> For the complete documentation index, see [llms.txt](https://docs.n8n.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.n8n.io/build/integrate-ai/ai-examples.md).

# AI examples

This section provides explanations of important AI concepts, and workflow templates that highlight those concepts, with explanations and configuration guides. The examples cover common use cases and highlight different features of advanced AI in n8n.

* **Agents and chains**

  Learn about agents[^1] and chains[^2] in AI, including exploring key differences using the example workflow.

  [→ What's a chain in AI?](/build/integrate-ai/understand-ai-components/what-chains-do.md)\
  [→ What's an agent in AI?](/build/integrate-ai/understand-ai-components/what-agents-do.md)\
  [→ Demonstration of key differences between agents and chains](/build/integrate-ai/understand-ai-components/agents-vs-chains.md)
* **Call n8n Workflow Tool**

  Learn about tools[^3] in AI, then explore examples that use n8n workflows as custom tools to give your AI workflow access to more data.

  [→ What's a tool in AI?](/build/integrate-ai/understand-ai-components/how-tools-work.md)\
  [→ Chat with Google Sheets](/build/integrate-ai/ai-examples/use-google-sheets-as-a-data-source.md)\
  [→ Call an API to fetch data](/build/integrate-ai/ai-examples/call-apis.md)\
  [→ Set up a human fallback](/build/integrate-ai/ai-examples/set-a-human-fallback-for-ai-workflows.md)\
  [→ Let AI specify tool parameters with `$fromAI()`](/build/integrate-ai/ai-examples/use-ai-for-parameters.md)
* **Vector databases**

  Learn about [vector databases](#user-content-fn-4)[^4] in AI, along with related concepts including embeddings[^5] and retrievers.

  [→ What's a vector database?](/build/integrate-ai/understand-ai-components/store-and-search-data-with-vectors.md)\
  [→ Populate a Pinecone vector database from a website](/build/integrate-ai/ai-examples/use-website-content.md)
* **Memory**

  Learn about memory[^6] in AI.

  [→ What's memory in AI?](/build/integrate-ai/understand-ai-components/how-memory-works.md)
* **AI workflow templates**

  You can browse AI templates, included community contributions, on the n8n website.

  [→ Browse all AI templates](https://n8n.io/workflows/?categories=25)

[^1]: AI agents are artificial intelligence systems capable of responding to requests, making decisions, and performing real-world tasks for users. They use large language models (LLMs) to interpret user input and make decisions about how to best process requests using the information and resources they have available.

[^2]: AI chains allow you to interact with large language models (LLMs) and other resources in sequences of calls to components. AI chains in n8n don't use persistent memory, so you can't use them to reference previous context (use AI agents for this).

[^3]: In an AI context, a tool is an add-on resource that the AI can refer to for specific information or functionality when responding to a request. The AI model can use a tool to interact with external systems or complete specific, focused tasks.

[^4]: 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.

[^5]: Embeddings are numerical representations of data using vectors. They're used by AI to interpret complex data and relationships by mapping values across many dimensions. Vector databases, or vector stores, are databases designed to store and access embeddings.

[^6]: In an AI context, memory allows AI tools to persist message context across interactions. This allows you to have a continuing conversations with AI agents, for example, without submitting ongoing context with each message. In n8n, AI agent nodes can use memory, but AI chains can't.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.n8n.io/build/integrate-ai/ai-examples.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.