LangChain Code#

Use the LangChain Code node to import LangChain. This means if there is functionality you need that n8n hasn't created a node for, you can still use it. By configuring the LangChain Code node connectors you can use it as a normal node, root node or sub-node.

On this page, you'll find the node parameters, guidance on configuring the node, and links to more resources.

Not available on Cloud

This node is only available on self-hosted n8n.

Node parameters#

Add Code#

Add your custom code. Choose either Execute or Supply Data mode. You can only use one mode.

Unlike the Code node, the LangChain Code node doesn't support Python.

Execute: use the LangChain Code node like n8n's own Code node. This takes input data from the workflow, processes it, and returns it as the node output. This mode requires a main input and output. You must create these connections in Inputs and Outputs.
Supply Data: use the LangChain Code node as a sub-node, sending data to a root node. This uses an output other than main.

By default, you can't load built-in or external modules in this node. Self-hosted users can enable built-in and external modules.

Inputs#

Choose the input types.

The main input is the normal connector found in all n8n workflows. If you have a main input and output set in the node, Execute code is required.

Outputs#

Choose the output types.

The main output is the normal connector found in all n8n workflows. If you have a main input and output set in the node, Execute code is required.

Node inputs and outputs configuration#

By configuring the LangChain Code node connectors (inputs and outputs) you can use it as an app node, root node or sub-node.

Node type	Inputs	Outputs	Code mode
App node. Similar to the Code node.	Main	Main	Execute
Root node	Main; at least one other type	Main	Execute
Sub-node	-	A type other than main. Must match the input type you want to connect to.	Supply Data
Sub-node with sub-nodes	A type other than main	A type other than main. Must match the input type you want to connect to.	Supply Data

Built-in methods#

n8n provides these methods to make it easier to perform common tasks in the LangChain Code node.

Method	Description
`this.addInputData(inputName, data)`	Populate the data of a specified non-main input. Useful for mocking data. `inputName` is the input connection type, and must be one of: `ai_agent`, `ai_chain`, `ai_document`, `ai_embedding`, `ai_languageModel`, `ai_memory`, `ai_outputParser`, `ai_retriever`, `ai_textSplitter`, `ai_tool`, `ai_vectorRetriever`, `ai_vectorStore` `data` contains the data you want to add. Refer to Data structure for information on the data structure expected by n8n.
`this.addOutputData(outputName, data)`	Populate the data of a specified non-main output. Useful for mocking data. `outputName` is the input connection type, and must be one of: `ai_agent`, `ai_chain`, `ai_document`, `ai_embedding`, `ai_languageModel`, `ai_memory`, `ai_outputParser`, `ai_retriever`, `ai_textSplitter`, `ai_tool`, `ai_vectorRetriever`, `ai_vectorStore` `data` contains the data you want to add. Refer to Data structure for information on the data structure expected by n8n.
`this.getInputConnectionData(inputName, itemIndex, inputIndex?)`	Get data from a specified non-main input. `inputName` is the input connection type, and must be one of: `ai_agent`, `ai_chain`, `ai_document`, `ai_embedding`, `ai_languageModel`, `ai_memory`, `ai_outputParser`, `ai_retriever`, `ai_textSplitter`, `ai_tool`, `ai_vectorRetriever`, `ai_vectorStore` `itemIndex` should always be `0` (this parameter will be used in upcoming functionality) Use `inputIndex` if there is more than one node connected to the specified input.
`this.getInputData(inputIndex?, inputName?)`	Get data from the main input.
`this.getNode()`	Get the current node.
`this.getNodeOutputs()`	Get the outputs of the current node.
`this.getExecutionCancelSignal()`	Use this to stop the execution of a function when the workflow stops. In most cases n8n handles this, but you may need to use it if building your own chains or agents. It replaces the Cancelling a running LLMChain code that you'd use if building a LangChain application normally.

Templates and examples#

Browse LangChain Code integration templates, or search all templates

View n8n's Advanced AI documentation.

completion: Completions are the responses generated by a model like GPT.
hallucinations: Hallucination in AI is when an LLM (large language model) mistakenly perceives patterns or objects that don't exist.
vector database: A vector database stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.
vector store: A vector store, or vector database, stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.