> ## 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.

# Web Search Agent

> Learn how to create AI agents for intelligent web searching and information gathering.

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

Web search agent using DuckDuckGo for real-time information gathering.

***

## Simple

**Agents: 1** — Single agent with search tool handles query and summarization.

### Workflow

1. Receive search query
2. Execute web search via DuckDuckGo
3. Filter and summarize results
4. Return formatted response

### Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents praisonai duckduckgo-search
export OPENAI_API_KEY="your-key"
```

### Run — Python

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonaiagents import duckduckgo

agent = Agent(
    name="WebSearcher",
    instructions="You are a web search agent. Search and summarize findings.",
    tools=[duckduckgo]
)

result = agent.start("What are the latest AI developments in 2024?")
print(result)
```

### Run — CLI

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "What are the latest AI developments?" --web-search
```

### Run — agents.yaml

```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
framework: praisonai
topic: Web Research
roles:
  web_searcher:
    role: Web Search Specialist
    goal: Find and summarize web information
    backstory: You are an expert at finding information online
    tools:
      - duckduckgo
    tasks:
      search_task:
        description: Search for the latest AI developments in 2024
        expected_output: A summary of key AI developments with sources
```

```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 duckduckgo

agent = Agent(
    name="WebSearcher",
    instructions="You are a web search agent.",
    tools=[duckduckgo]
)

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": "Search for Python 3.12 new features"}'
```

***

## Advanced Workflow (All Features)

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

### Workflow

1. Initialize session for resumable search context
2. Configure SQLite persistence for search history
3. Execute search with structured JSON output
4. Store results in memory for follow-up queries
5. Resume session to continue research

### Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents praisonai duckduckgo-search 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 duckduckgo
from pydantic import BaseModel

# Structured output schema
class SearchResult(BaseModel):
    query: str
    summary: str
    sources: list[str]
    key_findings: list[str]

# Create session for resumability
session = Session(session_id="search-session-001", user_id="user-1")

# Agent with memory and tools
agent = Agent(
    name="WebSearcher",
    instructions="Search the web and return structured results.",
    tools=[duckduckgo],
    memory=True
)

# Task with structured output
task = Task(
    description="Search for the latest AI developments in 2024",
    expected_output="Structured search results",
    agent=agent,
    output_pydantic=SearchResult
)

# Run with SQLite persistence
agents = AgentTeam(
    agents=[agent],
    tasks=[task],
    memory=True
)

result = agents.start()
print(result)

# Resume later
session2 = Session(session_id="search-session-001", user_id="user-1")
history = session2.search_memory("AI developments")
```

### Run — CLI

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# With memory and verbose
praisonai "Search for AI news" --web-search --memory --verbose

# Resume session
praisonai "Find more details" --web-search --memory --session search-001
```

### Run — agents.yaml

```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
framework: praisonai
topic: Web Research
memory: true
memory_config:
  provider: sqlite
  db_path: search.db
roles:
  web_searcher:
    role: Web Search Specialist
    goal: Find and summarize web information
    backstory: You are an expert at finding information online
    tools:
      - duckduckgo
    memory: true
    tasks:
      search_task:
        description: Search for the latest AI developments in 2024
        expected_output: Structured search results
        output_json:
          query: string
          summary: string
          sources: array
          key_findings: 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 duckduckgo

agent = Agent(
    name="WebSearcher",
    instructions="Search the web and return results.",
    tools=[duckduckgo],
    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": "Search for Python news", "session_id": "search-001"}'
```

***

## Monitor / Verify

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "test search" --web-search --verbose
```

## Cleanup

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

## Features Demonstrated

| Feature           | Implementation                |
| ----------------- | ----------------------------- |
| Workflow          | Single-step web search        |
| DB Persistence    | SQLite via `memory_config`    |
| Observability     | `--verbose` flag              |
| Tools             | DuckDuckGo search             |
| Resumability      | `Session` with `session_id`   |
| Structured Output | Pydantic `SearchResult` model |

## Next Steps

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