quickstart.py
"""
Parallel Quickstart - Web Research Agent
========================================
The smallest possible Parallel-powered agent: give an Agent the Parallel
Search API and ask it something that needs fresh information from the web.
Parallel's Search API is built for agents - it takes a natural-language
objective and returns ranked excerpts the model can reason over directly,
so a single tool call is usually enough to ground an answer.
Prerequisites:
- pip install parallel-web
- export PARALLEL_API_KEY=<your-api-key>
"""
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.parallel import ParallelTools
# ---------------------------------------------------------------------------
# Create the Agent
# ---------------------------------------------------------------------------
# ParallelTools enables the Search and Extract APIs by default.
agent = Agent(
model=OpenAIResponses(id="gpt-5.4"),
tools=[ParallelTools()],
markdown=True,
)
# ---------------------------------------------------------------------------
# Run the Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
agent.print_response(
"What did Parallel (parallel.ai) launch most recently, and when?",
stream=True,
)
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno openai parallel-web
3
Export your API keys
export OPENAI_API_KEY="your_openai_api_key_here"
export PARALLEL_API_KEY="your_parallel_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
$Env:PARALLEL_API_KEY="your_parallel_api_key_here"
4
Run the example
Save the code above as
quickstart.py, then run:python quickstart.py