@font-face {
  font-family: 'Reckless';
  src: url('https://exa.ai/search/fonts/RecklessTRIAL-Medium.woff2');
  font-weight: normal;
  font-style: normal;
  font-display: swap;
}

@font-face {
    font-family: 'ABCDiatype';
    src: url("https://exa.ai/search/fonts/ABCDiatype-Regular.otf") format("opentype");
}

@font-face {
  font-family: 'SuisseIntlMono';
  src: url("https://exa.ai/search/fonts/SuisseIntlMono-Regular-WebTrial.woff2") format('woff2');
}

h1, h2, h3 {
  font-family: 'Reckless';
  letter-spacing: normal !important;
  margin-bottom: 1rem !important;
}

p {
    font-family: 'ABCDiatype';
}

.card {
  border-radius: 15px;
  overflow: hidden;
  margin-bottom: 1rem !important;
}

pre {
  margin-top: 0.5rem !important;
  margin-bottom: 0.5rem !important;
}

section,
.section,
.content section {
  margin-bottom: 1rem !important;
}

h1, h2, h3, h4 {
  margin-top: 0.5rem !important;
  margin-bottom: 0.5rem !important;
}

hr {
  margin-bottom: 1.5rem !important;
  margin-top: 1.5rem !important;;
}

.anchor-icon-wrapper {
  border-radius: 50% !important;
}

.anchors-icon {
  border-radius: 50% !important;
}

.py-6 {
  padding-top: 0.5rem;
  padding-bottom: 0.5rem;
}

.mt-4 {
  margin-top: 0.5rem;
}

.text-primary-light {
  color: #FFFFFF !important;
}

.border-primary-light {
  border-color: #FFFFFF !important;
}

.border-primary {
  border-color: #FFFFFF !important;
}

.card-title {
  font-family: 'ABCDiatype' !important;
  font-size: 1.2rem !important;
  padding-top: 0.5rem !important;
}

.mt-2 {
  margin-top: .5rem;
  margin-bottom: .5rem;
}

/* Reduces spacing between title and text in steps */
.w-full.overflow-hidden.pl-12.pr-px p {
  margin-top: 0 !important;
  margin-bottom: 0rem !important;
  padding-top: 0.5rem !important;
}

/* Reduces spacing after code blocks */
.not-prose.rounded-xl {
  margin-bottom: 1rem !important;
} 

hr {
  margin-bottom: 1rem !important;
  margin-top: 1rem !important;
}

.mt-8 {
  margin-top: 1rem !important;
}

.mt-10 {
  margin-top: 1rem !important;
}

.max-h-\[300px\] {
  max-height: 500px !important;  
}

.api-button {
  align-items: center;
  background: rgb(var(--primary));
  color: var(--white, #fff);
  display: flex;
  font-family: var(SuisseIntlMono);
  text-transform: uppercase;
  font-size: 16px;
  font-style: normal;
  font-weight: 400;
  gap: 10px;
  height: 40px;
  justify-content: center;
  line-height: 27px;
  padding: 14px 32px;
  text-decoration: none;
  transition: 0.1s ease-in-out;
  border: 2px solid var(--primary);
  word-spacing: 8px;
  letter-spacing: 1px;
}

.api-button:hover {
  background-color: #000000;
}


OpenRouter

Overview

Welcome to Exa

Make your first request to one of Exa's API endpoints

Quickstart

The search endpoint lets you intelligently search the web and extract contents from the results. 

By default, it automatically chooses between traditional keyword search and Exa's embeddings-based model, to find the most relevant results for your query.

Search

Get the full page contents, summaries, and metadata for a list of URLs.

Returns instant results from our cache, with automatic live crawling as fallback for uncached pages.

Get contents

Find similar links to the link provided and optionally return the contents of the pages.

Find similar links

Get an LLM answer to a question informed by Exa search results. `/answer` performs an Exa search and uses an LLM to generate either:
1. A direct answer for specific queries. (i.e. "What is the capital of France?" would return "Paris")
2. A detailed summary with citations for open-ended queries (i.e. "What is the state of ai in healthcare?" would return a summary with citations to relevant sources)

The response includes both the generated answer and the sources used to create it. The endpoint also supports streaming (as `stream=True`), which will returns tokens as they are generated.

Alternatively, you can use the OpenAI compatible [chat completions interface](https://docs.exa.ai/reference/chat-completions#answer).


Answer

Websets

OpenAPI Specification

Learn how to build your first Retrieval Augmented Generation (RAG) system with Exa and OpenAI. 

RAG with Exa and OpenAI

How to use Exa's integration with LangChain to perform RAG.

RAG with LangChain

Enhance your OpenAI chat completetions with a simple Exa wrapper that handles search, chunking and prompting.

OpenAI Exa Wrapper

Learn how to add Exa retrieval capabilities to your CrewAI agents.

CrewAI agents with Exa

A quick-start guide on how to add Exa retrieval to a LlamaIndex Agent Application.

RAG with LlamaIndex

Learn to use OpenAI's tool call feature with Exa's Search Integration

Tool calling with GPT

Using Claude's "Tool Use" Feature with Exa Search Integration.

Tool calling with Claude

Use Exa's /chat/completions endpoint as a drop-in replacement for your OpenAI chat completions code.

OpenAI Chat Completions

Exa's web search can now be used with OpenAI's new Response API. This lets you add live web search to your AI app using OpenAI's function calling feature.

OpenAI Responses API

Exa is a novel search engine that utilizes the latest advancements in AI language processing to return the best possible results.

How Exa Search Works

We spend a lot of time and energy creating a high quality, curated index.

The Exa Index

Contents retrieval with Exa API

This page explains some of the available feature functionalities of Exa and some unique ways you might use Exa for your use-case

Exa's Capabilities Explained

Crawling Subpages with Exa

Exa LiveCrawl

Automate in-depth web research and receive structured JSON results with citations.

Exa Research

FAQs

Details on Team structure and account management for the Exa platform

Setting Up and Managing Your Team

Rate Limits

Enterprise Documentation & Security 

Exa MCP

A live demo that monitors the web semantically using the Websets API.

Websets News Monitor

A live demo that detects hallucinations in content using Exa's search.

Hallucination Detector

Writing Assistant

Chat app

Company researcher

Make your very own Hacker News powered by Exa

Hacker News Clone

Learn how to build an AI-powered news summarizer that searches and summarizes recent articles using Exa and GPT.

Building a News Summarizer

Learn how to build an AI-powered system that identifies and verifies claims using Exa and LangGraph.

Building a Hallucination Checker

Using Exa to enable retrieval-augmented generation.

RAG Q&A

Example project using the Exa Python SDK.

Company Analyst

Exa Researcher - JavaScript

Exa Researcher - Python

Recruiting Agent

Phrase Filters: Niche Company Finder

Tutorial for simple Exa searches on our front-end.

Job Search with Exa

Build a Retrieval Agent with LangGraph

Using Exa with instructor to generate structured outputs from web content.

Structured Outputs with Instructor

Vercel AI SDK

LangChain Docs

LlamaIndex Docs

CrewAI Docs

IBM WatsonX

Enumeration of methods and types in the Exa Python SDK (exa_py).

Python SDK Specification

TypeScript SDK Specification

Some common code you might want to use - don't miss the TypeScript tab below!

Python and TS Cheat Sheets

Our goal is to help you find anything you want on the web, no matter how complex.

Welcome to Websets

Welcome to the Websets Dashboard! Find anything you want on the web, no matter how complex.

Get started

Here are some examples for things to search for, to get you started!

Example queries

Turn your existing CSV data into a Webset

Import from CSV

Avoid duplicate results in your new searches by excluding URLs from previous Websets or CSV files.

Exclude Results

Connect your Websets with popular CRM and email tools

Integrations

The Websets API helps you find, verify, and process web data at scale to build your unique collection of web content.

How It Works

Markdown content is now the default format for all Exa API endpoints, providing cleaner, more readable content that's ideal for AI applications and text processing.

Markdown Contents as Default

Introducing the 'preferred' livecrawl option that tries to fetch fresh content but gracefully falls back to cached results when crawling fails, providing the best of both worlds.

New Livecrawl Option: Preferred

The /contents endpoint now returns detailed status information for each URL instead of HTTP error codes, providing better visibility into individual content fetch results.

Contents Endpoint Status Changes

Auto search, which intelligently combines Exa's proprietary neural search with traditional keyword search, is now the default search type for all queries.

Auto search as Default

Create an asynchronous research task that explores the web, gathers sources, synthesizes findings, and returns results with citations. Can be used to generate:
1. Structured JSON matching an `output.schema` you provide.
2. Structured data matching a schema generated by an LLM when `output.inferSchema` is provided.
3. A detailed markdown report when no schema is provided.

The API responds immediately with a task `id` for polling completion status. For more details, see [Exa Research](/reference/exa-research).

Alternatively, you can use the OpenAI compatible [chat completions interface](/reference/chat-completions#research).


Create a task

Retrieve the status and results of a previously created research task.

Use the unique task `id` returned from `POST /research/v0/tasks` to poll until the task is **completed** or **failed**. When the task is complete, the response includes your structured JSON output and citations.


Get a task

Retrieve a paginated list of your research tasks along with their current status and metadata. This endpoint allows you to monitor many tasks at once instead of polling each task individually.

The response follows a same cursor-based pagination pattern. Pass the `limit` parameter to control page size (max 200) and use the `cursor` token returned in the response to fetch subsequent pages.


List tasks

Here's how to create enrichments (also known as Adding Columns). 

Creating Enrichments 

Explore your Websets matched results, view summaries, criteria justification 

Exploring your results 

Adding and Managing Your Team Members in Websets

Here's how to prompt your query in Websets

Prompting Websets

Here's how to share or download your results and enrichments.

Downloading and Sharing Your Results

Creates a new import to upload your data into Websets. Imports can be used to:

- **Enrich**: Enhance your data with additional information using our AI-powered enrichment engine
- **Search**: Query your data using Websets' agentic search with natural language filters
- **Exclude**: Prevent duplicate or already known results from appearing in your searches

Once the import is created, you can upload your data to the returned `uploadUrl` until `uploadValidUntil` (by default 1 hour).

Create an Import

Get Import

Update Import

Delete Import

List Imports

Creates a new `Monitor` to continuously keep your Websets updated with fresh data.

Monitors automatically run on your defined schedule to ensure your Websets stay current without manual intervention:

- **Find new content**: Execute `search` operations to discover fresh items matching your criteria
- **Update existing content**: Run `refresh` operations to update items contents and enrichments
- **Automated scheduling**: Configure `cron` expressions and `timezone` for precise scheduling control

Create a Monitor

Get Monitor

Update Monitor

Delete Monitor

List Monitors

Create a Webhook

Get a Webhook

Update a Webhook

Delete a Webhook

List webhooks

List all attempts made by a Webhook ordered in descending order.

List webhook attempts

Learn how to securely verify webhook signatures to ensure requests are from Exa

Verifying Signatures

Learn about the events that occur within the Webset API

Types

Event Types

List all events that have occurred in the system.

You can paginate through the results using the `cursor` parameter.

List all Events

Get a single Event by id.

You can subscribe to Events by creating a Webhook.

Integrations External Docs