> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Invoices and receipts

> Header fields, line items, and the path from PDF to a database row.

The shape AP teams need: a header (vendor, totals, dates) and a list of line items. The schema is the contract with downstream systems.

```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 LineItem(BaseModel):
    description: str = Field(..., description="Line description as printed")
    quantity: Optional[float] = None
    unit_price: Optional[float] = None
    amount: Optional[float] = Field(None, description="Line total in invoice currency")


class Invoice(BaseModel):
    invoice_number: Optional[str] = None
    vendor: Optional[str] = None
    vendor_tax_id: Optional[str] = None
    bill_to: Optional[str] = None
    invoice_date: Optional[str] = Field(None, description="ISO 8601 if possible")
    due_date: Optional[str] = None
    currency: Optional[str] = Field(None, description="ISO 4217, e.g. USD, EUR")
    subtotal: Optional[float] = None
    tax: Optional[float] = None
    total: Optional[float] = None
    lines: List[LineItem] = Field(default_factory=list)


agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    instructions=(
        "Extract every field and every line item from the attached invoice. "
        "Numbers stay as numbers. Use ISO 8601 for dates when the format is "
        "unambiguous. Null for missing fields. Do not invent line items."
    ),
    output_schema=Invoice,
)

invoice = agent.run(
    "Extract this invoice.",
    files=[File(url="https://example.com/invoice-1042.pdf")],
).content
# Invoice(invoice_number='1042', vendor='Acme Corp', invoice_date='2026-04-12',
#         total=1296.0, currency='USD', lines=[LineItem(description='Pro plan',
#         quantity=12, unit_price=99.0, amount=1188.0), LineItem(...)])
```

The hard part is the missing-field discipline. A hallucinated `total` corrupts the AP ledger. Two lines in the instructions ("use exactly what the document shows", "null if missing") cut the hallucination rate substantially on noisy scans.

## Persist the row

The agent's job ends at a validated `Invoice`. The next step is a normal INSERT. Pydantic `.model_dump()` gives you a dict you can hand to any driver.

```python theme={null}
from sqlalchemy import create_engine, text

engine = create_engine("postgresql+psycopg://ai:ai@localhost:5532/ap")

with engine.begin() as conn:
    invoice_id = conn.execute(
        text(
            "INSERT INTO invoices (invoice_number, vendor, invoice_date, "
            "due_date, currency, subtotal, tax, total) "
            "VALUES (:invoice_number, :vendor, :invoice_date, :due_date, "
            ":currency, :subtotal, :tax, :total) RETURNING id"
        ),
        invoice.model_dump(exclude={"lines"}),
    ).scalar_one()

    for line in invoice.lines:
        conn.execute(
            text(
                "INSERT INTO invoice_lines (invoice_id, description, "
                "quantity, unit_price, amount) "
                "VALUES (:invoice_id, :description, :quantity, "
                ":unit_price, :amount)"
            ),
            {"invoice_id": invoice_id, **line.model_dump()},
        )
```

Two writes per invoice. The schema decides the table layout; the agent decides the values.

## Receipts

Receipts are invoices with a thinner header. Drop `due_date`, `vendor_tax_id`, and `bill_to`. Keep the same line-item shape. The same agent and the same instructions work; only `output_schema` changes.

```python theme={null}
class Receipt(BaseModel):
    merchant: Optional[str] = None
    purchase_date: Optional[str] = None
    currency: Optional[str] = None
    total: Optional[float] = None
    lines: List[LineItem] = Field(default_factory=list)
```

For phone-camera receipts (skewed, low light), the input becomes an image rather than a PDF. See [multimodal inputs](/use-cases/data-labeling/multimodal-inputs) for the input argument.

## Confidence on noisy scans

Production AP sees faxed copies, partial scans, and mixed-language invoices. When you need a flag for "send this to a human", wrap each value in a confidence carrier. The pattern is identical to the [data labeling pattern](/use-cases/data-labeling/structured-extraction#per-field-confidence) and feeds the routing logic in [human routing and eval](/use-cases/document-processing/human-routing-and-eval).

## Next steps

| Task                                             | Guide                                                                           |
| ------------------------------------------------ | ------------------------------------------------------------------------------- |
| Process a folder of invoices                     | [Batch and durability](/use-cases/document-processing/batch-and-durability)     |
| Route low-confidence invoices to AP review       | [Human routing and eval](/use-cases/document-processing/human-routing-and-eval) |
| Extract contract clauses with the same primitive | [Contracts](/use-cases/document-processing/contracts)                           |

## 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)