Parallel

Give agents Parallel’s Search, Task (deep research), and Monitor APIs for grounded web lookups, cited research, and scheduled web tracking.

Enable Agno agents with web search and extraction infrastructure from Parallel that prioritizes token efficiency and multi-hop reasoning.

Search

Natural-language web search that returns LLM-optimized excerpts. Use when the model needs current facts, specific entities, or web data to ground a response.

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(
        max_results=10,
        include_domains=["techcrunch.com", "wired.com"],
    )],
    markdown=True,
)

agent.print_response("What are the latest developments in AI agents?", stream=True)

Task

Deep research that takes a plain-language input and returns comprehensive, cited results. Choose the processor by complexity and latency. base typically completes in 15 to 100 seconds, while higher tiers can take minutes.

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

enrichment_tools = ParallelTools(
    enable_search=False,
    enable_extract=False,
    enable_task=True,
    default_processor="base",
    default_output_schema={
        "type": "json",
        "json_schema": {
            "type": "object",
            "properties": {
                "company_name": {"type": "string"},
                "headquarters": {"type": "string"},
                "total_funding": {"type": "string"},
                "key_investors": {"type": "array", "items": {"type": "string"}},
            },
            "required": ["company_name"],
        },
    },
)

agent = Agent(
    model=OpenAIResponses(id="gpt-5.4"),
    tools=[enrichment_tools],
    markdown=True,
    instructions="Use create_task() to research, then get_task_result() to retrieve.",
)

agent.print_response("Research Anthropic and return structured company data", stream=True)

Monitor

Continuously track the web for changes relevant to a natural-language query, on a schedule you control. Use for news tracking, regulatory watchlists, or competitor monitoring.

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

monitor_tools = ParallelTools(
    enable_search=False,
    enable_extract=False,
    enable_monitor=True,
    default_monitor_frequency="1d",
)

agent = Agent(
    model=OpenAIResponses(id="gpt-5.4"),
    tools=[monitor_tools],
    markdown=True,
)

# Create monitors
agent.print_response("Create a monitor to track OpenAI product launches", stream=True)

# Later: check for events
agent.print_response("List my monitors and fetch recent events", stream=True)

Run the Examples

1. Clone and set up

   git clone https://github.com/agno-agi/agno.git
   cd agno
   
2. Create virtual environment

   ./scripts/demo_setup.sh
   source .venvs/demo/bin/activate
   
3. Export the required API keys

   Mac/Linux

   export PARALLEL_API_KEY="your_parallel_api_key_here"
   export OPENAI_API_KEY="your_openai_api_key_here"
   

   $Env:PARALLEL_API_KEY="your_parallel_api_key_here"
   $Env:OPENAI_API_KEY="your_openai_api_key_here"
   
4. Run an example

   # Search
   python cookbook/91_tools/parallel/news_search.py
   
   # Task (deep research)
   python cookbook/91_tools/parallel/company_enrichment.py
   
   # Monitor (continuous tracking)
   python cookbook/91_tools/parallel/competitor_tracker.py
   

For more examples, see the Parallel cookbook.