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Conceptual guide

This section contains introductions to key parts of LangChain.


LangChain as a framework consists of a number of packages.


This package contains base abstractions of different components and ways to compose them together. The interfaces for core components like LLMs, vectorstores, retrievers and more are defined here. No third party integrations are defined here. The dependencies are kept purposefully very lightweight.

Partner packages​

While the long tail of integrations are in langchain-community, we split popular integrations into their own packages (e.g. langchain-openai, langchain-anthropic, etc). This was done in order to improve support for these important integrations.


The main langchain package contains chains, agents, and retrieval strategies that make up an application's cognitive architecture. These are NOT third party integrations. All chains, agents, and retrieval strategies here are NOT specific to any one integration, but rather generic across all integrations.


This package contains third party integrations that are maintained by the LangChain community. Key partner packages are separated out (see below). This contains all integrations for various components (LLMs, vectorstores, retrievers). All dependencies in this package are optional to keep the package as lightweight as possible.


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.

LangGraph exposes high level interfaces for creating common types of agents, as well as a low-level API for composing custom flows.


A package to deploy LangChain chains as REST APIs. Makes it easy to get a production ready API up and running.


A developer platform that lets you debug, test, evaluate, and monitor LLM applications.

Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.

LangChain Expression Language (LCEL)​

LangChain Expression Language, or LCEL, is a declarative way to chain LangChain components. LCEL was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest β€œprompt + LLM” chain to the most complex chains (we’ve seen folks successfully run LCEL chains with 100s of steps in production). To highlight a few of the reasons you might want to use LCEL:

First-class streaming support When you build your chains with LCEL you get the best possible time-to-first-token (time elapsed until the first chunk of output comes out). For some chains this means eg. we stream tokens straight from an LLM to a streaming output parser, and you get back parsed, incremental chunks of output at the same rate as the LLM provider outputs the raw tokens.

Async support Any chain built with LCEL can be called both with the synchronous API (eg. in your Jupyter notebook while prototyping) as well as with the asynchronous API (eg. in a LangServe server). This enables using the same code for prototypes and in production, with great performance, and the ability to handle many concurrent requests in the same server.

Optimized parallel execution Whenever your LCEL chains have steps that can be executed in parallel (eg if you fetch documents from multiple retrievers) we automatically do it, both in the sync and the async interfaces, for the smallest possible latency.

Retries and fallbacks Configure retries and fallbacks for any part of your LCEL chain. This is a great way to make your chains more reliable at scale. We’re currently working on adding streaming support for retries/fallbacks, so you can get the added reliability without any latency cost.

Access intermediate results For more complex chains it’s often very useful to access the results of intermediate steps even before the final output is produced. This can be used to let end-users know something is happening, or even just to debug your chain. You can stream intermediate results, and it’s available on every LangServe server.

Input and output schemas Input and output schemas give every LCEL chain Pydantic and JSONSchema schemas inferred from the structure of your chain. This can be used for validation of inputs and outputs, and is an integral part of LangServe.

Seamless LangSmith tracing As your chains get more and more complex, it becomes increasingly important to understand what exactly is happening at every step. With LCEL, all steps are automatically logged to LangSmith for maximum observability and debuggability.

Seamless LangServe deployment Any chain created with LCEL can be easily deployed using LangServe.

Runnable interface​

To make it as easy as possible to create custom chains, we've implemented a "Runnable" protocol. Many LangChain components implement the Runnable protocol, including chat models, LLMs, output parsers, retrievers, prompt templates, and more. There are also several useful primitives for working with runnables, which you can read about below.

This is a standard interface, which makes it easy to define custom chains as well as invoke them in a standard way. The standard interface includes:

  • stream: stream back chunks of the response
  • invoke: call the chain on an input
  • batch: call the chain on a list of inputs

These also have corresponding async methods that should be used with asyncio await syntax for concurrency:

  • astream: stream back chunks of the response async
  • ainvoke: call the chain on an input async
  • abatch: call the chain on a list of inputs async
  • astream_log: stream back intermediate steps as they happen, in addition to the final response
  • astream_events: beta stream events as they happen in the chain (introduced in langchain-core 0.1.14)

The input type and output type varies by component:

ComponentInput TypeOutput Type
ChatModelSingle string, list of chat messages or a PromptValueChatMessage
LLMSingle string, list of chat messages or a PromptValueString
OutputParserThe output of an LLM or ChatModelDepends on the parser
RetrieverSingle stringList of Documents
ToolSingle string or dictionary, depending on the toolDepends on the tool

All runnables expose input and output schemas to inspect the inputs and outputs:

  • input_schema: an input Pydantic model auto-generated from the structure of the Runnable
  • output_schema: an output Pydantic model auto-generated from the structure of the Runnable


LangChain provides standard, extendable interfaces and external integrations for various components useful for building with LLMs. Some components LangChain implements, some components we rely on third-party integrations for, and others are a mix.

Chat models​

Language models that use a sequence of messages as inputs and return chat messages as outputs (as opposed to using plain text). These are traditionally newer models (older models are generally LLMs, see below). Chat models support the assignment of distinct roles to conversation messages, helping to distinguish messages from the AI, users, and instructions such as system messages.

Although the underlying models are messages in, message out, the LangChain wrappers also allow these models to take a string as input. This means you can easily use chat models in place of LLMs.

When a string is passed in as input, it is converted to a HumanMessage and then passed to the underlying model.

LangChain does not host any Chat Models, rather we rely on third party integrations.

We have some standardized parameters when constructing ChatModels:

  • model: the name of the model

ChatModels also accept other parameters that are specific to that integration.


Tool Calling Some chat models have been fine-tuned for tool calling and provide a dedicated API for tool calling. Generally, such models are better at tool calling than non-fine-tuned models, and are recommended for use cases that require tool calling. Please see the tool calling section for more information.

For specifics on how to use chat models, see the relevant how-to guides here.


Language models that takes a string as input and returns a string. These are traditionally older models (newer models generally are Chat Models, see below).

Although the underlying models are string in, string out, the LangChain wrappers also allow these models to take messages as input. This gives them the same interface as Chat Models. When messages are passed in as input, they will be formatted into a string under the hood before being passed to the underlying model.

LangChain does not host any LLMs, rather we rely on third party integrations.

For specifics on how to use LLMs, see the relevant how-to guides here.


Some language models take a list of messages as input and return a message. There are a few different types of messages. All messages have a role, content, and response_metadata property.

The role describes WHO is saying the message. LangChain has different message classes for different roles.

The content property describes the content of the message. This can be a few different things:

  • A string (most models deal this type of content)
  • A List of dictionaries (this is used for multimodal input, where the dictionary contains information about that input type and that input location)


This represents a message from the user.


This represents a message from the model. In addition to the content property, these messages also have:


The response_metadata property contains additional metadata about the response. The data here is often specific to each model provider. This is where information like log-probs and token usage may be stored.


These represent a decision from an language model to call a tool. They are included as part of an AIMessage output. They can be accessed from there with the .tool_calls property.

This property returns a list of dictionaries. Each dictionary has the following keys:

  • name: The name of the tool that should be called.
  • args: The arguments to that tool.
  • id: The id of that tool call.


This represents a system message, which tells the model how to behave. Not every model provider supports this.


This represents the result of a function call. In addition to role and content, this message has a name parameter which conveys the name of the function that was called to produce this result.


This represents the result of a tool call. This is distinct from a FunctionMessage in order to match OpenAI's function and tool message types. In addition to role and content, this message has a tool_call_id parameter which conveys the id of the call to the tool that was called to produce this result.

Prompt templates​

Prompt templates help to translate user input and parameters into instructions for a language model. This can be used to guide a model's response, helping it understand the context and generate relevant and coherent language-based output.

Prompt Templates take as input a dictionary, where each key represents a variable in the prompt template to fill in.

Prompt Templates output a PromptValue. This PromptValue can be passed to an LLM or a ChatModel, and can also be cast to a string or a list of messages. The reason this PromptValue exists is to make it easy to switch between strings and messages.

There are a few different types of prompt templates:

String PromptTemplates​

These prompt templates are used to format a single string, and generally are used for simpler inputs. For example, a common way to construct and use a PromptTemplate is as follows:

from langchain_core.prompts import PromptTemplate

prompt_template = PromptTemplate.from_template("Tell me a joke about {topic}")

prompt_template.invoke({"topic": "cats"})
API Reference:PromptTemplate


These prompt templates are used to format a list of messages. These "templates" consist of a list of templates themselves. For example, a common way to construct and use a ChatPromptTemplate is as follows:

from langchain_core.prompts import ChatPromptTemplate

prompt_template = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant"),
("user", "Tell me a joke about {topic}")

prompt_template.invoke({"topic": "cats"})
API Reference:ChatPromptTemplate

In the above example, this ChatPromptTemplate will construct two messages when called. The first is a system message, that has no variables to format. The second is a HumanMessage, and will be formatted by the topic variable the user passes in.


This prompt template is responsible for adding a list of messages in a particular place. In the above ChatPromptTemplate, we saw how we could format two messages, each one a string. But what if we wanted the user to pass in a list of messages that we would slot into a particular spot? This is how you use MessagesPlaceholder.

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import HumanMessage

prompt_template = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant"),

prompt_template.invoke({"msgs": [HumanMessage(content="hi!")]})

This will produce a list of two messages, the first one being a system message, and the second one being the HumanMessage we passed in. If we had passed in 5 messages, then it would have produced 6 messages in total (the system message plus the 5 passed in). This is useful for letting a list of messages be slotted into a particular spot.

An alternative way to accomplish the same thing without using the MessagesPlaceholder class explicitly is:

prompt_template = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant"),
("placeholder", "{msgs}") # <-- This is the changed part

For specifics on how to use prompt templates, see the relevant how-to guides here.

Example selectors​

One common prompting technique for achieving better performance is to include examples as part of the prompt. This gives the language model concrete examples of how it should behave. Sometimes these examples are hardcoded into the prompt, but for more advanced situations it may be nice to dynamically select them. Example Selectors are classes responsible for selecting and then formatting examples into prompts.

For specifics on how to use example selectors, see the relevant how-to guides here.

Output parsers​


The information here refers to parsers that take a text output from a model try to parse it into a more structured representation. More and more models are supporting function (or tool) calling, which handles this automatically. It is recommended to use function/tool calling rather than output parsing. See documentation for that here.

Responsible for taking the output of a model and transforming it to a more suitable format for downstream tasks. Useful when you are using LLMs to generate structured data, or to normalize output from chat models and LLMs.

LangChain has lots of different types of output parsers. This is a list of output parsers LangChain supports. The table below has various pieces of information:

Name: The name of the output parser

Supports Streaming: Whether the output parser supports streaming.

Has Format Instructions: Whether the output parser has format instructions. This is generally available except when (a) the desired schema is not specified in the prompt but rather in other parameters (like OpenAI function calling), or (b) when the OutputParser wraps another OutputParser.

Calls LLM: Whether this output parser itself calls an LLM. This is usually only done by output parsers that attempt to correct misformatted output.

Input Type: Expected input type. Most output parsers work on both strings and messages, but some (like OpenAI Functions) need a message with specific kwargs.

Output Type: The output type of the object returned by the parser.

Description: Our commentary on this output parser and when to use it.

NameSupports StreamingHas Format InstructionsCalls LLMInput TypeOutput TypeDescription
JSONβœ…βœ…str | MessageJSON objectReturns a JSON object as specified. You can specify a Pydantic model and it will return JSON for that model. Probably the most reliable output parser for getting structured data that does NOT use function calling.
XMLβœ…βœ…str | MessagedictReturns a dictionary of tags. Use when XML output is needed. Use with models that are good at writing XML (like Anthropic's).
CSVβœ…βœ…str | MessageList[str]Returns a list of comma separated values.
OutputFixingβœ…str | MessageWraps another output parser. If that output parser errors, then this will pass the error message and the bad output to an LLM and ask it to fix the output.
RetryWithErrorβœ…str | MessageWraps another output parser. If that output parser errors, then this will pass the original inputs, the bad output, and the error message to an LLM and ask it to fix it. Compared to OutputFixingParser, this one also sends the original instructions.
Pydanticβœ…str | Messagepydantic.BaseModelTakes a user defined Pydantic model and returns data in that format.
YAMLβœ…str | Messagepydantic.BaseModelTakes a user defined Pydantic model and returns data in that format. Uses YAML to encode it.
PandasDataFrameβœ…str | MessagedictUseful for doing operations with pandas DataFrames.
Enumβœ…str | MessageEnumParses response into one of the provided enum values.
Datetimeβœ…str | Messagedatetime.datetimeParses response into a datetime string.
Structuredβœ…str | MessageDict[str, str]An output parser that returns structured information. It is less powerful than other output parsers since it only allows for fields to be strings. This can be useful when you are working with smaller LLMs.

For specifics on how to use output parsers, see the relevant how-to guides here.

Chat history​

Most LLM applications have a conversational interface. An essential component of a conversation is being able to refer to information introduced earlier in the conversation. At bare minimum, a conversational system should be able to access some window of past messages directly.

The concept of ChatHistory refers to a class in LangChain which can be used to wrap an arbitrary chain. This ChatHistory will keep track of inputs and outputs of the underlying chain, and append them as messages to a message database. Future interactions will then load those messages and pass them into the chain as part of the input.


A Document object in LangChain contains information about some data. It has two attributes:

  • page_content: str: The content of this document. Currently is only a string.
  • metadata: dict: Arbitrary metadata associated with this document. Can track the document id, file name, etc.

Document loaders​

These classes load Document objects. LangChain has hundreds of integrations with various data sources to load data from: Slack, Notion, Google Drive, etc.

Each DocumentLoader has its own specific parameters, but they can all be invoked in the same way with the .load method. An example use case is as follows:

from langchain_community.document_loaders.csv_loader import CSVLoader

loader = CSVLoader(
... # <-- Integration specific parameters here
data = loader.load()
API Reference:CSVLoader

For specifics on how to use document loaders, see the relevant how-to guides here.

Text splitters​

Once you've loaded documents, you'll often want to transform them to better suit your application. The simplest example is you may want to split a long document into smaller chunks that can fit into your model's context window. LangChain has a number of built-in document transformers that make it easy to split, combine, filter, and otherwise manipulate documents.

When you want to deal with long pieces of text, it is necessary to split up that text into chunks. As simple as this sounds, there is a lot of potential complexity here. Ideally, you want to keep the semantically related pieces of text together. What "semantically related" means could depend on the type of text. This notebook showcases several ways to do that.

At a high level, text splitters work as following:

  1. Split the text up into small, semantically meaningful chunks (often sentences).
  2. Start combining these small chunks into a larger chunk until you reach a certain size (as measured by some function).
  3. Once you reach that size, make that chunk its own piece of text and then start creating a new chunk of text with some overlap (to keep context between chunks).

That means there are two different axes along which you can customize your text splitter:

  1. How the text is split
  2. How the chunk size is measured

For specifics on how to use text splitters, see the relevant how-to guides here.

Embedding models​

The Embeddings class is a class designed for interfacing with text embedding models. There are lots of embedding model providers (OpenAI, Cohere, Hugging Face, etc) - this class is designed to provide a standard interface for all of them.

Embeddings create a vector representation of a piece of text. This is useful because it means we can think about text in the vector space, and do things like semantic search where we look for pieces of text that are most similar in the vector space.

The base Embeddings class in LangChain provides two methods: one for embedding documents and one for embedding a query. The former takes as input multiple texts, while the latter takes a single text. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself).

For specifics on how to use embedding models, see the relevant how-to guides here.

Vector stores​

One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding vectors, and then at query time to embed the unstructured query and retrieve the embedding vectors that are 'most similar' to the embedded query. A vector store takes care of storing embedded data and performing vector search for you.

Vector stores can be converted to the retriever interface by doing:

vectorstore = MyVectorStore()
retriever = vectorstore.as_retriever()

For specifics on how to use vector stores, see the relevant how-to guides here.


A retriever is an interface that returns documents given an unstructured query. It is more general than a vector store. A retriever does not need to be able to store documents, only to return (or retrieve) them. Retrievers can be created from vectorstores, but are also broad enough to include Wikipedia search and Amazon Kendra.

Retrievers accept a string query as input and return a list of Document's as output.

For specifics on how to use retrievers, see the relevant how-to guides here.


Tools are interfaces that an agent, a chain, or a chat model / LLM can use to interact with the world.

A tool consists of the following components:

  1. The name of the tool
  2. A description of what the tool does
  3. JSON schema of what the inputs to the tool are
  4. The function to call
  5. Whether the result of a tool should be returned directly to the user (only relevant for agents)

The name, description and JSON schema are provided as context to the LLM, allowing the LLM to determine how to use the tool appropriately.

Given a list of available tools and a prompt, an LLM can request that one or more tools be invoked with appropriate arguments.

Generally, when designing tools to be used by a chat model or LLM, it is important to keep in mind the following:

  • Chat models that have been fine-tuned for tool calling will be better at tool calling than non-fine-tuned models.
  • Non fine-tuned models may not be able to use tools at all, especially if the tools are complex or require multiple tool calls.
  • Models will perform better if the tools have well-chosen names, descriptions, and JSON schemas.
  • Simpler tools are generally easier for models to use than more complex tools.

For specifics on how to use tools, see the relevant how-to guides here.


Toolkits are collections of tools that are designed to be used together for specific tasks. They have convenient loading methods.

All Toolkits expose a get_tools method which returns a list of tools. You can therefore do:

# Initialize a toolkit
toolkit = ExampleTookit(...)

# Get list of tools
tools = toolkit.get_tools()


By themselves, language models can't take actions - they just output text. A big use case for LangChain is creating agents. Agents are systems that use an LLM as a reasoning engine to determine which actions to take and what the inputs to those actions should be. The results of those actions can then be fed back into the agent and it determine whether more actions are needed, or whether it is okay to finish.

LangGraph is an extension of LangChain specifically aimed at creating highly controllable and customizable agents. Please check out that documentation for a more in depth overview of agent concepts.

There is a legacy agent concept in LangChain that we are moving towards deprecating: AgentExecutor. AgentExecutor was essentially a runtime for agents. It was a great place to get started, however, it was not flexible enough as you started to have more customized agents. In order to solve that we built LangGraph to be this flexible, highly-controllable runtime.

If you are still using AgentExecutor, do not fear: we still have a guide on how to use AgentExecutor. It is recommended, however, that you start to transition to LangGraph. In order to assist in this we have put together a transition guide on how to do so.


Some models are multimodal, accepting images, audio and even video as inputs. These are still less common, meaning model providers haven't standardized on the "best" way to define the API. Multimodal outputs are even less common. As such, we've kept our multimodal abstractions fairly light weight and plan to further solidify the multimodal APIs and interaction patterns as the field matures.

In LangChain, most chat models that support multimodal inputs also accept those values in OpenAI's content blocks format. So far this is restricted to image inputs. For models like Gemini which support video and other bytes input, the APIs also support the native, model-specific representations.

For specifics on how to use multimodal models, see the relevant how-to guides here.


LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.

You can subscribe to these events by using the callbacks argument available throughout the API. This argument is list of handler objects, which are expected to implement one or more of the methods described below in more detail.

Callback Events​

EventEvent TriggerAssociated Method
Chat model startWhen a chat model startson_chat_model_start
LLM startWhen a llm startson_llm_start
LLM new tokenWhen an llm OR chat model emits a new tokenon_llm_new_token
LLM endsWhen an llm OR chat model endson_llm_end
LLM errorsWhen an llm OR chat model errorson_llm_error
Chain startWhen a chain starts runningon_chain_start
Chain endWhen a chain endson_chain_end
Chain errorWhen a chain errorson_chain_error
Tool startWhen a tool starts runningon_tool_start
Tool endWhen a tool endson_tool_end
Tool errorWhen a tool errorson_tool_error
Agent actionWhen an agent takes an actionon_agent_action
Agent finishWhen an agent endson_agent_finish
Retriever startWhen a retriever startson_retriever_start
Retriever endWhen a retriever endson_retriever_end
Retriever errorWhen a retriever errorson_retriever_error
TextWhen arbitrary text is runon_text
RetryWhen a retry event is runon_retry

Callback handlers​

Callback handlers can either be sync or async:

During run-time LangChain configures an appropriate callback manager (e.g., CallbackManager or AsyncCallbackManager which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered.

Passing callbacks​

The callbacks property is available on most objects throughout the API (Models, Tools, Agents, etc.) in two different places:

The callbacks are available on most objects throughout the API (Models, Tools, Agents, etc.) in two different places:

  • Request time callbacks: Passed at the time of the request in addition to the input data. Available on all standard Runnable objects. These callbacks are INHERITED by all children of the object they are defined on. For example, chain.invoke({"number": 25}, {"callbacks": [handler]}).
  • Constructor callbacks: chain = TheNameOfSomeChain(callbacks=[handler]). These callbacks are passed as arguments to the constructor of the object. The callbacks are scoped only to the object they are defined on, and are not inherited by any children of the object.

Constructor callbacks are scoped only to the object they are defined on. They are not inherited by children of the object.

If you're creating a custom chain or runnable, you need to remember to propagate request time callbacks to any child objects.

Async in Python<=3.10

Any RunnableLambda, a RunnableGenerator, or Tool that invokes other runnables and is running async in python<=3.10, will have to propagate callbacks to child objects manually. This is because LangChain cannot automatically propagate callbacks to child objects in this case.

This is a common reason why you may fail to see events being emitted from custom runnables or tools.

For specifics on how to use callbacks, see the relevant how-to guides here.



Individual LLM calls often run for much longer than traditional resource requests. This compounds when you build more complex chains or agents that require multiple reasoning steps.

Fortunately, LLMs generate output iteratively, which means it's possible to show sensible intermediate results before the final response is ready. Consuming output as soon as it becomes available has therefore become a vital part of the UX around building apps with LLMs to help alleviate latency issues, and LangChain aims to have first-class support for streaming.

Below, we'll discuss some concepts and considerations around streaming in LangChain.


The unit that most model providers use to measure input and output is via a unit called a token. Tokens are the basic units that language models read and generate when processing or producing text. The exact definition of a token can vary depending on the specific way the model was trained - for instance, in English, a token could be a single word like "apple", or a part of a word like "app".

When you send a model a prompt, the words and characters in the prompt are encoded into tokens using a tokenizer. The model then streams back generated output tokens, which the tokenizer decodes into human-readable text. The below example shows how OpenAI models tokenize LangChain is cool!:

You can see that it gets split into 5 different tokens, and that the boundaries between tokens are not exactly the same as word boundaries.

The reason language models use tokens rather than something more immediately intuitive like "characters" has to do with how they process and understand text. At a high-level, language models iteratively predict their next generated output based on the initial input and their previous generations. Training the model using tokens language models to handle linguistic units (like words or subwords) that carry meaning, rather than individual characters, which makes it easier for the model to learn and understand the structure of the language, including grammar and context. Furthermore, using tokens can also improve efficiency, since the model processes fewer units of text compared to character-level processing.


The lowest level way to stream outputs from LLMs in LangChain is via the callbacks system. You can pass a callback handler that handles the on_llm_new_token event into LangChain components. When that component is invoked, any LLM or chat model contained in the component calls the callback with the generated token. Within the callback, you could pipe the tokens into some other destination, e.g. a HTTP response. You can also handle the on_llm_end event to perform any necessary cleanup.

You can see this how-to section for more specifics on using callbacks.

Callbacks were the first technique for streaming introduced in LangChain. While powerful and generalizable, they can be unwieldy for developers. For example:

  • You need to explicitly initialize and manage some aggregator or other stream to collect results.
  • The execution order isn't explicitly guaranteed, and you could theoretically have a callback run after the .invoke() method finishes.
  • Providers would often make you pass an additional parameter to stream outputs instead of returning them all at once.
  • You would often ignore the result of the actual model call in favor of callback results.

.stream() and .astream()​

LangChain also includes the .stream() method (and the equivalent .astream() method for async environments) as a more ergonomic streaming interface. .stream() returns an iterator, which you can consume with a simple for loop. Here's an example with a chat model:

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-3-sonnet-20240229")

for chunk in"what color is the sky?"):
print(chunk.content, end="|", flush=True)
API Reference:ChatAnthropic

For models (or other components) that don't support streaming natively, this iterator would just yield a single chunk, but you could still use the same general pattern. Using .stream() will also automatically call the model in streaming mode without the need to provide additional config.

The type of each outputted chunk depends on the type of component - for example, chat models yield AIMessageChunks. Because this method is part of LangChain Expression Language, you can handle formatting differences from different outputs using an output parser to transform each yielded chunk.

You can check out this guide for more detail on how to use .stream().


While the .stream() method is easier to use than callbacks, it only returns one type of value. This is fine for single LLM calls, but as you build more complex chains of several LLM calls together, you may want to use the intermediate values of the chain alongside the final output - for example, returning sources alongside the final generation when building a chat over documents app.

There are ways to do this using the aforementioned callbacks, or by constructing your chain in such a way that it passes intermediate values to the end with something like .assign(), but LangChain also includes an .astream_events() method that combines the flexibility of callbacks with the ergonomics of .stream(). When called, it returns an iterator which yields various types of events that you can filter and process according to the needs of your project.

Here's one small example that prints just events containing streamed chat model output:

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-3-sonnet-20240229")

prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
parser = StrOutputParser()
chain = prompt | model | parser

async for event in chain.astream_events({"topic": "parrot"}, version="v2"):
kind = event["event"]
if kind == "on_chat_model_stream":
print(event, end="|", flush=True)

You can roughly think of it as an iterator over callback events (though the format differs) - and you can use it on almost all LangChain components!

See this guide for more detailed information on how to use .astream_events().

Structured output​

LLMs are capable of generating arbitrary text. This enables the model to respond appropriately to a wide range of inputs, but for some use-cases, it can be useful to constrain the LLM's output to a specific format or structure. This is referred to as structured output.

For example, if the output is to be stored in a relational database, it is much easier if the model generates output that adheres to a defined schema or format. Extracting specific information from unstructured text is another case where this is particularly useful. Most commonly, the output format will be JSON, though other formats such as YAML can be useful too. Below, we'll discuss a few ways to get structured output from models in LangChain.


For convenience, some LangChain chat models support a .with_structured_output() method. This method only requires a schema as input, and returns a dict or Pydantic object. Generally, this method is only present on models that support one of the more advanced methods described below, and will use one of them under the hood. It takes care of importing a suitable output parser and formatting the schema in the right format for the model.

For more information, check out this how-to guide.

Raw prompting​

The most intuitive way to get a model to structure output is to ask nicely. In addition to your query, you can give instructions describing what kind of output you'd like, then parse the output using an output parser to convert the raw model message or string output into something more easily manipulated.

The biggest benefit to raw prompting is its flexibility:

  • Raw prompting does not require any special model features, only sufficient reasoning capability to understand the passed schema.
  • You can prompt for any format you'd like, not just JSON. This can be useful if the model you are using is more heavily trained on a certain type of data, such as XML or YAML.

However, there are some drawbacks too:

  • LLMs are non-deterministic, and prompting a LLM to consistently output data in the exactly correct format for smooth parsing can be surprisingly difficult and model-specific.
  • Individual models have quirks depending on the data they were trained on, and optimizing prompts can be quite difficult. Some may be better at interpreting JSON schema, others may be best with TypeScript definitions, and still others may prefer XML.

While we'll next go over some ways that you can take advantage of features offered by model providers to increase reliability, prompting techniques remain important for tuning your results no matter what method you choose.

JSON mode​

Some models, such as Mistral, OpenAI, Together AI and Ollama, support a feature called JSON mode, usually enabled via config.

When enabled, JSON mode will constrain the model's output to always be some sort of valid JSON. Often they require some custom prompting, but it's usually much less burdensome and along the lines of, "you must always return JSON", and the output is easier to parse.

It's also generally simpler and more commonly available than tool calling.

Here's an example:

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain.output_parsers.json import SimpleJsonOutputParser

model = ChatOpenAI(
model_kwargs={ "response_format": { "type": "json_object" } },

prompt = ChatPromptTemplate.from_template(
"Answer the user's question to the best of your ability."
'You must always output a JSON object with an "answer" key and a "followup_question" key.'

chain = prompt | model | SimpleJsonOutputParser()

chain.invoke({ "question": "What is the powerhouse of the cell?" })
{'answer': 'The powerhouse of the cell is the mitochondrion. It is responsible for producing energy in the form of ATP through cellular respiration.',
'followup_question': 'Would you like to know more about how mitochondria produce energy?'}

For a full list of model providers that support JSON mode, see this table.

Function/tool calling​


We use the term tool calling interchangeably with function calling. Although function calling is sometimes meant to refer to invocations of a single function, we treat all models as though they can return multiple tool or function calls in each message.

Tool calling allows a model to respond to a given prompt by generating output that matches a user-defined schema. While the name implies that the model is performing some action, this is actually not the case! The model is coming up with the arguments to a tool, and actually running the tool (or not) is up to the user - for example, if you want to extract output matching some schema from unstructured text, you could give the model an "extraction" tool that takes parameters matching the desired schema, then treat the generated output as your final result.

For models that support it, tool calling can be very convenient. It removes the guesswork around how best to prompt schemas in favor of a built-in model feature. It can also more naturally support agentic flows, since you can just pass multiple tool schemas instead of fiddling with enums or unions.

Many LLM providers, including Anthropic, Cohere, Google, Mistral, OpenAI, and others, support variants of a tool calling feature. These features typically allow requests to the LLM to include available tools and their schemas, and for responses to include calls to these tools. For instance, given a search engine tool, an LLM might handle a query by first issuing a call to the search engine. The system calling the LLM can receive the tool call, execute it, and return the output to the LLM to inform its response. LangChain includes a suite of built-in tools and supports several methods for defining your own custom tools.

LangChain provides a standardized interface for tool calling that is consistent across different models.

The standard interface consists of:

  • ChatModel.bind_tools(): a method for specifying which tools are available for a model to call. This method accepts LangChain tools here.
  • AIMessage.tool_calls: an attribute on the AIMessage returned from the model for accessing the tool calls requested by the model.

The following how-to guides are good practical resources for using function/tool calling:

For a full list of model providers that support tool calling, see this table.


LangChain provides several advanced retrieval types. A full list is below, along with the following information:

Name: Name of the retrieval algorithm.

Index Type: Which index type (if any) this relies on.

Uses an LLM: Whether this retrieval method uses an LLM.

When to Use: Our commentary on when you should considering using this retrieval method.

Description: Description of what this retrieval algorithm is doing.

NameIndex TypeUses an LLMWhen to UseDescription
VectorstoreVectorstoreNoIf you are just getting started and looking for something quick and easy.This is the simplest method and the one that is easiest to get started with. It involves creating embeddings for each piece of text.
ParentDocumentVectorstore + Document StoreNoIf your pages have lots of smaller pieces of distinct information that are best indexed by themselves, but best retrieved all together.This involves indexing multiple chunks for each document. Then you find the chunks that are most similar in embedding space, but you retrieve the whole parent document and return that (rather than individual chunks).
Multi VectorVectorstore + Document StoreSometimes during indexingIf you are able to extract information from documents that you think is more relevant to index than the text itself.This involves creating multiple vectors for each document. Each vector could be created in a myriad of ways - examples include summaries of the text and hypothetical questions.
Self QueryVectorstoreYesIf users are asking questions that are better answered by fetching documents based on metadata rather than similarity with the text.This uses an LLM to transform user input into two things: (1) a string to look up semantically, (2) a metadata filer to go along with it. This is useful because oftentimes questions are about the METADATA of documents (not the content itself).
Contextual CompressionAnySometimesIf you are finding that your retrieved documents contain too much irrelevant information and are distracting the LLM.This puts a post-processing step on top of another retriever and extracts only the most relevant information from retrieved documents. This can be done with embeddings or an LLM.
Time-Weighted VectorstoreVectorstoreNoIf you have timestamps associated with your documents, and you want to retrieve the most recent onesThis fetches documents based on a combination of semantic similarity (as in normal vector retrieval) and recency (looking at timestamps of indexed documents)
Multi-Query RetrieverAnyYesIf users are asking questions that are complex and require multiple pieces of distinct information to respondThis uses an LLM to generate multiple queries from the original one. This is useful when the original query needs pieces of information about multiple topics to be properly answered. By generating multiple queries, we can then fetch documents for each of them.
EnsembleAnyNoIf you have multiple retrieval methods and want to try combining them.This fetches documents from multiple retrievers and then combines them.

For a high-level guide on retrieval, see this tutorial on RAG.

Text splitting​

LangChain offers many different types of text splitters. These all live in the langchain-text-splitters package.

Table columns:

  • Name: Name of the text splitter
  • Classes: Classes that implement this text splitter
  • Splits On: How this text splitter splits text
  • Adds Metadata: Whether or not this text splitter adds metadata about where each chunk came from.
  • Description: Description of the splitter, including recommendation on when to use it.
NameClassesSplits OnAdds MetadataDescription
RecursiveRecursiveCharacterTextSplitter, RecursiveJsonSplitterA list of user defined charactersRecursively splits text. This splitting is trying to keep related pieces of text next to each other. This is the recommended way to start splitting text.
HTMLHTMLHeaderTextSplitter, HTMLSectionSplitterHTML specific charactersβœ…Splits text based on HTML-specific characters. Notably, this adds in relevant information about where that chunk came from (based on the HTML)
MarkdownMarkdownHeaderTextSplitter,Markdown specific charactersβœ…Splits text based on Markdown-specific characters. Notably, this adds in relevant information about where that chunk came from (based on the Markdown)
Codemany languagesCode (Python, JS) specific charactersSplits text based on characters specific to coding languages. 15 different languages are available to choose from.
Tokenmany classesTokensSplits text on tokens. There exist a few different ways to measure tokens.
CharacterCharacterTextSplitterA user defined characterSplits text based on a user defined character. One of the simpler methods.
Semantic Chunker (Experimental)SemanticChunkerSentencesFirst splits on sentences. Then combines ones next to each other if they are semantically similar enough. Taken from Greg Kamradt
Integration: AI21 SemanticAI21SemanticTextSplitterβœ…Identifies distinct topics that form coherent pieces of text and splits along those.

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