SEO agencies run keyword research for dozens of clients. An AI agent can automate the repetitive parts: querying Google for SERP features, checking Reddit for topic demand, and scanning YouTube for content gaps. This tutorial builds an agent that produces a keyword brief from a single seed term.
Prerequisites
- Python 3.10+
- A Scavio API key
- Basic understanding of SEO keyword research
Walkthrough
Step 1: Set up the keyword research function
Query Google for SERP data including People Also Ask questions.
Python
import requests, os
H = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
URL = 'https://api.scavio.dev/api/v1/search'
def keyword_serp(keyword: str) -> dict:
resp = requests.post(URL, headers=H,
json={'platform': 'google', 'query': keyword}, timeout=10)
data = resp.json()
return {
'organic_count': len(data.get('organic', [])),
'paa': [q.get('question', '') for q in data.get('people_also_ask', [])],
'has_ai_overview': bool(data.get('ai_overview')),
'top_domains': [r.get('link', '').split('/')[2] for r in data.get('organic', [])[:5]],
}Step 2: Add Reddit demand signal
Check if the keyword has active Reddit discussions (indicates real user demand).
Python
def reddit_demand(keyword: str) -> dict:
resp = requests.post(URL, headers=H,
json={'platform': 'reddit', 'query': keyword}, timeout=10)
threads = resp.json().get('organic', [])
return {
'thread_count': len(threads),
'top_threads': [{'title': t.get('title',''), 'score': t.get('score',0)} for t in threads[:5]],
'has_demand': len(threads) >= 3,
}Step 3: Check YouTube content gap
Search YouTube to see if video content exists for this keyword.
Python
def youtube_gap(keyword: str) -> dict:
resp = requests.post(URL, headers=H,
json={'platform': 'youtube', 'query': keyword}, timeout=10)
videos = resp.json().get('organic', [])
return {
'video_count': len(videos),
'top_channels': list(set(v.get('channel','') for v in videos[:5] if v.get('channel'))),
'content_gap': len(videos) < 5,
}Step 4: Combine into keyword brief
Run all three checks and produce a structured brief for the client.
Python
def keyword_brief(seed: str) -> dict:
serp = keyword_serp(seed)
reddit = reddit_demand(seed)
youtube = youtube_gap(seed)
priority = 'high' if reddit['has_demand'] and youtube['content_gap'] else \
'medium' if reddit['has_demand'] or serp['has_ai_overview'] else 'low'
return {
'keyword': seed,
'priority': priority,
'serp_analysis': serp,
'reddit_demand': reddit,
'youtube_gap': youtube,
'paa_opportunities': serp['paa'],
'recommendation': f"Priority: {priority}. {'Strong Reddit demand + YouTube gap = opportunity.' if priority == 'high' else 'Monitor for changes.'}"
}
brief = keyword_brief('best crm for startups 2026')
print(f"Priority: {brief['priority']}")
print(f"PAA questions: {len(brief['paa_opportunities'])}")
print(f"Reddit threads: {brief['reddit_demand']['thread_count']}")Step 5: Scale for multiple keywords
Process a list of seed keywords for a client and output a ranked brief.
Python
def client_keyword_audit(keywords: list, client_name: str) -> dict:
briefs = [keyword_brief(kw) for kw in keywords]
briefs.sort(key=lambda b: {'high': 3, 'medium': 2, 'low': 1}[b['priority']], reverse=True)
return {
'client': client_name,
'total_keywords': len(briefs),
'high_priority': [b for b in briefs if b['priority'] == 'high'],
'medium_priority': [b for b in briefs if b['priority'] == 'medium'],
'low_priority': [b for b in briefs if b['priority'] == 'low'],
'total_credits_used': len(keywords) * 3, # 3 queries per keyword
}
# Usage:
audit = client_keyword_audit(
['best crm 2026', 'crm for startups', 'hubspot alternatives'],
'Acme Corp'
)
print(f"High priority: {len(audit['high_priority'])} keywords")Python Example
Python
import requests, os
H = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
def quick_brief(keyword):
serp = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'platform': 'google', 'query': keyword}).json()
reddit = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'platform': 'reddit', 'query': keyword}).json()
return {'paa': len(serp.get('people_also_ask',[])), 'reddit_threads': len(reddit.get('organic',[]))}JavaScript Example
JavaScript
async function quickBrief(keyword) {
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
const [serp, reddit] = await Promise.all([
fetch('https://api.scavio.dev/api/v1/search', {method:'POST', headers:H, body:JSON.stringify({platform:'google', query:keyword})}).then(r=>r.json()),
fetch('https://api.scavio.dev/api/v1/search', {method:'POST', headers:H, body:JSON.stringify({platform:'reddit', query:keyword})}).then(r=>r.json())
]);
return {paa: (serp.people_also_ask||[]).length, redditThreads: (reddit.organic||[]).length};
}Expected Output
JSON
An SEO keyword research agent that produces priority-ranked keyword briefs combining Google SERP data, Reddit demand signals, and YouTube content gaps.