> ## Documentation Index > Fetch the complete documentation index at: https://agno-v2-codex-docs-audit-20260719-0149.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Forms and intake > Resumes, applications, KYC. Lists inside lists, same File() plumbing. Forms and intake documents bring a different shape: a person's identity at the top, then several parallel lists (employment, education, skills, references). The agent fills out the nested structure in one pass. Place the resume to extract at `resume.pdf` in the directory where you run this code. ```python theme={null} 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(filepath="resume.pdf")], ).content # Illustrative output. Your values depend on resume.pdf: # Resume(full_name='Sarah Johnson', email='sarah@example.com', # 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. ```python theme={null} 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](/use-cases/data-labeling/structured-extraction#per-field-confidence) so the downstream queue knows what to send to a compliance reviewer. ## Multi-page applications Job applications arrive as one combined PDF or as a set of loose attachments. `files` takes a sequence, so the whole bundle goes into a single run against one schema. ```python theme={null} class Application(BaseModel): candidate: Resume cover_letter: Optional[str] = None references: List[str] = Field(default_factory=list) application_agent = Agent( model=OpenAIResponses(id="gpt-5.5"), instructions=( "Extract the application from the attached documents: a resume, a cover " "letter, and a reference list. Use null when a field is missing." ), output_schema=Application, ) application = application_agent.run( "Extract this application.", files=[ File(filepath="applications/sjohnson/cover-letter.pdf"), File(filepath="applications/sjohnson/resume.pdf"), File(filepath="applications/sjohnson/references.pdf"), ], ).content ``` Run once per attachment when you want an isolated schema per document, then compose in plain Python. Every run needs its own schema, because `output_schema` takes a Pydantic model: `Resume` for the resume, and a wrapper for the reference list. A bare `List[str]` is not a schema. ```python theme={null} class References(BaseModel): items: List[str] = Field(default_factory=list) ``` ## 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](/use-cases/document-processing/batch-and-durability) | | Flag low-confidence KYC fields for review | [Human routing and eval](/use-cases/document-processing/human-routing-and-eval) | | Validate extraction against a labeled set | [Human routing and eval](/use-cases/document-processing/human-routing-and-eval) | ## Developer Resources * [Document extraction cookbook](https://github.com/agno-agi/agno/tree/v2.7.4/cookbook/data_labeling/_16_document_extraction) * [Structured output](/input-output/structured-output/agent)