Screen

screen takes a DataFrame and a natural-language filter predicate, evaluates each row using web research agents, and returns only the rows that pass. The filter condition does not need to be computable from existing columns. Agents can research external information to make the determination.

Examples

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

class ScreenResult(BaseModel):
    passes: bool = Field(description="True if company meets criteria")

result = await screen(
    task="""
        Find companies with >75% recurring revenue that would benefit from
        Taiwan tensions. Include CHIPS Act beneficiaries, defense contractors,
        cybersecurity firms. Exclude companies dependent on Taiwan manufacturing.
    """,
    input=sp500,
    response_model=ScreenResult,
)
print(result.data.head())

Only passing rows come back.

Richer output

Want to know why something passed? Add fields:

class VendorRisk(BaseModel):
    approved: bool = Field(description="True if vendor is acceptable")
    risk_level: str = Field(description="low / medium / high")
    security_issues: str = Field(description="Any breaches or incidents")
    recommendation: str = Field(description="Summary")

result = await screen(
    task="""
        Assess each vendor for enterprise use. Research:
        1. Security incidents in past 3 years
        2. Financial stability (layoffs, funding issues)

        Approve only low/medium risk with no unresolved critical incidents.
    """,
    input=vendors,
    response_model=VendorRisk,
)
print(result.data.head())

Now you get risk_level, security_issues, and recommendation for every row that passed.

The pass/fail field

Your response model needs a boolean field. It can be named anything—passes, approved, include, whatever. The system figures out which field is the filter.

class Simple(BaseModel):
    passes: bool

class Detailed(BaseModel):
    approved: bool  # this is the filter
    confidence: float
    notes: str

Parameters

Performance

Compare: regex on "remote-friendly" job postings gets 68% precision.

Via MCP

MCP tool: everyrow_screen

Related docs

Guides

• Filter a DataFrame with LLMs

Case Studies

• LLM Screening at Scale
• Screen Job Postings by Criteria
• Screen Stocks by Investment Thesis
• Screen Stocks by Margin Sensitivity
• Multi-Stage Lead Qualification

Blog posts

• Thematic Stock Screen
• Job Posting Screen
• Screening Workflow