> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc-studio-tools-doc.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 identity at the top, then several parallel lists (employment, education, skills, references). The agent fills out the nested structure in one pass.

```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(url="https://example.com/resume-sjohnson.pdf")],
).content
# 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 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.

```python theme={null}
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](/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/main/cookbook/data_labeling/_16_document_extraction)
* [Structured output](/input-output/structured-output/agent)