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:

Component	Input Type	Output Type
Prompt	Dictionary	PromptValue
ChatModel	Single string, list of chat messages or a PromptValue	ChatMessage
LLM	Single string, list of chat messages or a PromptValue	String
OutputParser	The output of an LLM or ChatModel	Depends on the parser
Retriever	Single string	List of Documents
Tool	Single string or dictionary, depending on the tool	Depends 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