Adding web search to a DeerFlow agent gives it access to live data for research tasks instead of relying on static training knowledge. DeerFlow is a deep research framework that orchestrates multi-step investigation workflows, but out of the box it needs a search tool to gather external evidence. This tutorial registers a Scavio search tool in a DeerFlow agent, wires it into the research flow, and parses structured SERP data into the agent's context format.
Prerequisites
- Python 3.10+
- DeerFlow installed (pip install deerflow)
- Scavio API key from scavio.dev
- Basic understanding of agent tool registration
Walkthrough
Step 1: Create the search tool function
Define a search function that calls the Scavio API and returns results in the format DeerFlow expects.
Python
import os, requests
H = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
def web_search(query: str, num_results: int = 5) -> list[dict]:
'''Search the web and return structured results.'''
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=H, json={'query': query, 'country_code': 'us'}).json()
results = []
for r in data.get('organic_results', [])[:num_results]:
results.append({
'title': r.get('title', ''),
'url': r.get('link', ''),
'content': r.get('snippet', ''),
})
return resultsStep 2: Register the tool with DeerFlow
Register the search function as a tool in the DeerFlow agent configuration so it can be called during research flows.
Python
from deerflow import DeerFlowAgent, Tool
search_tool = Tool(
name='web_search',
description='Search the web for current information. Use for fact-checking, finding recent data, and research.',
function=web_search,
parameters={
'query': {'type': 'string', 'description': 'The search query', 'required': True},
'num_results': {'type': 'integer', 'description': 'Number of results to return', 'default': 5},
}
)
agent = DeerFlowAgent(
name='research_agent',
tools=[search_tool],
model='gpt-4o',
system_prompt='You are a research agent. Use web_search to find current information before answering.',
)Step 3: Add multi-platform search capability
Extend the tool to search across Google, Reddit, and YouTube for comprehensive research coverage.
Python
def multi_search(query: str, platforms: list[str] = None) -> dict:
'''Search across multiple platforms for comprehensive research.'''
if platforms is None:
platforms = ['google', 'reddit']
all_results = {}
for platform in platforms:
params = {'query': query, 'country_code': 'us'}
if platform != 'google':
params['platform'] = platform
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=H, json=params).json()
all_results[platform] = [{
'title': r.get('title', ''),
'url': r.get('link', ''),
'content': r.get('snippet', ''),
} for r in data.get('organic_results', [])[:3]]
return all_results
multi_search_tool = Tool(
name='multi_search',
description='Search multiple platforms (google, reddit, youtube) for research.',
function=multi_search,
parameters={
'query': {'type': 'string', 'required': True},
'platforms': {'type': 'array', 'items': {'type': 'string'}, 'default': ['google', 'reddit']},
}
)Step 4: Run a research task
Execute a research query and observe the agent using search tools to gather evidence before synthesizing an answer.
Python
async def run_research():
agent = DeerFlowAgent(
name='research_agent',
tools=[search_tool, multi_search_tool],
model='gpt-4o',
system_prompt='You are a research agent. Always search before answering. Cite sources.',
)
result = await agent.run(
'What are the top 3 LLM agent frameworks in 2026 and how do they compare?'
)
print(result.answer)
print(f'\nTools called: {len(result.tool_calls)}')
for call in result.tool_calls:
print(f' - {call.tool_name}({call.arguments.get("query", "")})')
import asyncio
asyncio.run(run_research())Python Example
Python
import os, requests, asyncio
from deerflow import DeerFlowAgent, Tool
H = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
def web_search(query: str, num_results: int = 5) -> list[dict]:
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=H, json={'query': query, 'country_code': 'us'}).json()
return [{'title': r.get('title', ''), 'url': r.get('link', ''),
'content': r.get('snippet', '')}
for r in data.get('organic_results', [])[:num_results]]
def multi_search(query: str, platforms: list[str] = None) -> dict:
platforms = platforms or ['google', 'reddit']
results = {}
for p in platforms:
params = {'query': query, 'country_code': 'us'}
if p != 'google': params['platform'] = p
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=H, json=params).json()
results[p] = [{'title': r.get('title',''), 'url': r.get('link',''),
'content': r.get('snippet','')} for r in data.get('organic_results',[])[:3]]
return results
async def main():
agent = DeerFlowAgent(
name='researcher',
tools=[
Tool(name='web_search', description='Search the web', function=web_search,
parameters={'query': {'type':'string','required':True}}),
Tool(name='multi_search', description='Multi-platform search', function=multi_search,
parameters={'query': {'type':'string','required':True},
'platforms': {'type':'array','default':['google','reddit']}}),
],
model='gpt-4o',
system_prompt='Research agent. Search before answering. Cite sources.',
)
result = await agent.run('Compare LangGraph vs CrewAI vs AutoGen in 2026')
print(result.answer)
asyncio.run(main())JavaScript Example
JavaScript
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
async function webSearch(query, numResults = 5) {
const data = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: H,
body: JSON.stringify({query, country_code: 'us'})
}).then(r => r.json());
return (data.organic_results || []).slice(0, numResults).map(r => ({
title: r.title || '', url: r.link || '', content: r.snippet || ''
}));
}
async function multiSearch(query, platforms = ['google', 'reddit']) {
const results = {};
for (const p of platforms) {
const params = {query, country_code: 'us'};
if (p !== 'google') params.platform = p;
const data = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: H, body: JSON.stringify(params)
}).then(r => r.json());
results[p] = (data.organic_results || []).slice(0, 3).map(r => ({
title: r.title, url: r.link, content: r.snippet
}));
}
return results;
}
// Register with DeerFlow (JS SDK)
// const agent = new DeerFlowAgent({tools: [{name: 'web_search', fn: webSearch}]});
console.log('DeerFlow search tools ready');
webSearch('LangGraph vs CrewAI 2026').then(r => console.log(\`\${r.length} results\`));Expected Output
JSON
Research agent output:
- LangGraph: Best for stateful, cyclic agent workflows. Most GitHub stars in 2026.
- CrewAI: Best for multi-agent orchestration with role-based agents.
- AutoGen: Best for conversational multi-agent patterns.
Tools called: 3
- web_search(LLM agent frameworks comparison 2026)
- multi_search(LangGraph vs CrewAI vs AutoGen)
- web_search(agent framework github stars 2026)