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*   [Introduction](/v0.2/docs/introduction)
*   [Tutorials](/v0.2/docs/tutorials/)
    
    *   [Build a Question Answering application over a Graph Database](/v0.2/docs/tutorials/graph)
    *   [Tutorials](/v0.2/docs/tutorials/)
    *   [Build a Simple LLM Application](/v0.2/docs/tutorials/llm_chain)
    *   [Build a Query Analysis System](/v0.2/docs/tutorials/query_analysis)
    *   [Build a Chatbot](/v0.2/docs/tutorials/chatbot)
    *   [Build an Agent](/v0.2/docs/tutorials/agents)
    *   [Build an Extraction Chain](/v0.2/docs/tutorials/extraction)
    *   [Summarize Text](/v0.2/docs/tutorials/summarization)
    *   [Tagging](/v0.2/docs/tutorials/classification)
    *   [Build a Local RAG Application](/v0.2/docs/tutorials/local_rag)
    *   [Conversational RAG](/v0.2/docs/tutorials/qa_chat_history)
    *   [Build a Retrieval Augmented Generation (RAG) App](/v0.2/docs/tutorials/rag)
    *   [Build a Question/Answering system over SQL data](/v0.2/docs/tutorials/sql_qa)
*   [How-to guides](/v0.2/docs/how_to/)
    
    *   [How-to guides](/v0.2/docs/how_to/)
    *   [How to use example selectors](/v0.2/docs/how_to/example_selectors)
    *   [Installation](/v0.2/docs/how_to/installation)
    *   [How to stream responses from an LLM](/v0.2/docs/how_to/streaming_llm)
    *   [How to stream chat model responses](/v0.2/docs/how_to/chat_streaming)
    *   [How to embed text data](/v0.2/docs/how_to/embed_text)
    *   [How to use few shot examples in chat models](/v0.2/docs/how_to/few_shot_examples_chat)
    *   [How to cache model responses](/v0.2/docs/how_to/llm_caching)
    *   [How to cache chat model responses](/v0.2/docs/how_to/chat_model_caching)
    *   [How to create a custom LLM class](/v0.2/docs/how_to/custom_llm)
    *   [How to use few shot examples](/v0.2/docs/how_to/few_shot_examples)
    *   [How to use output parsers to parse an LLM response into structured format](/v0.2/docs/how_to/output_parser_structured)
    *   [How to return structured data from a model](/v0.2/docs/how_to/structured_output)
    *   [How to add ad-hoc tool calling capability to LLMs and Chat Models](/v0.2/docs/how_to/tools_prompting)
    *   [How to create a custom chat model class](/v0.2/docs/how_to/custom_chat)
    *   [How to do per-user retrieval](/v0.2/docs/how_to/qa_per_user)
    *   [How to track token usage](/v0.2/docs/how_to/chat_token_usage_tracking)
    *   [How to track token usage](/v0.2/docs/how_to/llm_token_usage_tracking)
    *   [How to pass through arguments from one step to the next](/v0.2/docs/how_to/passthrough)
    *   [How to compose prompts together](/v0.2/docs/how_to/prompts_composition)
    *   [How to use legacy LangChain Agents (AgentExecutor)](/v0.2/docs/how_to/agent_executor)
    *   [How to add values to a chain's state](/v0.2/docs/how_to/assign)
    *   [How to attach runtime arguments to a Runnable](/v0.2/docs/how_to/binding)
    *   [How to cache embedding results](/v0.2/docs/how_to/caching_embeddings)
    *   [How to split by character](/v0.2/docs/how_to/character_text_splitter)
    *   [How to manage memory](/v0.2/docs/how_to/chatbots_memory)
    *   [How to do retrieval](/v0.2/docs/how_to/chatbots_retrieval)
    *   [How to use tools](/v0.2/docs/how_to/chatbots_tools)
    *   [How to split code](/v0.2/docs/how_to/code_splitter)
    *   [How to do retrieval with contextual compression](/v0.2/docs/how_to/contextual_compression)
    *   [How to write a custom retriever class](/v0.2/docs/how_to/custom_retriever)
    *   [How to create custom Tools](/v0.2/docs/how_to/custom_tools)
    *   [How to debug your LLM apps](/v0.2/docs/how_to/debugging)
    *   [How to load CSV data](/v0.2/docs/how_to/document_loader_csv)
    *   [How to write a custom document loader](/v0.2/docs/how_to/document_loader_custom)
    *   [How to load data from a directory](/v0.2/docs/how_to/document_loader_directory)
    *   [How to load PDF files](/v0.2/docs/how_to/document_loader_pdf)
    *   [How to load JSON data](/v0.2/docs/how_to/document_loaders_json)
    *   [How to select examples by length](/v0.2/docs/how_to/example_selectors_length_based)
    *   [How to select examples by similarity](/v0.2/docs/how_to/example_selectors_similarity)
    *   [How to use reference examples](/v0.2/docs/how_to/extraction_examples)
    *   [How to handle long text](/v0.2/docs/how_to/extraction_long_text)
    *   [How to do extraction without using function calling](/v0.2/docs/how_to/extraction_parse)
    *   [Fallbacks](/v0.2/docs/how_to/fallbacks)
    *   [Few Shot Prompt Templates](/v0.2/docs/how_to/few_shot)
    *   [How to run custom functions](/v0.2/docs/how_to/functions)
    *   [How to construct knowledge graphs](/v0.2/docs/how_to/graph_constructing)
    *   [How to map values to a database](/v0.2/docs/how_to/graph_mapping)
    *   [How to improve results with prompting](/v0.2/docs/how_to/graph_prompting)
    *   [How to add a semantic layer over the database](/v0.2/docs/how_to/graph_semantic)
    *   [How to reindex data to keep your vectorstore in-sync with the underlying data source](/v0.2/docs/how_to/indexing)
    *   [How to get log probabilities](/v0.2/docs/how_to/logprobs)
    *   [How to add message history](/v0.2/docs/how_to/message_history)
    *   [How to generate multiple embeddings per document](/v0.2/docs/how_to/multi_vector)
    *   [How to generate multiple queries to retrieve data for](/v0.2/docs/how_to/multiple_queries)
    *   [How to parse JSON output](/v0.2/docs/how_to/output_parser_json)
    *   [How to retry when output parsing errors occur](/v0.2/docs/how_to/output_parser_retry)
    *   [How to parse XML output](/v0.2/docs/how_to/output_parser_xml)
    *   [How to invoke runnables in parallel](/v0.2/docs/how_to/parallel)
    *   [How to retrieve the whole document for a chunk](/v0.2/docs/how_to/parent_document_retriever)
    *   [How to partially format prompt templates](/v0.2/docs/how_to/prompts_partial)
    *   [How to add chat history to a question-answering chain](/v0.2/docs/how_to/qa_chat_history_how_to)
    *   [How to return citations](/v0.2/docs/how_to/qa_citations)
    *   [How to return sources](/v0.2/docs/how_to/qa_sources)
    *   [How to stream from a question-answering chain](/v0.2/docs/how_to/qa_streaming)
    *   [How to construct filters](/v0.2/docs/how_to/query_constructing_filters)
    *   [How to add examples to the prompt](/v0.2/docs/how_to/query_few_shot)
    *   [How to deal with high cardinality categorical variables](/v0.2/docs/how_to/query_high_cardinality)
    *   [How to handle multiple queries](/v0.2/docs/how_to/query_multiple_queries)
    *   [How to handle multiple retrievers](/v0.2/docs/how_to/query_multiple_retrievers)
    *   [How to handle cases where no queries are generated](/v0.2/docs/how_to/query_no_queries)
    *   [How to recursively split text by characters](/v0.2/docs/how_to/recursive_text_splitter)
    *   [How to reduce retrieval latency](/v0.2/docs/how_to/reduce_retrieval_latency)
    *   [How to route execution within a chain](/v0.2/docs/how_to/routing)
    *   [How to chain runnables](/v0.2/docs/how_to/sequence)
    *   [How to split text by tokens](/v0.2/docs/how_to/split_by_token)
    *   [How to deal with large databases](/v0.2/docs/how_to/sql_large_db)
    *   [How to use prompting to improve results](/v0.2/docs/how_to/sql_prompting)
    *   [How to do query validation](/v0.2/docs/how_to/sql_query_checking)
    *   [How to stream](/v0.2/docs/how_to/streaming)
    *   [How to create a time-weighted retriever](/v0.2/docs/how_to/time_weighted_vectorstore)
    *   [How to use a chat model to call tools](/v0.2/docs/how_to/tool_calling)
    *   [How to call tools with multi-modal data](/v0.2/docs/how_to/tool_calls_multi_modal)
    *   [How to use LangChain tools](/v0.2/docs/how_to/tools_builtin)
    *   [How use a vector store to retrieve data](/v0.2/docs/how_to/vectorstore_retriever)
    *   [How to create and query vector stores](/v0.2/docs/how_to/vectorstores)
*   [Conceptual guide](/v0.2/docs/concepts)
*   Ecosystem
    
    *   [🦜🛠️ LangSmith](/v0.2/docs/langsmith/)
        
    *   [🦜🕸️LangGraph.js](/v0.2/docs/langgraph)
*   Versions
    
    *   [Overview](/v0.2/docs/versions/overview)
    *   [v0.2](/v0.2/docs/versions/v0_2)
    *   [Release Policy](/v0.2/docs/versions/release_policy)
    *   [Packages](/v0.2/docs/versions/packages)
*   [Security](/v0.2/docs/security)

*   [](/v0.2/)
*   Introduction

On this page

Introduction
============

**LangChain** is a framework for developing applications powered by large language models (LLMs).

LangChain simplifies every stage of the LLM application lifecycle:

*   **Development**: Build your applications using LangChain's open-source [building blocks](/v0.2/docs/how_to/#langchain-expression-language-lcel) and [components](/v0.2/docs/how_to/). Hit the ground running using [third-party integrations](/v0.2/docs/integrations/platforms/).
*   **Productionization**: Use [LangSmith](/v0.2/docs/langsmith/) to inspect, monitor and evaluate your chains, so that you can continuously optimize and deploy with confidence.
*   **Deployment**: Turn any chain into an API with [LangServe](https://www.langchain.com/langserve).

![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](/v0.2/svg/langchain_stack.svg "LangChain Framework Overview")![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](/v0.2/svg/langchain_stack_dark.svg "LangChain Framework Overview")

Concretely, the framework consists of the following open-source libraries:

*   **`@langchain/core`**: Base abstractions and LangChain Expression Language.
*   **`@langchain/community`**: Third party integrations.
    *   Partner packages (e.g. **`@langchain/openai`**, **`@langchain/anthropic`**, etc.): Some integrations have been further split into their own lightweight packages that only depend on **`@langchain/core`**.
*   **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
*   **[langgraph](https://www.langchain.com/langserveh)**: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
*   **[LangSmith](/v0.2/docs/langsmith)**: A developer platform that lets you debug, test, evaluate, and monitor LLM applications.

note

These docs focus on the JavaScript LangChain library. [Head here](https://python.langchain.com) for docs on the Python LangChain library.

[Tutorials](/v0.2/docs/tutorials)[​](#tutorials "Direct link to tutorials")
---------------------------------------------------------------------------

If you're looking to build something specific or are more of a hands-on learner, check out our [tutorials](/v0.2/docs/tutorials). This is the best place to get started.

These are the best ones to get started with:

*   [Build a Simple LLM Application](/v0.2/docs/tutorials/llm_chain)
*   [Build a Chatbot](/v0.2/docs/tutorials/chatbot)
*   [Build an Agent](/v0.2/docs/tutorials/agents)

Explore the full list of tutorials [here](/v0.2/docs/tutorials).

[How-To Guides](/v0.2/docs/how_to/)[​](#how-to-guides "Direct link to how-to-guides")
-------------------------------------------------------------------------------------

[Here](/v0.2/docs/how_to/) you'll find short answers to “How do I….?” types of questions. These how-to guides don't cover topics in depth - you'll find that material in the [Tutorials](/v0.2/docs/tutorials) and the [API Reference](https://v02.api.js.langchain.com). However, these guides will help you quickly accomplish common tasks.

[Conceptual Guide](/v0.2/docs/concepts)[​](#conceptual-guide "Direct link to conceptual-guide")
-----------------------------------------------------------------------------------------------

Introductions to all the key parts of LangChain you'll need to know! [Here](/v0.2/docs/concepts) you'll find high level explanations of all LangChain concepts.

[API reference](https://v02.api.js.langchain.com)[​](#api-reference "Direct link to api-reference")
---------------------------------------------------------------------------------------------------

Head to the reference section for full documentation of all classes and methods in the LangChain Python packages.

Ecosystem[​](#ecosystem "Direct link to Ecosystem")
---------------------------------------------------

### [🦜🛠️ LangSmith](/v0.2/docs/langsmith)[​](#️-langsmith "Direct link to ️-langsmith")

Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.

### [🦜🕸️ LangGraph](/v0.2/docs/langgraph)[​](#️-langgraph "Direct link to ️-langgraph")

Build stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain primitives.

Additional resources[​](#additional-resources "Direct link to Additional resources")
------------------------------------------------------------------------------------

[Security](/v0.2/docs/security)[​](#security "Direct link to security")
-----------------------------------------------------------------------

Read up on our [Security](/v0.2/docs/security) best practices to make sure you're developing safely with LangChain.

### [Integrations](/v0.2/docs/integrations/platforms/)[​](#integrations "Direct link to integrations")

LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/v0.2/docs/integrations/platforms/).

### [Contributing](/v0.2/docs/contributing)[​](#contributing "Direct link to contributing")

Check out the developer's guide for guidelines on contributing and help getting your dev environment set up.

* * *

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*   [Tutorials](#tutorials)
*   [How-To Guides](#how-to-guides)
*   [Conceptual Guide](#conceptual-guide)
*   [API reference](#api-reference)
*   [Ecosystem](#ecosystem)
    *   [🦜🛠️ LangSmith](#️-langsmith)
    *   [🦜🕸️ LangGraph](#️-langgraph)
*   [Additional resources](#additional-resources)
*   [Security](#security)
    *   [Integrations](#integrations)
    *   [Contributing](#contributing)

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