> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# 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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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?")
```

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "Wikipedia Agent"
        User[📋 Query] --> Agent[🤖 Agent]
        Agent --> Tools[🧠 Wikipedia Tools]
        Tools --> Result[✅ Knowledge Output]
    end

    classDef input fill:#6366F1,stroke:#7C90A0,color:#fff
    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef output fill:#10B981,stroke:#7C90A0,color:#fff

    class User,Agent input
    class Tools process
    class Result output
```

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

## Quick Start

<Steps>
  <Step title="Simple Usage">
    Attach the wiki tools and ask a knowledge question.

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    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?")
    ```
  </Step>

  <Step title="With Configuration">
    Return structured knowledge with a Pydantic schema.

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    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()
    ```
  </Step>
</Steps>

## How It Works

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
    participant User
    participant Agent
    participant Tools as Wikipedia Tools

    User->>Agent: "History of artificial intelligence?"
    Agent->>Tools: wiki_search / wiki_page / wiki_summary
    Tools-->>Agent: Articles and summaries
    Agent-->>User: Knowledge 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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents praisonai wikipedia
export OPENAI_API_KEY="your-key"
```

### Run — Python

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "Explain quantum computing from Wikipedia" --tools wikipedia
```

### Run — agents.yaml

```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai agents.yaml
```

### Serve API

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents praisonai wikipedia pydantic
export OPENAI_API_KEY="your-key"
```

### Run — Python

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "Explain AI history" --tools wikipedia --memory --verbose
```

### Run — agents.yaml

```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai agents.yaml --verbose
```

### Serve API

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "test wikipedia" --tools wikipedia --verbose
```

## Cleanup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
rm -f wiki.db
```

## Features Demonstrated

| Feature           | Implementation                          |
| ----------------- | --------------------------------------- |
| Workflow          | Multi-tool Wikipedia research           |
| DB Persistence    | SQLite via `memory_config`              |
| Observability     | `--verbose` flag                        |
| Tools             | wiki\_search, wiki\_summary, wiki\_page |
| Resumability      | `Session` with `session_id`             |
| Structured Output | Pydantic `WikiKnowledge` model          |

## Best Practices

<AccordionGroup>
  <Accordion title="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.
  </Accordion>

  <Accordion title="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.
  </Accordion>

  <Accordion title="Return structured knowledge for pipelines">
    Add `output_pydantic` with `key_facts` and `related_topics` so downstream code gets typed fields instead of a paragraph.
  </Accordion>

  <Accordion title="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.
  </Accordion>
</AccordionGroup>

## Related

<CardGroup cols={2}>
  <Card icon="globe" href="/docs/agents/websearch">
    Search the live web for current events.
  </Card>

  <Card icon="magnifying-glass-chart" href="/docs/agents/research">
    Synthesise multiple sources into a report.
  </Card>
</CardGroup>