Answer API - Web-enhanced AI responses
curl --request POST \
--url https://api.llmlayer.dev/api/v2/answer \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"query": "What are the latest developments in quantum computing?",
"model": "llmlayer-web",
"location": "us",
"provider_key": "<string>",
"system_prompt": "<string>",
"response_language": "auto",
"answer_type": "markdown",
"search_type": "general",
"json_schema": "{\"type\":\"object\",\"properties\":{\"summary\":{\"type\":\"string\"},\"key_points\":{\"type\":\"array\",\"items\":{\"type\":\"string\"}}}}",
"citations": false,
"return_sources": false,
"return_images": false,
"date_filter": "anytime",
"max_tokens": 1500,
"temperature": 0.7,
"domain_filter": [
"wikipedia.org",
"-reddit.com"
],
"max_queries": 3,
"search_context_size": "medium"
}
'import requests
url = "https://api.llmlayer.dev/api/v2/answer"
payload = {
"query": "What are the latest developments in quantum computing?",
"model": "llmlayer-web",
"location": "us",
"provider_key": "<string>",
"system_prompt": "<string>",
"response_language": "auto",
"answer_type": "markdown",
"search_type": "general",
"json_schema": "{\"type\":\"object\",\"properties\":{\"summary\":{\"type\":\"string\"},\"key_points\":{\"type\":\"array\",\"items\":{\"type\":\"string\"}}}}",
"citations": False,
"return_sources": False,
"return_images": False,
"date_filter": "anytime",
"max_tokens": 1500,
"temperature": 0.7,
"domain_filter": ["wikipedia.org", "-reddit.com"],
"max_queries": 3,
"search_context_size": "medium"
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
query: 'What are the latest developments in quantum computing?',
model: 'llmlayer-web',
location: 'us',
provider_key: '<string>',
system_prompt: '<string>',
response_language: 'auto',
answer_type: 'markdown',
search_type: 'general',
json_schema: '{"type":"object","properties":{"summary":{"type":"string"},"key_points":{"type":"array","items":{"type":"string"}}}}',
citations: false,
return_sources: false,
return_images: false,
date_filter: 'anytime',
max_tokens: 1500,
temperature: 0.7,
domain_filter: ['wikipedia.org', '-reddit.com'],
max_queries: 3,
search_context_size: 'medium'
})
};
fetch('https://api.llmlayer.dev/api/v2/answer', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.llmlayer.dev/api/v2/answer",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'query' => 'What are the latest developments in quantum computing?',
'model' => 'llmlayer-web',
'location' => 'us',
'provider_key' => '<string>',
'system_prompt' => '<string>',
'response_language' => 'auto',
'answer_type' => 'markdown',
'search_type' => 'general',
'json_schema' => '{"type":"object","properties":{"summary":{"type":"string"},"key_points":{"type":"array","items":{"type":"string"}}}}',
'citations' => false,
'return_sources' => false,
'return_images' => false,
'date_filter' => 'anytime',
'max_tokens' => 1500,
'temperature' => 0.7,
'domain_filter' => [
'wikipedia.org',
'-reddit.com'
],
'max_queries' => 3,
'search_context_size' => 'medium'
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.llmlayer.dev/api/v2/answer"
payload := strings.NewReader("{\n \"query\": \"What are the latest developments in quantum computing?\",\n \"model\": \"llmlayer-web\",\n \"location\": \"us\",\n \"provider_key\": \"<string>\",\n \"system_prompt\": \"<string>\",\n \"response_language\": \"auto\",\n \"answer_type\": \"markdown\",\n \"search_type\": \"general\",\n \"json_schema\": \"{\\\"type\\\":\\\"object\\\",\\\"properties\\\":{\\\"summary\\\":{\\\"type\\\":\\\"string\\\"},\\\"key_points\\\":{\\\"type\\\":\\\"array\\\",\\\"items\\\":{\\\"type\\\":\\\"string\\\"}}}}\",\n \"citations\": false,\n \"return_sources\": false,\n \"return_images\": false,\n \"date_filter\": \"anytime\",\n \"max_tokens\": 1500,\n \"temperature\": 0.7,\n \"domain_filter\": [\n \"wikipedia.org\",\n \"-reddit.com\"\n ],\n \"max_queries\": 3,\n \"search_context_size\": \"medium\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.llmlayer.dev/api/v2/answer")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"query\": \"What are the latest developments in quantum computing?\",\n \"model\": \"llmlayer-web\",\n \"location\": \"us\",\n \"provider_key\": \"<string>\",\n \"system_prompt\": \"<string>\",\n \"response_language\": \"auto\",\n \"answer_type\": \"markdown\",\n \"search_type\": \"general\",\n \"json_schema\": \"{\\\"type\\\":\\\"object\\\",\\\"properties\\\":{\\\"summary\\\":{\\\"type\\\":\\\"string\\\"},\\\"key_points\\\":{\\\"type\\\":\\\"array\\\",\\\"items\\\":{\\\"type\\\":\\\"string\\\"}}}}\",\n \"citations\": false,\n \"return_sources\": false,\n \"return_images\": false,\n \"date_filter\": \"anytime\",\n \"max_tokens\": 1500,\n \"temperature\": 0.7,\n \"domain_filter\": [\n \"wikipedia.org\",\n \"-reddit.com\"\n ],\n \"max_queries\": 3,\n \"search_context_size\": \"medium\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.llmlayer.dev/api/v2/answer")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"query\": \"What are the latest developments in quantum computing?\",\n \"model\": \"llmlayer-web\",\n \"location\": \"us\",\n \"provider_key\": \"<string>\",\n \"system_prompt\": \"<string>\",\n \"response_language\": \"auto\",\n \"answer_type\": \"markdown\",\n \"search_type\": \"general\",\n \"json_schema\": \"{\\\"type\\\":\\\"object\\\",\\\"properties\\\":{\\\"summary\\\":{\\\"type\\\":\\\"string\\\"},\\\"key_points\\\":{\\\"type\\\":\\\"array\\\",\\\"items\\\":{\\\"type\\\":\\\"string\\\"}}}}\",\n \"citations\": false,\n \"return_sources\": false,\n \"return_images\": false,\n \"date_filter\": \"anytime\",\n \"max_tokens\": 1500,\n \"temperature\": 0.7,\n \"domain_filter\": [\n \"wikipedia.org\",\n \"-reddit.com\"\n ],\n \"max_queries\": 3,\n \"search_context_size\": \"medium\"\n}"
response = http.request(request)
puts response.read_body{
"answer": "<string>",
"response_time": "2.34",
"input_tokens": 123,
"output_tokens": 123,
"llmlayer_cost": 123,
"sources": [
{
"title": "<string>",
"link": "<string>",
"snippet": "<string>"
}
],
"images": [
{
"title": "<string>",
"imageUrl": "<string>",
"thumbnailUrl": "<string>",
"source": "<string>",
"link": "<string>"
}
],
"model_cost": 123
}Endpoint
Answer API - Web-enhanced AI responses
Search the web and generate AI-powered answers using your chosen LLM model. Supports multiple search queries, custom prompts, and structured JSON output.
POST
/
api
/
v2
/
answer
Answer API - Web-enhanced AI responses
curl --request POST \
--url https://api.llmlayer.dev/api/v2/answer \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"query": "What are the latest developments in quantum computing?",
"model": "llmlayer-web",
"location": "us",
"provider_key": "<string>",
"system_prompt": "<string>",
"response_language": "auto",
"answer_type": "markdown",
"search_type": "general",
"json_schema": "{\"type\":\"object\",\"properties\":{\"summary\":{\"type\":\"string\"},\"key_points\":{\"type\":\"array\",\"items\":{\"type\":\"string\"}}}}",
"citations": false,
"return_sources": false,
"return_images": false,
"date_filter": "anytime",
"max_tokens": 1500,
"temperature": 0.7,
"domain_filter": [
"wikipedia.org",
"-reddit.com"
],
"max_queries": 3,
"search_context_size": "medium"
}
'import requests
url = "https://api.llmlayer.dev/api/v2/answer"
payload = {
"query": "What are the latest developments in quantum computing?",
"model": "llmlayer-web",
"location": "us",
"provider_key": "<string>",
"system_prompt": "<string>",
"response_language": "auto",
"answer_type": "markdown",
"search_type": "general",
"json_schema": "{\"type\":\"object\",\"properties\":{\"summary\":{\"type\":\"string\"},\"key_points\":{\"type\":\"array\",\"items\":{\"type\":\"string\"}}}}",
"citations": False,
"return_sources": False,
"return_images": False,
"date_filter": "anytime",
"max_tokens": 1500,
"temperature": 0.7,
"domain_filter": ["wikipedia.org", "-reddit.com"],
"max_queries": 3,
"search_context_size": "medium"
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
query: 'What are the latest developments in quantum computing?',
model: 'llmlayer-web',
location: 'us',
provider_key: '<string>',
system_prompt: '<string>',
response_language: 'auto',
answer_type: 'markdown',
search_type: 'general',
json_schema: '{"type":"object","properties":{"summary":{"type":"string"},"key_points":{"type":"array","items":{"type":"string"}}}}',
citations: false,
return_sources: false,
return_images: false,
date_filter: 'anytime',
max_tokens: 1500,
temperature: 0.7,
domain_filter: ['wikipedia.org', '-reddit.com'],
max_queries: 3,
search_context_size: 'medium'
})
};
fetch('https://api.llmlayer.dev/api/v2/answer', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.llmlayer.dev/api/v2/answer",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'query' => 'What are the latest developments in quantum computing?',
'model' => 'llmlayer-web',
'location' => 'us',
'provider_key' => '<string>',
'system_prompt' => '<string>',
'response_language' => 'auto',
'answer_type' => 'markdown',
'search_type' => 'general',
'json_schema' => '{"type":"object","properties":{"summary":{"type":"string"},"key_points":{"type":"array","items":{"type":"string"}}}}',
'citations' => false,
'return_sources' => false,
'return_images' => false,
'date_filter' => 'anytime',
'max_tokens' => 1500,
'temperature' => 0.7,
'domain_filter' => [
'wikipedia.org',
'-reddit.com'
],
'max_queries' => 3,
'search_context_size' => 'medium'
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.llmlayer.dev/api/v2/answer"
payload := strings.NewReader("{\n \"query\": \"What are the latest developments in quantum computing?\",\n \"model\": \"llmlayer-web\",\n \"location\": \"us\",\n \"provider_key\": \"<string>\",\n \"system_prompt\": \"<string>\",\n \"response_language\": \"auto\",\n \"answer_type\": \"markdown\",\n \"search_type\": \"general\",\n \"json_schema\": \"{\\\"type\\\":\\\"object\\\",\\\"properties\\\":{\\\"summary\\\":{\\\"type\\\":\\\"string\\\"},\\\"key_points\\\":{\\\"type\\\":\\\"array\\\",\\\"items\\\":{\\\"type\\\":\\\"string\\\"}}}}\",\n \"citations\": false,\n \"return_sources\": false,\n \"return_images\": false,\n \"date_filter\": \"anytime\",\n \"max_tokens\": 1500,\n \"temperature\": 0.7,\n \"domain_filter\": [\n \"wikipedia.org\",\n \"-reddit.com\"\n ],\n \"max_queries\": 3,\n \"search_context_size\": \"medium\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.llmlayer.dev/api/v2/answer")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"query\": \"What are the latest developments in quantum computing?\",\n \"model\": \"llmlayer-web\",\n \"location\": \"us\",\n \"provider_key\": \"<string>\",\n \"system_prompt\": \"<string>\",\n \"response_language\": \"auto\",\n \"answer_type\": \"markdown\",\n \"search_type\": \"general\",\n \"json_schema\": \"{\\\"type\\\":\\\"object\\\",\\\"properties\\\":{\\\"summary\\\":{\\\"type\\\":\\\"string\\\"},\\\"key_points\\\":{\\\"type\\\":\\\"array\\\",\\\"items\\\":{\\\"type\\\":\\\"string\\\"}}}}\",\n \"citations\": false,\n \"return_sources\": false,\n \"return_images\": false,\n \"date_filter\": \"anytime\",\n \"max_tokens\": 1500,\n \"temperature\": 0.7,\n \"domain_filter\": [\n \"wikipedia.org\",\n \"-reddit.com\"\n ],\n \"max_queries\": 3,\n \"search_context_size\": \"medium\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.llmlayer.dev/api/v2/answer")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"query\": \"What are the latest developments in quantum computing?\",\n \"model\": \"llmlayer-web\",\n \"location\": \"us\",\n \"provider_key\": \"<string>\",\n \"system_prompt\": \"<string>\",\n \"response_language\": \"auto\",\n \"answer_type\": \"markdown\",\n \"search_type\": \"general\",\n \"json_schema\": \"{\\\"type\\\":\\\"object\\\",\\\"properties\\\":{\\\"summary\\\":{\\\"type\\\":\\\"string\\\"},\\\"key_points\\\":{\\\"type\\\":\\\"array\\\",\\\"items\\\":{\\\"type\\\":\\\"string\\\"}}}}\",\n \"citations\": false,\n \"return_sources\": false,\n \"return_images\": false,\n \"date_filter\": \"anytime\",\n \"max_tokens\": 1500,\n \"temperature\": 0.7,\n \"domain_filter\": [\n \"wikipedia.org\",\n \"-reddit.com\"\n ],\n \"max_queries\": 3,\n \"search_context_size\": \"medium\"\n}"
response = http.request(request)
puts response.read_body{
"answer": "<string>",
"response_time": "2.34",
"input_tokens": 123,
"output_tokens": 123,
"llmlayer_cost": 123,
"sources": [
{
"title": "<string>",
"link": "<string>",
"snippet": "<string>"
}
],
"images": [
{
"title": "<string>",
"imageUrl": "<string>",
"thumbnailUrl": "<string>",
"source": "<string>",
"link": "<string>"
}
],
"model_cost": 123
}Authorizations
Bearer token authentication using your LLMLayer API key. Include in Authorization header as: Bearer YOUR_LLMLAYER_API_KEY
Body
application/json
The search query or question to answer
Example:
"What are the latest developments in quantum computing?"
LLM model to use (e.g., llmlayer-web, llmlayer-fast, openai/gpt-4o-mini, openai/gpt-5.1)
Example:
"llmlayer-web"
Country code for localized search results (us, uk, ca, etc.)
Example:
"us"
Deprecated. Accepted for backward compatibility but currently ignored by the API.
Custom system prompt to override default behavior. Use this to customize how the LLM processes the search results.
Language for the response. 'auto' detects from query, or specify language code (en, es, fr, etc.)
Example:
"auto"
Format of the response. Use 'json' with json_schema for structured output.
Available options:
markdown, html, json Type of web search to perform. 'news' provides recent news articles.
Available options:
general, news JSON schema as string for structured responses. Required when answer_type='json'. The LLM will format its response according to this schema.
Example:
"{\"type\":\"object\",\"properties\":{\"summary\":{\"type\":\"string\"},\"key_points\":{\"type\":\"array\",\"items\":{\"type\":\"string\"}}}}"
Include inline citations [1], [2] in the response text
Return the source documents used for answer generation
Return relevant images from image search. Adds $0.001 to cost.
Filter search results by recency. Useful for time-sensitive queries.
Available options:
anytime, hour, day, week, month, year Maximum tokens in the LLM response. Affects cost.
Required range:
x >= 1Controls response randomness. 0=deterministic, 2=very creative. Not supported by all models.
Required range:
0 <= x <= 2Include or exclude specific domains. Use '-' prefix to exclude (e.g., ['-reddit.com', 'wikipedia.org'])
Example:
["wikipedia.org", "-reddit.com"]
Number of search queries to generate from the user query. More queries = broader search and higher LLMLayer fee. Token-priced models use $0.004 per query; fixed-price LLMLayer models use their model-specific per-query price.
Required range:
1 <= x <= 4Example:
3
Amount of search context to extract and pass to LLM. 'high' provides more context but uses more tokens.
Available options:
low, medium, high Response
Successful response with AI-generated answer
The AI-generated answer. String for markdown/html, object for JSON responses.
Total processing time in seconds
Example:
"2.34"
Total input tokens processed (includes query generation + main LLM call + internal system prompts)
Total output tokens generated (includes query generation + main LLM response)
Cost in USD for LLMLayer search infrastructure. Base cost varies by max_queries and return_images.
Source documents used (only when return_sources=true)
Show child attributes
Show child attributes
Relevant images (only when return_images=true)
Show child attributes
Show child attributes
Cost in USD for LLM usage. Null for fixed-price LLMLayer models.