Agent Use Cases

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

View API Docs

agent.py

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

View API Docs

agent.py

# 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

View API Docs

agent.py

# 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 Intelligence at Scale

Extract product insights from massive review data. Millions of reviews become 10 actionable insights. Token cost reduced by 95%.

Millions of reviews → 10 insights. Token cost ↓95%.

Sentiment analysis at scale
Feature request extraction
Customer pain point identification

View API Docs

agent.py

# 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

View API Docs

agent.py

# 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

View API Docs

agent.py

# 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

LangChain

CrewAI

AutoGen

Claude MCP

OpenAI Functions

Custom Agents

Start Building Explore Skills

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

View API Docs

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)

# 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)

# 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 )

# 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 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 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)