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.")

How It Works

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

Configuration Options

WebConfig Fields

Search Provider Priority (auto-fallback)

web_crawl Output

Every fetched page comes back as a dict with four keys, and the content field is Markdown — headings, lists, links, and fenced code blocks survive even when no optional crawl provider is installed.

A failed fetch adds an error key instead of usable content.

Size limits on the built-in path

The built-in httpx path streams each response, and the urllib fallback does a single bounded read. Both read at most 5 MB of raw bytes before parsing. The returned content is still capped at 50,000 Markdown characters. The byte cap keeps memory bounded, so a very large page (or a never-ending stream) can’t use unlimited memory.

What survives on the zero-dependency path

The built-in httpx path uses only Python’s standard library, so it works on every install with no API key or extra package.

Example

from praisonaiagents.tools import web_crawl

result = web_crawl("https://example.com/docs/intro")
print(result["title"])     # "Intro — Example Docs"
print(result["provider"])  # "httpx"
print(result["content"][:120])
# # Intro
#
# Welcome to the docs. See [the quick start](https://example.com/docs/quickstart) for a tour.
#
# ```python
# from example import Client
# ```

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")

The agent reads the page as Markdown, so headings and code blocks are preserved — the summary can quote them directly.

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()

Best Practices

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