Best AI Agents for Search Tools in 2025

AI agents that can search the web are transforming how we build applications. Instead of hardcoding data sources, these agents retrieve real-time information on demand -- answering questions, researching topics, monitoring competitors, and making decisions based on live data.

This guide covers the best AI agent frameworks and tools that integrate web search capabilities, ranked by developer experience, reliability, and cost-effectiveness.

Key Takeaways

• LangChain + SearchHive is the most flexible combination for building custom search agents
• CrewAI excels at multi-agent workflows where multiple agents collaborate on research tasks
• Tavily offers a built-in agent that works out of the box but at premium pricing
• Perplexity API provides search-grounded answers with citations, ideal for Q&A applications
• SearchHive DeepDive enables multi-step research within a single API call, reducing agent complexity
• For production systems, the search API quality matters more than the agent framework

1. LangChain + SearchHive SwiftSearch

LangChain is the most popular framework for building LLM applications. Combined with SearchHive's SwiftSearch API, it gives you a fully customizable search agent with clean separation between retrieval and reasoning.

Why it works well:

• LangChain provides the agent loop (tool calling, memory, planning)
• SwiftSearch provides real-time web data
• You control the LLM, the prompt template, and the output format

from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
import requests

SEARCHHIVE_KEY = "your-key"

def web_search(query):
    """Search the web using SearchHive SwiftSearch."""
    response = requests.get(
        "https://api.searchhive.dev/v1/search",
        headers={"Authorization": f"Bearer {SEARCHHIVE_KEY}"},
        params={"q": query, "engine": "google", "num": 5}
    )
    results = response.json()["data"]["results"]
    return "\n".join([f"- {r['title']}: {r.get('snippet', '')}" for r in results])

def deep_research(query):
    """Multi-step research using SearchHive DeepDive."""
    response = requests.post(
        "https://api.searchhive.dev/v1/deepdive",
        headers={"Authorization": f"Bearer {SEARCHHIVE_KEY}"},
        json={"query": query, "output_format": "markdown"}
    )
    return response.json()["data"]["content"]

tools = [
    Tool(name="WebSearch", func=web_search,
         description="Search the web for real-time information. Use for factual queries, current events, and lookups."),
    Tool(name="DeepResearch", func=deep_research,
         description="Conduct multi-step research on a topic. Use for complex questions that require synthesizing multiple sources.")
]

llm = OpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent="zero-shot-react-description")

result = agent.run(
    "What are the main differences between SerpAPI and SearchHive for building AI agents?"
)
print(result)

2. CrewAI + SearchHive

CrewAI lets you define multiple AI agents that collaborate on tasks. Each agent can have its own search capability, creating research teams that divide and conquer complex questions.

from crewai import Agent, Task, Crew
import requests

def search_tool(query):
    response = requests.get(
        "https://api.searchhive.dev/v1/search",
        headers={"Authorization": "Bearer YOUR_KEY"},
        params={"q": query, "num": 5}
    )
    results = response.json()["data"]["results"]
    return "\n".join([f"{r['title']}: {r['snippet']}" for r in results])

researcher = Agent(
    role="Market Researcher",
    goal="Find current market data and competitor information",
    tools=[search_tool],
    backstory="You are an expert at finding competitive intelligence online."
)

analyst = Agent(
    role="Data Analyst",
    goal="Analyze research findings and produce actionable insights",
    tools=[search_tool],
    backstory="You analyze market data to find patterns and opportunities."
)

research_task = Task(
    description="Research the top 5 competitors in the web scraping API market. Find their pricing, features, and market positioning.",
    agent=researcher,
    expected_output="Summary of competitor landscape with pricing comparison"
)

analysis_task = Task(
    description="Analyze the research findings and identify gaps in the market that a new entrant could exploit.",
    agent=analyst,
    expected_output="Market opportunity analysis with recommendations"
)

crew = Crew(agents=[researcher, analyst], tasks=[research_task, analysis_task])
result = crew.kickoff()
print(result)

3. Tavily AI Search Agent

Tavily provides a pre-built search agent that requires minimal setup. It handles the search-retrieval-reasoning loop internally.

from tavily import TavilyClient

client = TavilyClient(api_key="your-tavily-key")

result = client.search(
    query="What are the best practices for building RAG pipelines in 2025?",
    search_depth="advanced",
    include_answer=True,
    max_results=5
)

print("Answer:", result["answer"])
for r in result["results"]:
    print(f"  - {r['title']}: {r['url']}")

Pros: Zero setup, optimized for AI context. Cons: At $0.008/credit, costs add up fast for production workloads.

4. Perplexity API

Perplexity offers a search-grounded LLM API. It combines search retrieval with answer generation in a single call, returning cited answers.

• Pricing: Free tier (limited), Pro plans from $20/month
• Best for: Q&A applications where cited, accurate answers matter more than raw search results

5. OpenAI Assistants + Web Search

OpenAI's Assistants API supports function calling, which you can wire to any search API. Combined with SearchHive, it creates a ChatGPT-like experience with live web access.

import requests
import json

def search_function(arguments):
    """Called by OpenAI when the assistant decides to search."""
    query = arguments.get("query", "")
    response = requests.get(
        "https://api.searchhive.dev/v1/search",
        headers={"Authorization": "Bearer YOUR_KEY"},
        params={"q": query, "num": 5}
    )
    results = response.json()["data"]["results"]
    return json.dumps([{"title": r["title"], "snippet": r["snippet"]} for r in results])

# Wire this as a function in the OpenAI Assistants API
# The assistant decides when to call it based on user queries

6. AutoGen + SearchHive

Microsoft's AutoGen framework enables multi-agent conversations where agents can delegate tasks to each other. Adding web search lets agents ground their discussions in real data.

7. LlamaIndex + SwiftSearch

LlamaIndex focuses on RAG (Retrieval Augmented Generation). Its web search integration fetches documents from the web and indexes them for retrieval.

8. Flowise + SearchHive

Flowise provides a visual, no-code builder for LangChain workflows. You can add SearchHive as a tool node and build search agents without writing code.

9. Dify + Web Search

Dify is an open-source LLM app platform with built-in web search tools. Good for teams that want a managed platform for deploying AI agents.

10. Custom Agents with DeepDive

For the simplest approach, skip the framework entirely. SearchHive DeepDive combines search, retrieval, and synthesis in a single API call:

import requests

response = requests.post(
    "https://api.searchhive.dev/v1/deepdive",
    headers={"Authorization": "Bearer YOUR_KEY"},
    json={
        "query": "Compare the pricing and features of the top 5 web scraping APIs for startups",
        "output_format": "markdown",
        "max_sources": 10
    }
)

print(response.json()["data"]["content"])

One API call, multiple sources researched and synthesized. No agent framework, no prompt engineering, no tool-calling loop.

Comparison Table

Recommendation

For production AI agents, use LangChain or CrewAI with SearchHive. You get maximum control over the agent's behavior, the search quality is excellent, and the total cost (LLM + search API) is significantly lower than all-in-one solutions like Tavily.

For quick prototypes and demos, use DeepDive. A single API call that does research and returns a synthesized answer. No framework needed.

For non-technical teams, use Flowise. Visual builder, drag-and-drop search integration, no code required.

The search API you choose matters more than the agent framework. SearchHive provides real-time, accurate results at a fraction of the cost of alternatives. Get started free with 500 credits and see the API documentation for integration examples. For more on integrating search with AI agents, see our MCP search server guide and AI agent web access guide.