protection.
utils.aiter.Tee(iterable[, n, lock])	Create n separate asynchronous iterators over iterable.
utils.aiter.aclosing(thing)	Async context manager for safely finalizing an asynchronously cleaned-up resource such as an async generator, calling its aclose() method.
utils.aiter.atee	alias of Tee
utils.formatting.StrictFormatter()	Formatter that checks for extra keys.
utils.function_calling.FunctionDescription	Representation of a callable function to send to an LLM.
utils.function_calling.ToolDescription	Representation of a callable function to the OpenAI API.
utils.iter.NoLock()	Dummy lock that provides the proper interface but no protection.
utils.iter.Tee(iterable[, n, lock])	Create n separate asynchronous iterators over iterable
utils.iter.safetee	alias of Tee
utils.mustache.ChevronError	Custom exception for Chevron errors.

Functions

utils.aiter.abatch_iterate(size, iterable)	Utility batching function for async iterables.
utils.aiter.py_anext(iterator[, default])	Pure-Python implementation of anext() for testing purposes.
utils.aiter.tee_peer(iterator, buffer, ...)	An individual iterator of a tee().
utils.env.env_var_is_set(env_var)	Check if an environment variable is set.
utils.env.get_from_dict_or_env(data, key, ...)	Get a value from a dictionary or an environment variable.
utils.env.get_from_env(key, env_key[, default])	Get a value from a dictionary or an environment variable.
utils.function_calling.convert_to_openai_function(...)	Convert a raw function/class to an OpenAI function. :param function: A dictionary, Pydantic BaseModel class, TypedDict class, a LangChain Tool object, or a Python function. If a dictionary is passed in, it is assumed to already be a valid OpenAI function, a JSON schema with top-level 'title' key specified, an Anthropic format tool, or an Amazon Bedrock Converse format tool. :param strict: If True, model output is guaranteed to exactly match the JSON Schema provided in the function definition. If None, strict argument will not be included in function definition.
utils.function_calling.convert_to_openai_tool(tool, *)	Convert a tool-like object to an OpenAI tool schema.
utils.function_calling.tool_example_to_messages(...)
utils.html.extract_sub_links(raw_html, url, *)	Extract all links from a raw HTML string and convert into absolute paths.
utils.html.find_all_links(raw_html, *[, pattern])	Extract all links from a raw HTML string.
utils.input.get_bolded_text(text)	Get bolded text.
utils.input.get_color_mapping(items[, ...])	Get mapping for items to a support color.
utils.input.get_colored_text(text, color)	Get colored text.
utils.input.print_text(text[, color, end, file])	Print text with highlighting and no end characters.
utils.interactive_env.is_interactive_env()	Determine if running within IPython or Jupyter.
utils.iter.batch_iterate(size, iterable)	Utility batching function.
utils.iter.tee_peer(iterator, buffer, peers, ...)	An individual iterator of a tee().
utils.json.parse_and_check_json_markdown(...)	Parse a JSON string from a Markdown string and check that it contains the expected keys.
utils.json.parse_json_markdown(json_string, *)	Parse a JSON string from a Markdown string.
utils.json.parse_partial_json(s, *[, strict])	Parse a JSON string that may be missing closing braces.
utils.json_schema.dereference_refs(schema_obj, *)	Try to substitute $refs in JSON Schema.
utils.mustache.grab_literal(template, l_del)	Parse a literal from the template.
utils.mustache.l_sa_check(template, literal, ...)	Do a preliminary check to see if a tag could be a standalone.
utils.mustache.parse_tag(template, l_del, r_del)	Parse a tag from a template.
utils.mustache.r_sa_check(template, ...)	Do a final check to see if a tag could be a standalone.
utils.mustache.render([template, data, ...])	Render a mustache template.
utils.mustache.tokenize(template[, ...])	Tokenize a mustache template.
utils.pydantic.create_model(__model_name[, ...])	Create a pydantic model with the given field definitions.
utils.pydantic.create_model_v2(model_name, *)	Create a pydantic model with the given field definitions.
utils.pydantic.get_fields()	Get the field names of a Pydantic model.
utils.pydantic.get_pydantic_major_version()	Get the major version of Pydantic.
utils.pydantic.is_basemodel_instance(obj)	Check if the given class is an instance of Pydantic BaseModel.
utils.pydantic.is_basemodel_subclass(cls)	Check if the given class is a subclass of Pydantic BaseModel.
utils.pydantic.is_pydantic_v1_subclass(cls)	Check if the installed Pydantic version is 1.x-like.
utils.pydantic.is_pydantic_v2_subclass(cls)	Check if the installed Pydantic version is 1.x-like.
utils.pydantic.pre_init(func)	Decorator to run a function before model initialization.
utils.strings.comma_list(items)	Convert a list to a comma-separated string.
utils.strings.stringify_dict(data)	Stringify a dictionary.
utils.strings.stringify_value(val)	Stringify a value.
utils.utils.build_extra_kwargs(extra_kwargs, ...)	Build extra kwargs from values and extra_kwargs.
utils.utils.check_package_version(package[, ...])	Check the version of a package.
utils.utils.convert_to_secret_str(value)	Convert a string to a SecretStr if needed.
utils.utils.from_env()	Create a factory method that gets a value from an environment variable.
utils.utils.get_pydantic_field_names(...)	Get field names, including aliases, for a pydantic class.
utils.utils.guard_import(module_name, *[, ...])	Dynamically import a module and raise an exception if the module is not installed.
utils.utils.mock_now(dt_value)	Context manager for mocking out datetime.now() in unit tests.
utils.utils.raise_for_status_with_text(response)	Raise an error with the response text.
utils.utils.secret_from_env()	Secret from env.
utils.utils.xor_args(*arg_groups)	Validate specified keyword args are mutually exclusive."

Deprecated functions

utils.function_calling.convert_pydantic_to_openai_function(...)
utils.function_calling.convert_pydantic_to_openai_tool(...)
utils.function_calling.convert_python_function_to_openai_function(...)
utils.function_calling.format_tool_to_openai_function(tool)
utils.function_calling.format_tool_to_openai_tool(tool)
utils.loading.try_load_from_hub(*args, **kwargs)

vectorstores

Classes

vectorstores.base.VectorStore()	Interface for vector store.
vectorstores.base.VectorStoreRetriever	Base Retriever class for VectorStore.
vectorstores.in_memory.InMemoryVectorStore(...)	In-memory vector store implementation.

Functions

vectorstores.utils.maximal_marginal_relevance(...)

Calculate maximal marginal relevance.