Tavily Search API
Scrape Tavily Search data with one API call. Runs a web search via Tavily and returns ranked results plus an optional LLM-generated `answer` synthesised from the top sources. Set `include_answer=true` to enable answer synthesis. Use `search_depth=advanced` for higher-relevance results (also unlocks `chunks_per_source`). Filter results to specific domains via `include_domains` (comma-separated), or exclude via `exclude_domains`. Time-bounded queries via `time_range` (`d` / `w` / `m` / `y`) or explicit `start_date` / `end_date` (YYYY-MM-DD).
Last updated August 2026Maintained by the SocialCrawl team
Returns ranked web search results for a query, and optionally a written answer put together from the top sources.
Use it when you want search results you can narrow by domain, date, or topic, with a short written answer alongside them if you ask for one.
Searching 48 platforms in parallel
What can you do with the Search API?
The Search endpoint gives you structured Tavily data with computed fields in a single request. No scraping infrastructure to build or maintain.
Example Request
curl -H "x-api-key: YOUR_API_KEY" \
"https://www.socialcrawl.dev/v1/tavily/search?query=claude+opus+4.7+release+notes"import requests
response = requests.get(
"https://www.socialcrawl.dev/v1/tavily/search",
params={
'query': 'claude opus 4.7 release notes',
},
headers={"x-api-key": "YOUR_API_KEY"},
)
data = response.json()const response = await fetch(
"https://www.socialcrawl.dev/v1/tavily/search?query=claude+opus+4.7+release+notes",
{
headers: { "x-api-key": "YOUR_API_KEY" },
},
);
const data = await response.json();Parameters
| Parameter | Required | Description |
|---|---|---|
| query | Yes | The search query — natural-language free text. |
| search_depth | No | Latency-vs-relevance tradeoff. `basic` is the default; `advanced` unlocks `chunks_per_source` and higher-relevance ranking. (basic | advanced | fast | ultra-fast) |
| topic | No | Search category. Defaults to `general`. Use `news` for time-sensitive queries and `finance` for market data. (general | news | finance) |
| time_range | No | Time window relative to now. Accepts `day` / `week` / `month` / `year` (or shorthand `d` / `w` / `m` / `y`). (day | week | month | year | d | w | m | y) |
| max_results | No | Number of results to return (1–20). Defaults to 5. |
| chunks_per_source | No | Max relevant chunks returned per source (1–5). Only honoured when `search_depth=advanced`. Defaults to 3. |
| include_images | No | Include images alongside the result content. |
| include_image_descriptions | No | Include AI-generated descriptions for the returned images. |
| include_answer | No | Include an LLM-generated answer string synthesised from the top sources. |
| include_raw_content | No | Include the raw HTML/text alongside the cleaned content. |
| include_domains | No | Comma-separated list of domains to restrict results to (e.g. `nytimes.com,reuters.com`). |
| exclude_domains | No | Comma-separated list of domains to exclude from results. |
| country | No | ISO 3166-1 alpha-2 country code to bias results toward. |
| start_date | No | Inclusive lower bound on result publish date (YYYY-MM-DD). |
| end_date | No | Inclusive upper bound on result publish date (YYYY-MM-DD). |
What does the Tavily Search API return?
Every response follows one unified schema. Here is a real, unmodified response body, so you can see the exact fields you get back before spending a credit.
Example response
{
"success": true,
"platform": "instagram",
"endpoint": "/v1/instagram/engagement",
"data": {
"engagement_rate_percentages": 38.33,
"recent_posts": 12,
"followers": 87608035,
"comments": 528912,
"likes": 33049046,
"recent_posts_explanation": "Statistics based on the last 12 posts",
"id_user": "2278169415",
"username": "mrbeast",
"is_private": false,
"posts_details": [
{
"likes": 5636982,
"comments": 69484,
"taken_at": 1781457954,
"datetime": "2026-06-14 20:25:54",
"hours_since_post": 461,
"time_ago": "19 days ago",
"likes_per_hour": 12228,
"comments_per_hour": 151
},
{
"likes": 20000768,
"comments": 223510,
"taken_at": 1732824650,
"datetime": "2024-11-28 23:10:50",
"hours_since_post": 13971,
"time_ago": "2 years ago",
"likes_per_hour": 1432,
"comments_per_hour": 16
},
{
"likes": 929226,
"comments": 30633,
"taken_at": 1782232475,
"datetime": "2026-06-23 19:34:35",
"hours_since_post": 246,
"time_ago": "10 days ago",
"likes_per_hour": 3777,
"comments_per_hour": 125
},
{
"likes": 487761,
"comments": 22482,
"taken_at": 1781799425,
"datetime": "2026-06-18 19:17:05",
"hours_since_post": 366,
"time_ago": "15 days ago",
"likes_per_hour": 1333,
"comments_per_hour": 61
},
{
"likes": 712265,
"comments": 15716,
"taken_at": 1781366405,
"datetime": "2026-06-13 19:00:05",
"hours_since_post": 487,
"time_ago": "20 days ago",
"likes_per_hour": 1463,
"comments_per_hour": 32
},
{
"likes": 1475116,
"comments": 35386,
"taken_at": 1781277094,
"datetime": "2026-06-12 18:11:34",
"hours_since_post": 512,
"time_ago": "21 days ago",
"likes_per_hour": 2881,
"comments_per_hour": 69
},
{
"likes": 1108220,
"comments": 26632,
"taken_at": 1780160249,
"datetime": "2026-05-30 19:57:29",
"hours_since_post": 822,
"time_ago": "1 months ago",
"likes_per_hour": 1348,
"comments_per_hour": 32
},
{
"likes": 542948,
"comments": 28476,
"taken_at": 1779375582,
"datetime": "2026-05-21 17:59:42",
"hours_since_post": 1040,
"time_ago": "1 months ago",
"likes_per_hour": 522,
"comments_per_hour": 27
},
{
"likes": 698514,
"comments": 24401,
"taken_at": 1779120014,
"datetime": "2026-05-18 19:00:14",
"hours_since_post": 1111,
"time_ago": "2 months ago",
"likes_per_hour": 629,
"comments_per_hour": 22
},
{
"likes": 468000,
"comments": 13548,
"taken_at": 1778947209,
"datetime": "2026-05-16 19:00:09",
"hours_since_post": 1159,
"time_ago": "2 months ago",
"likes_per_hour": 404,
"comments_per_hour": 12
},
{
"likes": 526594,
"comments": 24411,
"taken_at": 1777737719,
"datetime": "2026-05-02 19:01:59",
"hours_since_post": 1495,
"time_ago": "2 months ago",
"likes_per_hour": 352,
"comments_per_hour": 16
},
{
"likes": 462652,
"comments": 14233,
"taken_at": 1777580305,
"datetime": "2026-04-30 23:18:25",
"hours_since_post": 1538,
"time_ago": "2 months ago",
"likes_per_hour": 301,
"comments_per_hour": 9
}
]
},
"credits_used": 5,
"credits_remaining": 9999,
"request_id": "req-8Kq2ZmR4vT9xLb3P",
"cached": false
}Example captured from the Instagram API. Every SocialCrawl endpoint returns this same unified schema, so your Tavily Search response has the same fields.
How does the Tavily Search API work?
Send a GET request with your API key and get back clean, structured JSON in our unified schema. Supported computed fields are populated when the source provides the required inputs.
Method
GET
Response
JSON
How do you scrape social media data in seconds?
The fastest social media scraping API for developers. Scrape profiles, posts, comments, and analytics from 48 platforms covering 10B+ monthly active users.
One schema, every platform
Query 48 platforms with identical response structures. Write your integration once.
Computed fields, not just scraped
When an endpoint supports these metrics and the source provides the required inputs, the normalized record includes engagement_rate, estimated_reach, content_category, and language — ready to use.
See your data before you code
Visual Data Explorer — paste any URL, get rich result cards, sortable tables, CSV export.
import requests
response = requests.get(
'https://www.socialcrawl.dev/v1/tiktok/profile',
params={'handle': 'charlidamelio'},
headers={'x-api-key': 'sc_YOUR_API_KEY'}
)
data = response.json(){
"success": true,
"platform": "tiktok",
"data": {
"author": {
"username": "charlidamelio",
"followers": 152400000
},
"engagement": {
"likes": 12400000000,
"engagement_rate": 0.087
},
"metadata": {
"language": "en",
"content_category": "lifestyle"
}
}
}Have a question? We got answers
Find answers to frequently asked questions about SocialCrawl's API, pricing, and capabilities.
Contact usHow do I run a Tavily web search with an API?
Can Tavily search return an LLM-generated answer?
What filters does the Tavily search endpoint support?
Is this a Tavily alternative or the real Tavily?
How much does the Tavily Search API cost?
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