> ## Documentation Index
> Fetch the complete documentation index at: https://docs.praison.ai/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.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
flowchart LR
    In[Query] --> Agent[Wikipedia Agent]
    Agent --> Out[Knowledge Output]
    
    style In fill:#8B0000,color:#fff
    style Agent fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff
```

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

***

## 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 praisonaiagents 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 praisonaiagents 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 praisonaiagents 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 praisonaiagents 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          |

## Next Steps

* [Research Agent](/agents/research) for web research
* [Deep Research](/agents/deep-research) for comprehensive analysis
* [Memory](/features/advanced-memory) for persistent context