What Search Stack Do AI Agents Actually Need?

Most agent tutorials show a single Google search tool and call it done. Real agents need more. A product research agent needs Amazon prices. A content agent needs YouTube metadata. A sentiment agent needs Reddit threads. A competitive intelligence agent needs Google SERP features. One search tool searching one platform is a toy demo, not a production stack.

What agents actually query

After reviewing 40+ open-source agent repos and production agent deployments in early 2026, the pattern is clear. Agents fan out across platforms depending on the task:

• Google -- factual lookup, company info, pricing pages, documentation
• Reddit -- user sentiment, real experiences, complaints, recommendations
• YouTube -- tutorials, product demos, review videos, how-to content
• Amazon -- product prices, ratings, availability, competitor products
• Google News -- recent events, press releases, industry developments

The four-API approach (before)

The traditional approach is to wire up separate APIs for each platform. This means four API keys, four billing accounts, four response formats to normalize, and four rate limit strategies.

# The old way: 4 separate APIs, 4 keys, 4 response formats
import os

GOOGLE_KEY = os.environ['SERPER_KEY']       # $50/mo for 50K
REDDIT_KEY = os.environ['REDDIT_CLIENT_ID'] # OAuth + rate limits
YOUTUBE_KEY = os.environ['YOUTUBE_API_KEY'] # 10K units/day free
AMAZON_KEY = os.environ['SERPAPI_KEY']       # $75/mo for 5K

# Each returns a different shape. Each has different rate limits.
# Each needs its own error handling and retry logic.
# Total cost: ~$125/mo minimum before you search anything.

The unified approach (after)

A single API that accepts a platform parameter and returns typed JSON for each. One key, one billing account, one response normalization layer.

import requests, os

H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
URL = 'https://api.scavio.dev/api/v1/search'

def agent_search(query: str, platforms: list[str]) -> dict:
    """Fan out across platforms and merge results."""
    results = {}
    for platform in platforms:
        resp = requests.post(URL, headers=H,
            json={'platform': platform, 'query': query}, timeout=15)
        results[platform] = resp.json().get('organic_results', [])
    return results

# Product research agent: needs prices, reviews, and videos
data = agent_search('Sony WH-1000XM6 review', ['google', 'amazon', 'youtube', 'reddit'])

print(f"Google: {len(data['google'])} results")
print(f"Amazon: {len(data['amazon'])} products")
print(f"YouTube: {len(data['youtube'])} videos")
print(f"Reddit: {len(data['reddit'])} threads")

Building the agent tool properly

The search tool your agent exposes should let the LLM choose which platforms to query. Do not hardcode platform selection. The LLM is better at deciding whether a question needs Reddit sentiment or Amazon pricing than your if-else logic.

from typing import Literal

Platform = Literal['google', 'reddit', 'youtube', 'amazon', 'google_news']

def search_tool(
    query: str,
    platforms: list[Platform],
    num_results: int = 5
) -> dict:
    """
    Search across multiple platforms. The agent chooses which platforms
    are relevant for the current task.

    Args:
        query: Search query string
        platforms: Which platforms to search (let the LLM decide)
        num_results: Results per platform (default 5)
    """
    import requests, os
    H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
    URL = 'https://api.scavio.dev/api/v1/search'

    merged = {}
    for p in platforms:
        resp = requests.post(URL, headers=H,
            json={'platform': p, 'query': query, 'num': num_results})
        merged[p] = resp.json().get('organic_results', [])[:num_results]
    return merged

Honest tradeoffs

The unified API approach is simpler but not strictly better in all cases. Direct YouTube Data API gives you view counts, channel subscriber data, and comment threads that a SERP API does not. Direct Reddit API gives you upvote counts, comment trees, and user profiles. If your agent needs deep platform-specific data beyond search results, you still need the native APIs. The unified approach is best when your agent needs breadth (search across platforms) more than depth (full platform data).

Cost comparison

Four separate APIs: roughly $125-200/mo for moderate usage. Scavio unified: $30/mo for 7,000 credits across all platforms, or 500 free credits per month to start. The math works if your agent makes fewer than 7,000 total searches per month. Above that, per-credit pricing at $0.005 still beats running four separate subscriptions for most workloads.

What to build this week

1. Identify which platforms your agent actually queries
2. If it is Google-only, any SERP API works fine
3. If multi-platform, evaluate unified APIs to reduce complexity
4. Let the LLM choose platforms per query instead of hardcoding
5. Start with the free tier (500 credits) and measure real usage