Integrations

Search 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

  1. Sign in or create an account at maton.ai
  2. Go to maton.ai/settings
  3. 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

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:

ParameterTypeRequiredDescription
querystringYesSearch query string
numResultsintegerNoNumber of results (max 100, default 10)
typestringNoSearch type: neural, auto (default), keyword
categorystringNoFilter by category: company, research paper, news, tweet, personal site, financial report, people
includeDomainsarrayNoOnly include these domains
excludeDomainsarrayNoExclude these domains
startPublishedDatestringNoISO 8601 date filter (after)
endPublishedDatestringNoISO 8601 date filter (before)
contentsobjectNoContent extraction options (see below)

Contents Options:

{
  "contents": {
    "text": true,
    "highlights": true,
    "summary": true
  }
}
OptionTypeDescription
textboolean/objectExtract full page text
highlightsboolean/objectExtract relevant snippets
summaryboolean/objectGenerate 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:

ParameterTypeRequiredDescription
idsarrayYesList of URLs to fetch content from
textbooleanNoInclude full page text
highlightsboolean/objectNoInclude relevant snippets
summaryboolean/objectNoGenerate 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:

ParameterTypeRequiredDescription
urlstringYesURL to find similar pages for
numResultsintegerNoNumber of results (max 100, default 10)
includeDomainsarrayNoOnly include these domains
excludeDomainsarrayNoExclude these domains
contentsobjectNoContent 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:

ParameterTypeRequiredDescription
querystringYesQuestion to answer
textbooleanNoInclude 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:

ParameterTypeRequiredDescription
instructionsstringYesWhat to research (max 4096 chars)
modelstringNoModel to use: exa-research-fast, exa-research (default), exa-research-pro
outputSchemaobjectNoJSON 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:

ParameterTypeDescription
eventsstringSet to true to include event log
streamstringSet 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:

ParameterTypeDescription
limitintegerResults per page (1-50, default 10)
cursorstringPagination 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 people and company have restricted filter support
  • Timestamps are in ISO 8601 format
  • IMPORTANT: When piping curl output to jq or other commands, environment variables like $MATON_API_KEY may not expand correctly in some shell environments

Error Handling

StatusMeaning
400Missing Exa connection or invalid request
401Invalid or missing Maton API key
429Rate limited
4xx/5xxPassthrough error from Exa API

Resources

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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