curl --request POST \
  --url https://api-lr.agent.ai/v1/action/meeting_prep_generate_meeting_sections \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "processed_research": "{{processed_research}}",
  "target_company_identity": "{{prepared_contacts.target_company}}",
  "meeting_classification": "{{meeting_classification}}",
  "processed_gcal_event": "{{processed_gcal_event}}",
  "output_variable_name": "meeting_sections",
  "topic_signals": "{{topic_signals}}",
  "meeting_relationships": "{{meeting_relationships}}",
  "user_context": "{{user_context}}",
  "sections_to_generate": "[\"overview\", \"attendees\", \"company\", \"strategy\", \"goals\"]",
  "fast_model": "gpt-5-mini",
  "quality_model": "gpt-5"
}
'

import requests

url = "https://api-lr.agent.ai/v1/action/meeting_prep_generate_meeting_sections"

payload = {
    "processed_research": "{{processed_research}}",
    "target_company_identity": "{{prepared_contacts.target_company}}",
    "meeting_classification": "{{meeting_classification}}",
    "processed_gcal_event": "{{processed_gcal_event}}",
    "output_variable_name": "meeting_sections",
    "topic_signals": "{{topic_signals}}",
    "meeting_relationships": "{{meeting_relationships}}",
    "user_context": "{{user_context}}",
    "sections_to_generate": "[\"overview\", \"attendees\", \"company\", \"strategy\", \"goals\"]",
    "fast_model": "gpt-5-mini",
    "quality_model": "gpt-5"
}
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({
    processed_research: '{{processed_research}}',
    target_company_identity: '{{prepared_contacts.target_company}}',
    meeting_classification: '{{meeting_classification}}',
    processed_gcal_event: '{{processed_gcal_event}}',
    output_variable_name: 'meeting_sections',
    topic_signals: '{{topic_signals}}',
    meeting_relationships: '{{meeting_relationships}}',
    user_context: '{{user_context}}',
    sections_to_generate: '["overview", "attendees", "company", "strategy", "goals"]',
    fast_model: 'gpt-5-mini',
    quality_model: 'gpt-5'
  })
};

fetch('https://api-lr.agent.ai/v1/action/meeting_prep_generate_meeting_sections', 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-lr.agent.ai/v1/action/meeting_prep_generate_meeting_sections",
  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([
    'processed_research' => '{{processed_research}}',
    'target_company_identity' => '{{prepared_contacts.target_company}}',
    'meeting_classification' => '{{meeting_classification}}',
    'processed_gcal_event' => '{{processed_gcal_event}}',
    'output_variable_name' => 'meeting_sections',
    'topic_signals' => '{{topic_signals}}',
    'meeting_relationships' => '{{meeting_relationships}}',
    'user_context' => '{{user_context}}',
    'sections_to_generate' => '["overview", "attendees", "company", "strategy", "goals"]',
    'fast_model' => 'gpt-5-mini',
    'quality_model' => 'gpt-5'
  ]),
  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-lr.agent.ai/v1/action/meeting_prep_generate_meeting_sections"

	payload := strings.NewReader("{\n  \"processed_research\": \"{{processed_research}}\",\n  \"target_company_identity\": \"{{prepared_contacts.target_company}}\",\n  \"meeting_classification\": \"{{meeting_classification}}\",\n  \"processed_gcal_event\": \"{{processed_gcal_event}}\",\n  \"output_variable_name\": \"meeting_sections\",\n  \"topic_signals\": \"{{topic_signals}}\",\n  \"meeting_relationships\": \"{{meeting_relationships}}\",\n  \"user_context\": \"{{user_context}}\",\n  \"sections_to_generate\": \"[\\\"overview\\\", \\\"attendees\\\", \\\"company\\\", \\\"strategy\\\", \\\"goals\\\"]\",\n  \"fast_model\": \"gpt-5-mini\",\n  \"quality_model\": \"gpt-5\"\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-lr.agent.ai/v1/action/meeting_prep_generate_meeting_sections")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"processed_research\": \"{{processed_research}}\",\n  \"target_company_identity\": \"{{prepared_contacts.target_company}}\",\n  \"meeting_classification\": \"{{meeting_classification}}\",\n  \"processed_gcal_event\": \"{{processed_gcal_event}}\",\n  \"output_variable_name\": \"meeting_sections\",\n  \"topic_signals\": \"{{topic_signals}}\",\n  \"meeting_relationships\": \"{{meeting_relationships}}\",\n  \"user_context\": \"{{user_context}}\",\n  \"sections_to_generate\": \"[\\\"overview\\\", \\\"attendees\\\", \\\"company\\\", \\\"strategy\\\", \\\"goals\\\"]\",\n  \"fast_model\": \"gpt-5-mini\",\n  \"quality_model\": \"gpt-5\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api-lr.agent.ai/v1/action/meeting_prep_generate_meeting_sections")

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  \"processed_research\": \"{{processed_research}}\",\n  \"target_company_identity\": \"{{prepared_contacts.target_company}}\",\n  \"meeting_classification\": \"{{meeting_classification}}\",\n  \"processed_gcal_event\": \"{{processed_gcal_event}}\",\n  \"output_variable_name\": \"meeting_sections\",\n  \"topic_signals\": \"{{topic_signals}}\",\n  \"meeting_relationships\": \"{{meeting_relationships}}\",\n  \"user_context\": \"{{user_context}}\",\n  \"sections_to_generate\": \"[\\\"overview\\\", \\\"attendees\\\", \\\"company\\\", \\\"strategy\\\", \\\"goals\\\"]\",\n  \"fast_model\": \"gpt-5-mini\",\n  \"quality_model\": \"gpt-5\"\n}"

response = http.request(request)
puts response.read_body

{
  "status": 123,
  "response": {}
}

{
  "status": 123,
  "response": {}
}

{
  "status": 123,
  "response": {}
}

{
  "status": 123,
  "response": {}
}