Parallel + Groq: Fast Web Search for Real-Time AI Research
Parallel provides a web search MCP server that gives AI models access to real-time web data. Combined with Groq's industry-leading inference speeds (1000+ tokens/second), you can build research agents that find and analyze current information in seconds, not minutes.
Key Features:
- Real-Time Information: Access current events, breaking news, and live data
- Parallel Processing: Search multiple sources simultaneously
- Ultra-Fast: Groq's inference makes tool calling nearly instant
- Source Transparency: See exactly which websites were searched
- Accurate Results: Fresh data means current answers, not outdated information
Quick Start
1. Install the required packages:
bash
pip install openai python-dotenv2. Get your API keys:
- Groq: console.groq.com/keys
- Parallel: platform.parallel.ai
bash
export GROQ_API_KEY="your-groq-api-key"
export PARALLEL_API_KEY="your-parallel-api-key"3. Create your first real-time research agent:
python
import os
from openai import OpenAI
from openai.types import responses as openai_responses
client = OpenAI(
base_url="https://api.groq.com/api/openai/v1",
api_key=os.getenv("GROQ_API_KEY")
)
tools = [
openai_responses.tool_param.Mcp(
server_label="parallel_web_search",
server_url="https://mcp.parallel.ai/v1beta/search_mcp/",
headers={"x-api-key": os.getenv("PARALLEL_API_KEY")},
type="mcp",
require_approval="never",
)
]
response = client.responses.create(
model="openai/gpt-oss-120b",
input="What does Anthropic do? Find recent product launches from past year.",
tools=tools,
temperature=0.1,
top_p=0.4,
)
print(response.output_text)Advanced Examples
Multi-Company Comparison
Compare multiple companies side-by-side:
python
companies = ["OpenAI", "Anthropic", "Google AI", "Meta AI"]
for company in companies:
response = client.responses.create(
model="openai/gpt-oss-120b",
input=f"""Research {company}:
- Main products
- Latest announcements (6 months)
- Company size and funding
- Key differentiators""",
tools=tools,
temperature=0.1,
)
print(f"{company}:\n{response.output_text}\n")Real-Time Market Data
Get current financial information:
python
stocks = ["GOOGL", "MSFT", "NVDA", "TSLA"]
for ticker in stocks:
response = client.responses.create(
model="openai/gpt-oss-120b",
input=f"Current stock price of {ticker}? Include today's change and 52-week range.",
tools=tools,
temperature=0.1,
)
print(f"{ticker}: {response.output_text}")Breaking News Monitoring
Track developing stories:
python
topics = [
"artificial intelligence breakthroughs",
"quantum computing developments",
"renewable energy innovations"
]
for topic in topics:
response = client.responses.create(
model="openai/gpt-oss-120b",
input=f"Latest breaking news about {topic} from today?",
tools=tools,
temperature=0.1,
)
print(f"{topic}:\n{response.output_text}\n")Performance Comparison
Real comparison from testing:
- Groq (openai/gpt-oss-120b): 11.15s, 472 chars/sec
- OpenAI (gpt-5): 88.38s, 42 chars/sec
Groq is 8x faster due to LPU architecture, instant tool call decisions, and fast synthesis of search results.
Challenge: Build a real-time market intelligence platform that monitors news, tracks competitor activities, analyzes trends, compares products, and generates daily briefings!