Search the web, extract page content, crawl sites, and run research tasks through the Tavily API via the Maton gateway.
Integrations
Exa
Try itSearch the web, fetch page contents, and run async research through the Exa API with managed key authentication.
What it does
Routes requests to the Exa API endpoints (search, contents, findSimilar, answer, research/v1) through the Maton gateway, which injects your Exa key automatically. Authentication uses a Bearer token from the MATON_API_KEY environment variable, with optional connection selection via the Maton-Connection header. Supports neural, keyword, and hybrid search types, plus content extraction for text, highlights, and AI summaries.
When to use it
- Searching the web in neural, keyword, or auto mode with up to 100 results per call
- Fetching full text, highlights, or AI summaries from a list of known URLs
- Getting a direct AI answer with source citations to a specific question
- Running async research tasks on exa-research-fast, exa-research, or exa-research-pro models and polling for results
The skill document
Exa
Access the Exa API with managed API key authentication. Perform neural web searches, retrieve page contents, find similar pages, get AI-generated answers with citations, and run async research tasks.
Quick Start
# Search the web
python <<'EOF'
import urllib.request, os, json
data = json.dumps({"query": "latest AI research", "numResults": 5}).encode()
req = urllib.request.Request('https://gateway.maton.ai/exa/search', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF
Base URL
https://gateway.maton.ai/exa/{endpoint}
Replace {endpoint} with the Exa API endpoint (search, contents, findSimilar, answer, research/v1). The gateway proxies requests to api.exa.ai and automatically injects your API key.
Authentication
All requests require the Maton API key in the Authorization header:
Authorization: Bearer $MATON_API_KEY
Environment Variable: Set your API key as MATON_API_KEY:
export MATON_API_KEY="YOUR_API_KEY"
Getting Your API Key
- Sign in or create an account at maton.ai
- Go to maton.ai/settings
- Copy your API key
Connection Management
Manage your Exa API key connections at https://ctrl.maton.ai.
List Connections
python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://ctrl.maton.ai/connections?app=exa&status=ACTIVE')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF
Create Connection
python <<'EOF'
import urllib.request, os, json
data = json.dumps({'app': 'exa', 'method': 'API_KEY'}).encode()
req = urllib.request.Request('https://ctrl.maton.ai/connections', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF
Open the returned url in a browser to enter your Exa API key.
Get Connection
python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://ctrl.maton.ai/connections/{connection_id}')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF
Delete Connection
python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://ctrl.maton.ai/connections/{connection_id}', method='DELETE')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF
Specifying Connection
If you have multiple Exa connections, specify which one to use with the Maton-Connection header:
python <<'EOF'
import urllib.request, os, json
data = json.dumps({"query": "AI news"}).encode()
req = urllib.request.Request('https://gateway.maton.ai/exa/search', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
req.add_header('Maton-Connection', '{connection_id}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF
If omitted, the gateway uses the default (oldest) active connection.
API Reference
Search
Perform a neural web search with optional content extraction.
POST /exa/search
Content-Type: application/json
{
"query": "latest AI research papers",
"numResults": 10
}
Request Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
| query | string | Yes | Search query string |
| numResults | integer | No | Number of results (max 100, default 10) |
| type | string | No | Search type: neural, auto (default), keyword |
| category | string | No | Filter by category: company, research paper, news, tweet, personal site, financial report, people |
| includeDomains | array | No | Only include these domains |
| excludeDomains | array | No | Exclude these domains |
| startPublishedDate | string | No | ISO 8601 date filter (after) |
| endPublishedDate | string | No | ISO 8601 date filter (before) |
| contents | object | No | Content extraction options (see below) |
Contents Options:
{
"contents": {
"text": true,
"highlights": true,
"summary": true
}
}
| Option | Type | Description |
|---|---|---|
| text | boolean/object | Extract full page text |
| highlights | boolean/object | Extract relevant snippets |
| summary | boolean/object | Generate AI summary |
Response:
{
"requestId": "abc123",
"resolvedSearchType": "neural",
"results": [
{
"id": "https://example.com/article",
"title": "Article Title",
"url": "https://example.com/article",
"publishedDate": "2024-01-15T00:00:00.000Z",
"author": "Author Name",
"text": "Full page content...",
"highlights": ["Relevant snippet 1", "Relevant snippet 2"],
"summary": "AI-generated summary..."
}
],
"costDollars": {
"total": 0.005
}
}
Get Contents
Retrieve full page contents for specific URLs.
POST /exa/contents
Content-Type: application/json
{
"ids": ["https://example.com/page1", "https://example.com/page2"],
"text": true
}
Request Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
| ids | array | Yes | List of URLs to fetch content from |
| text | boolean | No | Include full page text |
| highlights | boolean/object | No | Include relevant snippets |
| summary | boolean/object | No | Generate AI summary |
Response:
{
"requestId": "abc123",
"results": [
{
"id": "https://example.com/page1",
"url": "https://example.com/page1",
"title": "Page Title",
"text": "Full page content..."
}
]
}
Find Similar
Find pages similar to a given URL.
POST /exa/findSimilar
Content-Type: application/json
{
"url": "https://example.com",
"numResults": 10
}
Request Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
| url | string | Yes | URL to find similar pages for |
| numResults | integer | No | Number of results (max 100, default 10) |
| includeDomains | array | No | Only include these domains |
| excludeDomains | array | No | Exclude these domains |
| contents | object | No | Content extraction options |
Response:
{
"requestId": "abc123",
"results": [
{
"id": "https://similar-site.com",
"title": "Similar Site",
"url": "https://similar-site.com",
"score": 0.95
}
],
"costDollars": {
"total": 0.005
}
}
Answer
Get an AI-generated answer to a question with citations.
POST /exa/answer
Content-Type: application/json
{
"query": "What is machine learning?",
"text": true
}
Request Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
| query | string | Yes | Question to answer |
| text | boolean | No | Include source text in response |
Response:
{
"requestId": "abc123",
"answer": "Machine learning is a subset of artificial intelligence...",
"citations": [
{
"id": "https://example.com/ml-guide",
"url": "https://example.com/ml-guide",
"title": "Machine Learning Guide"
}
]
}
Research Tasks
Run asynchronous research tasks that explore the web, gather sources, and synthesize findings with citations.
Create Research Task
POST /exa/research/v1
Content-Type: application/json
{
"instructions": "What are the top AI companies and their main products?",
"model": "exa-research"
}
Request Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
| instructions | string | Yes | What to research (max 4096 chars) |
| model | string | No | Model to use: exa-research-fast, exa-research (default), exa-research-pro |
| outputSchema | object | No | JSON Schema for structured output |
Response:
{
"researchId": "r_01abc123",
"createdAt": 1772969504083,
"model": "exa-research",
"instructions": "What are the top AI companies...",
"status": "running"
}
Get Research Task
GET /exa/research/v1/{researchId}
Query Parameters:
| Parameter | Type | Description |
|---|---|---|
| events | string | Set to true to include event log |
| stream | string | Set to true for SSE streaming |
Response (completed):
{
"researchId": "r_01abc123",
"status": "completed",
"createdAt": 1772969504083,
"finishedAt": 1772969520000,
"model": "exa-research",
"instructions": "What are the top AI companies...",
"output": {
"content": "Based on my research, the top AI companies are..."
},
"costDollars": {
"total": 0.15,
"numSearches": 5,
"numPages": 20,
"reasoningTokens": 1500
}
}
Status values: pending, running, completed, canceled, failed
List Research Tasks
GET /exa/research/v1?limit=10
Query Parameters:
| Parameter | Type | Description |
|---|---|---|
| limit | integer | Results per page (1-50, default 10) |
| cursor | string | Pagination cursor |
Response:
{
"data": [
{
"researchId": "r_01abc123",
"status": "completed",
"model": "exa-research",
"instructions": "What are the top AI companies..."
}
],
"hasMore": false,
"nextCursor": null
}
Code Examples
JavaScript
// Search with content extraction
const response = await fetch('https://gateway.maton.ai/exa/search', {
method: 'POST',
headers: {
'Authorization': `Bearer ${process.env.MATON_API_KEY}`,
'Content-Type': 'application/json'
},
body: JSON.stringify({
query: 'latest AI news',
numResults: 5,
contents: { text: true, highlights: true }
})
});
const data = await response.json();
Python
import os
import requests
# Search with content extraction
response = requests.post(
'https://gateway.maton.ai/exa/search',
headers={'Authorization': f'Bearer {os.environ["MATON_API_KEY"]}'},
json={
'query': 'latest AI news',
'numResults': 5,
'contents': {'text': True, 'highlights': True}
}
)
data = response.json()
Notes
- Search types:
neural(semantic),auto(hybrid),keyword(traditional) - Maximum 100 results per search request
- Content extraction (text, highlights, summary) incurs additional costs
- Categories like
peopleandcompanyhave restricted filter support - Timestamps are in ISO 8601 format
- IMPORTANT: When piping curl output to
jqor other commands, environment variables like$MATON_API_KEYmay not expand correctly in some shell environments
Error Handling
| Status | Meaning |
|---|---|
| 400 | Missing Exa connection or invalid request |
| 401 | Invalid or missing Maton API key |
| 429 | Rate limited |
| 4xx/5xx | Passthrough error from Exa API |
Resources
- Exa API Documentation
- Exa API Reference
- Exa Dashboard
- Maton Community
- Maton Support
Questions people ask
- How is the Exa API key handled?
- It is managed by the Maton gateway. You authenticate each request with a Bearer MATON_API_KEY, and the gateway injects the underlying Exa key for you. Multiple connections can be selected per request via the Maton-Connection header.
- Which Exa endpoints are exposed?
- Five endpoints are available: /exa/search, /exa/contents, /exa/findSimilar, /exa/answer, and /exa/research/v1, which supports creating, retrieving, and listing asynchronous research tasks.
- Do research tasks support structured output?
- Yes. The /exa/research/v1 endpoint accepts an outputSchema field defined as JSON Schema, and returns the synthesized result in that shape once the task finishes.
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