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Web tools let any agent search the internet or read any webpage, with automatic provider fallback so it works even without an API key. 
from praisonaiagents import Agent

agent = Agent(
    name="Researcher",
    instructions="Answer questions using current information from the web.",
    web=True,
)
agent.start("What are the latest developments in AI this week?")
The user asks for current information; the agent searches or fetches pages with automatic provider fallback.

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Quick Start

1

Level 1 — Bool (simplest)

Add web=True and the agent automatically searches the internet when it needs current information.
from praisonaiagents import Agent

agent = Agent(
    name="Researcher",
    instructions="Answer questions using current information from the web.",
    web=True,
)

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

Level 2 — String (pick a provider)

Pass a provider name to route searches through a specific engine.
from praisonaiagents import Agent

agent = Agent(
    name="Researcher",
    instructions="Answer questions using current information from the web.",
    web="tavily",
)

agent.start("What are the latest developments in AI this week?")
3

Level 3 — Config class (full control)

Use WebConfig to combine search, fetch, provider, and result limits.
from praisonaiagents import Agent, WebConfig

agent = Agent(
    name="Researcher",
    instructions="Research topics thoroughly.",
    web=WebConfig(
        search=True,
        fetch=True,
        search_provider="tavily",
        max_results=10,
    )
)

agent.start("Summarize the latest news on quantum computing.")
4

Alternative — Web tools directly

Use search_web and web_crawl as standalone tools inside any agent.
from praisonaiagents import Agent
from praisonaiagents.tools import search_web, web_crawl

agent = Agent(
    name="Web Agent",
    instructions="Search and read web pages to answer questions.",
    tools=[search_web, web_crawl],
)

agent.start("Find and summarize the homepage of OpenAI.")

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How It Works

The agent decides when to search or fetch based on your query. Results flow back as context for the final answer.

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Configuration Options

WebConfig API Reference

Full API reference for WebConfig — search, fetch, providers, max_results

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WebConfig Fields

FieldTypeDefaultDescription
searchboolTrueEnable web search
fetchboolTrueEnable web page fetching
search_providerstr"duckduckgo"Provider to use: tavily, brave, exa, youdotcom, duckduckgo, searxng
max_resultsint5Maximum search results returned
search_configdict | NoneNoneProvider-specific search settings
fetch_configdict | NoneNoneProvider-specific fetch settings

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Search Provider Priority (auto-fallback)

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Common Patterns

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Research Agent with Sources

Search the web and return cited sources alongside the answer. 
from praisonaiagents import Agent

agent = Agent(
    name="Research Assistant",
    instructions="Answer questions with sources. Always cite URLs.",
    web=True,
)

agent.start("What is the current state of nuclear fusion research?")

​
Crawl and Summarize a URL

Fetch a specific webpage and summarize it. 
from praisonaiagents import Agent
from praisonaiagents.tools import web_crawl

agent = Agent(
    name="Summarizer",
    instructions="Fetch the given URL and write a 3-sentence summary.",
    tools=[web_crawl],
)

agent.start("Summarize https://openai.com/research/overview")

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Multi-Agent Research Pipeline

One agent searches, another writes the report. 
from praisonaiagents import Agent, Task, PraisonAIAgents

searcher = Agent(
    name="Searcher",
    instructions="Search the web for information on the given topic.",
    web=True,
)

writer = Agent(
    name="Writer",
    instructions="Write a structured report based on research findings.",
)

search_task = Task(
    description="Find the top 5 sources on AI regulation in 2025",
    agent=searcher,
    expected_output="List of sources with summaries",
)

write_task = Task(
    description="Write a 500-word report on AI regulation",
    agent=writer,
    context=[search_task],
    expected_output="Formatted report",
)

agents = PraisonAIAgents(agents=[searcher, writer], tasks=[search_task, write_task])
agents.start()

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Best Practices

DuckDuckGo requires no API key and no package beyond ddgs. It’s the best choice for local development, prototypes, and open-source projects where you can’t bundle API keys.
Tavily returns higher-quality snippets and supports full-page extraction. Set TAVILY_API_KEY in your environment and the agent will prefer it automatically over free providers.
Each search result adds tokens to the agent’s context. Keep max_results=5 (the default) for most queries. Only increase it when the task genuinely requires broad coverage, such as academic literature surveys.
search=True finds the best URLs; fetch=True reads the full page content. Use both together when a snippet is not enough — for example, reading full API documentation or news articles.

Search Providers

Choose DuckDuckGo, Tavily, or Serper for web search

Deep Research

Multi-step research agents that synthesize information from many sources