Why Agents Need Web Search

LLMs have a knowledge cutoff. When a user asks about today's news, recent prices, or current documentation, the model either hallucinates or says "I don't know." Web search tools solve this by letting the agent retrieve real-time information from Google.

Prerequisites

pip install langchain langchain-openai httpx

You'll also need a [Searlo API key](https://dashboard.searlo.tech/auth) (free, 3,000 credits included).

Step 1: Create the Search Tool

import httpx

from langchain.tools import StructuredTool

from pydantic import BaseModel, Field

SEARLO_API_KEY = "your_api_key_here"

class SearchInput(BaseModel):

query: str = Field(description="The search query")

def web_search(query: str) -> str:

"""Search the web using Searlo API and return results."""

response = httpx.get(

"https://api.searlo.tech/api/v1/search",

params={"q": query, "num": 5},

headers={"X-API-Key": SEARLO_API_KEY},

timeout=10.0,

)

data = response.json()

results = data.get("organic", [])

return "\n".join(

f"[{r['position']}] {r['title']}\n{r['snippet']}\nURL: {r['link']}"

for r in results

)

search_tool = StructuredTool.from_function(

func=web_search,

name="web_search",

description="Search Google for real-time information. Use this when you need current data, news, or facts you're not sure about.",

args_schema=SearchInput,

)

Step 2: Create the Agent

from langchain_openai import ChatOpenAI

from langchain.agents import AgentExecutor, create_openai_functions_agent

from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder

llm = ChatOpenAI(model="gpt-4o", temperature=0)

prompt = ChatPromptTemplate.from_messages([

("system", "You are a helpful research assistant. Use the web_search tool to find current information when needed. Always cite your sources with URLs."),

("human", "{input}"),

MessagesPlaceholder("agent_scratchpad"),

])

agent = create_openai_functions_agent(llm, [search_tool], prompt)

executor = AgentExecutor(agent=agent, tools=[search_tool], verbose=True)

Step 3: Run It

result = executor.invoke({

"input": "What are the latest developments in quantum computing this week?"

})

print(result["output"])

The agent will:

1. Recognize it needs current information

2. Call `web_search` with an appropriate query

3. Read the results

4. Synthesize a response with source citations

Tips for Production

• **Set a timeout**: Searlo responds in ~300ms, but network conditions vary. Use a 10-second timeout.

• **Limit results**: `num=5` is usually enough. More results cost the same (1 credit per search) but add more tokens to the LLM context.

• **Use TOON format**: Add `&format=toon` to the API call to get token-optimized output that's 60% smaller — saves money on LLM calls.

• **Cache frequent queries**: If multiple users ask similar questions, cache search results for 5-10 minutes.

Next Steps

• See the full [Integrations page](/integrations) for CrewAI, LlamaIndex, and n8n examples

• Learn about [MCP protocol](/mcp) for zero-code AI tool integration

• Read about [SERP API for AI agents](/search-api-for-ai-agents) for more architecture patterns

bash
pip install langchain langchain-openai httpx

python
import httpx
from langchain.tools import StructuredTool
from pydantic import BaseModel, Field

SEARLO_API_KEY = "your_api_key_here"

class SearchInput(BaseModel):
    query: str = Field(description="The search query")

def web_search(query: str) -> str:
    """Search the web using Searlo API and return results."""
    response = httpx.get(
        "https://api.searlo.tech/api/v1/search",
        params={"q": query, "num": 5},
        headers={"X-API-Key": SEARLO_API_KEY},
        timeout=10.0,
    )
    data = response.json()
    results = data.get("organic", [])
    return "\n".join(
        f"[{r['position']}] {r['title']}\n{r['snippet']}\nURL: {r['link']}"
        for r in results
    )

search_tool = StructuredTool.from_function(
    func=web_search,
    name="web_search",
    description="Search Google for real-time information. Use this when you need current data, news, or facts you're not sure about.",
    args_schema=SearchInput,
)

python
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder

llm = ChatOpenAI(model="gpt-4o", temperature=0)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful research assistant. Use the web_search tool to find current information when needed. Always cite your sources with URLs."),
    ("human", "{input}"),
    MessagesPlaceholder("agent_scratchpad"),
])

agent = create_openai_functions_agent(llm, [search_tool], prompt)
executor = AgentExecutor(agent=agent, tools=[search_tool], verbose=True)

python
result = executor.invoke({
    "input": "What are the latest developments in quantum computing this week?"
})
print(result["output"])

How to Add Web Search to LangChain Agents (Python)