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HTTP Request Tool node#

Legacy tool version

New instances of the HTTP Request tool node that you add to workflows use the standard HTTP Request node as a tool. This page is describes the legacy, standalone HTTP Request tool node.

You can identify which tool version is in your workflow by checking if the node has an Add option property when you open the node on the canvas. If that button is present, you're using the new version, not the one described on this page.

The HTTP Request tool works just like the HTTP Request node, but it's designed to be used with an AI agent as a tool to collect information from a website or API.

On this page, you'll find a list of operations the HTTP Request node supports and links to more resources.

Credentials

Refer to HTTP Request credentials for guidance on setting up authentication.

Parameter resolution in sub-nodes

Sub-nodes behave differently to other nodes when processing multiple items using an expression.

Most nodes, including root nodes, take any number of items as input, process these items, and output the results. You can use expressions to refer to input items, and the node resolves the expression for each item in turn. For example, given an input of five name values, the expression {{ $json.name }} resolves to each name in turn.

In sub-nodes, the expression always resolves to the first item. For example, given an input of five name values, the expression {{ $json.name }} always resolves to the first name.

Templates and examples#

Browse HTTP Request Tool node documentation integration templates, or search all templates

Refer to LangChain's documentation on tools for more information about tools in LangChain.

View n8n's Advanced AI documentation.

AI glossary#

  • 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.