Wikipedia PraisonAI Integration

Guide for integrating Wikipedia search capabilities with PraisonAI agents using the Wikipedia API wrapper

from praisonaiagents import Agent

agent = Agent(
    name="WikiAgent",
    instructions="Search Wikipedia for factual information.",
)
agent.start("Explain the history of the internet")

The user asks for encyclopaedic facts; the agent searches Wikipedia and returns a grounded summary.

Wikipedia PraisonAI Integration

pip install wikipedia langchain_community

# tools.py
from langchain_community.utilities import WikipediaAPIWrapper
class WikipediaSearchTool(BaseTool):
    name: str = "WikipediaSearchTool"
    description: str = "Search Wikipedia for relevant information based on a query."

    def _run(self, query: str):
        api_wrapper = WikipediaAPIWrapper(top_k_results=4, doc_content_chars_max=100)
        results = api_wrapper.load(query=query)
        return results

# agents.yaml
framework: crewai
topic: research about nvidia growth
agents:  # Canonical: use 'agents' instead of 'roles'
  data_collector:
    instructions:  # Canonical: use 'instructions' instead of 'backstory' An experienced researcher with the ability to efficiently collect and
      organize vast amounts of data.
    goal: Gather information on Nvidia's growth by providing the Ticket Symbol to YahooFinanceNewsTool
    role: Data Collector
    tasks:
      data_collection_task:
        description: Collect data on Nvidia's growth from various sources such as
          financial reports, news articles, and company announcements.
        expected_output: A comprehensive document detailing data points on Nvidia's
          growth over the years.
    tools:
    - 'WikipediaSearchTool'

Quick Start

1. Install

   pip install praisonaiagents wikipedia-api
   
2. Search Wikipedia with agent

   from praisonaiagents import Agent
   
   agent = Agent(
       name="WikiAgent",
       instructions="Search Wikipedia for factual information.",
   )
   
   agent.start("Explain the history of the internet")
   

Best Practices

Use Wikipedia for factual background

Wikipedia tools are best for general knowledge and background context, not breaking news.

Extract specific sections

Extract specific sections rather than full articles to reduce token usage.

Follow links for depth

Use Wikipedia link extraction to discover and follow related concepts.

Pair with web search for recency

Combine Wikipedia (background facts) with web search (recent information) for comprehensive research.

Related

Custom Tools

Build your own agent tools

Tools Overview

Browse PraisonAI tool documentation