Recommendation Agent

A workflow demonstrating how the Recommendation Agent can analyze preferences and generate personalized recommendations.

​

Quick Start

pip install praisonaiagents

export OPENAI_API_KEY=your_api_key_here

from praisonaiagents import Agent, Tools
from praisonaiagents.tools import duckduckgo

# Create Recommendation Agent
recommendation_agent = Agent(
    name="RecommendationEngine",
    role="Recommendation Specialist",
    goal="Generate personalized recommendations based on preferences",
    instructions="You are a Recommendation Agent",
    tools=[duckduckgo]
)

# Get recommendations
response = recommendation_agent.start(
    "Recommend me a good movie to watch in 2025"
)

# Save recommendations
with open('recommendations.txt', 'w') as f:
    f.write(response)

​

Understanding Recommendation System

The Recommendation Agent uses multiple approaches to generate personalized suggestions:

1. Content Search: Uses DuckDuckGo to find current options
2. Preference Analysis: Understands user preferences
3. Recommendation Generation: Creates personalized suggestions
4. Content Filtering: Filters based on relevance and quality

​

Features

​

Example Usage

# Example: Get personalized recommendations
from praisonaiagents import Agent, Tools
from praisonaiagents.tools import duckduckgo

agent = Agent(instructions="You are a Recommendation Agent", tools=[duckduckgo])
agent.start("Recommend me a good movie to watch in 2025")

​

Next Steps

• Learn about Prompt Chaining for complex recommendation systems
• Explore Evaluator Optimizer for improving recommendation accuracy
• Check out the Research Agent for detailed content research