Praison AI

Shopping Agent

Learn how to create AI agents for price comparison and shopping assistance.

Compare prices across stores and surface the best deal with a single Agent using web search.

python
from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="ShoppingAssistant",
    instructions="You are a shopping agent. Compare prices in table format.",
    tools=[duckduckgo],
)

agent.start("Compare prices for iPhone 16 Pro Max")
Diagram

Shopping Agent

📋 Product Query

🤖 Agent

🧠 Web Search

✅ Price Comparison

Shopping assistant with web search for price comparison across stores.

Quick Start

  1. Simple Usage

    Attach a search tool and name a product.

    python
    from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="ShoppingAssistant",
    instructions="You are a shopping agent. Compare prices in table format.",
    tools=[duckduckgo],
)

agent.start("Compare prices for iPhone 16 Pro Max")
  2. With Configuration

    Enable memory so the agent tracks prices over time.

    python
    from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="ShoppingAssistant",
    instructions="Compare prices and remember what the user is watching.",
    tools=[duckduckgo],
    memory=True,
)

agent.start("Compare MacBook Pro prices, then watch for a drop.")

How It Works

Diagram
Web SearchAgentUserWeb SearchAgentUser“Compare prices for iPhone 16 Pro Max”Query multiple storesPrices and availabilityComparison table + best deal

Simple

Agents: 1 — Single agent with search tool handles product research and comparison.

Workflow

  1. Receive product query
  2. Search multiple stores
  3. Compare prices and generate report

Setup

bash
pip install praisonaiagents praisonai duckduckgo-search
export OPENAI_API_KEY="your-key"

Run — Python

python
from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="ShoppingAssistant",
    instructions="You are a shopping agent. Compare prices in table format.",
    tools=[duckduckgo]
)

result = agent.start("Compare prices for iPhone 16 Pro Max")
print(result)

Run — CLI

bash
praisonai "Compare MacBook Pro prices" --web-search

Run — agents.yaml

yaml
framework: praisonai
topic: Price Comparison
roles:
  shopping_assistant:
    role: Shopping Specialist
    goal: Find the best prices across stores
    backstory: You are an expert at finding deals
    tools:
      - duckduckgo
    tasks:
      compare_prices:
        description: Compare prices for iPhone 16 Pro Max
        expected_output: A price comparison table
bash
praisonai agents.yaml

Serve API

python
from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="ShoppingAssistant",
    instructions="You are a shopping agent.",
    tools=[duckduckgo]
)

agent.launch(port=8080)
bash
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Find best deals on Sony headphones"}'

Advanced Workflow (All Features)

Agents: 1 — Single agent with memory, persistence, structured output, and session resumability.

Workflow

  1. Initialize session for shopping history
  2. Configure SQLite persistence for price tracking
  3. Search and compare with structured output
  4. Store results in memory for price alerts
  5. Resume session for ongoing comparisons

Setup

bash
pip install praisonaiagents praisonai duckduckgo-search pydantic
export OPENAI_API_KEY="your-key"

Run — Python

python
from praisonaiagents import Agent, Task, AgentTeam, Session
from praisonaiagents.tools import duckduckgo
from pydantic import BaseModel

class PriceComparison(BaseModel):
    product: str
    stores: list[str]
    prices: list[str]
    best_deal: str
    recommendation: str

session = Session(session_id="shop-001", user_id="user-1")

agent = Agent(
    name="ShoppingAssistant",
    instructions="Compare prices and return structured results.",
    tools=[duckduckgo],
    memory=True
)

task = Task(
    description="Compare iPhone 16 Pro Max prices across stores",
    expected_output="Structured price comparison",
    agent=agent,
    output_pydantic=PriceComparison
)

agents = AgentTeam(
    agents=[agent],
    tasks=[task],
    memory=True
)

result = agents.start()
print(result)

Run — CLI

bash
praisonai "Compare iPhone prices" --web-search --memory --verbose

Run — agents.yaml

yaml
framework: praisonai
topic: Price Comparison
memory: true
memory_config:
  provider: sqlite
  db_path: shopping.db
roles:
  shopping_assistant:
    role: Shopping Specialist
    goal: Find best prices with structured output
    backstory: You are an expert at finding deals
    tools:
      - duckduckgo
    memory: true
    tasks:
      compare_prices:
        description: Compare iPhone 16 Pro Max prices
        expected_output: Structured price comparison
        output_json:
          product: string
          stores: array
          prices: array
          best_deal: string
          recommendation: string
bash
praisonai agents.yaml --verbose

Serve API

python
from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="ShoppingAssistant",
    instructions="Compare prices and return structured results.",
    tools=[duckduckgo],
    memory=True
)

agent.launch(port=8080)
bash
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Compare laptop prices", "session_id": "shop-001"}'

Monitor / Verify

bash
praisonai "test shopping" --web-search --verbose

Cleanup

bash
rm -f shopping.db

Features Demonstrated

FeatureImplementation
WorkflowMulti-store price comparison
DB PersistenceSQLite via memory_config
Observability--verbose flag
ToolsDuckDuckGo search
ResumabilitySession with session_id
Structured OutputPydantic PriceComparison model

Best Practices

Ask for a comparison table

Instruct the agent to return results as a table with store, price, and link. Structured output makes the best deal obvious at a glance.

Enable memory for price watching

Set memory=True so the agent remembers watched products and flags changes on the next run instead of starting over.

Verify prices are current

Web results can be stale. Ask the agent to note the source date so users know how fresh each quote is.

Pair with Recommendation for discovery

Use the Recommendation Agent to pick what to buy, then hand the chosen item to this agent for a price comparison.

Related

Card

Get personalised suggestions before comparing prices.

 Card

Research a product in depth before buying.