Langchain memory documentation.
LangChain Python API Reference langchain: 0.
Langchain memory documentation. It’ll be removed in 0. param buffer: str = '' # param human_prefix: str = 'Human' # param input_key: str | None = None # param output_key: str | None = None # async aclear() → None Memory 📄️ Astra DB DataStax Astra DB is a serverless vector-capable database built on Cassandra and made conveniently available through an easy-to-use JSON API. With a swappable entity store, persisting entities across conversations. It seamlessly integrates with LangChain, and you can use it to inspect and debug individual steps of your chains as you build. It provides a standard interface for chains, many integrations with other tools, and end-to-end chains for common applications. You can peruse LangSmith tutorials here. Add short-term memory Short-term memory (thread-level persistence) enables BaseChatMemory # class langchain. 2 or 0. Entity extractor & summarizer memory. ConversationKGMemory # class langchain_community. Parameters: maxsize (Optional[int]) – The maximum number of items to store in the cache. param ai_prefix: str = 'AI' # Prefix to use for AI generated responses. InMemoryCache(*, maxsize: int | None = None) [source] # Cache that stores things in memory. param chat_memory: BaseChatMessageHistory [Optional] # param input_key: str | None = None # param output_key: str | None = None # param return_messages: bool = False # async aclear() → None [source] # Clear memory contents. Fortunately, LangChain provides several memory management solutions, suitable for different use cases. Below is a minimal implementation, analogous to using ConversationChain with the default ConversationBufferMemory: This guide demonstrates how to use both memory types with agents in LangGraph. This chatbot will be able to have a conversation and remember previous interactions. This makes a Chain stateful. ConversationStringBufferMemory [source] # Bases: BaseMemory Buffer for storing conversation memory. Evaluation LangSmith helps you evaluate the performance of your LLM applications. Source code for langchain. 2, which is no longer actively maintained. param ai_prefix: str = 'AI' # param buffer: str = '' # param chat_memory: BaseChatMessageHistory [Optional] # param human_prefix: str = 'Human' # param input_key: str | None = None # param llm: BaseLanguageModel [Required For longer-term persistence across chat sessions, you can swap out the default in-memory chatHistory that backs chat memory classes like BufferMemory for a MongoDB instance. ConversationKGMemory [source] # Bases: BaseChatMemory Knowledge graph conversation memory. Available in both Python- and Javascript-based libraries, LangChain’s tools and APIs simplify the process of building LLM-driven applications like chatbots and AI agents. param memory_key: str = 'history' # Key name to locate the memories in the result of load_memory_variables. Predefined; using static LangChain. You can use its core API with any storage InMemoryStore # class langchain_core. Dec 9, 2024 · Importantly, Index keeps on working even if the content being written is derived via a set of transformations from some source content (e. Use to build complex pipelines and workflows. 1, we recommend that you first upgrade to 0. Google Cloud Memorystore for Redis is a fully-managed service that is powered by the Redis in-memory data store to build application caches that provide sub-millisecond data access. Now let's take a look at using a slightly more complex type of memory - ConversationSummaryMemory. memory. It provides tooling to extract information from conversations, optimize agent behavior through prompt updates, and maintain long-term memory about behaviors, facts, and events. If None, the cache has no maximum size. Extend your database application to build AI-powered experiences leveraging Memorystore for Redis's Langchain integrations. Discover how each tool fits into the LLM application stack and when to use them. Class hierarchy for Memory: langchain-community: Community-driven components for LangChain. langgraph: Powerful orchestration layer for LangChain. Hit the ground running using third-party integrations and Templates. For more see the how-to guide for setting up LangSmith with LangChain or setting up LangSmith with LangGraph. No third-party integrations are defined here. async aclear() → None # Async clear memory contents. caches. Use VectorStore. For example, for conversational Chains Memory can be used to store conversations and automatically add them to future model prompts so that the model has the necessary context to respond Architecture LangChain is a framework that consists of a number of packages. 29: This was a beta API that was added in 0. , indexing children documents that were derived from parent documents by chunking. If the cache exceeds the maximum size, the oldest items are removed. It does NOT support native tool calling capabilities for chat models and will fail SILENTLY if used with a chat model that has native tool calling. 📄️ Cassandra Apache Cassandra® is a NoSQL, row-oriented, highly The platform for reliable agents Frameworks LangChain - Python Open-source framework for developing applications powered by large language models (LLMs). 📄️ Google Spanner using LangChain. Add long-term memory to store user-specific or application-level data across sessions. Contribute to langchain-ai/langgraph-memory development by creating an account on GitHub. LangSmith documentation is hosted on a separate site. store # The underlying dictionary that stores the key-value pairs. param memories: list[BaseMemory] [Required] # For tracking all the memories that should be accessed. _api import deprecated from langchain_core. The agent extracts key information from conversations, maintains memory consistency, and knows when to search past interactions. langchain: A package for higher level components (e. Return type: None LangSmith is framework-agnostic — it can be used with or without LangChain's open source frameworks langchain and langgraph. summary_buffer. simple. Parameters: messages (Sequence[BaseMessage]) – The messages to add. 15 # Main entrypoint into package. 1, which is no longer actively maintained. Introduction LangChain is a framework for developing applications powered by large language models (LLMs). In LangGraph, you can add two types of memory: Add short-term memory as a part of your agent's state to enable multi-turn conversations. One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. Help us out by providing feedback on this documentation page: langchain: 0. abc import Sequence from typing import Any, Optional, Union from langchain_core. This state management can take several forms, including: Simply stuffing previous messages into a chat model prompt. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves. Sep 16, 2024 · See the deprecated chains and associated migration guides here. buffer. Return type: None async aload Abstract base class for chat memory. When you use all LangChain products, you'll build better, get to production quicker, and grow visibility -- all with less set up and friction. 27 memory ConversationTokenBufferMemory A basic memory implementation that simply stores the conversation history. 27 memory ConversationBufferWindowMemory langchain: 0. Jul 23, 2025 · LangChain is an open-source framework designed to simplify the creation of applications using large language models (LLMs). 27 memory Source code for langchain_core. langchain-core: Core langchain package. ConversationalRetrievalChain # class langchain. 3 release of LangChain, we recommend that LangChain users take advantage of LangGraph persistence to incorporate memory into new LangChain applications. Note that this chatbot that we build will only use the language model to have a conversation. 20,<0. We will add memory to a question/answering chain. The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by ml-distance. ) Classes ¶ langchain. OpenAI. ConversationalRetrievalChain [source] # Bases: BaseConversationalRetrievalChain In-memory This guide will help you getting started with such a retriever backed by an in-memory vector store. Chain; internal class Program { private static async Task Main(string[] args) { // Pull the API key from the environment, so it's never checked in with source var apiKey = Environment. param ai_prefix: str = 'AI' # param chat_memory: BaseChatMessageHistory [Optional] # param entity_extraction_prompt This is documentation for LangChain v0. LangChain's products work seamlessly together to provide an integrated solution for every step of the application development journey. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions RunnableWithMessageHistory offers several benefits, including: Stream, batch, and async support; More flexible memory handling, including the ability to manage memory outside the chain; Support for multiple threads. LangSmith Dec 9, 2024 · Buffer for storing conversation memory. LLMs are stateless by default, meaning that they have no built-in memory. Add and manage memory AI applications need memory to share context across multiple interactions. chat_memory. vectorstores import VectorStoreRetriever from pydantic import Field from langchain. embeddings langchain_together. This will work with either 0. The interfaces for core components like chat models, vector stores, tools and more are defined here. BaseChatMemory # class langchain. This memory allows for storing messages and then extracts the messages in a variable. Framework to build resilient language agents as graphs. Providers. conversational_retrieval. CombinedMemory [source] # Bases: BaseMemory Combining multiple memories’ data together. This notebook covers how to do that. ATTENTION This abstraction was created prior to when chat models had native tool calling capabilities. This chain takes as inputs both related documents and a user question. 2 days ago · LangChain is a powerful framework that simplifies the development of applications powered by large language models (LLMs). Deprecated since version langchain-core==0. ConversationSummaryBufferMemory [source] # Bases: BaseChatMemory, SummarizerMixin Buffer with This tutorial shows how to implement an agent with long-term memory capabilities using LangGraph. Return type: None async aload_memory_variables( inputs: dict[str, Any], ) → dict[str, Any] # Async This is documentation for LangChain v0. Follow their code on GitHub. 0. stores. This notebook shows how to use ConversationBufferMemory. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis. utils import pre_init from langchain. param chat_memory: BaseChatMessageHistory [Optional] # param input SimpleMemory # class langchain. Return type: None async aload LangChain Python API Reference langchain: 0. 0 or 0. memory """**Memory** maintains Chain state, incorporating context from past runs. param return_docs: bool = False # Overview We’ll go over an example of how to design and implement an LLM-powered chatbot. Includes base interfaces and in-memory implementations. It provides essential building blocks like chains, agents, and memory components that enable developers to create sophisticated AI workflows beyond simple prompt-response interactions. , some pre-built chains). For detailed documentation of all features and configurations head to the API reference. messages import BaseMessage, get_buffer_string from langchain_core. summary import SummarizerMixin ConversationSummaryMemory # class langchain. kg. Memory management can be challenging to get right, especially if you add additional tools for the bot to choose between. combined. sentence_transformers langchain_text_splitters. An all-in-one developer platform for every step of the llm-powered application lifecycle, whether you’re building with LangChain or not. These are applications that can answer questions about specific source information. This is documentation for LangChain v0. Providers; using LangChain. To tune the frequency and quality of memories your bot is saving, we recommend starting from an evaluation set, adding to it over time as you find and address common errors in your service. LangChain implements a standard interface for large language models and related technologies, such as embedding models and vector stores, and integrates with hundreds of providers. 27 # Main entrypoint into package. . memory # Memory maintains Chain state, incorporating context from past runs. InMemoryCache # class langchain_core. llms langchain_unstructured. document_parse langchain_upstage. param ai_prefix: str = 'AI' # param chat_history_key: str = 'history' # param chat_memory LangChain Python API Reference langchain: 0. summary. Memory in the Multi-Input Chain Most memory objects assume a single input. GetEnvironmentVariable("OPENAI_API_KEY") ?? throw new InvalidOperationException Introduction LangChain is a framework for developing applications powered by large language models (LLMs). The tutorial below is a great way to get This notebook goes over how to use the Memory class with an LLMChain. BaseChatMemory [source] # Bases: BaseMemory, ABC Abstract base class for chat memory. This allows the retriever to account for underlying document metadata in MemoryVectorStore LangChain offers is an in-memory, ephemeral vectorstore that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. Raises ConversationBufferMemory # class langchain. Initialize with empty cache. How to add memory to chatbots A key feature of chatbots is their ability to use content of previous conversation turns as context. This is a relatively simple LLM application - it's just a single LLM call plus some prompting. documents import Document from langchain_core. LangChain is a framework for building LLM-powered applications. LangGraph documentation is currently hosted on a separate site. 3 versions of all the base packages. memory ¶ Memory maintains Chain state, incorporating context from past runs. """Class for a VectorStore-backed memory object. param memories: dict[str, Any] = {} # async aclear() → None # Async clear memory contents. This application will translate text from English into another language. LangChain has 208 repositories available. Deploy and scale with LangGraph Platform, with APIs for state management, a visual studio for debugging, and multiple deployment options. param ai_prefix: str = 'AI' # param chat_memory: BaseChatMessageHistory [Optional] # param human_prefix: str = 'Human' # param input_key: str | None = None # param output_key: str | None = None # param return_messages: bool = False # async In this quickstart we'll show you how to build a simple LLM application with LangChain. ConversationBufferMemory [source] # Bases: BaseChatMemory Buffer for storing conversation memory. Type: dict [str, Any] Examples The memory tools (create_manage_memory_tool and create_search_memory_tool) let you control what gets stored. The above, but trimming old messages to reduce the amount of distracting information the model has to deal with. Still, this is a great way to get started with LangChain - a lot of features can be built with just some prompting and an LLM call! A list of messages stored in memory. 4 days ago · Learn the key differences between LangChain, LangGraph, and LangSmith. 3. This stores the entire conversation history in memory without any additional processing. """ from collections. Stateful: add Memory to any Chain to give it state, Observable: pass Callbacks to a Chain to execute additional functionality, like logging, outside the main sequence of component calls, Composable: combine Chains with other components, including other Chains. document_parse_parsers langchain_upstage. Default is None. Productionization: Use LangSmith to inspect, monitor Stateful: add Memory to any Chain to give it state, Observable: pass Callbacks to a Chain to execute additional functionality, like logging, outside the main sequence of component calls, Composable: combine Chains with other components, including other Chains. Memory; using LangChain. from langchain_core. embeddings ConversationSummaryBufferMemory combines the two ideas. chat_models langchain_upstage. 11. Defaults to an in-memory entity store, and can be swapped out for a Redis, SQLite, or other entity store. chat_models langchain_together. 3 days ago · Learn how to use the LangChain ecosystem to build, test, deploy, monitor, and visualize complex agentic workflows. x. LangChain Python API Reference langchain-community: 0. document_loaders langchain_upstage. For a deeper understanding of memory concepts, refer to the LangGraph memory documentation. Class hierarchy for LangChain's products work seamlessly together to provide an integrated solution for every step of the application development journey. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source building blocks, components, and third-party integrations. InMemoryStore [source] # In-memory store for any type of data. If you're using langgraph, upgrade to langgraph>=0. base. This type of memory creates a summary of the conversation over time. Head to Integrations for documentation on built-in chat message history integrations with 3rd-party databases and tools. spacy langchain_together. chains. But sometimes we need memory to implement applications such like conversational systems, which may have to remember previous information provided by the user. LangChain's products work seamlessly together to provide an integrated solution for every step of the application development journey. Integrates with external knowledge graph to store and retrieve information about knowledge triples in the conversation. 2. More complex modifications like There are many different types of memory. DO NOT USE THIS ABSTRACTION FOR NEW CODE. In this notebook, we go over how to add memory to a chain that has multiple inputs. Below is a minimal implementation, analogous to using ConversationChain with the default ConversationBufferMemory: ConversationStringBufferMemory # class langchain. ConversationSummaryBufferMemory # class langchain. This memory allows for storing of messages, then later formats the messages into a prompt input variable. The dependencies are kept purposefully very lightweight Build controllable agents with LangGraph, our low-level agent orchestration framework. For the current stable version, see this version (Latest). LangChain is an open source orchestration framework for application development using large language models (LLMs). LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. param input_key: str | None = None # Key name to index the inputs to load_memory_variables. This module contains memory abstractions from LangChain v0. You can peruse LangGraph how-to guides here. 1 billion valuation, helps developers at companies like Klarna and Rippling use off-the-shelf AI models to create new applications. param ai_prefix: str = 'AI' ¶ param chat_memory: BaseChatMessageHistory [Optional] ¶ param human_prefix: str = 'Human' ¶ param input_key: Optional[str] = None ¶ param output_key: Optional[str] = None ¶ param return_messages: bool = False ¶ async abuffer() → Any [source] ¶ String buffer of memory SimpleMemory # class langchain. 📄️ AWS DynamoDB Amazon AWS DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. How to update your code If you're using langchain / langchain-community / langchain-core 0. vectorstores import InMemoryVectorStore from langchain_openai import OpenAIEmbeddings vector_store = InMemoryVectorStore(OpenAIEmbeddings()) langchain_text_splitters. Chains can be initialized with a Memory object, which will persist data across calls to the chain. Both short-term and long-term memory require persistent storage to maintain continuity across LLM interactions. param retriever: VectorStoreRetriever [Required] # VectorStoreRetriever object to connect to. g. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source building blocks and components. memory import BaseMemory from langchain_core. async aadd_messages(messages: Sequence[BaseMessage]) → None [source] # Async add messages to the store. langchain-core This package contains base abstractions for different components and ways to compose them together. param ai_prefix: str = 'AI' # param chat_memory: BaseChatMessageHistory [Optional] # param human_prefix: str = 'Human' # param input_key: str | None = None # param output_key: str | None = None # param return_messages: bool = False # async How to: pass runtime secrets to a runnable LangGraph LangGraph is an extension of LangChain aimed at building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Chains. Jul 9, 2025 · The startup, which sources say is raising at a $1. Overview A self-query retriever retrieves documents by dynamically generating metadata filters based on some input query. Feb 18, 2025 · Today we're releasing the LangMem SDK, a library that helps your agents learn and improve through long-term memory. Return type: None async aclear() → None [source] # Async clear all messages from the store. If you are using either of these, you can enable LangSmith tracing with a single environment variable. These applications use a technique known as Retrieval Augmented Generation, or RAG. aadd_documents instead. summary_buffer from typing import Any, Dict, List, Union from langchain_core. Return type: None async aload_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any] # Async return key-value ConversationBufferMemory # class langchain. param memories: Dict[str, Any] = {} # async aclear() → None # Async clear memory contents. chat_memory import BaseChatMemory from langchain. utils import get_prompt Although there are a few predefined types of memory in LangChain, it is highly possible you will want to add your own type of memory that is optimal for your application. As of the v0. RunnableWithMessageHistory offers several benefits, including: Stream, batch, and async support; More flexible memory handling, including the ability to manage memory outside the chain; Support for multiple threads. ConversationSummaryMemory [source] # Bases: BaseChatMemory, SummarizerMixin Conversation summarizer to chat memory. SimpleMemory [source] # Bases: BaseMemory Simple memory for storing context or other information that shouldn’t ever change between prompts. Return type: None async aload_memory_variables( inputs: dict[str, Any], ) → dict[str, Any] # Async return key Memory can be used to store information aboutpast executions of a Chain and inject that information into the inputs of future executions of the Chain. This can be useful for condensing information from the conversation over time. There are several other related concepts that you may be looking for: Conversational RAG: Enable a chatbot experience over an CombinedMemory # class langchain. This notebook shows how to use BufferMemory. Extracts named entities from the recent chat history and generates summaries. The agent can store, retrieve, and use memories to enhance its interactions with users. dqzdcijdcfkgvrwohddrsspisvcpsrubptaoiuvmxcwfgub