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uv add langchain-classicLegacy chains, langchain-community re-exports, indexing API, deprecated functionality, and more.
In most cases, you should be using the main langchain package.
For full documentation, see the API reference. For conceptual guides, tutorials, and examples on using LangChain, see the LangChain Docs.
See our Releases and Versioning policies.
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.
A utility to experiment with and compare the performance of different models.
Interface for caching results from embedding models.
Output parser for a list of lines.
Given a query, use an LLM to write a set of queries.
Time Weighted Vector Store Retriever.
Given a query, use an LLM to re-phrase it.
Retrieve small chunks then retrieve their parent documents.
Retriever that wraps a base retriever and compresses the results.
Retriever that ensembles the multiple retrievers.
Retriever that merges the results of multiple retrievers.
Enumerator of the types of search to perform.
Retriever that supports multiple embeddings per parent document.
Self Query Retriever.
Document compressor that uses Zero-Shot Listwise Document Reranking.
Filter that drops documents that aren't relevant to the query.
Parse outputs that could return a null string of some sort.
LLM Chain Extractor.
Document compressor that uses CrossEncoder for reranking.
Embeddings Filter.
Document compressor that uses a pipeline of Transformers.
BaseStore interface that works on the local file system.
Wraps a store with key and value encoders/decoders.
Parse an output using Pandas DataFrame format.
Parse the output of an LLM call to a datetime.
Parse the output of an LLM call using a regex.
Combine multiple output parsers into one.
Schema for a response from a structured output parser.
Parse the output of an LLM call to a structured output.
Retry chain input for RetryOutputParser.
Retry chain input for RetryWithErrorOutputParser.
Wrap a parser and try to fix parsing errors.
Wrap a parser and try to fix parsing errors.
Input for the retry chain of the OutputFixingParser.
Wrap a parser and try to fix parsing errors.
Parse the output of an LLM call to a boolean.
Parse the output of an LLM call into a Dictionary using a regex.
Parse an output that is one of a set of values.
Parse YAML output using a Pydantic model.
Configuration for a given run evaluator.
Configuration for a run evaluator that only requires a single key.
Configuration for a run evaluation.
Configuration for a reference-free criteria evaluator.
Configuration for a labeled (with references) criteria evaluator.
Configuration for an embedding distance evaluator.
Configuration for a string distance evaluator.
Configuration for a QA evaluator.
Configuration for a context-based QA evaluator.
Configuration for a context-based QA evaluator.
Configuration for a json validity evaluator.
Configuration for a json equality evaluator.
Configuration for an exact match string evaluator.
Configuration for a regex match string evaluator.
Configuration for a score string evaluator.
Configuration for a labeled score string evaluator.
A simple progress bar for the console.
Raised when the input format is invalid.
A dictionary of the results of a single test run.
Your architecture raised an error.
Input for a chat model.
Extract items to evaluate from the run object.
Extract items to evaluate from the run object.
Extract items to evaluate from the run object from a chain.
Map an input to the tool.
Map an example, or row in the dataset, to the inputs of an evaluation.
Evaluate Run and optional examples.
Conversation chat memory with token limit and vectordb backing.
Simple Memory.
Memory wrapper that is read-only and cannot be changed.
Combining multiple memories' data together.
Wrapper around a VectorStore for easy access.
Logic for creating indexes.
Table used to keep track of when a key was last updated.
A SQL Alchemy based implementation of the record manager.
Tool that is run when invalid tool name is encountered by agent.
Base Single Action Agent class.
Base Multi Action Agent class.
Base class for parsing agent output into agent action/finish.
Base class for parsing agent output into agent actions/finish.
Agent powered by Runnables.
Agent powered by Runnables.
Tool that just returns the query.
Agent that is using tools.
Chat prompt template for the agent scratchpad.
Iterator for AgentExecutor.
Output parser for the structured chat agent.
Output parser with retries for the structured chat agent.
Output parser for the ReAct agent.
Output parser for the conversational agent.
Output parser for the chat agent.
MRKL Output parser for the chat agent.
Configuration for a chain to use in MRKL system.
Memory used to save agent output AND intermediate steps.
Parses tool invocations and final answers from XML-formatted agent output.
Parses self-ask style LLM calls.
Tool agent action.
Parses a message into agent actions/finish.
Parses a message into agent actions/finish.
Parses tool invocations and final answers in JSON format.
Parses ReAct-style LLM calls that have a single tool input in json format.
Parses ReAct-style LLM calls that have a single tool input.
Parses a message into agent action/finish.
Information about a VectorStore.
Toolkit for interacting with a VectorStore.
Toolkit for routing between Vector Stores.
Output parser for the conversational agent.
AgentFinish with run and thread metadata.
AgentAction with info needed to submit custom tool output to existing run.
Run an OpenAI Assistant.
Callback handler that returns an async iterator.
Callback handler for streaming in agents.
Callback handler that returns an async iterator.
Tracer that logs via the input Logger.
The types of the evaluators.
A base class for evaluators that use an LLM.
String evaluator interface.
Compare the output of two models (or two outputs of the same model).
Interface for evaluating agent trajectories.
A parser for the output of the PairwiseStringEvalChain.
Pairwise String Evaluation Chain.
Labeled Pairwise String Evaluation Chain.
A Criteria to evaluate.
A parser for the output of the CriteriaEvalChain.
LLM Chain for evaluating runs against criteria.
Criteria evaluation chain that requires references.
Compute a regex match between the prediction and the reference.
A parser for the output of the ScoreStringEvalChain.
A chain for scoring on a scale of 1-10 the output of a model.
A chain for scoring the output of a model on a scale of 1-10.
Distance metric to use.
Compute string distances between the prediction and the reference.
Compute string edit distances between two predictions.
An evaluator that calculates the edit distance between JSON strings.
An evaluator that validates a JSON prediction against a JSON schema reference.
Evaluate whether the prediction is valid JSON.
Json Equality Evaluator.
LLM Chain for evaluating question answering.
LLM Chain for evaluating QA w/o GT based on context.
LLM Chain for evaluating QA using chain of thought reasoning.
LLM Chain for generating examples for question answering.
A named tuple containing the score and reasoning for a trajectory.
Trajectory output parser.
A chain for evaluating ReAct style agents.
Compute an exact match between the prediction and the reference.
Embedding Distance Metric.
Embedding distance evaluation chain.
Use embedding distances to score semantic difference between two predictions.
A function description for ChatOpenAI.
A runnable that routes to the selected function.
Base class for prompt selectors.
Prompt collection that goes through conditionals.
Pass input through a moderation endpoint.
Chain where the outputs of one chain feed directly into next.
Simple chain where the outputs of one step feed directly into next.
Chain that transforms the chain output.
Abstract base class for creating structured sequences of calls to components.
A typed dictionary containing information about elements in the viewport.
A crawler for web pages.
Parser for output of router chain in the multi-prompt chain.
Multi Retrieval QA Chain.
Chain that uses embeddings to route between options.
A route to a destination chain.
Chain that outputs the name of a destination chain and the inputs to it.
Use a single chain to route an input to one of multiple candidate chains.
Interface for loading the combine documents chain.
Question-answering with sources over an index.
Question-answering with sources over a vector database.
Interface for loading the combine documents chain.
Raise an ImportError if APIChain is used without langchain_community.
Interface for loading the combine documents chain.
Output parser that checks if the output is finished.
Chain that generates questions from uncertain spans.
Flare chain.
Input for a SQL Chain.
Input for a SQL Chain.
Information about a data source attribute.
A date in ISO 8601 format (YYYY-MM-DD).
A datetime in ISO 8601 format (YYYY-MM-DDTHH:MM:SS).
Transform a query string into an intermediate representation.
Output parser that parses a structured query.
Input type for ConversationalRetrievalChain.
Chain for chatting with an index.
Chain for chatting with a vector database.
Class representing a single statement.
A question and its answer as a list of facts.
An answer to the question, with sources.
Interface for the combine_docs method.
Interface for the combine_docs method.
Base interface for chains combining documents.
Class for a constitutional principle.
Generate hypothetical document for query, and then embed that.
Chain for interacting with Elasticsearch Database.
Abstract base class for memory in Chains.
Document compressor that uses Cohere Rerank API.
Buffer with summarizer for storing conversation memory.
Vector Store Retriever Memory.
A basic memory implementation that simply stores the conversation history.
A basic memory implementation that simply stores the conversation history.
Abstract base class for chat memory.
Mixin for summarizer.
Continually summarizes the conversation history.
Abstract base class for Entity store.
In-memory Entity store.
Upstash Redis backed Entity store.
Redis-backed Entity store.
SQLite-backed Entity store with safe query construction.
Entity extractor & summarizer memory.
Conversation chat memory with token limit.
Use to keep track of the last k turns of a conversation.
Base class for single action agents.
Agent that calls the language model and deciding the action.
An enum for agent types.
Agent for the self-ask-with-search paper.
[Deprecated] Chain that does self-ask with search.
Structured Chat Agent.
Agent for the ReAct chain.
Class to assist with exploration of a document store.
Agent for the ReAct TextWorld chain.
[Deprecated] Chain that implements the ReAct paper.
An agent designed to hold a conversation in addition to using tools.
Chat Agent.
Agent for the MRKL chain.
Chain that implements the MRKL system.
An Agent driven by OpenAIs function powered API.
Agent that uses XML tags.
Agent driven by OpenAIs function powered API.
An agent that holds a conversation in addition to using tools.
An instance of a runnable stored in the LangChain Hub.
Map-reduce chain.
Chain to run queries against LLMs.
Chain that interprets a prompt and executes python code to do math.
Implement an LLM driven browser.
A router chain that uses an LLM chain to perform routing.
A multi-route chain that uses an LLM router chain to choose amongst prompts.
Question answering chain with sources over documents.
Question answering with sources over documents.
Chain for question-answering with self-verification.
Chain for having a conversation based on retrieved documents.
Chain for making a simple request to an API endpoint.
Chain for question-answering with self-verification.
Combining documents by mapping a chain over them, then reranking results.
Combining documents by mapping a chain over them, then combining results.
Chain that combines documents by stuffing into context.
Combine documents by doing a first pass and then refining on more documents.
Combine documents by recursively reducing them.
Chain that splits documents, then analyzes it in pieces.
Chain for applying constitutional principles.
Base class for question-answer generation chains.
Base class for question-answering chains.
Chain for question-answering against an index.
Chain for question-answering against a vector database.
Chain to have a conversation and load context from memory.
Get information about the LangChain runtime environment.
Create a function that helps retrieve objects from their new locations.
Determine if running within IPython or Jupyter.
Get the major version of Pydantic.
Yield unique elements of an iterable based on a key function.
Return the compression chain input.
Return the compression chain input.
Create a store for LangChain serializable objects from a bytes store.
Create a store for langchain Document objects from a bytes store.
Load an output parser.
Generate a random name.
Run on dataset.
Run on dataset.
Get the prompt input key.
Validate tools for single input.
Create an agent that uses self-ask with search prompting.
Create an agent aimed at supporting tools with multiple inputs.
Create an agent that uses ReAct prompting.
Create an agent that uses OpenAI function calling.
Create an agent that uses OpenAI tools.
Create an agent that uses JSON to format its logic, build for Chat Models.
Parse an AI message potentially containing tool_calls.
Parse an AI message potentially containing tool_calls.
Format the intermediate steps as XML.
Convert (AgentAction, tool output) tuples into ToolMessage objects.
Construct the scratchpad that lets the agent continue its thought process.
Construct the scratchpad that lets the agent continue its thought process.
Convert (AgentAction, tool output) tuples into FunctionMessages.
A convenience method for creating a conversational retrieval agent.
Create an agent that uses tools.
Create an agent that uses XML to format its logic.
Callback Handler that writes to a Streamlit app.
Load a dataset from the LangChainDatasets on HuggingFace.
Load the requested evaluation chain specified by a string.
Load evaluators specified by a list of evaluator types.
Resolve the criteria for the pairwise evaluator.
Resolve the criteria to evaluate.
Resolve the criteria for the pairwise evaluator.
Check if the language model is a LLM.
Check if the language model is a chat model.
Import error for load_llm.
Import error for load_llm_from_config.
Create retrieval chain that retrieves documents and then passes them on.
Create a chain that takes conversation history and returns documents.
Return another example given a list of examples for a prompt.
Get the appropriate function output parser given the user functions.
Load summarizing chain.
Create a chain that generates SQL queries.
Dummy decorator for when lark is not installed.
Return a parser for the query language.
Fix invalid filter directive.
Construct examples from input-output pairs.
Create query construction prompt.
Load a query constructor runnable chain.
Create a citation fuzzy match Runnable.
Return the kwargs for the LLMChain constructor.
Create a chain for passing a list of Documents to a model.
Split Document objects to subsets that each meet a cumulative len. constraint.
Execute a collapse function on a set of documents and merge their metadatas.
Execute a collapse function on a set of documents and merge their metadatas.
Push an object to the hub and returns the URL it can be viewed at in a browser.
Pull an object from the hub and returns it as a LangChain object.
Initialize an embeddings model from a model name and optional provider.
Load agent from Config Dict.
Unified method for loading an agent from LangChainHub or local fs.
Load an agent executor given tools and LLM.
Construct a VectorStore agent from an LLM and tools.
Construct a VectorStore router agent from an LLM and tools.
Initialize a chat model from any supported provider using a unified interface.
Load chain from Config Dict.
Unified method for loading a chain from LangChainHub or local fs.
Create a runnable sequence that uses OpenAI functions.
Create a runnable for extracting structured outputs.
Load a question answering with sources chain.
Load question answering chain.
Creates a chain that extracts information from a passage.
Load a query constructor chain.
Creates a chain that extracts information from a passage.
Creates a chain that extracts information from a passage using Pydantic schema.
OpenAPI spec to OpenAI function JSON Schema.
Create a chain for querying an API from a OpenAPI spec.
Create a citation fuzzy match chain.
Create tagging chain from schema.
Create tagging chain from Pydantic schema.
Create a question answering chain with structure.
Create a question answering chain that returns an answer with sources.
[Legacy] Create an LLM chain that uses OpenAI functions.
[Legacy] Create an LLMChain that uses an OpenAI function to get a structured output.
Main entrypoint into package.
Experiment with different models.
DEPRECATED: Kept for backwards compatibility.
DEPRECATED: Kept for backwards compatibility.
Global values and configuration that apply to all of LangChain.
For backwards compatibility.
Keep here for backwards compatibility.
Interface with the LangChain Hub.
For backwards compatibility.
Memory maintains Chain state, incorporating context from past runs.
DEPRECATED: Kept for backwards compatibility.
Deprecated module for BaseLanguageModel class, kept for backwards compatibility.
Keep here for backwards compatibility.
Kept for backwards compatibility.
Embedding models.
Module contains code for a cache backed embedder.
Utility functions for LangChain.
Utilities are the integrations with third-part systems and packages.
Shims for asyncio features that may be missing from older python versions.
For backwards compatibility.
Retriever class returns Documents given a text query.
Ensemble Retriever.
Retriever that generates and executes structured queries over its own data source.
Filter that uses an LLM to rerank documents listwise and select top-k.
Filter that uses an LLM to drop documents that aren't relevant to the query.
DocumentFilter that uses an LLM chain to extract the relevant parts of documents.
Implementations of key-value stores and storage helpers.
In memory store that is not thread safe and has no eviction policy.
OutputParser classes parse the output of an LLM call.
Schemas are the LangChain Base Classes and Interfaces.
LangChain Runnable and the LangChain Expression Language (LCEL).
Docstores are classes to store and load Documents.
LangSmith utilities.
LangSmith evaluation utilities.
Configuration for run evaluators.
A simple progress bar for the console.
Utilities for running language models or Chains over datasets.
Run evaluator wrapper for string evaluators.
Vector store stores embedded data and performs vector search.
Memory maintains Chain state, incorporating context from past runs.
Class for a conversation memory buffer with older messages stored in a vectorstore .
Class for a VectorStore-backed memory object.
Deprecated as of LangChain v0.3.4 and will be removed in LangChain v1.0.0.
Graphs provide a natural language interface to graph databases.
LLMs.
This module provides backward-compatible exports of core language model classes.
Indexes.
Vectorstore stubs for the indexing api.
Graphs provide a natural language interface to graph databases.
Relevant prompts for constructing indexes.
Agent is a class that uses an LLM to choose a sequence of actions to take.
Interface for tools.
Chain that takes in an input and produces an action and action input.
Functionality for loading agents.
Module definitions of agent types together with corresponding agents.
Load agent.
Chain that does self ask with search.
Chain that does self-ask with search.
Implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf.
Chain that implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf.
An agent designed to hold a conversation in addition to using tools.
An agent designed to hold a conversation in addition to using tools.
Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf.
Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf.
Memory used to save agent output AND intermediate steps.
Module implements an agent that uses OpenAI's APIs function enabled API.
Parsing utils to go from string to AgentAction or Agent Finish.
Logic for formatting intermediate steps into an agent scratchpad.
Agent toolkits contain integrations with various resources and services.
SQL agent.
GitHub Toolkit.
Office365 toolkit.
Local file management toolkit.
GitLab Toolkit.
AINetwork toolkit.
Spark SQL agent.
Agent toolkit for interacting with vector stores.
Toolkit for interacting with a vector store.
VectorStore agent.
Slack toolkit.
Gmail toolkit.
Zapier Toolkit.
Json agent.
NASA Toolkit.
Jira Toolkit.
Power BI agent.
Steam Toolkit.
OpenAPI spec agent.
Playwright browser toolkit.
MultiOn Toolkit.
Module implements an agent that uses OpenAI's APIs function enabled API.
An agent designed to hold a conversation in addition to using tools.
An agent designed to hold a conversation in addition to using tools.
Callback handlers allow listening to events in LangChain.
Callback Handler streams to stdout on new llm token.
Callback Handler streams to stdout on new llm token.
Base callback handler that can be used to handle callbacks in langchain.
Tracers that record execution of LangChain runs.
A Tracer implementation that records to LangChain endpoint.
A tracer that runs evaluators over completed runs.
Base interfaces for tracing runs.
Evaluation chains for grading LLM and Chain outputs.
Loading datasets and evaluators.
Interfaces to be implemented by general evaluators.
Comparison evaluators.
Prompts for comparing the outputs of two models for a given question.
Base classes for comparing the output of two models.
Criteria or rubric based evaluators.
Scoring evaluators.
Prompts for scoring the outputs of a models for a given question.
Base classes for scoring the output of a model on a scale of 1-10.
String distance evaluators.
String distance evaluators based on the RapidFuzz library.
Evaluators for parsing strings.
Chains and utils related to evaluating question answering functionality.
LLM Chains for evaluating question answering.
LLM Chain for generating examples for question answering.
Chains for evaluating ReAct style agents.
A chain for evaluating ReAct style agents.
Prompt for trajectory evaluation chain.
Evaluators that measure embedding distances.
A chain for comparing the output of two models using embeddings.
LangChain Runnable and the LangChain Expression Language (LCEL).
Serialization and deserialization.
Prompt is the input to the model.
Logic for selecting examples to include in prompts.
Chat Loaders load chat messages from common communications platforms.
Document Transformers are classes to transform Documents.
Document Loaders are classes to load Documents.
Chat Models are a variation on language models.
Chains are easily reusable components linked together.
Pass input through a moderation endpoint.
Functionality for loading chains.
Map-reduce chain.
Chain pipeline where the outputs of one step feed directly into next.
Chain that just formats a prompt and calls an LLM.
Chain that runs an arbitrary python function.
Base interface that all chains should implement.
Chain that interprets a prompt and executes python code to do math.
Chain that interprets a prompt and executes python code to do math.
Implement a GPT-3 driven browser.
Implement an LLM driven browser.
Base classes for LLM-powered router chains.
Prompt for the router chain in the multi-prompt chain.
Use a single chain to route an input to one of multiple retrieval qa chains.
Prompt for the router chain in the multi-retrieval qa chain.
Use a single chain to route an input to one of multiple llm chains.
Base classes for chain routing.
Load question answering with sources chains.
Load question answering with sources chains.
Question-answering with sources over an index.
Question-answering with sources over a vector database.
Question answering with sources over documents.
Load summarizing chains.
Chain that makes API calls and summarizes the responses to answer a question.
Chain that makes API calls and summarizes the responses to answer a question.
Chain that tries to verify assumptions before answering a question.
Chain for question-answering with self-verification.
Load question answering chains.
Adapted from https://github.com/jzbjyb/FLARE.
Chain for interacting with SQL Database.
Internal representation of a structured query language.
LLM Chain for turning a user text query into a structured query.
Chain for chatting with a vector database.
Chain for chatting with a vector database.
Methods for creating chains that use OpenAI function-calling APIs.
Summarization checker chain for verifying accuracy of text generation.
Chain for summarization with self-verification.
Different ways to combine documents.
Combining documents by mapping a chain over them first, then reranking results.
Combining documents by mapping a chain over them first, then combining results.
Chain that combines documents by stuffing into context.
Combine documents by doing a first pass and then refining on more documents.
Combine many documents together by recursively reducing them.
Base interface for chains combining documents.
Constitutional AI.
Models for the Constitutional AI chain.
Constitutional principles.
Chain for applying constitutional principles to the outputs of another chain.
Hypothetical Document Embeddings.
Hypothetical Document Embeddings.
Chain for question-answering against a vector database.
Chain for question-answering against a vector database.
Chain that carries on a conversation from a prompt plus history.
Memory modules for conversation prompts.
Chain that carries on a conversation and calls an LLM.
Chain for interacting with Elasticsearch Database.
Tools are classes that an Agent uses to interact with the world.
Different methods for rendering Tools to be passed to LLMs.
Sleep tool.
DuckDuckGo Search API toolkit.
GitHub Tool.
Eleven Labs Services Tools.
O365 tools.
Tool to generate an image.
Tavily Search API toolkit.
Merriam-Webster API toolkit.
File Management Tools.
Google Cloud Tools.
OpenWeatherMap API toolkit.
Amadeus tools.
Shell tool.
GitLab Tool.
SceneXplain API toolkit.
Google Places API Toolkit.
Tools for interacting with a SQL database.
For backwards compatibility.
Tools for interacting with Spark SQL.
Simple tool wrapper around VectorDBQA chain.
Google Trends API Toolkit.
Google Scholar API Toolkit.
Wikipedia API toolkit.
Google Lens API Toolkit.
Metaphor Search API toolkit.
Tools for making requests to an API endpoint.
Tool for asking for human input.
Slack tools.
Gmail tools.
StackExchange API toolkit.
Google Finance API Toolkit.
Zapier Tool.
This module provides dynamic access to deprecated Zapier tools in LangChain.
DataForSeo API Toolkit.
Tools for interacting with a JSON file.
This module provides dynamic access to deprecated JSON tools in LangChain.
Tools for interacting with the user.
Jira Tool.
This module provides dynamic access to deprecated Jira tools.
Tools for interacting with a PowerBI dataset.
Arxiv API toolkit.
Google Search API Toolkit.
Steam API toolkit.
Google Jobs API Toolkit.
Unsupervised learning based memorization.
Utility functions for parsing an OpenAPI spec. Kept for backwards compat.
Edenai Tools.
Golden API toolkit.
Browser tools and toolkit.
PubMed API toolkit.
Tools for interacting with a GraphQL API.
Azure Cognitive Services Tools.
Wolfram Alpha API toolkit.
MutliOn Client API tools.
Bing Search API toolkit.