Pagination

Overview

The Explorium API supports two pagination modes for the Fetch Businesses (POST /v1/businesses) and Fetch Prospects (POST /v1/prospects) endpoints:

Mode	Best For	Max Results
Offset-based (page / page_size)	Small to medium datasets, random page access	Up to 60,000 records
Cursor-based (search_after)	Large datasets, streaming all results efficiently	No limit

Both modes return results in pages of up to 500 records each.

Offset-Based Pagination

Offset-based pagination uses page and page_size parameters to retrieve specific pages of results. This is the simplest approach and works well when you need random access to any page.

Parameters

Parameter	Type	Required	Description
`size`	Number	No	Maximum total records to return across all pages. Defaults to 60,000. Must be ≤ 60,000.
`page_size`	Number	Yes	Number of records per page. Maximum: 500.
`page`	Number	No	Page number to retrieve (1-based). Defaults to 1.

How It Works

Send your first request with page: 1 and your desired page_size.
The response includes total_results, total_pages, and page so you know how many pages are available.
Increment page to retrieve subsequent pages until you reach total_pages.

Example Request

curl -X POST "https://api.explorium.ai/v1/businesses" \
  -H "api_key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "filters": {
      "country_code": { "values": ["US"] },
      "company_size": { "values": ["51-200"] }
    },
    "mode": "full",
    "size": 500,
    "page_size": 100,
    "page": 1
  }'

Example Response

{
  "response_context": {
    "correlation_id": "abc123",
    "request_status": "success",
    "time_took_in_seconds": 0.45
  },
  "data": [ ... ],
  "total_results": 500,
  "page": 1,
  "total_pages": 5
}

Full Python Example

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.explorium.ai/v1/businesses"

headers = {
    "api_key": API_KEY,
    "Content-Type": "application/json"
}

payload = {
    "filters": {
        "country_code": {"values": ["US"]},
        "company_size": {"values": ["51-200"]}
    },
    "mode": "full",
    "size": 500,
    "page_size": 100,
    "page": 1
}

all_results = []

while True:
    response = requests.post(BASE_URL, headers=headers, json=payload)
    data = response.json()

    all_results.extend(data["data"])
    print(f"Page {data['page']}/{data['total_pages']} — fetched {len(data['data'])} records")

    if data["page"] >= data["total_pages"]:
        break

    payload["page"] += 1

print(f"Total records collected: {len(all_results)}")

Cursor-Based Pagination (Search After)

Cursor-based pagination uses an opaque next_cursor token to iterate through results sequentially. This mode is more efficient for large datasets because it avoids the performance overhead of deep page offsets.

Parameters

Parameter	Type	Required	Description
`page_size`	Number	Yes	Number of records per page. Maximum: 100.
`next_cursor`	String	Yes	Cursor token from a previous response. Omit or set to `null` for the first request.

When using cursor-based pagination, the size parameter is optional and ignored. The response will return the true total_results count regardless of any size value provided.

How It Works

Send your first request withnext_cursor set to null.
The response includes a page object with size (number of records returned) and next_cursor.
Pass the next_cursor value from the response into your next request.
Repeat until next_cursor is null, which indicates you’ve reached the last page.

Example Request — First Page

curl -X POST "https://api.explorium.ai/v1/businesses" \
  -H "api_key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "filters": {
      "country_code": { "values": ["US"] },
      "company_size": { "values": ["51-200"] }
    },
    "mode": "full",
    "page_size": 100,
    "next_cursor": null
  }'

Example Response — First Page

{
  "response_context": {
    "correlation_id": "def456",
    "request_status": "success",
    "time_took_in_seconds": 0.38
  },
  "data": [ ... ],
  "total_results": 12350,
  "page": {
    "size": 100,
    "next_cursor": "eyJzZWFyY2hfYWZ0ZXIiOiBbMTcwOTEyMzQ1Nl19"
  }
}

Example Request — Next Page

Pass the next_cursor from the previous response:

curl -X POST "https://api.explorium.ai/v1/businesses" \
  -H "api_key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "filters": {
      "country_code": { "values": ["US"] },
      "company_size": { "values": ["51-200"] }
    },
    "mode": "full",
    "page_size": 100,
    "next_cursor": "eyJzZWFyY2hfYWZ0ZXIiOiBbMTcwOTEyMzQ1Nl19"
  }'

Example Response — Last Page

When there are no more results, next_cursor is null:

{
  "response_context": {
    "correlation_id": "ghi789",
    "request_status": "success",
    "time_took_in_seconds": 0.29
  },
  "data": [ ... ],
  "total_results": 12350,
  "page": {
    "size": 50,
    "next_cursor": null
  }
}

Full Python Example

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.explorium.ai/v1/businesses"

headers = {
    "api_key": API_KEY,
    "Content-Type": "application/json"
}

payload = {
    "filters": {
        "country_code": {"values": ["US"]},
        "company_size": {"values": ["51-200"]}
    },
    "mode": "full",
    "page_size": 100,
    "next_cursor": None
}

all_results = []
page_count = 0

while True:
    response = requests.post(BASE_URL, headers=headers, json=payload)
    data = response.json()

    all_results.extend(data["data"])
    page_count += 1

    page_info = data["page"]
    print(f"Page {page_count} — fetched {page_info['size']} records (total available: {data['total_results']})")

    if page_info["next_cursor"] is None:
        break

    payload["next_cursor"] = page_info["next_cursor"]

print(f"Total records collected: {len(all_results)}")

Response Format Comparison

The response structure differs slightly depending on which pagination mode you use.

Offset-Based Response
Cursor-Based Response

{
  "response_context": { ... },
  "data": [ ... ],
  "total_results": 500,
  "page": 1,
  "total_pages": 5
}

Field	Type	Description
`total_results`	Number	Total matching records (capped by `size`).
`page`	Number	Current page number.
`total_pages`	Number	Total number of pages available.

{
  "response_context": { ... },
  "data": [ ... ],
  "total_results": 12350,
  "page": {
    "size": 100,
    "next_cursor": "eyJzZWFyY2hfYWZ0..."
  }
}

Field	Type	Description
`total_results`	Number	True total count of matching records.
`page.size`	Number	Number of records returned in this page.
`page.next_cursor`	String \| null	Cursor for the next page. `null` when no more results.

Choosing the Right Mode

Use Offset-Based When

You need to jump to a specific page number.
Your result set is small to medium (under ~10,000 records).
You want to display a page selector UI (e.g., “Page 3 of 12”).
You need to know the total page count upfront.

Use Cursor-Based When

You’re iterating through all results sequentially.
Your result set is large (10,000+ records).
You want consistent performance regardless of how deep you paginate.
You need the true total_results count independent of the size parameter.

Best Practices

Always set page_size explicitly

While page_size has a default, setting it explicitly (recommended: 100) ensures predictable response sizes and makes your code easier to reason about.

Handle the last page gracefully

The last page of results will often contain fewer records than page_size. In offset mode, check page >= total_pages. In cursor mode, check next_cursor == null.

Don't mix pagination modes

Use either page (offset-based) or next_cursor (cursor-based) in a given request — not both. If next_cursor is provided, the API uses cursor-based pagination and the page parameter is ignored.

Cursor tokens are opaque

Treat next_cursor values as opaque strings. Don’t attempt to parse, modify, or construct them. Always use the exact value returned by the API.

Keep filters consistent across pages

When paginating through results (in either mode), always send the same filters and mode parameters with each request. Changing filters mid-pagination will produce inconsistent results.

Implement retry logic

For large pagination jobs, implement retry logic with exponential backoff in case of transient errors. Save the last successful next_cursor or page number so you can resume without re-fetching.

Supported Endpoints

Both pagination modes are available on the following endpoints:

Endpoint	Method	Path
Fetch Businesses	POST	`/v1/businesses`
Fetch Prospects	POST	`/v1/prospects`

Explorium AgentSource

Partners

Overview

Parameters

How It Works

Example Request

Example Response

Full Python Example

Parameters

How It Works

Example Request — First Page

Example Response — First Page

Example Request — Next Page

Example Response — Last Page

Full Python Example

Response Format Comparison

Choosing the Right Mode

Use Offset-Based When

Use Cursor-Based When

Best Practices

Supported Endpoints

​Overview

​Offset-Based Pagination

​Parameters

​How It Works

​Example Request

​Example Response

​Full Python Example

​Cursor-Based Pagination (Search After)

​Parameters

​How It Works

​Example Request — First Page

​Example Response — First Page

​Example Request — Next Page

​Example Response — Last Page

​Full Python Example

​Response Format Comparison

​Choosing the Right Mode

Use Offset-Based When

Use Cursor-Based When

​Best Practices

​Supported Endpoints

Overview

Offset-Based Pagination

Parameters

How It Works

Example Request

Example Response

Full Python Example

Cursor-Based Pagination (Search After)

Parameters

How It Works

Example Request — First Page

Example Response — First Page

Example Request — Next Page

Example Response — Last Page

Full Python Example

Response Format Comparison

Choosing the Right Mode

Best Practices

Supported Endpoints