How to Build an AI Agent with Web Access

An AI agent with web access can search the internet, read web pages, and make decisions based on real-time information — capabilities that static LLMs completely lack. Whether you're building a research assistant, a monitoring bot, or an autonomous data-gathering pipeline, web access transforms an AI from a knowledge-base into a live intelligence system.

This guide covers the frameworks, patterns, and tools you need — including how to use SearchHive as the web access layer for your agent.

Key Takeaways

• LangChain is the most mature framework for building web-enabled agents — built-in search and scraping tools
• Tool-calling (function calling) is the standard pattern — the LLM decides when to search, your code executes the search
• SearchHive APIs are ideal as agent tools — clean free JSON formatter responses, low latency, no HTML parsing needed
• MCP (Model Context Protocol) is emerging as a universal standard for connecting agents to web tools
• Token budgets matter — always truncate and filter scraped content before passing it to the LLM

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What frameworks can I use to build a web-enabled AI agent?

LangChain

The most widely-adopted agent framework. Ships with built-in web search tools (TavilySearchResults, DuckDuckGoSearchRun), scraping tools (WebBaseLoader), and extensive documentation.

• Best for: Single-agent and multi-agent workflows with extensive integrations
• Web support: Multiple built-in search and scraping tools, custom tool creation via @tool decorator
• Ease of use: ⭐⭐⭐⭐ (mature, well-documented)

LlamaIndex

Originally a RAG framework, now supports full agent workflows. Excellent for agents that need to ingest, index, and reason over large amounts of web content.

• Best for: Data-intensive agents that process and query scraped content
• Web support: Multiple web readers (WebPageReader, TrafilaturaWebReader)
• Ease of use: ⭐⭐⭐⭐ (clean data connector abstractions)

CrewAI

Multi-agent orchestration where agents have assigned roles (Researcher, Writer, Analyst) and collaborate on tasks. Built on LangChain's tool ecosystem.

• Best for: Content pipelines and research workflows with role-based agent teams
• Web support: Via LangChain tools (SerperDevTool, ScrapeWebsiteTool)
• Ease of use: ⭐⭐⭐⭐⭐ (intuitive role-based design)

AutoGen (Microsoft)

Conversational multi-agent framework. No built-in web tools — you implement custom functions that agents call. Maximum flexibility, steepest learning curve.

• Best for: Complex multi-agent reasoning with full control over tool implementations
• Web support: Custom implementation required
• Ease of use: ⭐⭐ (verbose setup, but powerful)

How does web access actually work in an AI agent?

The standard pattern is tool-calling (also called function calling):

1. Define tools — describe web search as a function in the LLM's tool schema
2. LLM decides — based on the user's query, the model outputs a tool call when it needs web data
3. Execute locally — your code runs the actual search/scrape request
4. Return results — function output is fed back to the LLM
5. LLM synthesizes — the model incorporates the web data into its response

import json
from searchhive import SwiftSearch

client = SwiftSearch(api_key='your-key')

# Define the web search tool schema
tools = [{
    'name': 'web_search',
    'description': 'Search the web for current information. Use when you need facts, news, or data not in your training data.',
    'parameters': {
        'type': 'object',
        'properties': {
            'query': {'type': 'string', 'description': 'The search query'},
            'num_results': {'type': 'integer', 'default': 5, 'description': 'Number of results to return'}
        },n        'required': ['query']
    }
}]

def execute_web_search(query, num_results=5):
    """Called when the LLM invokes the web_search tool."""
    results = client.query(query, count=num_results)
    formatted = []
    for r in results:
        formatted.append({
            'title': r['title'],
            'url': r['url'],
            'snippet': r['snippet']
        })
    return json.dumps(formatted)

The LLM never touches the web directly. It outputs a function call, your code executes it, and the results flow back. This keeps the architecture clean and secure.

When should I use search vs. scraping in an agent?

Use search when the agent needs to discover information:

• "What are the latest AI regulation developments?"
• "Find competitors to this product"
• "What's the current price of Bitcoin?"

Use scraping when the agent needs full content from a specific page:

• "Summarize this article at [URL]"
• "Extract data from this product page"
• "Read this documentation page and answer questions about it"

Use both for deep research:

from searchhive import SwiftSearch, ScrapeForge

search = SwiftSearch(api_key='your-key')
scraper = ScrapeForge(api_key='your-key')

# Agent tool: search for sources, then scrape the most relevant one
def research_topic(query):
    # Step 1: Find sources
    results = search.query(query, count=5)
    # Step 2: Scrape the top result
    if results:
        content = scraper.scrape(results[0]['url'])
        return {
            'source': results[0]['url'],
            'title': results[0]['title'],
            'content': content['markdown'][:3000]  # Truncate for token budget
        }
    return {'error': 'No results found'}

Critical rule: Always truncate scraped content before passing it to the LLM. A full web page can be 50,000+ tokens — well beyond context windows and incredibly wasteful. Extract the relevant sections or summarize first.

What is MCP and why does it matter for agents?

MCP (Model Context Protocol) is an open standard by Anthropic that standardizes how AI models connect to external tools. Think of it as "USB-C for AI agents" — a universal connector.

How it works:

• MCP Servers expose tools (like web search) over a standardized JSON-RPC interface
• MCP Clients (Claude Desktop, IDEs, agent frameworks) discover and use these tools
• Any MCP-compatible client can use any MCP server — no custom integration needed

Current state (2026):

• LangChain has MCP adapter support
• LlamaIndex has experimental MCP integration
• Claude Desktop is the most mature MCP client
• CrewAI and AutoGen don't yet have native MCP support

The trajectory is clear: MCP is becoming the standard way to wire web tools into AI agents. A SearchHive MCP server would let any MCP-compatible agent use web search with zero custom code.

What are the most common mistakes when building web-enabled agents?

• Returning too much text — full page dumps blow up token budgets. Always truncate and filter.
• Not handling rate limits — agents that fire 20 search queries in 10 seconds get blocked. Implement throttling.
• Scraping bot-protected sites — without proper headers, delays, and proxies, most requests fail.
• No relevance filtering — passing irrelevant search results to the LLM wastes tokens and degrades output quality.
• Ignoring robots.txt generator — see Is Web Scraping Legal for why this matters.
• No caching — agents often search for the same thing repeatedly. Cache results with a TTL.

Why use SearchHive as the web layer for AI agents?

SearchHive's APIs are designed for exactly this use case:

• Clean JSON responses — no HTML parsing, no messy extraction logic
• Low latency — sub-second search results, agents stay responsive
• Consistent schema — API responses don't break when websites change their layout
• Built-in rate management — no 429s, no backoff logic, no proxy pools
• Three capabilities in one SDK — search (SwiftSearch), scrape (ScrapeForge), extract (DeepDive)

from searchhive import SwiftSearch, ScrapeForge

search = SwiftSearch(api_key='your-key')
scraper = ScrapeForge(api_key='your-key')

# LangChain-compatible tool definition
from langchain.tools import tool

@tool
def search_web(query: str) -> str:
    """Search the web for current information."""
    results = search.query(query, count=5)
    return '\n'.join(f"- {r['title']}: {r['snippet']}" for r in results)

@tool
def read_webpage(url: str) -> str:
    """Read and extract content from a web page."""
    page = scraper.scrape(url)
    return page['markdown'][:4000]  # Token-safe truncation

# Use with any LangChain agent
from langchain.agents import create_tool_calling_agent
agent = create_tool_calling_agent(llm, [search_web, read_webpage], prompt)

Compared to alternatives — SerpAPI (search only, $50+/month), Firecrawl (scraping only, $49+/month) — SearchHive provides search + scraping + extraction in one SDK at a lower price point. Compare options.

What's the recommended stack for getting started?

For a web-enabled AI agent in 2026:

1. Framework: LangChain (most mature, best tool ecosystem)
2. LLM: Any model with function calling support (GPT-4o, Claude 3.5, Llama 3.1)
3. Web search: SearchHive SwiftSearch (structured results, low latency)
4. Web scraping: SearchHive ScrapeForge (handles JS, CAPTCHAs, proxies)
5. Optional AI extraction: SearchHive DeepDive (structured data from unstructured content)

Start with the free tier — no credit card needed.

Summary

Building an AI agent with web access comes down to three pieces: a framework (LangChain/CrewAI), a tool-calling pattern, and a web data layer (SearchHive). The LLM decides when to search via function calling, your code executes through the SearchHive SDK, and clean JSON results flow back to the model. No proxy management, no HTML parsing, no CAPTCHA solving.

Get started at searchhive.dev/docs and wire web access into your first agent in under 30 minutes.

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Related reading: What Is SwiftSearch API | How to Use SearchHive with Python | How to Handle Rate Limiting in Web Scraping