> For the complete documentation index, see [llms.txt](https://agpt.co/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://agpt.co/docs/integrations/block-integrations/answers.md).

# Exa Answers

Blocks for getting AI-generated answers to questions using Exa's search-informed answer API.

## Exa Answer

### What it is

Get an LLM answer to a question informed by Exa search results

### How it works

This block sends your question to the Exa Answer API, which performs a semantic search across billions of web pages to find relevant information. The API then uses an LLM to synthesize the search results into a coherent answer with citations.

The block returns both the generated answer and the source citations that informed it. You can optionally include full text content from the search results for more comprehensive answers.

### Inputs

| Input | Description                                                         | Type | Required |
| ----- | ------------------------------------------------------------------- | ---- | -------- |
| query | The question or query to answer                                     | str  | Yes      |
| text  | Include full text content in the search results used for the answer | bool | No       |

### Outputs

| Output    | Description                                  | Type                  |
| --------- | -------------------------------------------- | --------------------- |
| error     | Error message if the request failed          | str                   |
| answer    | The generated answer based on search results | str                   |
| citations | Search results used to generate the answer   | List\[AnswerCitation] |
| citation  | Individual citation from the answer          | AnswerCitation        |

### Possible use case

**Research Assistance**: Get quick, sourced answers to complex questions without manually searching multiple websites.

**Fact Verification**: Verify claims or statements by getting answers backed by real web sources with citations.

**Content Creation**: Generate research-backed content by asking questions about topics and using the cited sources.

***


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# 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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://agpt.co/docs/integrations/block-integrations/answers.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.