Web

Give agents live internet access — search the web or fetch any URL in one line

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.

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

WebConfig Fields

Field	Type	Default	Description
search	bool	True	Enable web search
fetch	bool	True	Enable web page fetching
search_provider	str	"duckduckgo"	Provider to use: tavily, brave, exa, youdotcom, duckduckgo, searxng
max_results	int	5	Maximum search results returned
search_config	dict | None	None	Provider-specific search settings
fetch_config	dict | None	None	Provider-specific fetch settings

Search Provider Priority (auto-fallback)

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Common Patterns

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")

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

Use DuckDuckGo for zero-configuration setups

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.

Switch to Tavily for production accuracy

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.

Limit max_results to control token costs

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.

Combine search and fetch for deep research

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.

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Related

Search Providers

Choose DuckDuckGo, Tavily, or Serper for web search

Deep Research

Multi-step research agents that synthesize information from many sources