Praison AI

Wikipedia Agent

Learn how to create AI agents for searching and extracting information from Wikipedia.

Search Wikipedia, pull articles, and summarise them with a single Agent using dedicated wiki tools.

python
from praisonaiagents import Agent
from praisonai_tools import wiki_search, wiki_summary, wiki_page

agent = Agent(
    name="WikiResearcher",
    instructions="Search and summarize Wikipedia content.",
    tools=[wiki_search, wiki_summary, wiki_page],
)

agent.start("What is the history of artificial intelligence?")
Diagram

Wikipedia Agent

📋 Query

🤖 Agent

🧠 Wikipedia Tools

✅ Knowledge Output

Wikipedia research agent with search, page retrieval, and summarization tools.

Quick Start

  1. Simple Usage

    Attach the wiki tools and ask a knowledge question.

    python
    from praisonaiagents import Agent
from praisonai_tools import wiki_search, wiki_summary, wiki_page

agent = Agent(
    name="WikiResearcher",
    instructions="Search and summarize Wikipedia content.",
    tools=[wiki_search, wiki_summary, wiki_page],
)

agent.start("What is the history of artificial intelligence?")
  2. With Configuration

    Return structured knowledge with a Pydantic schema.

    python
    from praisonaiagents import Agent, Task, AgentTeam
from praisonai_tools import wiki_search, wiki_summary, wiki_page
from pydantic import BaseModel

class WikiKnowledge(BaseModel):
    topic: str
    summary: str
    key_facts: list[str]
    related_topics: list[str]

agent = Agent(
    name="WikiResearcher",
    instructions="Extract structured knowledge from Wikipedia.",
    tools=[wiki_search, wiki_summary, wiki_page],
)

task = Task(
    description="What is the history of artificial intelligence?",
    expected_output="Structured knowledge summary",
    agent=agent,
    output_pydantic=WikiKnowledge,
)

AgentTeam(agents=[agent], tasks=[task]).start()

How It Works

Diagram
Wikipedia ToolsAgentUserWikipedia ToolsAgentUser“History of artificial intelligence?”wiki_search / wiki_page / wiki_summaryArticles and summariesKnowledge summary

Simple

Agents: 1 — Single agent with Wikipedia tools handles search and content extraction.

Workflow

  1. Receive knowledge query
  2. Search Wikipedia articles
  3. Summarize findings

Setup

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

Run — Python

python
from praisonaiagents import Agent
from praisonai_tools import wiki_search, wiki_summary, wiki_page

agent = Agent(
    name="WikiResearcher",
    instructions="Search and summarize Wikipedia content.",
    tools=[wiki_search, wiki_summary, wiki_page]
)

result = agent.start("What is the history of artificial intelligence?")
print(result)

Run — CLI

bash
praisonai "Explain quantum computing from Wikipedia" --tools wikipedia

Run — agents.yaml

yaml
framework: praisonai
topic: Wikipedia Research
roles:
  wiki_researcher:
    role: Wikipedia Research Specialist
    goal: Extract and summarize Wikipedia content
    backstory: You are an expert at finding knowledge
    tools:
      - wiki_search
      - wiki_summary
      - wiki_page
    tasks:
      research:
        description: What is the history of artificial intelligence?
        expected_output: A comprehensive summary
bash
praisonai agents.yaml

Serve API

python
from praisonaiagents import Agent
from praisonai_tools import wiki_search, wiki_summary, wiki_page

agent = Agent(
    name="WikiResearcher",
    instructions="You are a Wikipedia research agent.",
    tools=[wiki_search, wiki_summary, wiki_page]
)

agent.launch(port=8080)
bash
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Tell me about the Roman Empire"}'

Advanced Workflow (All Features)

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

Workflow

  1. Initialize session for knowledge tracking
  2. Configure SQLite persistence for research history
  3. Search and extract with structured output
  4. Store findings in memory for follow-up queries
  5. Resume session for continued research

Setup

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

Run — Python

python
from praisonaiagents import Agent, Task, AgentTeam, Session
from praisonai_tools import wiki_search, wiki_summary, wiki_page
from pydantic import BaseModel

class WikiKnowledge(BaseModel):
    topic: str
    summary: str
    key_facts: list[str]
    related_topics: list[str]

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

agent = Agent(
    name="WikiResearcher",
    instructions="Extract structured knowledge from Wikipedia.",
    tools=[wiki_search, wiki_summary, wiki_page],
    memory=True
)

task = Task(
    description="What is the history of artificial intelligence?",
    expected_output="Structured knowledge summary",
    agent=agent,
    output_pydantic=WikiKnowledge
)

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

result = agents.start()
print(result)

Run — CLI

bash
praisonai "Explain AI history" --tools wikipedia --memory --verbose

Run — agents.yaml

yaml
framework: praisonai
topic: Wikipedia Research
memory: true
memory_config:
  provider: sqlite
  db_path: wiki.db
roles:
  wiki_researcher:
    role: Wikipedia Research Specialist
    goal: Extract structured knowledge
    backstory: You are an expert at finding knowledge
    tools:
      - wiki_search
      - wiki_summary
      - wiki_page
    memory: true
    tasks:
      research:
        description: What is the history of artificial intelligence?
        expected_output: Structured knowledge summary
        output_json:
          topic: string
          summary: string
          key_facts: array
          related_topics: array
bash
praisonai agents.yaml --verbose

Serve API

python
from praisonaiagents import Agent
from praisonai_tools import wiki_search, wiki_summary, wiki_page

agent = Agent(
    name="WikiResearcher",
    instructions="Extract structured knowledge from Wikipedia.",
    tools=[wiki_search, wiki_summary, wiki_page],
    memory=True
)

agent.launch(port=8080)
bash
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Tell me about Rome", "session_id": "wiki-001"}'

Monitor / Verify

bash
praisonai "test wikipedia" --tools wikipedia --verbose

Cleanup

bash
rm -f wiki.db

Features Demonstrated

FeatureImplementation
WorkflowMulti-tool Wikipedia research
DB PersistenceSQLite via memory_config
Observability--verbose flag
Toolswiki_search, wiki_summary, wiki_page
ResumabilitySession with session_id
Structured OutputPydantic WikiKnowledge model

Best Practices

Attach all three wiki tools

Pass wiki_search, wiki_summary, and wiki_page together. The agent needs search to find an article, page to read it, and summary to condense it.

Prefer Wikipedia for encyclopaedic facts

Reach for this agent when accuracy on established facts matters. For breaking news or current events, use the Web Search Agent instead.

Return structured knowledge for pipelines

Add output_pydantic with key_facts and related_topics so downstream code gets typed fields instead of a paragraph.

Combine with Research for broader coverage

Wikipedia is one source. Pair with the Research Agent when a question needs perspectives beyond a single encyclopaedia entry.

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