The Problem

CrewAI agents are powerful for multi-step reasoning, but they're limited to their training data. A research agent that can't search the web is like a librarian locked out of the library.

Setup

pip install crewai httpx

Get a free API key from [Searlo Dashboard](https://dashboard.searlo.tech/auth) (includes 3,000 credits).

Create the Search Tool

import httpx

from crewai.tools import BaseTool

from pydantic import Field

class WebSearchTool(BaseTool):

name: str = "Web Search"

description: str = "Search Google for real-time information. Use for current events, facts, or research."

api_key: str = Field(default="your_searlo_api_key")

def _run(self, query: str) -> str:

response = httpx.get(

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

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

headers={"X-API-Key": self.api_key},

timeout=10.0,

)

data = response.json()

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

if not results:

return "No results found."

return "\n\n".join(

f"**{r['title']}**\n{r['snippet']}\nSource: {r['link']}"

for r in results

)

Use It in a Crew

from crewai import Agent, Task, Crew

search = WebSearchTool(api_key="your_searlo_api_key")

researcher = Agent(

role="Senior Research Analyst",

goal="Find accurate, current information on the given topic",

backstory="Expert researcher who always verifies facts with multiple sources.",

tools=[search],

verbose=True,

)

writer = Agent(

role="Content Writer",

goal="Write clear, well-sourced articles based on research",

backstory="Experienced writer who transforms research into readable content.",

verbose=True,

)

research_task = Task(

description="Research the current state of {topic}. Find at least 3 recent sources.",

expected_output="A research brief with key findings and source URLs.",

agent=researcher,

)

write_task = Task(

description="Write a 500-word article based on the research brief.",

expected_output="A well-structured article with source citations.",

agent=writer,

)

crew = Crew(agents=[researcher, writer], tasks=[research_task, write_task])

result = crew.kickoff(inputs={"topic": "AI regulation in the EU"})

print(result)

What Happens

1. The **researcher** agent searches Google via Searlo for current information

2. It can make multiple search calls to verify facts across sources

3. The **writer** agent receives the research and creates the article

4. Total cost: ~5-10 searches = less than $0.01

Why Searlo for CrewAI

• **Speed**: ~300ms responses don't slow down multi-agent workflows

• **TOON format**: 60% fewer tokens means cheaper LLM calls in the pipeline

• **No subscription**: One-time credits work well for batch agent runs

• **Structured JSON**: Clean data that agents can parse reliably

More Resources

• [Full integrations guide](/integrations) with LangChain, LlamaIndex, and n8n

• [Search API for AI Agents](/search-api-for-ai-agents) — architecture patterns and use cases

• [Pricing](/pricing) — credit packs start at $2.99

bash
pip install crewai httpx

python
import httpx
from crewai.tools import BaseTool
from pydantic import Field

class WebSearchTool(BaseTool):
    name: str = "Web Search"
    description: str = "Search Google for real-time information. Use for current events, facts, or research."
    api_key: str = Field(default="your_searlo_api_key")

    def _run(self, query: str) -> str:
        response = httpx.get(
            "https://api.searlo.tech/api/v1/search",
            params={"q": query, "num": 5, "format": "toon"},
            headers={"X-API-Key": self.api_key},
            timeout=10.0,
        )
        data = response.json()
        results = data.get("organic", [])
        if not results:
            return "No results found."
        return "\n\n".join(
            f"**{r['title']}**\n{r['snippet']}\nSource: {r['link']}"
            for r in results
        )

python
from crewai import Agent, Task, Crew

search = WebSearchTool(api_key="your_searlo_api_key")

researcher = Agent(
    role="Senior Research Analyst",
    goal="Find accurate, current information on the given topic",
    backstory="Expert researcher who always verifies facts with multiple sources.",
    tools=[search],
    verbose=True,
)

writer = Agent(
    role="Content Writer",
    goal="Write clear, well-sourced articles based on research",
    backstory="Experienced writer who transforms research into readable content.",
    verbose=True,
)

research_task = Task(
    description="Research the current state of {topic}. Find at least 3 recent sources.",
    expected_output="A research brief with key findings and source URLs.",
    agent=researcher,
)

write_task = Task(
    description="Write a 500-word article based on the research brief.",
    expected_output="A well-structured article with source citations.",
    agent=writer,
)

crew = Crew(agents=[researcher, writer], tasks=[research_task, write_task])
result = crew.kickoff(inputs={"topic": "AI regulation in the EU"})
print(result)

Add Web Search to CrewAI Agents in 5 Minutes