Forms and intake

Resumes, applications, KYC. Lists inside lists, same File() plumbing.

Forms and intake documents bring a different shape: a person identity at the top, then several parallel lists (employment, education, skills, references). The agent fills out the nested structure in one pass.

from typing import List, Optional

from agno.agent import Agent
from agno.media import File
from agno.models.openai import OpenAIResponses
from pydantic import BaseModel, Field


class Employment(BaseModel):
    company: str
    title: Optional[str] = None
    start_date: Optional[str] = None
    end_date: Optional[str] = Field(None, description="Null if current")
    summary: Optional[str] = Field(None, description="Bullet points joined into one string")


class Education(BaseModel):
    institution: str
    degree: Optional[str] = None
    field_of_study: Optional[str] = None
    graduation_year: Optional[int] = None


class Resume(BaseModel):
    full_name: Optional[str] = None
    email: Optional[str] = None
    phone: Optional[str] = None
    location: Optional[str] = None
    headline: Optional[str] = Field(None, description="Top-of-page summary line")
    employment: List[Employment] = Field(default_factory=list)
    education: List[Education] = Field(default_factory=list)
    skills: List[str] = Field(default_factory=list)


agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    instructions=(
        "Extract every field from the attached resume PDF. Preserve the "
        "candidate's wording for titles and summaries. Use null when a "
        "field is missing. Do not infer skills that are not on the page."
    ),
    output_schema=Resume,
)

resume = agent.run(
    "Extract this resume.",
    files=[File(url="https://example.com/resume-sjohnson.pdf")],
).content
# Resume(full_name='Sarah Johnson', email='[e-mail]',
#        headline='Senior Platform Engineer',
#        employment=[Employment(company='Acme Corp', title='Staff Engineer',
#                               start_date='2023-02', end_date=None, ...),
#                    Employment(company='Beta Labs', title='Senior Engineer',
#                               start_date='2019-06', end_date='2023-01', ...)],
#        education=[Education(institution='University of Texas',
#                             degree='B.S.', field_of_study='Computer Science',
#                             graduation_year=2018)],
#        skills=['Python', 'PostgreSQL', 'Kubernetes', 'Terraform'])

The same shape covers job applications and KYC intake. Swap the schema’s outer model and the instructions; the File() plumbing and the agent definition do not change.

KYC intake

Identity verification forms add typed fields the downstream system has to accept verbatim (passport numbers, dates of birth, addresses). The schema should be conservative about types: keep IDs as strings to preserve leading zeros and country-specific formats.

class KYCSubmission(BaseModel):
    full_name: str
    date_of_birth: Optional[str] = Field(None, description="ISO 8601")
    country_of_residence: Optional[str] = Field(None, description="ISO 3166-1 alpha-2")
    national_id_type: Optional[str] = Field(None, description="passport, driver_license, national_id")
    national_id_number: Optional[str] = Field(None, description="As printed, including any leading zeros")
    address: Optional[str] = None
    declared_source_of_funds: Optional[str] = None

For KYC, every field is review-worthy. Combine this schema with the confidence pattern so the downstream queue knows what to send to a compliance reviewer.

Multi-page applications

Job applications often arrive as multi-page PDFs with attachments. File(url=...) handles a single combined PDF. For loose attachments (cover letter, resume, references), run the agent once per attachment, each with the right output_schema, and merge.

class Application(BaseModel):
    candidate: Resume
    cover_letter: Optional[str] = None
    references: List[str] = Field(default_factory=list)

For the resume and the references list, two agent.run(...) calls return typed objects. Compose them into an Application in plain Python.

Schema-shape comparison

Workload	Header	Repeated structure	Notes
Invoice	Vendor, totals, dates	List[LineItem]	Numbers stay numeric
Contract	Parties, dates, governing law	List[Clause] with category Literal	Verbatim clause text
Resume	Identity, headline	Parallel lists: employment, education, skills	Preserve candidate wording
KYC	Identity	Few sub-lists; conservative typing	Keep IDs as strings

The agent code is the same across all four. The schema decides the workload.

Next steps

Task	Guide
Process every PDF in a Drive folder	Batch and durability
Flag low-confidence KYC fields for review	Human routing and eval
Validate extraction against a labeled set	Human routing and eval

Developer Resources

• Document extraction cookbook
• Structured output