Built for Agent Workflows
From autonomous research to multi-agent orchestration — power every Agent in your commerce stack.
The difference?
Human: 100 products/day. Agent with APIClaw: 10,000+ products/day.
Autonomous Selection Agent
Let your Agent continuously scan the market and discover blue ocean opportunities. A human can review 100 products per day. Your Agent can review 10,000.
Human: 100 products/day. Agent: 10,000 products/day.
- Automated market opportunity scanning
- 14 built-in selection presets
- Multi-dimensional filtering logic
from apiclaw import APIClawClient
client = APIClawClient(api_key="hms_live_xxx")
# Discover blue ocean opportunities
products = client.products.search(
category_path=["Electronics", "Headphones"],
mode="blue_ocean", # 14 preset modes available
filters={
"monthly_revenue_min": 10000,
"review_count_max": 100,
"rating_min": 4.0
}
)
# Your agent processes 10,000+ results
for product in products:
analyze_opportunity(product)Competitive Intelligence Network
Multi-agent collaborative monitoring of competitors. Price changes, new listings, review shifts — all detected in seconds.
24/7 monitoring, second-level price change alerts.
- Real-time price change notifications
- New product launch detection
- Review trend monitoring
# Multi-dimensional competitor lookup
competitors = client.competitors.lookup(
keyword="wireless earbuds",
brand="Sony",
asin="B09V3KXJPB" # Any dimension
)
# Real-time monitoring
for competitor in competitors:
product = client.realtime.product(
asin=competitor["asin"]
)
if price_changed(product):
alert_agent(product)Dynamic Pricing Agent
Automatic price adjustments based on real-time market signals. This is not a pricing SaaS — it's the data layer for your pricing Agent.
Not a pricing SaaS, but the data layer for your pricing Agent.
- Real-time competitive price tracking
- Price band analysis
- Margin optimization signals
# Batch real-time price lookup
asins = ["B09V3KXJPB", "B08N5WRWNW", ...]
for asin in asins:
product = client.realtime.product(asin=asin)
market_price = product["price"]
competitor_prices = get_competitor_prices(asin)
# Your agent decides optimal price
optimal_price = pricing_agent.decide(
current_price=my_price,
market_price=market_price,
competitor_prices=competitor_prices
)Review Mining at Scale
Extract product insights from massive review data. 10,000 reviews become 10 actionable insights. Token cost reduced by 95%.
10,000 reviews → 10 insights. Token cost ↓95%.
- Sentiment analysis at scale
- Feature request extraction
- Customer pain point identification
# AI-powered review analysis
insights = client.reviews.analyze(
asins=["B09V3KXJPB"],
analysis_types=[
"sentiment",
"keywords",
"pain_points",
"feature_requests"
]
)
# Pre-processed, structured output
# No need to run your own NLP
print(insights["top_complaints"])
print(insights["feature_gaps"])
print(insights["sentiment_score"])Market Radar
Continuous monitoring of category trends, capturing rising signals. Flowing signals, not static reports.
Flowing signals, not static reports.
- Category trend tracking
- BSR movement alerts
- Emerging opportunity detection
# Market trend monitoring
def daily_scan():
categories = get_watch_list()
for category in categories:
market = client.markets.search(
category_path=category,
sample_type="by_sale_100"
)
# BSR updated every 15 minutes
signals = extract_signals(market)
if signals["trending_up"]:
notify_agent(category, signals)
# Run continuously
schedule.every(15).minutes.do(daily_scan)End-to-End Listing Automation
Fully automated pipeline from discovery to listing. OpenClaw ecosystem: data layer + execution layer.
OpenClaw ecosystem: data layer + execution layer.
- Automated product discovery
- Listing content generation
- Inventory sync automation
# End-to-end automation workflow
class ListingAgent:
def run(self, category):
# Step 1: Discover opportunities
products = client.products.search(
category_path=category,
mode="emerging"
)
# Step 2: Validate with real-time data
for product in products:
live = client.realtime.product(
asin=product["asin"]
)
if self.validate(live):
# Step 3: Generate listing
listing = self.generate_listing(live)
# Step 4: Publish (via OpenClaw)
self.publish(listing)Works With Your AI Stack
OpenAPI 3.0 spec means one-click import into any framework