Research

single_agent runs one web research agent on a single input (or no input). agent_map runs an agent on every row of a DataFrame in parallel. Both dispatch agents that search the web, read pages, and return structured results. The transform is live web research: agents fetch and synthesize external information to populate new columns.

Examples

single_agent

from pydantic import BaseModel
from everyrow.ops import single_agent

class CompanyInput(BaseModel):
    company: str

result = await single_agent(
    task="Find the company's most recent annual revenue and employee count",
    input=CompanyInput(company="Stripe"),
)
print(result.data.head())

No input required

You can run an agent without any input data.

result = await single_agent(
    task="""
        What company has reported the greatest cost reduction
        due to internal AI usage over the past 12 months?
    """,
)
print(result.data.head())

agent_map

from pandas import DataFrame
from everyrow.ops import agent_map

companies = DataFrame([
    {"company": "Stripe"},
    {"company": "Databricks"},
    {"company": "Canva"},
])

result = await agent_map(
    task="Find the company's most recent annual revenue",
    input=companies,
)
print(result.data.head())

Each row gets its own agent that researches independently.

Parameters

single_agent and agent_map have the nearly the same parameters.

Effort levels

The effort level lets you control how thorough the research is.

• LOW: Just a single LLM call, not a real agent, cheapest & fastest
• MEDIUM: More thorough research, multiple sources consulted (default)
• HIGH: Deep research, cross-referencing sources, higher accuracy

Response model

Both single_agent and agent_map support structured output via custom Pydantic models.

from pandas import DataFrame
from pydantic import BaseModel, Field
from everyrow.ops import agent_map

companies = DataFrame([
    {"company": "Stripe"},
    {"company": "Databricks"},
    {"company": "Canva"},
])

class CompanyFinancials(BaseModel):
    annual_revenue_usd: int = Field(description="Most recent annual revenue in USD")
    employee_count: int = Field(description="Current number of employees")
    last_funding_round: str = Field(description="Most recent funding round, e.g. 'Series C'")

result = await agent_map(
    task="Research each company's financials and latest funding",
    input=companies,
    response_model=CompanyFinancials,
)
print(result.data.head())

Now the output has annual_revenue_usd, employee_count, and last_funding_round columns.

Returning a table

With single_agent, you can generate a dataset from scratch by setting return_table=True.

from pydantic import BaseModel, Field
from everyrow.ops import single_agent

class CompanyInfo(BaseModel):
    company: str = Field(description="Company name")
    market_cap: int = Field(description="Market cap in USD")

companies = await single_agent(
    task="Find the three largest US healthcare companies by market cap",
    response_model=CompanyInfo,
    return_table=True,  # Return a table of companies
)

Via MCP

MCP tools: everyrow_agent (DataFrame), everyrow_single_agent (single question)

everyrow_agent:

everyrow_single_agent:

Related docs

Guides

• Add a Column with Web Lookup
• Classify and Label Data with an LLM

Case Studies