research_assistant.py
"""
Parallel Research Assistant - Persistent, Multi-API Agent
=========================================================
A research assistant you can come back to. It combines all of Parallel's
agent APIs (Search, Extract, Task) with Agno persistence: a SQLite-backed
session, conversation history, and user memory.
Ask a question, then a follow-up - the assistant remembers what you are
working on and what it already found.
Prerequisites:
- pip install parallel-web
- export PARALLEL_API_KEY=<your-api-key>
"""
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
from agno.tools.parallel import ParallelTools
# ---------------------------------------------------------------------------
# Setup - persistence and tools
# ---------------------------------------------------------------------------
# SqliteDb gives the assistant a place to store sessions and memories.
db = SqliteDb(db_file="tmp/parallel_assistant.db")
# Search + Extract + Task in a single toolkit.
research_tools = ParallelTools(
enable_search=True,
enable_extract=True,
enable_task=True,
default_processor="base",
)
# ---------------------------------------------------------------------------
# Create the Agent
# ---------------------------------------------------------------------------
assistant = Agent(
name="Research Assistant",
model=OpenAIResponses(id="gpt-5.4"),
tools=[research_tools],
db=db,
add_history_to_context=True,
num_history_runs=5,
update_memory_on_run=True,
markdown=True,
instructions=[
"You are a research assistant.",
"Use Search for quick facts, Extract to read specific URLs, and the "
"Task API for deep research that needs citations.",
"Remember what the user is researching across the conversation.",
],
)
# ---------------------------------------------------------------------------
# Run the Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
user_id = "researcher@example.com"
session_id = "parallel-research-session"
# First turn - establish the topic.
assistant.print_response(
"I'm evaluating web-research APIs for an agent we're building. "
"Start by finding the main options.",
stream=True,
user_id=user_id,
session_id=session_id,
)
# Follow-up - the assistant remembers the context from the first turn.
assistant.print_response(
"Of those, which support deep research with citations?",
stream=True,
user_id=user_id,
session_id=session_id,
)
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 sqlalchemy
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
research_assistant.py, then run:python research_assistant.py