Parallel - Agno

ParallelTools enable an Agent to perform AI-optimized web search and content extraction using Parallel’s APIs.

API	Speed	Use Case
Search	1-5s	Quick web lookups, gather sources
Extract	1-5s	Pull content from specific URLs
Task	10s-25min	Deep research with structured output and citations
Monitor	Scheduled	Track topics over time, detect changes

Prerequisites

The following examples require the parallel-web library and an API key which can be obtained from Parallel.

uv pip install -U parallel-web

export PARALLEL_API_KEY=***

The API key is optional. Keyless access is rate-limited; setting a key raises the ceiling.

Search API

Fast web search for recent information. Returns raw results that your agent synthesizes.

from agno.agent import Agent
from agno.tools.parallel import ParallelTools

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

agent.print_response("What are the latest developments in AI agents?")

Extract API

Pull content from specific URLs in clean markdown format. Handles JavaScript-heavy pages and PDFs.

from agno.agent import Agent
from agno.tools.parallel import ParallelTools

agent = Agent(
    tools=[ParallelTools(
        enable_search=False,
        enable_extract=True,
    )],
    markdown=True,
)

agent.print_response(
    "Extract the product features from https://parallel.ai and https://docs.parallel.ai"
)

Task API

Deep research with structured output and citations. Use for company enrichment, market research, and analysis tasks.

from agno.agent import Agent
from agno.tools.parallel import ParallelTools

# Define the schema for structured output
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"},
                "employee_count": {"type": "string"},
                "total_funding": {"type": "string"},
                "key_investors": {
                    "type": "array",
                    "items": {"type": "string"},
                },
            },
            "required": ["company_name"],
        },
    },
)

agent = Agent(
    tools=[enrichment_tools],
    markdown=True,
    instructions="Use create_task() to research, then get_task_result() to retrieve data.",
)

agent.print_response("Research Anthropic and return structured company data")

Task Processors

Processor	Speed	Depth	Use Case
`lite`	~10s	Surface	Quick facts, simple lookups
`base`	~60s	Standard	Company research, market analysis
`pro`	~5min	Deep	Comprehensive reports
`ultra`	~25min	Exhaustive	Due diligence, academic research

Set with default_processor at toolkit level.

Output Schema Types

Type	Description
`{"type": "auto"}`	Auto-detect structure from query
`{"type": "text"}`	Plain text response
`{"type": "json", "json_schema": {...}}`	Structured JSON matching your schema
`"description string"`	Auto-generate schema from description

Monitor API

Track topics over time and detect changes. Run monitors on a schedule to catch new developments.

from agno.agent import Agent
from agno.tools.parallel import ParallelTools

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

agent = Agent(
    tools=[monitor_tools],
    markdown=True,
    instructions="""Track competitors using:
    - create_monitor(query): Start tracking
    - list_monitors(): See active monitors
    - get_monitor_events(monitor_id): Get detected changes
    - cancel_monitor(monitor_id): Stop tracking
    """,
)

# Create monitors
agent.print_response("Create monitors to track OpenAI and Anthropic product launches")

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

Monitor Frequencies

Frequency	Value	Use Case
Hourly	`"1h"`	Fast-moving markets, breaking news
Daily	`"1d"`	General competitive intel
Weekly	`"1w"`	Industry trends
Monthly	`"30d"`	Quarterly reviews

Toolkit Params

Parameter	Type	Default	Description
`api_key`	`Optional[str]`	`None`	Parallel API key. Falls back to `PARALLEL_API_KEY` env var.
`enable_search`	`bool`	`True`	Enable Search API.
`enable_extract`	`bool`	`True`	Enable Extract API.
`enable_task`	`bool`	`False`	Enable Task API (deep research).
`enable_monitor`	`bool`	`False`	Enable Monitor API (web tracking).
`all`	`bool`	`False`	Enable all tools. Overrides individual flags.
`max_results`	`int`	`10`	Default max results for search.
`max_chars_per_result`	`int`	`10000`	Default max characters per search result.
`mode`	`Optional[str]`	`None`	Search mode: `"one-shot"` or `"agentic"`.
`include_domains`	`Optional[List[str]]`	`None`	Restrict search to these domains.
`exclude_domains`	`Optional[List[str]]`	`None`	Exclude these domains from search.
`max_age_seconds`	`Optional[int]`	`None`	Cache age threshold (minimum 600).
`disable_cache_fallback`	`Optional[bool]`	`None`	Disable cache fallback behavior.
`default_processor`	`str`	`"base"`	Task API processor tier.
`default_monitor_processor`	`Literal["lite", "base"]`	`"lite"`	Monitor API processor.
`default_monitor_frequency`	`str`	`"1d"`	Monitor run frequency.
`default_timeout`	`int`	`1800`	Task result timeout in seconds.
`default_output_schema`	`Optional[Union[Dict, str]]`	`None`	Schema for structured task output.
`beta_version`	`str`	`"search-extract-2025-10-10"`	Beta API version header.

Toolkit Functions

Search API

Function	Description
`parallel_search`	Search the web with natural language objective. Returns URLs, titles, publish dates, and excerpts. Parameters: `objective` (str), `search_queries` (list), `max_results` (int), `max_chars_per_result` (int).
`parallel_extract`	Extract content from URLs. Returns clean markdown with excerpts or full content. Parameters: `urls` (list), `objective` (str), `excerpts` (bool), `full_content` (bool).

Task API

Function	Description
`create_task`	Create a research task without waiting. Returns `run_id` for later retrieval. Parameter: `query` (str).
`get_task_result`	Get completed task result. Blocks until done or timeout. Returns structured content and citations. Parameter: `run_id` (str).
`get_task_status`	Check task status without blocking. Returns status, processor, and timestamps. Parameter: `run_id` (str).

Monitor API

Function	Description
`create_monitor`	Create a monitor for a query. Returns `monitor_id`, status, frequency. Parameter: `query` (str).
`get_monitor`	Retrieve monitor configuration. Parameter: `monitor_id` (str).
`update_monitor`	Update frequency or query. Parameters: `monitor_id` (str), `frequency` (str), `query` (str).
`list_monitors`	List all monitors with optional filters. Parameters: `status` (“active”/“cancelled”), `monitor_type` (“event_stream”/“snapshot”), `limit` (int).
`get_monitor_events`	Get detected changes. Returns events with content and citations. Parameters: `monitor_id` (str), `include_completions` (bool), `limit` (int).
`cancel_monitor`	Permanently stop a monitor. Parameter: `monitor_id` (str).

​Prerequisites

​Search API

​Extract API

​Task API

​Task Processors

​Output Schema Types

​Monitor API

​Monitor Frequencies

​Toolkit Params

​Toolkit Functions

​Search API

​Task API

​Monitor API

​Developer Resources

Prerequisites

Search API

Extract API

Task API

Task Processors

Output Schema Types

Monitor API

Monitor Frequencies

Toolkit Params

Toolkit Functions

Search API

Task API

Monitor API

Developer Resources