Recommendation Agent

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

from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="Recommender",
    instructions="Provide personalized suggestions based on preferences.",
    tools=[duckduckgo]
)

result = agent.start("Recommend 5 sci-fi movies from 2024")
print(result)

praisonai "Recommend good books about AI" --web-search

framework: praisonai
topic: Recommendations
roles:
  recommender:
    role: Recommendation Specialist
    goal: Generate personalized recommendations
    backstory: You are an expert at finding great content
    tools:
      - duckduckgo
    tasks:
      recommend:
        description: Recommend 5 sci-fi movies from 2024
        expected_output: A list of recommendations

praisonai agents.yaml

from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

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

agent.launch(port=8080)

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Recommend podcasts about technology"}'

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

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

class Recommendation(BaseModel):
    category: str
    items: list[str]
    descriptions: list[str]
    ratings: list[str]

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

agent = Agent(
    name="Recommender",
    instructions="Generate structured recommendations.",
    tools=[duckduckgo],
    memory=True
)

task = Task(
    description="Recommend 5 sci-fi movies from 2024 with ratings",
    expected_output="Structured recommendations",
    agent=agent,
    output_pydantic=Recommendation
)

agents = Agents(
    agents=[agent],
    tasks=[task],
    memory=True,
    memory_config={"provider": "sqlite", "db_path": "recommendations.db"},
    verbose=1
)

result = agents.start()
print(result)

praisonai "Recommend sci-fi movies" --web-search --memory --verbose

framework: praisonai
topic: Recommendations
memory: true
memory_config:
  provider: sqlite
  db_path: recommendations.db
roles:
  recommender:
    role: Recommendation Specialist
    goal: Generate structured recommendations
    backstory: You are an expert at finding great content
    tools:
      - duckduckgo
    memory: true
    tasks:
      recommend:
        description: Recommend 5 sci-fi movies from 2024
        expected_output: Structured recommendations
        output_json:
          category: string
          items: array
          descriptions: array
          ratings: array

praisonai agents.yaml --verbose

from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

agent = Agent(
    name="Recommender",
    instructions="Generate structured recommendations.",
    tools=[duckduckgo],
    memory=True
)

agent.launch(port=8080)

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Recommend books", "session_id": "rec-001"}'

praisonai "test recommendations" --web-search --verbose

rm -f recommendations.db