Agent Map

agent_map runs one web research agent per row of a DataFrame, in parallel. Each agent searches the web, reads pages, and returns structured results that populate new columns. The transform is live web research: agents fetch and synthesize external information per row.

For a single question, or to generate a list from scratch, use multi_agent instead.

single_agent is deprecated. Use multi_agent for a single question.

Examples

agent_map

from pandas import DataFrame
from futuresearch.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.

Response model

agent_map supports structured output via a custom Pydantic model.

from pandas import DataFrame
from pydantic import BaseModel, Field
from futuresearch.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())

The output now has annual_revenue_usd, employee_count, and last_funding_round columns.

Parameters

Effort levels

The effort level controls how thorough the research is on each row.

• LOW: a single LLM call, cheapest and fastest
• MEDIUM: multiple sources consulted (default)
• HIGH: deep research, cross-referencing sources, higher accuracy

Via MCP

MCP tool: futuresearch_agent

Related docs

Guides

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

Reference

• multi_agent for a single question or to generate a list