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Answer questions with real-time information by giving a single Agent a web search tool. 
from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

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

agent.start("What are the latest AI developments in 2024?")
Web search agent using DuckDuckGo for real-time information gathering.

​
Quick Start

1

Simple Usage

Attach the search tool and ask a question.
from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

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

agent.start("What are the latest AI developments in 2024?")
2

With Configuration

Return structured search results with a Pydantic schema.
from praisonaiagents import Agent, Task, AgentTeam
from praisonaiagents.tools import duckduckgo
from pydantic import BaseModel

class SearchResult(BaseModel):
    summary: str
    sources: list[str]
    key_findings: list[str]

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

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

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

​
How It Works

​
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

pip install praisonaiagents praisonai duckduckgo-search
export OPENAI_API_KEY="your-key"

​
Run — Python

from praisonaiagents import Agent
from praisonaiagents.tools 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

praisonai "What are the latest AI developments?" --web-search

​
Run — agents.yaml

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

praisonai agents.yaml

​
Serve API

from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

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

agent.launch(port=8080)

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

pip install praisonaiagents praisonai duckduckgo-search pydantic
export OPENAI_API_KEY="your-key"

​
Run — Python

from praisonaiagents import Agent, Task, AgentTeam, Session
from praisonaiagents.tools 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

# 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

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

praisonai agents.yaml --verbose

​
Serve API

from praisonaiagents import Agent
from praisonaiagents.tools import duckduckgo

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

agent.launch(port=8080)

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Search for Python news", "session_id": "search-001"}'

​
Monitor / Verify

praisonai "test search" --web-search --verbose

​
Cleanup

rm -f search.db

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Features Demonstrated

FeatureImplementation
WorkflowSingle-step web search
DB PersistenceSQLite via memory_config
Observability--verbose flag
ToolsDuckDuckGo search
ResumabilitySession with session_id
Structured OutputPydantic SearchResult model

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Best Practices

Instruct it to include the URLs it used. Search answers without provenance are hard to verify and easy to distrust.
Tell the agent to synthesise findings into a concise answer rather than pasting raw snippets. Users want the takeaway, not ten links.
Set memory=True so the agent builds on earlier queries instead of re-searching the same ground on each turn.
For multi-source synthesis and long reports, hand off to the Research or Deep Research agents rather than a single search pass.

Synthesise multiple sources into a report.

Provider-native deep research for in-depth analysis.