Parallel

Build web-research agents on Parallel with Agno.

Parallel offers APIs built for agents:

API	Speed	Use Case
Search	1-5s	Quick lookups, gather sources for an answer
Extract	1-5s	Clean text from specific URLs (incl. JS pages and PDFs)
Task	10s-25min	Deep research with structured output and citations
Monitor	Scheduled	Track topics over time, detect changes

Cookbooks

A progression from a single agent to a deployable research app:

File	Focus
`01_quickstart.py`	Minimal research agent (Search)
`02_extract_content.py`	Read specific URLs with the Extract API
`03_deep_research.py`	Cited reports with the Task API
`04_research_assistant.py`	Persistent assistant (DB, session, memory) using every agent API
`05_web_plus_knowledge.py`	Hybrid: Parallel live web + Agno Knowledge (vector RAG)
`06_research_team.py`	A Team of Parallel-backed agents
`07_research_workflow.py`	A deterministic gather-then-synthesize pipeline
`08_competitive_intel_monitor.py`	Monitor API as a standing intelligence desk
`09_agent_os_app.py`	Deploy a research agent as an AgentOS app

Looking for the tool-by-tool reference (one example per API and use case)? See cookbook/91_tools/parallel.

Setup

bash

pip install parallel-web
export PARALLEL_API_KEY=<your-api-key>

Some examples need extra packages: 05_web_plus_knowledge.py uses chromadb for the local vector store.

Quick Start

python

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.parallel import ParallelTools

agent = Agent(
    model=OpenAIResponses(id="gpt-5.4"),
    tools=[ParallelTools()],          # Search + Extract by default
)
agent.print_response("What did Parallel launch most recently?", stream=True)

Enable the deeper APIs with flags:

python

ParallelTools(enable_task=True)       # deep research with citations
ParallelTools(enable_monitor=True)    # track topics over time

Running Examples

bash

.venvs/demo/bin/python cookbook/integrations/parallel/<file>.py