Category: Automated Document Pipelines

Build end-to-end automation chains for PDFs, forms, and signatures.

How to extract invoice data from PDFs into structured JSON

Diagram showing an invoice PDF flowing through upload, extract, and download steps into a JSON object with vendor and line item fields

Invoice PDFs rarely follow one layout, so generic text extraction breaks on multi-column tables, wrapped cells, and scanned pages. This guide walks through the four API calls that convert a raw invoice PDF into typed JSON, plus the post-processing code that maps the response onto a clean invoice schema your backend can use right away.

Invoice PDFs are structurally unpredictable. Vendor A ships a five-column line-item table, vendor B embeds the same information in a paragraph block, and half the scanned copies in a legacy archive have no text layer at all. Generic text extraction reads characters in the order they were written to the file rather than the reading order a human sees, so field values drift with every layout variation.

The Foxit PDF Structural Extraction API, also known as the PDF Structural Analysis API, addresses this directly. You submit a PDF invoice and receive a typed, hierarchical JSON document with named element types, bounding regions, and an addressable table cell grid. This guide covers the four REST calls that turn a raw PDF into a StructureInfo.json file, the Python post-processing that maps that output onto a clean invoice schema, and the edge cases your pipeline will hit on real vendor documents. Every response shape and field name below comes from a live run against the API, not from the reference docs alone.

Why invoice PDFs break generic parsers

Three structural problems cause most invoice parsing failures, and OCR accuracy is only one of them.

The first is layout variance across vendors. A PDF’s text layer records characters in the order they were drawn, which often follows vector rendering order rather than left-to-right, top-to-bottom reading order. Extract raw text from a five-column line-item table and you frequently get interleaved fragments, where description text from one column mixes with unit prices from another because the writer rendered all rows of one column before moving to the next. No string-parsing logic reliably recovers column boundaries from that flattened sequence.

The second is merged and multi-row cells. Line-item tables routinely span cells across rows for items with multi-line descriptions. Text extraction collapses those cell boundaries into a flat string and drops the row-to-total relationship an accounting system needs.

The third is rasterized scans with no text layer. A PDF created by scanning a paper invoice contains only an embedded image, so anything that reads the text layer alone comes back empty. That kind of file has image data but no searchable text at all. Tools built for scanned input bundle an OCR step rather than skipping it, and any pipeline you build has to do the same before extraction can happen.

Structure-aware extraction addresses all three by classifying document regions into typed elements before exposing their content.

Invoice fields to target before touching the API

Define the target schema before writing code. A concrete target tells you which elements to read from StructureInfo.json and which to skip, which saves iteration time on every invoice you process.

A workable invoice schema covers three groups:

  • Header fields, including vendor name, invoice number, invoice date, due date, and payment terms
  • Line items, a repeating array of description, quantity, unit price, and line total
  • Footer totals, including subtotal, tax amount, and total amount due
{
  "vendor_name": "",
  "invoice_number": "",
  "invoice_date": "",
  "due_date": "",
  "payment_terms": "",
  "line_items": [
    {
      "description": "",
      "quantity": "",
      "unit_price": "",
      "line_total": ""
    }
  ],
  "subtotal": "",
  "tax": "",
  "total_due": ""
}

Keep every value a string at extraction time. Type conversion, currency parsing, and date normalization belong downstream, after validation, where a bad value can be rejected with context instead of raising inside the parser.

The sample invoice used throughout this guide is invoice_full_test.pdf, so you can run every call below against the same document.

Sample invoice PDF with header fields, a line item table, and Subtotal, Tax Rate, and Total Due labels in the second to last column

The input document. Notice that the Subtotal, Tax Rate, Tax Amount, and Total Due labels sit in the second-to-last column rather than the first. That detail determines how the post-processing code has to find them.

Prerequisites

You need the following before the first API call:

Scaffold the workspace in one shot:


mkdir invoice-extraction && cd invoice-extraction && python3 -m venv .venv && source .venv/bin/activate && pip install requests

Then download the sample invoice into that folder:


curl -L -o invoice_full_test.pdf https://github.com/lucienchemaly/foxit-demo-templates/raw/main/invoice_full_test.pdf

Foxit API authentication and setup

Signing up activates a free Developer plan that includes 500 credits per year with no credit card required. A structural extraction call costs one credit, while the upload, polling, and download calls are not billed, so a full run of the workflow below costs a single credit.

The account creation screen. The free Developer plan is enough to work through this entire guide.

Foxit authenticates PDF Services requests with a client ID and client secret passed as HTTP headers, so there is no OAuth token exchange to implement. Both values come from the default application created in your Developer Portal dashboard, alongside the base URL your calls need.

Foxit developer portal panel showing the base URL, client ID, and a masked client secret

The credentials panel. Copy the Client ID and Client Secret into environment variables rather than pasting them into source files.

Export them into your shell so no credential is ever committed:

export FOXIT_CLIENT_ID="your_client_id"
export FOXIT_CLIENT_SECRET="your_client_secret"

The structural extraction reference page carries a Test Request button that fires live calls straight from the browser, which is the quickest way to confirm your credentials work before writing any Python. The endpoint is currently labelled Trial in the reference, so expect its surface to evolve.

Pin your parser to the version field inside the analyzeResult response. The current schema ships as 1.0.7, and pinning prevents silent breakage if that changes.

The four-step PDF to JSON invoice extraction workflow

The API is asynchronous. You upload a document, start a task, poll until the task completes, then download the result.

Four-step diagram of the Foxit structural extraction workflow, from upload through polling to downloading the result

All four paths sit under https://na1.fusion.foxit.com/pdf-services. Calling them without that prefix returns 404.

The path prefix matters more than it looks. The four endpoints live under /pdf-services/api/..., and requesting a bare /documents/{id}/download returns 404 rather than a helpful error.

import io
import json
import os
import time
import zipfile

import requests

BASE_URL = "https://na1.fusion.foxit.com/pdf-services"
HEADERS = {
    "client_id": os.environ["FOXIT_CLIENT_ID"],
    "client_secret": os.environ["FOXIT_CLIENT_SECRET"],
}
POLL_SECONDS = 2
POLL_TIMEOUT = 120


def extract_structure(pdf_path: str) -> dict:
    # Step 1: upload the PDF (multipart/form-data, 100 MB maximum)
    with open(pdf_path, "rb") as handle:
        upload = requests.post(
            f"{BASE_URL}/api/documents/upload",
            headers=HEADERS,
            files={"file": (os.path.basename(pdf_path), handle, "application/pdf")},
        )
    upload.raise_for_status()
    document_id = upload.json()["documentId"]

    # Step 2: start the structural extraction task
    started = requests.post(
        f"{BASE_URL}/api/documents/pdf-structural-extract",
        headers=HEADERS,
        json={"documentId": document_id},
    )
    started.raise_for_status()
    task_id = started.json()["taskId"]

    # Step 3: poll until COMPLETED, bounded, and handle FAILED
    deadline = time.monotonic() + POLL_TIMEOUT
    while True:
        response = requests.get(f"{BASE_URL}/api/tasks/{task_id}", headers=HEADERS)
        response.raise_for_status()
        task = response.json()
        if task["status"] == "COMPLETED":
            break
        if task["status"] == "FAILED":
            raise RuntimeError(f"extraction task {task_id} FAILED: {task}")
        if time.monotonic() > deadline:
            raise TimeoutError(f"task {task_id} stuck at {task['status']}")
        time.sleep(POLL_SECONDS)

    # Step 4: download the result ZIP and read StructureInfo.json
    result = requests.get(
        f"{BASE_URL}/api/documents/{task['resultDocumentId']}/download",
        headers=HEADERS,
    )
    result.raise_for_status()
    with zipfile.ZipFile(io.BytesIO(result.content)) as archive:
        return json.loads(archive.read("StructureInfo.json"))

This code:

  • Reads both credentials from the environment.
  • Uploads the PDF as multipart form data and captures the returned documentId.
  • Hands that id to the extraction endpoint to receive a taskId.
  • Polls the task endpoint every two seconds, bounded by a deadline, checking explicitly for FAILED so a rejected document raises instead of spinning forever.
  • Once the status reads COMPLETED, downloads the ZIP archive using the task’s resultDocumentId and reads StructureInfo.json out of it in memory.
Terminal output showing four API calls succeed and the final parsed invoice JSON with line items and totals

A real run against the sample invoice. The task reports IN_PROGRESS at 20 percent before reaching COMPLETED, and the final object carries every field from the schema defined earlier.

How to map raw output to a clean invoice schema

Choosing an invoice data extraction API is only half the work. The other half is mapping whatever it returns onto a schema your backend already understands, and that mapping is where the shape of the response starts to matter.

StructureInfo.json wraps everything in an analyzeResult object with four top-level keys, version, pages, info, and elements. The elements array is where the work happens. Each element carries a type drawn from twelve values, including paragraph, table, title, image, form, and formula, along with its bounding region and content. The same API can also extract embedded images as standalone image files, in addition to returning them as typed image elements within StructureInfo.json.

Two details in that structure cause most of the bugs in a first implementation, and neither is obvious from the field names.

Annotated JSON showing table cells nested at content.body.cells and text at paragraph.content.text

The actual response shape from a live extraction. A table’s cells are nested at content.body.cells, cell text sits at paragraph.content.text, and region.boundingBox is an eight-number polygon rather than an x, y, width, height rectangle.

A table element does not expose a top-level cells array. Its grid is nested at content.body.cells, where each cell carries rowIndex, columnIndex, and a paragraph object. Cell text then sits one level deeper still, at paragraph.content.text, because content is an object rather than a string. Reaching for cell["paragraph"]["content"] returns a dict, not the text you want.

Blank cells are the second detail. When a vendor leaves a cell empty, the API still returns the cell with its indices and a paragraph object, but that paragraph has no content key at all. An unguarded read raises KeyError partway through a document that looked fine in testing.

def element_text(element: dict) -> str:
    """Return an element's text, or an empty string when it carries none."""
    text = element.get("content", {}).get("text", "")
    return " ".join(text.split())


def cell_text(cell: dict) -> str:
    """Return a table cell's text, or an empty string when the cell is blank."""
    return element_text(cell.get("paragraph", {}))

In this code, element_text reads the nested content.text value and normalizes its whitespace, which matters because cell text can contain a literal \r\n where a label wraps across two lines. Using " ".join(text.split()) collapses those into single spaces, whereas .strip() leaves a mid-string newline untouched. cell_text then reuses that helper for table cells, returning an empty string for a blank cell instead of raising.

With the accessors in place, build a grid and read it row by row.

import re

FOOTER_LABELS = ("subtotal", "tax rate", "tax amount", "tax", "total due", "total")
HEADER_PATTERNS = {
    "vendor_name":    r"bill to:\s*(.+)",
    "invoice_number": r"invoice number:\s*(.+)",
    "invoice_date":   r"invoice date:\s*(.+)",
    "due_date":       r"due date:\s*(.+)",
    "payment_terms":  r"payment is due within (.+?) of",
}


def parse_invoice(structure_info: dict) -> dict:
    elements = structure_info["analyzeResult"]["elements"]
    invoice = {key: "" for key in HEADER_PATTERNS}
    invoice.update(line_items=[], subtotal="", tax="", total_due="")

    # Header fields come from paragraph elements above the table
    for element in elements:
        if element["type"] != "paragraph":
            continue
        text = element_text(element)
        for field, pattern in HEADER_PATTERNS.items():
            match = re.search(pattern, text, re.IGNORECASE)
            if match and not invoice[field]:
                invoice[field] = match.group(1).strip()

    tables = [element for element in elements if element["type"] == "table"]
    if not tables:
        return invoice

    grid: dict = {}
    for cell in tables[0]["content"]["body"]["cells"]:
        grid.setdefault(cell["rowIndex"], {})[cell["columnIndex"]] = cell_text(cell)

    # Resolve columns from the header row instead of assuming positions
    columns = {name.lower(): index for index, name in grid.get(0, {}).items() if name}

    def column_for(*candidates, default):
        for candidate in candidates:
            for name, index in columns.items():
                if candidate in name:
                    return index
        return default

    description_col = column_for("description", "item", default=1)
    quantity_col = column_for("qty", "quantity", default=2)
    unit_price_col = column_for("unit price", default=3)
    line_total_col = column_for("total", "amount", default=4)

    for row_index in sorted(index for index in grid if index > 0):
        row = grid[row_index]
        label = next(
            (value.lower().rstrip(":").strip() for value in row.values()
             if value.lower().rstrip(":").strip() in FOOTER_LABELS),
            None,
        )
        if label:
            value = row[max(row)]
            if label == "subtotal":
                invoice["subtotal"] = value
            elif label == "tax amount":
                invoice["tax"] = value
            elif label in ("total due", "total"):
                invoice["total_due"] = value
            continue
        if row.get(description_col):
            invoice["line_items"].append({
                "description": row.get(description_col, ""),
                "quantity": row.get(quantity_col, ""),
                "unit_price": row.get(unit_price_col, ""),
                "line_total": row.get(line_total_col, ""),
            })

    return invoice

In this code, you first walk the paragraph elements and pull header fields out with labelled regular expressions, which works because Foxit exposes each header line as its own element with its reading order preserved. You then flatten the table into a {rowIndex: {columnIndex: text}} grid and resolve column positions from the header row by name, so a vendor who adds a leading row-number column does not shift every field by one. Each subsequent row is classified before it is read, so that if any cell in the row matches a known footer label the row is treated as a total and its value taken from the last populated column, and otherwise the row becomes a line item. Scanning the whole row for the label is the part that matters, because footer labels do not sit in the first column.

Footer totals living inside the line-item table is convenient rather than awkward, since one pass over the cell grid covers line items and totals together. Form elements do not appear on a typical invoice, so there is no need to look for them.

Before and after

The two blocks below show the same data on either side of that mapping. First, one real cell exactly as the API returns it, taken verbatim from the run above:

{
  "paragraph": {
    "type": "paragraph",
    "content": {
      "text": "API Integration\r\nConsulting"
    },
    "region": {
      "page": 1,
      "boundingBox": [171, 300, 258, 300, 258, 328, 171, 328]
    },
    "id": "paragraph12",
    "paragraphOrder": 12
  },
  "rowSpan": 1,
  "columnSpan": 1,
  "rowIndex": 1,
  "columnIndex": 1,
  "region": {
    "page": 1,
    "boundingBox": [171, 300, 258, 300, 258, 328, 171, 328]
  },
  "score": 0.8555269837379456
}

That fragment is the shape of every cell you will handle. The text is nested at paragraph.content.text rather than sitting directly on the cell. The position arrives as an eight-number boundingBox polygon on both the cell and its paragraph, not as a rectangle. And the value itself contains a literal \r\n where the description wrapped onto a second line in the source table, which is the case .strip() silently fails to clean.

Second, the complete object parse_invoice returns for the whole document, which is what your backend actually consumes:

{
  "vendor_name": "Acme Corporation",
  "invoice_number": "INV-2025-0042",
  "invoice_date": "07/15/2025",
  "due_date": "08/14/2025",
  "payment_terms": "30 days",
  "line_items": [
    {
      "description": "API Integration Consulting",
      "quantity": "8",
      "unit_price": "$ 195.00",
      "line_total": "$1,560.00"
    },
    {
      "description": "Document Automation Setup",
      "quantity": "1",
      "unit_price": "$ 750.00",
      "line_total": "$ 750.00"
    }
  ],
  "subtotal": "$2,310.00",
  "tax": "$ 184.80",
  "total_due": "$2,494.80"
}

The wrapped description has become the single clean string "API Integration Consulting", the header fields have been lifted out of the paragraph elements above the table, and the four footer rows have been separated from the two genuine line items. That output is backend-ready, so you can write it straight to a database, push it to a reporting pipeline, or validate it against an accounts-payable schema without further parsing.

Common mistakes

  • Dropping the path prefix. All four endpoints sit under /pdf-services/api/.... A bare /documents/{id}/download returns 404.
  • Reading cells off the table element. The grid is nested at content.body.cells. A top-level cells lookup raises KeyError.
  • Treating paragraph.content as a string. It is an object, so the text is at paragraph.content.text.
  • Assuming footer labels are in column 0. On real invoices they commonly sit in the second-to-last column, with the value beside them.
  • Forgetting blank cells. A blank cell keeps its paragraph object but carries no content key.
  • Polling without a bound. A FAILED task never becomes COMPLETED, so an unbounded while True loop hangs.
  • Trusting info.basicInfo.elementCounts. It can disagree with the length of the elements array, so size loops from the array itself.
  • Using .strip() to clean cell text. A wrapped label contains a mid-string \r\n that .strip() leaves in place. Use " ".join(text.split()).

Invoice parsing FAQ

Invoice data extraction from PDF is the programmatic conversion of semi-structured PDF invoice content into a schema-typed data object. The source can be a digital-native PDF with an embedded text layer or a scanned image-only PDF that needs OCR first. The output is a structured record, typically JSON, with typed fields for header values, line items, and totals that downstream systems consume without manual parsing.

Line items live in table elements inside StructureInfo.json. Read the grid from content.body.cells, where every cell carries a rowIndex and columnIndex, build a {rowIndex: {columnIndex: text}} dictionary, resolve the column positions from the header row, then read each data row in column order. Classify rows before reading them so footer totals are not appended as line items.

The PDF Structural Extraction API expects a PDF that already has a text layer. Tested against an image-only PDF, it returns no text and an empty table rather than an error. To handle scans, run the document through Foxit’s OCR endpoint first (POST /pdf-services/api/documents/analyze/pdf-ocr with outputFormat set to PDF), then pass the OCR output through the four-step workflow. That makes five calls rather than four, and the OCR step is billed as its own credit.

The downloaded ZIP unpacks to StructureInfo.json, holding an analyzeResult object with version, pages, info, and elements keys. The elements array carries every classified region, each with a type drawn from twelve values. A table element nests its grid at content.body.cells, and each cell’s text sits at paragraph.content.text.

A grid keyed by row and column decouples your field mapping from the order the API happens to return cells in, and it lets you address a specific position directly, which is what the footer-label check needs. It also makes missing cells visible as absent keys rather than as silently shifted values.

The API classifies regions into twelve element types, including paragraph, table, title, image, form, and formula. On a typical invoice, paragraph elements carry the header fields while a single table element carries both line items and footer totals, so filtering by type lets you target only what your schema needs.

Some layouts place totals in a separate table element or in standalone paragraph elements below the main table. Keep the row-classification logic in its own function so you can apply it to a second table element, then fall back to scanning paragraph elements for currency-formatted strings next to known label text.

Wrapping up

The four-step workflow of upload, extract, poll, and download produces a typed, hierarchical JSON document from any digital-native invoice PDF. Post-processing the elements array through a row and column grid turns that into a clean invoice object your backend can consume directly. The same four calls extend to purchase orders, receipts, and any other tabular financial document, with only the mapping logic changing to match the target schema.

The Foxit PDF Structural Extraction API is part of the broader Foxit PDF Services API, which covers conversion, compression, OCR, and other document operations under the same credential set. The infrastructure is SOC 2 Type II certified, with GDPR-supporting features and HIPAA-aligned controls including BAA availability, which matters for teams processing financial documents under compliance review.

The complete script from this guide is available as extract_invoice.py if you want to run it before adapting it.

Create your free developer account and start turning invoice PDFs into structured JSON today. No credit card required, and the 500 credits on the free Developer plan are enough to build and test something real.

How Foxit compares to Google Document AI for document data extraction

Split graphic comparing Google Document AI cloud icons to Foxit OCR, layout, and AI extraction pipeline

Seven Google Document AI processors are being retired in June 2026, pushing teams to look at alternatives. This comparison walks through integration setup, extraction architecture, output schema, and data residency for Foxit’s PDF Structural Extraction API against Google Document AI, so you can decide with real implementation detail instead of a feature list.

Seven Google Document AI processors hit end-of-life on 30 June 2026. Google is retiring the Enterprise Document OCR, Expense, Custom classifier, Custom splitter, Invoice, Pay slip, and Bank statement parsers, and processor versions follow a rolling schedule where each version is deprecated six months after a newer one ships.

For many teams, the deprecation notice does more than prompt a migration ticket. It opens a broader question about whether Google Document AI is still the right foundation for the extraction stack, and what the credible Google Document AI alternatives actually look like once you compare them on implementation detail rather than feature lists.

This article gives you the technical specifics to make that call, comparing the Foxit PDF Structural Extraction API and Google Document AI across five dimensions that drive the real build-vs-switch decision, covering integration overhead, extraction architecture, output schema, document and language coverage, and data residency.

Five axes for evaluating Google Document AI alternatives

Any extraction API comparison lives or dies on the criteria it uses. These five dimensions cover what a production engineering team actually cares about, going well beyond a proof-of-concept benchmark.

Integration complexity measures how many external dependencies you must provision before your first call returns data. A tool that requires a GCP project, service account, IAM role grants, and billing enablement adds meaningful friction before a single byte of document gets processed. For teams with CI/CD pipelines and strict access-control policies, every new cloud dependency is a potential blocker.

Extraction architecture covers how the API reads a document internally, including what happens when a file mixes scanned pages, machine-typed text, and embedded tables in the same document.

Output schema determines how much post-processing your downstream systems require. A flat token list forces you to reconstruct document structure yourself, while a pre-labeled semantic taxonomy reduces that burden before the data reaches your RAG pipeline or BI dashboard.

Document and language coverage sets the practical ceiling on what you can run through the API in production. Language breadth matters especially for multi-region workloads processing invoices or contracts in non-Latin scripts.

Data residency encompasses where documents travel during processing, how long they remain on third-party infrastructure, and what audit evidence you can produce for compliance reviews. For regulated industries, this dimension often decides the question before the others are evaluated.

Integration setup and authentication overhead

Getting to a first call on Google Document AI requires a GCP project, a service account with an IAM role assignment (at minimum roles/documentai.apiUser), billing enablement on the project, and an environment variable pointing to a downloaded service account JSON key. Teams outside the GCP ecosystem absorb all of that as onboarding cost before any extraction runs.

Foxit’s path is shorter. Create a free developer account at the Foxit Developer Portal, retrieve your client_id and client_secret from the default application, and attach them as two HTTP headers on every request. The entire setup takes minutes and requires nothing from GCP.

Prerequisites

To run the code below you need Python 3.8+, the requests library installed into an isolated virtual environment with pip, an editor such as VS Code with the Python extension (PyCharm or Sublime Text work equally well), and a free Foxit developer account from app.developer-api.foxit.com/sign-up to supply the two credential values. Scaffold the workspace in one shot:


mkdir foxit-extract && cd foxit-extract && python3 -m venv .venv && source .venv/bin/activate && pip install requests

Extraction then follows a four-step asynchronous workflow, annotated at each step:

export FOXIT_CLIENT_ID="your_client_id"
export FOXIT_CLIENT_SECRET="your_client_secret"

Extraction then follows a four-step asynchronous workflow, annotated at each step:

import os
import requests
import time

BASE_URL = "https://na1.fusion.foxit.com/pdf-services/api"
HEADERS = {
    "client_id": os.environ["FOXIT_CLIENT_ID"],          # lowercase snake_case, not Authorization: Bearer
    "client_secret": os.environ["FOXIT_CLIENT_SECRET"]
}

# Step 1: Upload the document (multipart/form-data, field name "file", max 100 MB)
with open("contract.pdf", "rb") as f:
    upload_resp = requests.post(
        f"{BASE_URL}/documents/upload",
        headers=HEADERS,
        files={"file": f}
    )
document_id = upload_resp.json()["documentId"]

# Step 2: Start structural extraction
extract_resp = requests.post(
    f"{BASE_URL}/documents/pdf-structural-extract",
    headers=HEADERS,
    json={"documentId": document_id}
)
task_id = extract_resp.json()["taskId"]

# Step 3: Poll every 2 seconds until COMPLETED (statuses are uppercase)
result_doc_id = None
for _ in range(60):
    status_resp = requests.get(
        f"{BASE_URL}/tasks/{task_id}",
        headers=HEADERS
    )
    payload = status_resp.json()
    if payload["status"] == "COMPLETED":
        result_doc_id = payload["resultDocumentId"]
        break
    if payload["status"] == "FAILED":
        raise RuntimeError(f"Extraction failed: {payload}")
    time.sleep(2)

if result_doc_id is None:
    raise TimeoutError("Extraction did not complete within 120 seconds")

# Step 4: Download the ZIP archive containing StructureInfo.json
result_resp = requests.get(
    f"{BASE_URL}/documents/{result_doc_id}/download",
    headers=HEADERS
)
with open("extraction_result.zip", "wb") as out:
    out.write(result_resp.content)

Authentication uses lowercase snake_case header names on every call. The upload endpoint accepts multipart/form-data with the PDF file under the field name file, with a 100 MB size limit per document.

Terminal output showing four curl calls to the Foxit extraction API: upload, extract, poll, and download

 The four calls against the live API. Note the 202 on the extract call and the COMPLETED status before any download is attempted.

The response shape from that run. Every element sits under analyzeResult, and text elements carry region.boundingBox as an eight-number polygon rather than a four-number rectangle. A table element is the exception, since its region comes back empty and its geometry sits in a regions array instead.

The table below puts both platforms side by side on the integration and output dimensions:

DimensionGoogle Document AIFoxit PDF Structural Extraction API
Account setupGCP project, service account, IAM role, billingFree developer account, no credit card
AuthenticationService account JSON key via GOOGLE_APPLICATION_CREDENTIALSclient_id and client_secret as HTTP headers
Call patternSynchronous or async depending on processorFour-step async (upload, extract, poll, download)
Output formatDocument proto (blocks, paragraphs, tokens)StructureInfo.json with 12 semantic element types
Cloud dependencyGCP-native; Vertex AI integration availableCloud-agnostic, any stack
Language coverageVaries by processor200+ languages via dedicated OCR layer

Common mistakes and troubleshooting

Four failure modes account for most of the time lost on a first integration. Only the first one reports itself clearly; the other three surface as exceptions in your own code rather than as API errors.

  • Sending an Authorization: Bearer header : PDF Services authenticates with two separate lowercase headers, client_id and client_secret. There is no token exchange step and no OAuth flow to implement. This is the one mistake the API names outright, returning HTTP 400 with {"allow": false, "reason": "Missing credentials: provide both 'client_id' and 'client_secret' headers."}.
  • Treating the extract call as synchronous : pdf-structural-extract returns HTTP 202 with a taskId, never the result. Read the status from GET /tasks/{taskId}, which also returns a progress percentage, and note that the values are uppercase (PENDING, IN_PROGRESS, COMPLETED, FAILED). Comparing against lowercase strings produces a poll loop that never exits, which is why the sample above also breaks out on FAILED and caps its attempts.
  • Reading region.boundingBox on every element : text elements such as title, head, and paragraph carry region as {page, boundingBox}, but a table element returns an empty region and puts its geometry in a regions array instead. A loop that assumes one shape raises KeyError on the first document containing a table.
  • Indexing elements at the JSON root : every result nests under analyzeResult, so data["elements"] raises KeyError while data["analyzeResult"]["elements"] works. The same applies to uploads above the 100 MB per-document limit, which fail at the upload step rather than during extraction.

Extraction architecture, output format, and document coverage

Google Document AI’s processing model is processor-centric. You select a processor type (Invoice Parser, Form Parser, Document OCR), and the service returns a Document proto containing position-anchored blocks, paragraphs, and tokens. Semantic meaning depends on which processor you deployed, so a Form Parser and an Invoice Parser return structurally similar protos but with different field-level annotations.

Foxit’s PDF Structural Extraction API runs three coordinated layers on every document, regardless of document type. The OCR layer handles rasterized content across more than 200 languages. The layout recognition layer maps spatial relationships and table cell grids, resolving multi-column text blocks, overlapping text-image regions, stamped signatures on top of text fields, and engineering drawing annotations. The AI parsing layer then classifies content semantically, assigning each element a type from a fixed taxonomy of twelve labels.

The rendered pipeline. Every document takes the same path, so there is no processor to choose per document type.

Those twelve types appear in StructureInfo.json inside the returned ZIP archive, covering title, head, paragraph, table, image, headerFooter, form, hyperlink, footnote, sidebar, annotation, and formula. Each element carries its reading-order position and spatial coordinates, so the structure your downstream system receives reflects how a human would read the original document rather than raw storage order.

The practical delta shows up in RAG pipeline integration. Google Document AI’s Document proto gives you text and coordinates, but your pipeline needs a post-processing step to decide what each block means semantically. With StructureInfo.json, you filter to table elements, iterate rows, and pass the content directly to your embedding model, because the semantic classification happened upstream.

The output schema comparison below clarifies where each platform puts the interpretive work:

Schema dimensionGoogle Document AI (Document proto)Foxit (StructureInfo.json)
Structure modelHierarchical, covering pages, blocks, paragraphs, and tokensFlat list of semantically typed elements with spatial metadata
Semantic labelsField-level labels tied to specific processor selection12 fixed element types, processor-independent
Table representationCell tokens within a table blockDedicated table element type with cell grid coordinates
Reading orderImplicit (coordinate ordering required client-side)Explicit, preserved in element sequence
Formula supportLimitedDedicated formula element type

Document coverage on both platforms is broad. Foxit processes scanned PDFs, image-based PDFs, multi-page contracts, invoices, and form-heavy documents. The layout layer handles edge cases that trip up simpler OCR tools, including stamped signatures overlapping text fields, mixed raster-vector pages, and footnote regions that appear spatially disconnected from their reference markers.

Data privacy and ecosystem independence

Regulated workloads ask two questions before any technical evaluation, starting with where the document goes and what audit evidence you can produce.

Foxit’s API compliance page documents SOC 2 Type II independent audit, HIPAA-aligned features with Business Associate Agreement (BAA) support, and GDPR-supporting features including redaction, anonymization, and secure metadata handling. Foxit’s AI service documentation states that input documents and results are held temporarily and deleted within 24 hours. If your organization requires a BAA, Foxit can provide one.

Google Document AI routes all processing through GCP infrastructure. Teams subject to data residency requirements need to select the appropriate GCP region, review Google’s data processing addendum, and confirm their cloud agreement covers the specific data types being processed. That review is standard for teams already operating within GCP, but it adds a compliance surface for teams that are not.

Foxit’s API is cloud-agnostic. Your team calls it from any existing stack, passing two credential headers, and extracts documents without spinning up a GCP project, provisioning a storage bucket, or accepting GCP billing terms. For teams evaluating outside GCP, that independence cuts both technical and commercial risk from the decision.

When to use Google Document AI and when to use Foxit

The right choice depends on your existing infrastructure and what your extraction output needs to do.

ScenarioBest fitKey reason
Teams already deep in GCP who want Vertex AI integrationGoogle Document AINative Vertex AI pipeline support and Google-managed processor versions reduce ops overhead
High-volume PDF processing outside the GCP ecosystemFoxitCloud-agnostic with credit-based pricing, with no GCP billing or IAM dependency
Workloads with strict data residency or BAA requirementsFoxitSOC 2 Type II audit, HIPAA-aligned features with BAA support, and GDPR-supporting features
Teams that need semantic element classification ready for downstream consumptionFoxitStructureInfo.json delivers 12 pre-labeled element types without a client-side post-processing step

The fourth scenario is worth walking through in detail. Your team has a contract review pipeline that needs to extract all tables and footnotes from multi-page PDFs and push them into a downstream system. With the Foxit API, the workflow runs like this:

  1. Upload the PDF to /pdf-services/api/documents/upload and receive a documentId.
  2. POST to /pdf-services/api/documents/pdf-structural-extract with the documentId and receive a taskId.
  3. Poll /pdf-services/api/tasks/{taskId} every two seconds until status equals COMPLETED.
  4. Download the result ZIP from /pdf-services/api/documents/{resultDocumentId}/download and parse StructureInfo.json.

Once you have StructureInfo.json, filtering to table and footnote elements is a single-pass list comprehension. The labeled elements arrive with spatial coordinates and reading order intact, so your downstream system receives structured, ordered data ready for indexing, embedding, or display, with no second model call, no coordinate sorting, and no block-level classification required.

Teams running active Vertex AI pipelines in GCP, with processors not among the seven being retired, have no technical reason to switch. For everyone else, the four API calls above give you a working extraction against your own documents in minutes.

Google Document AI FAQ

Google Document AI is a managed cloud service for document parsing and data extraction, built on GCP processors. You select a processor type (such as Invoice Parser, Form Parser, or Document OCR), send a document via the API, and receive a structured Document proto containing text, coordinates, and field-level annotations. All processing runs on Google Cloud Platform infrastructure.

Foxit’s API runs three coordinated processing layers on every document (OCR, layout recognition, and AI parsing) regardless of document type, while Google Document AI uses a processor model where semantic classification depends on the specific processor you select. The output schemas also differ. Foxit returns StructureInfo.json inside a ZIP archive with twelve pre-labeled element types preserving reading order and spatial relationships, while Google Document AI returns a Document proto with hierarchical blocks, paragraphs, and tokens that require client-side semantic interpretation. Foxit is also cloud-agnostic and needs no GCP project, while Google Document AI is GCP-native.

Foxit’s API compliance page documents SOC 2 Type II independent audit, HIPAA-aligned features with Business Associate Agreement (BAA) support, and GDPR-supporting features including redaction, anonymization, and secure metadata handling. Foxit’s AI service documentation states that input documents and results are held temporarily and deleted within 24 hours. Foxit does not claim HIPAA certification or GDPR certification, and the API compliance page does not state that documents are never stored, so teams with specific retention requirements should review the documentation directly and request a BAA where applicable.

The API returns a ZIP archive containing StructureInfo.json. That file classifies every element in the document using one of twelve labeled types, including title, head, paragraph, table, image, headerFooter, form, hyperlink, footnote, sidebar, annotation, and formula. Each element includes spatial coordinates and reading-order position, so the structure your downstream system receives reflects how a human would read the original document. Table elements carry cell grid coordinates, making row-level data extraction straightforward without additional parsing.

Yes. Create a free developer account at app.developer-api.foxit.com/sign-up. No credit card is required. Once you register, your client_id and client_secret are available immediately in the Developer Portal, and you can run extractions against your own documents using the API Playground or the downloadable Postman collection.

Foxit’s OCR layer processes rasterized content across more than 200 languages, so scanned PDFs and image-based documents (including TIFFs) are processable without pre-conversion. The layout recognition layer then maps spatial relationships and resolves edge cases such as multi-column text blocks, overlapping text-image regions, and footnote regions that appear spatially disconnected from their reference markers.

After 30 June 2026, Google is retiring the Enterprise Document OCR, Expense, Custom classifier, Custom splitter, Invoice, Pay slip, and Bank statement processors. Remaining processors (including Form Parser and Document OCR for non-deprecated versions) continue to operate on their own rolling deprecation schedule, where each version is deprecated six months after a newer one ships. Teams relying on any of the seven retired processors need to migrate before that date.

Conclusion

Across all five axes, the practical difference between Google Document AI alternatives comes down to how much infrastructure you take on to reach a first result. Foxit requires two credential headers. Google’s setup adds a GCP project, service account, IAM configuration, and billing enablement before you process a single document. Foxit’s three-layer extraction model (OCR, layout recognition, AI parsing) delivers semantic classification across every document type without processor selection. StructureInfo.json‘s twelve labeled element types reduce client-side post-processing compared to Google’s Document proto. Both platforms handle the major document types, and Foxit’s 200-plus language OCR covers non-Latin scripts across all document categories. Foxit’s SOC 2 Type II audit, HIPAA-aligned BAA support, and GDPR-supporting features give regulated teams a documented compliance baseline, and the cloud-agnostic model means your team runs document extraction without taking on GCP billing, IAM governance, or ecosystem lock-in.

Create a free developer account at app.developer-api.foxit.com/sign-up, no credit card required, and run the four-step extraction against your own document today.

How to Turn PDFs into Structured Data with Foxit’s PDF Structural Extraction API

Diagram showing pdf data extraction turning an invoice into a JSON file with typed elements and table cells

PDF data extraction with Foxit’s Structural Extraction API turns messy invoices and tables into typed JSON, complete with bounding regions and addressable cells. This tutorial walks through the four REST calls, upload, extract, poll, and download, and shows working Python code that builds a clean dictionary from an invoice’s line items. It also covers common mistakes like case-sensitive auth headers and stale document IDs.

Pull text out of a multi-column invoice and you get a flat string with column headers mixed into values, row boundaries gone, and field labels indistinguishable from the data they describe. Foxit’s PDF Structural Extraction API returns typed JSON instead, where every element carries a type, its text, a bounding region, and, for tables, an addressable grid of cells.

This tutorial walks the four REST calls that get you there, uploading a PDF, starting the analysis, polling the task, and downloading the result. By the end you’ll have working Python code that turns an invoice into a dictionary your pipeline can address by key.

Raw text vs. structured extraction

What separates raw text extraction from structured extraction is the shape of the output, not the accuracy of the characters.

Take a vendor invoice with a line-item table covering description, quantity, and unit price. Text extraction returns something like "1 API Integration Consulting 10 $ 150.00 $1,500.00". The content is all there, but the row and column relationships are gone, so your parsing code has to reconstruct structure the PDF already encoded, and it has to do that differently for every layout you encounter.

Structured extraction preserves what raw text discards. The pdf-structural-extract endpoint returns each element with a type, a content object holding the text and its font styling, and a region giving the page number and bounding polygon. Tables come back as a cell grid with explicit rowIndex and columnIndex values, so a cell’s position is data rather than something you infer from coordinates.

Prerequisites

Scaffold the workspace in one shot:

mkdir foxit-extract && cd foxit-extract
python3 -m venv .venv && source .venv/bin/activate
pip install requests
curl -L -o invoice.pdf https://github.com/lucienchemaly/foxit-demo-templates/raw/main/invoice_table_test.pdf
export FOXIT_CLIENT_ID="your_client_id"
export FOXIT_CLIENT_SECRET="your_client_secret"

Here is the invoice the rest of the tutorial extracts from.

Sample invoice PDF with billing details and a five column line item table used for pdf data extraction

The source document. The five-column table and the labeled fields above it are what the extraction turns into addressable JSON.

Authentication

PDF Services authenticates with two named request headers on every call, client_id and client_secret, both lowercase with an underscore. There is no OAuth exchange and no bearer token, and wrapping the credentials in an Authorization: Bearer header returns a 400 instead. The base host for every endpoint in this tutorial is https://na1.fusion.foxit.com/pdf-services.

Keep the values in environment variables rather than in the file, so nothing secret travels with your code.

The four-call extraction flow

Structural extraction is an asynchronous job, so it runs in four steps.

Flow diagram of four API calls for pdf data extraction: upload, extract, poll, and download JSON

The four calls and what each one hands to the next. The id you download with comes from the finished task, not the upload.

  1. Upload the PDF and receive a documentId.
  2. Start the analysis against that id and receive a taskId.
  3. Poll the task until its status reaches COMPLETED, which also returns a resultDocumentId.
  4. Download the result, a ZIP archive holding the structured JSON.

Step 1: Upload the document

Send the PDF as multipart/form-data to the upload endpoint, using the form field name file.

curl -X POST "https://na1.fusion.foxit.com/pdf-services/api/documents/upload" \
  -H "client_id: $FOXIT_CLIENT_ID" \
  -H "client_secret: $FOXIT_CLIENT_SECRET" \
  -F "[email protected]"

The -F flag is what makes curl send a multipart body, and the @ prefix tells it to read the file from disk rather than treat the value as a literal string. Uploads are capped at 100 MB, and an uploaded document is deleted after 24 hours, so treat the documentId as short-lived rather than a permanent handle.

A successful upload returns a single key:

{
  "documentId": "6a6c9834a820c33d30d222e5"
}

Step 2: Start the structural analysis

POST that id to the extraction endpoint with a JSON body.

curl -X POST "https://na1.fusion.foxit.com/pdf-services/api/documents/pdf-structural-extract" \
  -H "client_id: $FOXIT_CLIENT_ID" \
  -H "client_secret: $FOXIT_CLIENT_SECRET" \
  -H "Content-Type: application/json" \
  -d '{"documentId": "6a6c9834a820c33d30d222e5"}'

The call returns HTTP 202 with a taskId rather than the finished document, since analysis runs asynchronously. documentId is the only required field in the body, and a password-protected source PDF takes an optional password alongside it. Full request and response details live in the PDF Structural Extraction reference, which also carries the endpoint’s Trial designation, so pin the schema version you parse against rather than assuming it is stable.

{
  "taskId": "6a6c9835d24a2429666f61b6"
}

Step 3: Poll the task

Ask for the task by id until it finishes.

curl "https://na1.fusion.foxit.com/pdf-services/api/tasks/6a6c9835d24a2429666f61b6" \
  -H "client_id: $FOXIT_CLIENT_ID" \
  -H "client_secret: $FOXIT_CLIENT_SECRET"

The response carries the state, a percentage, and, once the work is done, the id of the result document:

{
  "taskId": "6a6c9835d24a2429666f61b6",
  "status": "COMPLETED",
  "progress": 100,
  "resultDocumentId": "6a6c98375e2cab6bb50e740b"
}

Task statuses are uppercase. The schema enum runs PENDING, IN_PROGRESS, COMPLETED, and FAILED, so a comparison against a lowercase "completed" never matches and your loop spins until it times out. Portal copy sometimes says “processing” in prose, but IN_PROGRESS is the value on the wire.

Step 4: Download the result

Fetch the finished artifact using the resultDocumentId from the poll, not the documentId from the upload. Confusing the two is the most common 4xx at this step.

curl -o extract.zip \
  "https://na1.fusion.foxit.com/pdf-services/api/documents/6a6c98375e2cab6bb50e740b/download" \
  -H "client_id: $FOXIT_CLIENT_ID" \
  -H "client_secret: $FOXIT_CLIENT_SECRET"

The response comes back as application/zip. Unzipping it gives you StructureInfo.json, the structured output, alongside a rendered PNG of each analyzed page (page_p0.pdf_0.png for a one-page file).

The whole flow in Python

Here is the complete script, reading credentials from the environment.

import os
import time
import zipfile
import json
import requests

BASE = "https://na1.fusion.foxit.com/pdf-services/api"
HEADERS = {
    "client_id": os.environ["FOXIT_CLIENT_ID"],
    "client_secret": os.environ["FOXIT_CLIENT_SECRET"],
}


def upload(path):
    with open(path, "rb") as fh:
        r = requests.post(f"{BASE}/documents/upload", headers=HEADERS, files={"file": fh})
    r.raise_for_status()
    return r.json()["documentId"]


def start_extract(document_id):
    r = requests.post(
        f"{BASE}/documents/pdf-structural-extract",
        headers={**HEADERS, "Content-Type": "application/json"},
        json={"documentId": document_id},
    )
    r.raise_for_status()
    return r.json()["taskId"]


def wait_for_task(task_id, interval=3, timeout=180):
    deadline = time.time() + timeout
    while time.time() < deadline:
        r = requests.get(f"{BASE}/tasks/{task_id}", headers=HEADERS)
        r.raise_for_status()
        body = r.json()
        if body["status"] == "COMPLETED":
            return body["resultDocumentId"]
        if body["status"] == "FAILED":
            raise RuntimeError(f"Extraction failed: {body.get('error')}")
        time.sleep(interval)
    raise TimeoutError(f"Task {task_id} unfinished after {timeout}s")


def download_zip(result_id, out="extract.zip"):
    r = requests.get(f"{BASE}/documents/{result_id}/download", headers=HEADERS)
    r.raise_for_status()
    with open(out, "wb") as fh:
        fh.write(r.content)
    return out


document_id = upload("invoice.pdf")
result_id = wait_for_task(start_extract(document_id))
archive = download_zip(result_id)

with zipfile.ZipFile(archive) as z:
    structure = json.loads(z.read("StructureInfo.json"))

analyze = structure["analyzeResult"]
print("schema", analyze["version"]["schema"], "pages", len(analyze["pages"]))

In this code, you upload the invoice and keep the returned documentId, hand that id to the extraction endpoint to get a taskId, then poll the task on a fixed interval until it reports COMPLETED and yields a resultDocumentId. The download call writes the ZIP to disk, and rather than unpacking it to a folder you read StructureInfo.json straight out of the archive. The top-level key is analyzeResult, which is where the schema version, the page list, and the element array all live.

A three-second interval with a 180-second ceiling is comfortable for single-page documents. Back off rather than tightening the loop if you process long files, since polling every second only burns request budget without finishing the job sooner.

Reading the structured JSON

analyzeResult holds four things worth knowing about, an info block of document metadata, a version block, the pages array, and the elements array that carries the content.

{
  "analyzeResult": {
    "version": {
      "schema": "1.0.7",
      "software": "FoxitPDFAnalyzer",
      "model": "idp-analysis"
    },
    "pages": [
      { "pageNumber": 1, "size": {}, "state": {} }
    ],
    "elements": [
      {
        "type": "title",
        "content": {
          "text": "INVOICE",
          "style": { "fontFamilyName": "Arial", "fontSize": 24.0 }
        },
        "region": {
          "page": 1,
          "boundingBox": [90, 71, 189, 71, 189, 99, 90, 99]
        },
        "score": 0.88,
        "id": "title1"
      }
    ]
  }
}

Each element follows the same shape. The type classifies it, and extracting this invoice returns title, head, paragraph, and table. The schema defines a wider set, adding image, headerFooter, form, hyperlink, footnote, sidebar, annotation, and formula, so branch on the types your documents actually produce rather than assuming only four exist. The text and its font styling sit under content, so you read content.text rather than a top-level text key. The region gives the one-based page plus a boundingBox, and that box is an eight-number polygon listing four corner pairs in order, not a four-number rectangle. Every element also carries a confidence score and a stable id such as title1 or paragraph2, and paragraphs additionally carry a paragraphOrder for reading sequence.

Tables are the interesting case. Rather than a headers array and a two-dimensional rows array, a table exposes a cell list under content.body:

{
  "type": "table",
  "content": {
    "body": {
      "rowCount": 4,
      "columnCount": 5,
      "cells": [
        {
          "paragraph": { "type": "paragraph", "content": { "text": "Description" } },
          "rowSpan": 1,
          "columnSpan": 1,
          "rowIndex": 0,
          "columnIndex": 1
        }
      ]
    }
  }
}

Each cell states its own rowIndex and columnIndex along with rowSpan and columnSpan, and its text lives at paragraph.content.text. That is more verbose than a plain grid, but it means merged cells stay describable and you never have to infer column membership from x-coordinates.

Turning the cell list into rows

Since the API hands you cells rather than rows, build the grid yourself once and work with it afterwards.

def table_to_grid(table):
    body = table["content"]["body"]
    grid = [["" for _ in range(body["columnCount"])] for _ in range(body["rowCount"])]
    for cell in body["cells"]:
        text = cell.get("paragraph", {}).get("content", {}).get("text", "")
        grid[cell["rowIndex"]][cell["columnIndex"]] = text.replace("\r\n", " ")
    return grid


tables = [e for e in analyze["elements"] if e["type"] == "table"]
header, *data_rows = table_to_grid(tables[0])

line_items = [dict(zip(header, row)) for row in data_rows]
text_blocks = {
    e["id"]: e["content"].get("text", "")
    for e in analyze["elements"]
    if e["type"] in ("title", "head", "paragraph")
}

print(header)
for item in line_items:
    print(item)

The code above allocates an empty grid from rowCount and columnCount, then drops each cell’s text into its stated position, which sidesteps any assumption about cell ordering in the array. Cell text can contain literal \r\n where a label wraps inside its column, so the replace call flattens that to a space before it reaches your data layer. Splitting the first row off as the header lets you zip each remaining row into a dictionary keyed by column name, and the same comprehension pattern collects the title, heading, and paragraph text by element id.

Running it against the sample invoice prints the real extraction:

['#', 'Description', 'Qty', 'Unit Price', 'Line Total']
{'#': '1', 'Description': 'API Integration Consulting', 'Qty': '10', 'Unit Price': '$ 150.00', 'Line Total': '$1,500.00'}
{'#': '2', 'Description': 'Compliance Review', 'Qty': '5', 'Unit Price': '$ 200.00', 'Line Total': '$1,000.00'}
{'#': '', 'Description': '', 'Qty': '', 'Unit Price': 'Subtotal:', 'Line Total': '$2,500.00'}

Two things in that output are worth designing around. The unit price arrives as the string "$ 150.00", so currency parsing is still your job, and the final row is a subtotal rather than a line item, which is a reminder that the analyzer reports table geometry rather than business meaning. Filter trailing rows on an empty # or Description before you treat them as products. If you want to inspect a full result without running the calls yourself, the StructureInfo_sample.json from this exact run is available to read.

Feeding the output into an agent or downstream workflow

Once the table is a list of dictionaries and the labeled text is keyed by id, the payload is already agent-ready.

agent_context = {
    "document": {"schema": analyze["version"]["schema"], "pages": len(analyze["pages"])},
    "text_blocks": text_blocks,
    "line_items": line_items,
}

Because every element also carries a region, you can layer spatial checks on top, such as confirming a table sits below a particular heading by comparing the y values in their bounding polygons before you trust the association. Foxit also publishes an MCP server for PDF Services, so the same operations are reachable from an agent that speaks the Model Context Protocol rather than raw HTTP.

Common mistakes

  • PascalCase auth headers : the keys are lowercase client_id and client_secret. ClientId and ClientSecret do not authenticate, and neither does an Authorization: Bearer header, which returns a 400.
  • Comparing status to a lowercase string : task statuses are uppercase, so test against COMPLETED and FAILED.
  • Downloading with the upload id : the download path takes the resultDocumentId from the completed task, not the documentId from the upload.
  • Reading elements from the root : the array is nested under analyzeResult, so structure["analyzeResult"]["elements"] is the path.
  • Expecting bbox or a rows array : positions arrive as region.boundingBox with eight numbers, and tables arrive as content.body.cells with index fields rather than a headers plus rows pair.
  • Treating the ZIP as the JSON : the download is an archive, and the structured output is the StructureInfo.json entry inside it.
  • Reusing a stale documentId : uploads are removed after 24 hours, and the cap on an upload is 100 MB.
  • Polling every second : that exhausts request budget without speeding anything up. A few seconds between checks is enough.

PDF data extraction FAQ

Extracting this invoice produces title, head, paragraph, and table elements. Every element carries type, content, region, score, and id, with tables adding a cell grid under content.body.

Raw text collapses a table into one string and loses row and column boundaries. Structural extraction reports rowCount, columnCount, and a cell list where each cell states its own rowIndex and columnIndex, so position is data rather than inference.

No. The extract call returns HTTP 202 with a taskId, and you poll GET /pdf-services/api/tasks/{taskId} until the status reaches COMPLETED, which is when resultDocumentId appears.

A ZIP archive holding StructureInfo.json plus a rendered PNG per analyzed page. The JSON is the structured output and the PNG is useful for visual spot checks.

The sample run reports analyzeResult.version.schema of 1.0.7, produced by FoxitPDFAnalyzer with the idp-analysis model. Read the version from the payload rather than hardcoding it, since it can move.

The task object surfaces the failure state in status as FAILED, so branch on that and log the whole task body when it happens.

Each upload and each task is independent, so run them concurrently and keep one taskId per document. The upload cap is 100 MB per file.

Get started with Foxit’s PDF Structural Extraction API

The pattern is four calls. Upload the PDF for a documentId, start pdf-structural-extract for a taskId, poll until COMPLETED for a resultDocumentId, then download the ZIP and read StructureInfo.json. From there analyzeResult.elements gives you typed titles, headings, paragraphs, and a table cell grid you can turn into dictionaries in a few lines.

Create a free developer account (no credit card) at account.foxit.com/site/sign-up, grab your Client ID and Secret from the APIs Dashboard, and run the script above against invoice_table_test.pdf to see the structured output for yourself.

eSignature API: A Developer’s Guide to Adding Signing to Your App

Illustration of code calling an esignature API alongside a Sign Document screen with a signature field

Adding a signing step to your app involves more than it first appears. Authentication, document preparation, session handling, and completion tracking all need to work together. This guide walks through a full esignature API integration with Foxit eSign, from your first authenticated request to a signed, webhook-confirmed document.

Adding a signing step to an existing app sounds straightforward until you try to implement it. Getting a document signed is a simple idea, but the actual API surface, how authentication works, how you mark up a PDF, and how you learn that signing finished all take longer to figure out than they should. This guide walks a complete Foxit eSign API integration from the first authenticated request through to a webhook-confirmed, digitally signed document. Comfort with REST APIs and bearer tokens is enough to follow along.

What an eSignature API is and how signing workflows work

An eSignature API is a REST interface that owns the document-signing lifecycle, covering preparation, delivery, the signing session, and the audit trail. You supply the document and the signers, and the API handles field rendering, identity capture, signature application, and tamper-evident recordkeeping, so none of that infrastructure is yours to build.

The distinction that matters most for app developers is redirect-based versus embedded signing. Redirect-based signing sends the user to a hosted URL to sign and returns them afterwards, which means they leave your product mid-task. Embedded signing renders the session inside your own application, typically in an iframe, so the user never changes context. If signing sits in the middle of an onboarding or checkout flow, embedded keeps that flow intact.

Foxit eSign supports both. The lifecycle you implement runs through five stages, starting with OAuth 2.0 authentication, then PDF preparation with Text Tags, a POST to /esign/api/v1/folders/createfolder that defines signers and mints a session, the signer completing the embedded session, and a folder_executed webhook confirming the document is final.

Diagram of the esignature API signing lifecycle from OAuth authentication through webhook confirmation

The five stages this guide implements in order. Each one maps to a section below.

Prerequisites

Signing up for eSign API access is self-serve on the Foxit API Platform — no sales ticket and no waiting for an administrator. The platform provisions a trial eSign account for you and manages its credentials, so the whole setup is a short walkthrough:

  1. Create and sign in to your Foxit API Platform account. You need an active Foxit API plan and a complete CAS profile — first name, last name, email address, and company name (a company address is required for eSign provisioning). Your eSign account is provisioned from those profile details, so complete them before you start.
  2. Open the Dashboard and select Get started with eSign in the Dashboard header. That takes you to the eSign activation page.
  3. Choose your document-storage region. The activation card preselects United States, with European Union and Canada as alternatives. Select Activate with [region] storage. Confirm the region before activating: once the remote account exists, the region is locked, and changing it later means submitting a case with Foxit Support.
  4. Wait for provisioning to complete. You may see “Creating your eSign account” and then “Confirming your eSign account” — the second is a reliability check that reconciles ambiguous creation results, so let it finish instead of retrying activation.
  5. Confirm eSign is ready. A successful activation displays “eSign is ready”, your eSign company number, and the selected region. The platform manages the API account and its credentials, and the same unified client_id/client_secret you use for PDF Services also authenticate eSign API, Document Generation, and Embed API — there is no separate eSign key pair to generate. If credential retrieval is still pending or failed, use Resume credentials or Retry credentials on the page; the first-call button stays disabled until credentials are ready.
  6. (Optional but worth it) Run the sample request on the activation page. It uses your profile’s name and email as the first signing party, sends a Base64-encoded one-page contract, adds Signer Name, Today’s Date, Signature, and Date Signed fields, creates a draft with sending disabled, and returns an embedded sending session URL — a quick end-to-end check that everything is wired up. The button switches to Running and then reports “Sample draft created. The embedded sending session is ready.”

Two things to know before you build: your provisioned account is an eSign Business trial that lasts 30 days and starts in TEST mode, so every envelope carries a watermark. Moving the account to Production mode is a Sales/Operations step performed in Foxit Monitor, not something the API can do.

Beyond the account, the rest of the prerequisites are lightweight:

  • Python 3.8+ with pip and a venv, for the webhook handler later in this guide.
  • Flask and requests for the sample code.
  • curl for the token exchange, and ngrok or any tunnel that gives your local webhook endpoint a public HTTPS URL.
  • A code editor, VS Code with the Python extension being a reasonable default alongside PyCharm.
  • A tagged sample PDF, so you do not have to author one. This guide uses agreement-signable.pdf, which already carries Text Tags for a single signer.

Scaffold the workspace in one shot:

mkdir foxit-esign && cd foxit-esign
python3 -m venv .venv && source .venv/bin/activate
pip install flask requests
export ESIGN_HOST="https://na1.foxitesign.foxit.com"export ESIGN_CLIENT_ID="your_api_key"
export ESIGN_CLIENT_SECRET="your_api_secret"
export WEBHOOK_SECRET="your_webhook_secret"

Step 1: Authenticate with the Foxit eSign API

The platform provisions your eSign account and manages its credentials, and that single pair is shared across eSign API, PDF Services, Document Generation, and Embed API. No bearer token, no client_credentials grant, no expires_in to watch.

The quickest way to prove a credential pair works is a minimal folder creation — the same /esign/api/v1/folders/createfolder endpoint Step 3 explains field by field:

curl -X POST "$ESIGN_HOST/esign/api/v1/folders/createfolder" \
  -H "client_id: $ESIGN_CLIENT_ID" \
  -H "client_secret: $ESIGN_CLIENT_SECRET" \
  -H "Content-Type: application/json" \
  -d '{
    "folderName": "Auth check",
    "inputType": "url",
    "fileUrls": ["https://github.com/lucienchemaly/foxit-demo-templates/raw/main/esign/agreement-signable.pdf"],
    "fileNames": ["agreement.pdf"],
    "parties": [
      {
        "firstName": "Jane",
        "lastName": "Smith",
        "emailId": "[email protected]",
        "permission": "FILL_FIELDS_AND_SIGN",
        "sequence": 1
      }
    ],
    "processTextTags": false,
    "processAcroFields": false,
    "createEmbeddedSigningSession": false,
    "createEmbeddedSendingSession": true,
    "sendNow": false
  }'

A valid credential pair returns JSON with a folder object carrying folderId and folderStatus (DRAFT here, since sendNow is false), so the response doubles as your auth check — a rejected pair comes back as an error before any folder exists. Send the same two headers on every subsequent request. There is nothing to mint or refresh like an OAuth token; if the platform ever flags the stored credentials as stale, refresh them from the activation page with Resume credentials or Retry credentials. And because eSign shares the PDF Services credential pair, the two integrations are interchangeable — one pair of credentials covers both.

Step 2: Prepare a document and define signature fields with Text Tags

Foxit eSign reads field definitions out of the PDF itself at upload time. You embed them as Text Tags, plain-text strings placed where each field belongs, and the API converts them into interactive fields on ingest.

The syntax is ${fieldtype:party_number:required:field_name:width}, where y marks a field required and n marks it optional, the party number maps to a signer’s sequence, and width is expressed as underscores.

${signfield:1:y:____}                 # required signature, party 1
${datefield:1:y::____}                # required date, party 1
${i:1:______}                         # initials field, party 1
${t:1:y:Full_Name:__________}         # required text field, party 1, named "Full_Name"
${signfield:2:y:____}                 # required signature, party 2

Each tag names a type, either in full or by its short alias. The supported set covers signfield (s), initialfield (i), datefield (d), textfield (t), textboxfield (tb), checkboxfield (c), radiobuttonfield (rb), securedfield (sc), attachmentfield (a), imagefield (img), accept (ab), decline (db), payfield (pf), and formulafield (ff). Author them in lowercase to match the documented syntax.

Express width as underscores and never as a literal space, because a space stops the tag from being recognized. A tag written ${s:1: } renders in the signing UI as plain ${s:1: } text with no field attached, while ${signfield:1:y:____} becomes a real signature field. That failure is silent, so the create call still succeeds and you only notice when a signer has nothing to sign.

Two preparation details save support tickets later. Foxit eSign converts tags to fields but does not delete the tag text, so set the tag’s text color to match the page background if you do not want signers reading raw ${...} strings. And paste tags through a plain-text editor first, because smart-quote autocorrect in Word or Google Docs silently swaps straight ASCII characters for typographic ones and tag parsing then fails without an error.

If you would rather not tag a document by hand for a first run, agreement-signable.pdf is already prepared and hosted at a public URL you can pass straight to the next step.

Step 3: Send the document and mint an embedded session

One call to /esign/api/v1/folders/createfolder submits the document, defines the signers, and, when you ask for it, returns a ready-to-render signing URL. Foxit calls the signing container a folder rather than an envelope.

{
  "folderName": "Customer Agreement - Acme Corp",
  "fileUrls": ["https://github.com/lucienchemaly/foxit-demo-templates/raw/main/esign/agreement-signable.pdf"],
  "fileNames": ["agreement.pdf"],
  "parties": [
    {
      "firstName": "Jane",
      "lastName": "Smith",
      "emailId": "[email protected]",
      "permission": "FILL_FIELDS_AND_SIGN",
      "sequence": 1
    }
  ],
  "processTextTags": true,
  "createEmbeddedSigningSession": true,
  "embeddedSignersEmailIds": ["[email protected]"],
  "sendNow": false
}

In this body you point the API at the tagged PDF with fileUrls and give it a display name through fileNames, define the single signer in parties with firstName, lastName, emailId, the FILL_FIELDS_AND_SIGN permission and a signing sequence, then set processTextTags to true so the embedded tags become real fields. Asking for createEmbeddedSigningSession and naming the signer in embeddedSignersEmailIds returns a session URL in the same response, and sendNow set to false keeps Foxit from emailing an invitation, which is what you want while testing.

One nuance to expect here. sendNow: false on its own produces a DRAFT folder, but pairing it with createEmbeddedSigningSession returns folderStatus of SHARED, since the folder has to be live for the session URL to open. No email goes out either way, so the flag still suppresses the invitation, and the status you see just reflects that the document is now signable. To attach the file as bytes instead of a URL, send base64FileString as an array together with inputType set to "base64".

The response nests the folder identifier at folder.folderId and carries an embeddedSigningSessions array. Each entry holds emailIdOfSigner, the raw embeddedToken, and the renderable embeddedSessionURL, which follows this shape:

https://{HOST_NAME}/embedded/embeddedsign?eetid={URL-ENCODED-EMBEDDED-TOKEN}

Three behaviors are worth designing around before you ship. Omitting embeddedSignersEmailIds returns email id of embedded signer(s) not submitted, so always list embedded signers explicitly. Every party number used in a Text Tag needs a matching entry in parties, because a sendNow: true create still reports success while silently dropping the fields of a party that is not listed, which means a mandatory signature is never routed. And for a multi-party document where everyone signs in your app, createEmbeddedSigningSessionForAllParties set to true covers all recipients rather than naming them one by one.

To dispatch a draft later, POST to /api/folders/sendDraftFolder.

Folder state moves through DRAFT, SHARED, COMPLETED, and finally EXECUTED once the digital signature is applied. A run of this guide’s single-signer flow produced exactly that path, with the activity log labelling the creation event CREATED and then recording Envelope viewed, Jane Smith signed this folder at COMPLETED, and Document(s) successfully executed at EXECUTED. PARTIALLY SIGNED appears only when a folder has more than one party and some but not all of them have signed, so you will not see it on a single-signer document.

Step 4: Render the signing session in your app

Load the embeddedSessionURL in an iframe. The sandbox attribute needs a specific minimum set of permissions, and trimming it is a common way to break the signing UI with no visible error.

<iframe
  id="signing-session"
  src="PASTE_EMBEDDED_SESSION_URL_HERE"
  width="100%"
  height="780px"
  style="border: none;"
  sandbox="allow-scripts allow-same-origin allow-forms allow-popups allow-top-navigation"
></iframe>

In this markup the src receives the embeddedSessionURL from the createfolder response, and the five sandbox permissions are the minimum the signing UI needs. Removing allow-popups or allow-top-navigation breaks the flow in ways that surface no obvious error, so keep all five unless you have tested a reduced set end to end. Session URLs are short-lived, so generate one when the user is ready to sign rather than caching it, and request a fresh one per signer through /esign/api/v1/embedded/regenerateEmbeddedSigningSession when a session goes stale.

To check the flow before wiring it into your own UI, download the ready-to-run iFrame test page, open it in a browser, paste your embeddedSessionURL into the input, and load it. A correctly tagged document renders with active signing controls.

Embedded esignature API signing session showing four required fields still outlined and unfilled

A real session opened from the embeddedSessionURL this guide’s request returns. All four tags in the sample became required fields, which is what the counter is reporting. The raw tag text still shows through each box, which is exactly why you color it to match the page background before shipping.

Step 5: Confirm completion with webhooks

Polling for completion wastes requests and adds latency. Register a webhook instead and Foxit eSign posts to your endpoint as each signing event happens.

Registration lives on the eSign portal’s API settings page at /consumer/consumerdetails, under Configure Webhooks, where you set the callback URL, a webhook secret, and the events you want. That page is visible only to the account owner, so an admin-level user will not find it. Your endpoint has to be reachable over public HTTPS.

Foxit eSign webhook configuration screen with callback URL, webhook secret, and signing event checkboxes

The owner-only webhook settings. The event checkboxes control which callbacks reach your endpoint.

The events available are folder_sent, folder_viewed, folder_signed, folder_cancelled, folder_executed, folder_deleted, folder_completed, folder_assigned, and folder_access_code_failure. In practice folder_viewed, folder_signed, folder_completed, and folder_executed are the ones that fire on an API-dispatched folder.

The event to build on is folder_executed. folder_completed fires once every party has signed, but folder_executed fires after Foxit applies the digital signature and locks the audit trail, so it is the point at which a download gives you the final document.

Foxit signs every callback. It delivers the POST as <your-url>?signature=<base64>, where the signature is the base64 of an HMAC-SHA-256 over the raw request body keyed with your webhook secret. Verify it against the unparsed bytes, since re-serializing the JSON changes whitespace or key order and breaks the comparison.

import base64
import hashlib
import hmac
import os

from flask import Flask, request, jsonify

app = Flask(__name__)
WEBHOOK_SECRET = os.environ["WEBHOOK_SECRET"]


def verify_signature(raw_body: bytes, signature: str) -> bool:
    expected = base64.b64encode(
        hmac.new(WEBHOOK_SECRET.encode(), raw_body, hashlib.sha256).digest()
    ).decode()
    return hmac.compare_digest(expected, signature)


@app.route("/webhooks/esign", methods=["POST"])
def handle_esign_event():
    raw_body = request.get_data()
    if not verify_signature(raw_body, request.args.get("signature", "")):
        return jsonify({"error": "invalid signature"}), 403

    payload = request.get_json(silent=True) or {}
    event_name = payload.get("event_name")
    folder = payload.get("data", {}).get("folder", {})

    if event_name == "folder_executed":
        # The document is final here. Archive it, update your record, notify the user.
        app.logger.info("Signing finished for folder %s", folder.get("folderId"))

    return jsonify({"status": "received"}), 200

In this handler you read the unparsed body first, recompute the HMAC over those exact bytes with your webhook secret, and compare it against the signature query parameter using hmac.compare_digest so the check is not timing-dependent. A mismatch returns 403 before any business logic runs, which stops a spoofed POST to your public URL from triggging an archive. Only after the signature passes do you parse the JSON, read event_name, and branch on folder_executed to run downstream work, returning 200 so Foxit records the delivery as successful. A ready-to-run version of this receiver lives at webhook_receiver.py in the demo repo.

Test the whole path against a draft. Create the folder with sendNow set to false, dispatch it, sign in the embedded session, and watch the events arrive. Once the signer finishes, the session redirects to a result URL carrying event=signing_success, and the document reaches EXECUTED within a few seconds.

Completed embedded signing session with name, initials, date, and signature filled in and Finish enabled

The session once every field is filled. The counter reads zero and Finish activates, which is the state that produces folder_completed and then folder_executed.

Step 6: Retrieve the executed document and its audit trail

With folder_executed in hand, pull the final PDF. The download takes the folder id as a query parameter.

curl -o executed.pdf \
  "$ESIGN_HOST/api/folders/download?folderId=35117696" \
  -H "Authorization: Bearer $ACCESS_TOKEN"

The response streams the executed PDF, which arrives with a content type of application/octet-stream rather than application/pdf, so write the bytes to a file rather than sniffing the header. The returned document carries the filled field values and the signature certificate, so a text extraction of it contains the signer’s typed name, the date, and the per-signer Signer ID recorded on the certificate page.

For the audit trail itself, GET /api/folders/viewActivityHistory?folderId={id} returns a details object holding the folder metadata plus an activities array, where each entry carries an activity description and the folderStatus at that moment. That endpoint is GET-only and only returns data once the folder has been shared, so a pure DRAFT folder reports nothing useful.

The signature certificate attached to a document once folder_executed has fired. Each signer’s adopted signature sits beside the identity record Foxit captured, which is the tamper-evident audit trail you are archiving alongside the PDF.

Common mistakes

  • Dropping the /api/ prefix : every eSign path sits under /api/, so it is /api/folders/createfolder, not /folders/createfolder.
  • snake_case request fields : the body is camelCase. folderName, fileUrls, emailId, and sendNow work, while folder_name, document_url, email, and send_now do not.
  • Omitting processTextTags : without it the tags stay inert text, the signer sees raw ${...} strings, and they can finish without signing anything.
  • A tag party with no matching parties entry : the create returns success and that party’s fields disappear silently, so their signature is never collected.
  • Skipping signature verification : a public webhook URL that acts on any POST is a spoofing target. Verify the HMAC before you touch the payload.
  • Archiving on folder_completed : that fires before the digital signature is applied. Wait for folder_executed.
  • Caching an embeddedSessionURL : sessions expire. Mint one when the signer is ready, and regenerate when needed.
  • Trimming the iframe sandbox list : removing allow-popups or allow-top-navigation breaks signing with no error message.
  • Smart quotes or a space inside a tag : both stop tag recognition. Use straight ASCII and underscores.
  • Reusing PDF Services credentials : eSign has its own portal, host, and key pair.

eSignature API FAQ

A REST interface that handles the document-signing lifecycle, covering preparation, delivery, the signing session, and the audit trail, so your app makes calls rather than building signing infrastructure.

Embedded signing renders the session inside your own application instead of redirecting to a hosted page, so the signer finishes without leaving your product. That matters most when signing sits inside a flow you do not want to interrupt, like onboarding or checkout.

The party number in a tag maps to a signer’s sequence in the parties array, so ${s:1: } is signed by the party with sequence: 1. Keeping those aligned is the difference between fields routing correctly and vanishing silently.

folder_completed fires when all parties have signed. folder_executed fires after Foxit applies the digital signature and locks the audit trail, which is why downstream archiving should trigger on folder_executed.

Recompute a base64 HMAC-SHA-256 of the raw request body using your webhook secret and compare it with the signature query parameter. Verify against raw bytes, never re-serialized JSON.

Yes. Pass additional entries in fileUrls with matching fileNames. Each document carries its own Text Tags, and party assignments stay consistent across the folder.

No. The free tier gives you credentials and lets you run the full flow, and no credit card is required to create the account.

Yes. Create it with sendNow set to false for a DRAFT folder, inspect it in the eSign dashboard, then dispatch with /api/folders/sendDraftFolder.

Wrapping up

That is the full path for an eSignature API integration. Authenticate with the client_credentials grant, prepare a PDF with Text Tags, create the folder with processTextTags and an embedded session, render the returned embeddedSessionURL in an iframe, and act on a signature-verified folder_executed webhook.

The same shape extends to multi-signer approval chains, CRM-triggered signing when a deal closes, and template-based documents where fields arrive pre-filled from your own data. Those build on the pieces already in place rather than replacing them.

Ready to build? Create a free account (no credit card required) at account.foxit.com/site/sign-up, generate your API Key and Secret from the API tab, and run the token call above against agreement-signable.pdf to see a session URL come back.

Build a CRM-Triggered PDF Generation and eSign Workflow in Power Automate with Foxit’s REST APIs

Diagram of an api workflow automation pipeline connecting a CRM trigger, Foxit document generation, eSign, and cloud archive

This guide shows how to trigger a Word-to-PDF contract from a closed CRM deal, route it for signature through Foxit’s eSign API, and archive the signed copy automatically, using nothing but HTTP actions and a webhook.

Most Power Automate document tutorials stop at a SharePoint file move or an AI Builder extraction, and the ones that touch signatures assume a native connector that does not exist for a headless, API-first pipeline. The gap is the full chain, where a CRM deal closes, a contract is generated from a Word template, the PDF is routed for signature, and the signed copy is archived, with no manual export and no polling. This article builds that pipeline in Power Automate using the HTTP action to call Foxit’s REST endpoints directly, which is the same pattern that carries over to n8n, Zapier, or any orchestrator that can make an HTTP request.

Foxit exposes two REST APIs that chain cleanly for this. The Document Generation API takes a base64-encoded Word template plus a JSON payload and returns a base64 PDF, and the eSign API handles the signing lifecycle behind an OAuth2 token. The base64 PDF from generation drops straight into the eSign upload call, so the handoff stays inside one flow with no SDK to install and no desktop agent. This Power Automate Foxit API integration walks the two hosts and their two auth models, the document generation call, the send-for-signature call with embedded signature fields, and a second flow that receives Foxit’s webhook and archives the executed document.

Prerequisites

Power Automate runs in the browser, so there is no local runtime to install. What you need are the right accounts and one sample file.

  • A Power Automate account with the HTTP action : the HTTP action is a premium connector, so you need a per-user or per-flow premium license. This is the one paid dependency in the tutorial. On the free Microsoft 365 tier you will hit a wall at the first HTTP action, so confirm the plan before you start.

  • A Foxit Developer account : create one at account.foxit.com/site/sign-up and activate the free Developer plan, which includes 500 credits per year with no credit card. From the APIs Dashboard, copy the Document Generation Client ID and Secret, and separately the eSign Client ID and Secret. These are two different credential pairs for two different APIs.

  • A CRM that can trigger a flow : this article uses the Salesforce connector as the example. Any CRM with a Power Automate connector, or any system that can POST to a webhook URL, works the same way.

  • The sample contract template : download contract_signing.docx so you do not have to author one. It already carries both Document Generation merge tags and eSign signature tags, which is what makes the two-API handoff work.

  • A REST client : Postman or curl, to test each Foxit call in isolation before wiring it into a flow.

Store both Foxit credential pairs in the Power Automate secure store or as environment variables in your solution, never pasted as literals into an action, so they do not travel in exported flow definitions.

Foxit APIs Dashboard displaying the base URL, Client ID, and Client Secret for an API application

The APIs Dashboard is where you retrieve the Client ID and Secret. Document Generation and eSign each have their own pair.

How the Power Automate Foxit API Integration Works: Two APIs, Two Hosts, Two Flows

The pipeline has four stages. A CRM Closed Won event triggers document generation, the generated PDF is sent for signature, and the executed document is archived once every party has signed.

Salesforce: Opportunity → Closed Won
        │
        ▼
HTTP: POST GenerateDocumentBase64   (na1.fusion.foxit.com)   → base64 PDF
        │
        ▼
HTTP: POST createfolder             (na1.foxitesign.foxit.com) → folderId, email sent
        │
   … signer signs (async) …
        ▼
Flow 2: When a HTTP request is received  <- Foxit webhook (folder_executed)
        │
        ▼
HTTP: GET download → OneDrive: Create file

Power Automate owns orchestration and control flow. Foxit owns document rendering and the signature lifecycle. The single most common mistake in this integration is treating the two Foxit products as one, so keep them separate from the start. Document Generation runs on https://na1.fusion.foxit.com and authenticates with lowercase client_id and client_secret request headers. eSign runs on https://na1.foxitesign.foxit.com and authenticates with an OAuth 2.0 bearer token obtained from its own credential pair. They do not share credentials or a portal.

The work also splits into two flows for a reason. Generation and sending are synchronous, so they belong in one flow that runs when the deal closes. Signing completes minutes or days later, so a second flow triggered by Foxit’s webhook handles the archive step. That separation is what removes any polling from the design.

Here is the main flow built in the Power Automate designer, with the four HTTP actions chained after the trigger.

Power Automate designer showing the api workflow automation sequence from trigger through Create signing folder

The four HTTP actions in order. This build uses a manual trigger so the flow runs on demand while you test; in production the Salesforce trigger from Step 1 takes its place as the entry point, and nothing downstream changes.

Step 1: CRM Trigger, Firing the Flow on a Closed Deal

Create an automated cloud flow and choose the Salesforce trigger for a created or modified record, pointing it at the Opportunity object. Add a condition so the flow only proceeds when the Stage equals Closed Won, which keeps every mid-pipeline edit from generating a contract.

Map the CRM fields the contract needs. For the sample template, that is the client name, the contract date, the deal value, and the signer’s name and email. Read them from the trigger output with expressions like triggerOutputs()?['body/Account_Name'] and store each in a variable or reference it inline, so the next two steps can assemble their payloads cleanly.

This step is swappable. Any CRM with a Power Automate connector, or any system that can POST JSON to a Power Automate HTTP-request trigger, drops in here without touching the Foxit calls that follow. If you use HubSpot or Dynamics 365, only the trigger and field paths change, so the rest of this tutorial stays identical.

Step 2: Generate the Contract PDF with GenerateDocumentBase64

The contract_signing.docx template drives this step. It contains Document Generation merge tags for the scalar fields and a table loop for line items, using Foxit’s {{ }} syntax:

{{clientName}}
{{contractDate \@ MM/dd/yyyy}}
{{dealValue \# "$#,##0.00"}}

{{TableStart:lineItems}} {{description}}   {{amount}} {{TableEnd:lineItems}}

The \@ switch formats a date and the \# switch formats a currency value, so you can pass a raw ISO date and a plain number and let the template render them. The {{TableStart:lineItems}} and {{TableEnd:lineItems}} tokens sit in a single Word table row and repeat that row for each object in the lineItems array.

Before the flow can send the template, it needs the file as a base64 string. The build here gets it with an HTTP GET action named Get template, pointed at the raw contract_signing.docx URL, and base64-encodes the response with the base64() expression. For a template you maintain yourself, store it in OneDrive or SharePoint and use a Get file content action instead of the GET. Then add a second HTTP action named Generate PDF, set to POST https://na1.fusion.foxit.com/document-generation/api/GenerateDocumentBase64 with client_id and client_secret headers and this JSON body:

{
  "base64FileString": "@{base64(body('Get_template'))}",
  "documentValues": {
    "clientName": "@{variables('clientName')}",
    "contractDate": "2026-07-17",
    "dealValue": 48500,
    "lineItems": [
      { "description": "Platform license (annual)", "amount": "$36,000.00" },
      { "description": "Onboarding and training", "amount": "$12,500.00" }
    ]
  },
  "outputFormat": "pdf"
}

In this code, you send the base64-encoded template as base64FileString, pass the CRM-sourced fields as documentValues whose keys match the template tags exactly, and request a PDF with outputFormat set to the lowercase "pdf". The keys clientName, dealValue, and the lineItems array with its description and amount fields have to match the tag names in the Word file, since a mismatch leaves the tag unrendered rather than raising an error. The call is synchronous and returns HTTP 200 with a JSON response containing message, fileExtension, and base64FileString, where base64FileString is the rendered PDF as a base64 string.

The call is synchronous, so there is no task to poll and no status to check, and the generated contract is available immediately. Reference the rendered PDF in the next step directly with the expression body('Generate_PDF')?['base64FileString'], or add a Parse JSON action first if you prefer typed outputs. When you need the raw bytes rather than the string, for archiving or a file action, convert with the base64ToBinary() expression, one of the Workflow Definition Language conversion functions.

One limit to plan for is the upload size. Document Generation rejects .docx payloads larger than 4 MB after base64 encoding, which is roughly a 3 MB raw file, and the limit is not surfaced in a friendly error. If you hit it, compress images through Word’s Picture Format tools, drop embedded fonts and OLE objects, and split oversized templates. The sample here is about 37 KB, so first runs stay well clear of the cap. For template authoring detail and common payload errors, Foxit’s Document Generation API quickstart is the reference.

Generated Service Agreement PDF with client details, line-item table, and embedded signature fields

The generated contract. The signature and date fields at the bottom come from the eSign Text Tags embedded in the same template, ready for Step 3.

Step 3: Send for Signature with OAuth2 and createfolder

The eSign API needs a bearer token first. Add an HTTP action that POSTs to https://na1.foxitesign.foxit.com/api/oauth2/access_token with content type application/x-www-form-urlencoded and this body:

grant_type=client_credentials&client_id=YOUR_ESIGN_CLIENT_ID&client_secret=YOUR_ESIGN_CLIENT_SECRET&scope=read-write

Name this action Get eSign token. Its JSON response carries access_token, token_type set to bearer, expires_in, and instance_url, and you reference the token in the next call as body('Get_eSign_token')?['access_token']. This is the client-credentials grant, which fits a server-to-server flow where no human is present to log in.

The signature fields are already defined in the template. contract_signing.docx carries two eSign Text Tags, ${signfield:1:y} and ${datefield:1:y}, which follow the ${fieldtype:party:mandatory} syntax. Here both are mandatory fields assigned to party 1, the client who signs. Because the tags live in the Word file, the PDF that Document Generation produced in Step 2 already has a signature and date field in place, so there is no manual field placement after generation. Keep two rules in mind when you author your own tags. Replace any space inside a tag with an underscore, since a literal space breaks tag recognition, and set the tag text color to match the document background so the tokens do not show in the final PDF.

Now add the send action, an HTTP action named Create signing folder, set to POST https://na1.foxitesign.foxit.com/api/folders/createfolder with an Authorization: Bearer @{body('Get_eSign_token')?['access_token']} header and this body:

{
  "folderName": "Acme Corp Contract",
  "inputType": "base64",
  "base64FileString": ["@{body('Generate_PDF')?['base64FileString']}"],
  "fileNames": ["contract_signing.pdf"],
  "parties": [
    {
      "firstName": "Jordan",
      "lastName": "Lee",
      "emailId": "[email protected]",
      "permission": "FILL_FIELDS_AND_SIGN",
      "sequence": 1
    }
  ],
  "processTextTags": true,
  "sendNow": true
}

In this code, you attach the Step 2 PDF by passing it as the single element of the base64FileString array together with inputType set to "base64", which is the pairing the API requires for base64 uploads. The parties array names the signer with firstName, lastName, emailId, and a permission of FILL_FIELDS_AND_SIGN, and its sequence of 1 matches the party number in the ${signfield:1:y} tag. Setting processTextTags to true is what converts those embedded tags into real, interactive fields, and sendNow set to true dispatches the signing invitation immediately. The response returns result as success and nests the identifier at folder.folderId, which you store for the archive flow. Foxit calls this container a folder rather than an envelope.

Create signing folder HTTP action configured with the eSign bearer token and base64 PDF body

The Create signing folder action. The access_token and base64FileString chips are dynamic references to the two prior HTTP actions, so the eSign token and the generated PDF flow straight into this call with no copy-paste.

Two behaviors are worth handling before you ship. Every party number referenced by a Text Tag must have a matching entry in the parties array, because if a tag points at a party that is not listed, a sendNow: true create still returns success but silently drops that party’s fields, so their signature is never routed. If you need a human to approve the contract before it leaves, set sendNow to false to create a draft without emailing anyone, then dispatch it later with POST https://na1.foxitesign.foxit.com/api/folders/sendDraftFolder.

Foxit eSign signing view showing an active click-to-sign field on the generated contract

What the recipient sees after createfolder sends the invitation. The signature field is the one defined by the ${signfield:1:y} tag.

Step 4: Receive the Webhook and Archive the Signed Document

Create a second automated flow using the When a HTTP request is received trigger. Saving the flow generates a callback URL. Register that URL as the webhook endpoint on the eSign portal’s API settings page, which is owner-only, and select the signing events you want delivered.

Archive on the right event. The folder status moves through DRAFT, then SHARED, then PARTIALLY SIGNED, then COMPLETED, then EXECUTED. Trigger the archive on folder_executed, not folder_completed. The folder_completed event fires when all signatures are in but before the digital signature has been applied to the PDF, whereas folder_executed guarantees the file you download is the final, digitally signed document.

Verify each callback before acting on it. Foxit delivers every webhook as POST <your-url>?signature=<base64>, where the signature is the base64 of an HMAC-SHA-256 of the raw request body keyed with your webhook secret. Power Automate’s expression language has no native HMAC function, so there are two practical paths. The stronger one calls a small Azure Function or an Office Script that recomputes the HMAC over the raw body and returns a match boolean, which the flow checks in a Condition. The lighter one restricts the trigger to your tenant and treats the webhook secret as a shared value checked in a Condition, which is simpler but weaker. Choose based on how exposed the endpoint is.

Once a callback is verified and the event is folder_executed, parse folderId from the payload and download the signed file. Add an HTTP action with the bearer token set to GET https://na1.foxitesign.foxit.com/api/folders/download?folderId=@{triggerBody()?['folderId']}, which returns the executed PDF as application/pdf. Pass the response body to the OneDrive for Business Create file action, naming the destination folder by deal name or date so contracts stay retrievable.

The owner-only webhook settings, where you paste the Power Automate callback URL and pick the events to receive.

If you cannot expose a public endpoint, poll instead. GET https://na1.foxitesign.foxit.com/api/folders/viewActivityHistory?folderId={id} returns the audit trail, with actions such as Created, Invitation Sent, Opened, Viewed, Signed, and Folder Executed. This endpoint is GET-only and only returns data once the folder has been shared or sent, so run it on a schedule from a separate flow.

Common Mistakes

Most failures in this pipeline come from a small set of recurring errors. Check these first when something does not work.

  • Using one credential pair or one token for both APIs : Document Generation uses client_id and client_secret headers on na1.fusion.foxit.com, while eSign uses a bearer token on na1.foxitesign.foxit.com. They are separate.
  • Sending the eSign PDF without inputType : the base64 upload needs inputType set to "base64" alongside the base64FileString array, or the API returns fileUrls or base64FileString cannot be empty.
  • Omitting processTextTags : without processTextTags set to true, the tags stay as inert text and the signer can finish without signing.
  • A tag party number with no matching party : if ${signfield:2:y} appears but the parties array has no party 2, the send succeeds and that party’s fields vanish silently.
  • Missing a party email field : each party needs emailId, not email. The wrong key returns email id of party cannot be empty.
  • Archiving on folder_completed : download on folder_executed instead, or you may pull a PDF before the digital signature is applied.
  • A stray space or smart quote in a tag : a literal space inside ${...} or a curly quote from Word autocorrect breaks tag recognition. Use straight quotes and underscores.

API Workflow Automation FAQ

Yes. Any platform that can make HTTP requests and receive a webhook works, since the Foxit calls are identical. Only the orchestration layer changes.

No. The free Developer plan gives 500 credits per year with instant activation and no credit card. The paid dependency here is the Power Automate premium license for the HTTP action.

No. Each API has its own Client ID and Secret, so store two credential pairs and use each on its own host.

Yes. The createfolder call accepts a base64 PDF directly, or a public file URL through fileUrls and fileNames, so the generation step is optional if you already have the document.

Add party-2 Text Tags to the template, such as ${signfield:2:y}, and a matching party-2 entry to the parties array. Keep the party numbers in the tags and the array aligned.

Next Step

The fastest way to confirm the pieces before building the flow is to run one call by hand. Create your free Foxit Developer account, pull your Document Generation and eSign Client IDs and Secrets from the APIs Dashboard, and run a single GenerateDocumentBase64 call against contract_signing.docx in a REST client. Once it returns a base64 PDF, you know the credentials and payload are right, and the rest of the Power Automate Foxit API integration is just wiring the same calls into actions. Create your free account to get started.

Build a CRM-Triggered Document Pipeline with Foxit APIs in n8n, with a Vercel Workflows Alternative

Diagram of a CRM-triggered document pipeline: contract generation, eSign, and cloud archive

This tutorial shows how to wire a CRM webhook, the Foxit Document Generation API, and the Foxit eSign API into one durable pipeline, without manual copying of data between tools. You’ll build it twice: once visually in n8n, and once as a resumable serverless function in Vercel Workflows.

Most document automation tutorials stop at the happy path. Generate a PDF, send it for signature, done. What they skip is the plumbing, which is where a real pipeline lives or dies. How does a CRM event actually hand off to a document API? How do you pass a signed document downstream without a polling loop? What happens when one step fails halfway through and you do not want to re-run the steps that already succeeded?

If you have tried to stitch a multi-step document workflow across a CRM, a generator, and an eSign service, you know exactly where the seams are. The deal data lives in the CRM, the template lives somewhere else, the signature lives in a third system, and a person ends up copying state between them.

This tutorial closes those seams. A CRM deal-closed event fires a webhook, a workflow renders a contract from a Word template, routes it for signature, waits for completion, and archives the signed file, with no human in the loop. You will build it first in n8n, a source-available workflow tool, and then rebuild the same pipeline as a durable function with Vercel Workflows. Both use the Foxit Document Generation API and the Foxit eSign API with real endpoints and real request shapes.

Durability is the theme. A production pipeline must survive a step failure, retry without repeating completed work, and hold state across an asynchronous signing wait that can last minutes or days.

Architecture Overview: Two Ways to Build the Same Pipeline

The pipeline is four stages, and each hands its output directly to the next.

  1. Trigger. A CRM webhook (a HubSpot deal-stage change in this example) delivers the deal data.

  2. Generate. The workflow renders a contract PDF from a Word template through Document Generation, a synchronous REST call with no SDK to install.

  3. Sign. The rendered PDF goes straight to the eSign API, which creates a signing folder and routes it to the signer.

  4. Archive. When signing completes, the workflow downloads the executed PDF and writes it to storage.

In n8n, that maps to a Webhook node, an HTTP Request node for generation, an HTTP Request node for signing, a second Webhook node that waits for the eSign completion callback, and a final archive step.

n8n canvas for Foxit API pipeline

The core chain in the local n8n editor. Each stage is a plain HTTP Request node, and the base64 PDF from the generation node flows straight into the eSign create-folder node.

The same pipeline can be written as code with Vercel Workflows, where the whole flow is one durable function, each Foxit call is a retrying step, and the eSign wait is a hook that resumes when the completion webhook arrives. Reach for n8n when you want visual orchestration and fast prototyping, and for Vercel Workflows when you want code-native durability and already run on Vercel. Section 7 builds that version.

One detail shapes every call. The two APIs live on two hosts and authenticate differently. Document Generation runs on https://na1.fusion.foxit.com and takes client_id and client_secret request headers. eSign runs on https://na1.foxitesign.foxit.com and takes an OAuth2 bearer token obtained from /api/oauth2/access_token.

Step 1: Credentials and Template Prep

The whole build runs on free infrastructure. Run n8n as the self-hosted Community Edition in Docker, and use Foxit’s free Developer plan. There is no n8n Cloud plan and no paid Foxit tier involved.

Register a Foxit account at account.foxit.com/site/sign-up, verify your email, and activate the free Developer plan. From the APIs Dashboard, copy your Document Generation Client ID and Client Secret. The eSign API is provisioned separately, so once eSign is active on your account, retrieve its API Key and API Secret as a distinct pair. You end up with two credential sets, and they are not interchangeable.

Store the Document Generation pair once in n8n’s credential store rather than pasting keys into nodes. Because Foxit needs two custom headers, use the Custom Auth credential type, which is the same type Foxit’s published template uses. Create a credential with this JSON:

{
  "headers": {
    "client_id": "YOUR_FOXIT_CLIENT_ID",
    "client_secret": "YOUR_FOXIT_CLIENT_SECRET"
  }
}
n8n Custom Auth credential for Foxit API

The Custom Auth credential holds both Foxit headers in one place, so your keys never appear in a node field or an exported workflow.

Now the template. Because the goal is a signing-ready contract, one Word file carries two families of tags. Document Generation merge tags use double braces for data, and eSign Text Tags use dollar-brace syntax for signature fields. Download the ready-to-use sample, contract_signing.docx. It carries {{clientName}}, {{dealValue \# "$#,##0.00"}}, {{contractDate \@ MM/dd/yyyy}}, a {{TableStart:lineItems}} / {{TableEnd:lineItems}} loop for line items, and the eSign Text Tags ${signfield:1:y} and ${datefield:1:y}. The Text Tag format is ${fieldtype:party:mandatory}, so ${signfield:1:y} is a required signature for party 1. Keep the raw file under about 3 MB, for the reason covered in Step 2.

Step 2: CRM Webhook to DocGen, Generating the Contract

Start the workflow with a Webhook node, which is how a CRM will call your pipeline when a deal closes. Set the method to POST and give it a path such as deal-closed. n8n gives the node a Test URL for building and a Production URL for live traffic.

The Webhook node’s Test URL. Click “Listen for test event” to capture a sample request while building.

Because a local build has no real CRM pointed at it, fire the webhook yourself. Click Listen for test event, copy the Test URL, and send a sample deal payload with cURL:

curl -X POST "http://localhost:5678/webhook-test/deal-closed" \
  -H "Content-Type: application/json" \
  -d '{ "clientName": "Acme Robotics", "dealValue": 48500, "contractDate": "2026-07-02" }'

In this request, you POST a small JSON object standing in for the CRM’s deal-closed payload. The Webhook node captures it and its fields become available to later nodes as {{ $json.body.clientName }}. In production, register the node’s Production URL in your CRM’s webhook settings, and expose your local instance with a tunnel such as ngrok if you are testing before deploying.

Add an HTTP Request node for generation. Set the method to POST, the URL to https://na1.fusion.foxit.com/document-generation/api/GenerateDocumentBase64, attach the Custom Auth credential, turn on Send Body, choose JSON, and send:

{
  "base64FileString": "<base64-encoded contract_signing.docx>",
  "documentValues": {
    "clientName": "{{ $json.body.clientName }}",
    "dealValue": "{{ $json.body.dealValue }}",
    "contractDate": "{{ $json.body.contractDate }}",
    "lineItems": [
      { "description": "Platform license (annual)", "amount": "$36,000.00" },
      { "description": "Premium support", "amount": "$5,000.00" }
    ]
  },
  "outputFormat": "pdf"
}

The HTTP Request config panel. Set the method and URL, attach the credential, and turn on Send Body for the JSON payload.

In this body, base64FileString is the template encoded as base64, documentValues is a flat object whose keys match the template tags exactly and whose values come from the webhook payload, and outputFormat is the lowercase "pdf". The keys must match the tag names character for character, because a mismatch does not raise an error. The call returns HTTP 200 and that field simply renders blank, so confirm names with POST /document-generation/api/AnalyzeDocumentBase64 if a field comes back empty. One more limit to plan for, since it surfaces as a bare HTTP 500 rather than a friendly message. Document Generation rejects a .docx payload larger than 4 MB after base64 encoding, which is roughly a 3 MB raw file, so compress images and drop embedded fonts if the template is heavy.

The endpoint is synchronous. The response carries message, fileExtension, and base64FileString, where the last holds the rendered PDF as base64. There is no job id and no polling, which keeps the graph linear. The generated contract has the data merged in and the Text Tags still present in the text, ready for signing.

Generated Foxit contract PDF with signature tags

The generated PDF. The merge tags are filled and the ${signfield:1:y} and ${datefield:1:y} Text Tags survive into the output, where eSign will convert them into fields.

Step 3: Routing the PDF into eSign

The eSign leg starts by turning your eSign credentials into a bearer token. Add an HTTP Request node that POSTs to https://na1.foxitesign.foxit.com/api/oauth2/access_token, with the body sent as Form Urlencoded and four fields, grant_type set to client_credentials, client_id, client_secret, and scope set to read-write. The response is JSON with access_token, token_type (the string bearer), expires_in, and instance_url. This is a standard OAuth2 client-credentials grant defined in RFC 6749. Cache the token against expires_in and refresh before it lapses rather than minting one per run.

The token node uses a Form URL Encoded body with the four OAuth fields. Click Execute step to confirm it returns a token before wiring the rest.

Successful Foxit eSign token response

A successful token call. The green check and the returned access_token confirm the credentials and endpoint are correct.

Now add the send-for-signature node. POST to https://na1.foxitesign.foxit.com/api/folders/createfolder with an Authorization: Bearer <access_token> header and this body:

{
  "folderName": "Contract - {{ $('Webhook').item.json.body.clientName }}",
  "inputType": "base64",
  "base64FileString": ["{{ $('Generate PDF').item.json.base64FileString }}"],
  "fileNames": ["contract.pdf"],
  "processTextTags": true,
  "sendNow": true,
  "parties": [
    {
      "firstName": "Alex",
      "lastName": "Rivera",
      "emailId": "[email protected]",
      "permission": "FILL_FIELDS_AND_SIGN",
      "sequence": 1,
      "workflowSequence": 1
    }
  ]
}
Foxit eSign create-folder request node

The create-folder node. The Authorization header value Bearer {{ $json.access_token }} pulls the token from the previous node.

In this call, inputType is "base64" and base64FileString is an array holding the PDF from Step 2, which is the direct handoff between the two APIs. processTextTags: true converts the embedded Text Tags into real fields, and sendNow: true dispatches the folder in the same request. The response nests the identifier at folder.folderId, which you keep for the completion step. One rule fails silently, so check it. Every party number used in a Text Tag must have a matching entry in parties, or a sendNow: true call returns success while dropping that party’s fields. If you prefer a review gate, set sendNow: false and dispatch later with POST /api/folders/sendDraftFolder.

Foxit eSign signer view with signature field

What the signer receives, from a real run of this exact pipeline. The ${signfield:1:y} tag in the template has become an interactive signature field, which confirms processTextTags did its job on the generated contract.

Step 4: eSign Completion Webhook and Archive

Register your callback URL in the eSign portal’s API settings page, under the Configure Webhooks section. This page is visible to the account owner once API access is active, so a non-owner login will not see it. Provide the HTTPS callback URL, a Webhook Secret, and the events you want delivered.

Archive at the right moment by understanding the lifecycle. A folder moves through DRAFT, then SHARED, then PARTIALLY SIGNED, then COMPLETED when all signatures are collected, then EXECUTED a few seconds later once the digital signature is applied. Hook folder_executed, not folder_completed, so the file you store is the finalized, digitally signed PDF.

Add a second Webhook node to receive the callback. Each callback POSTs a JSON body with event_name, event_date in Unix milliseconds, and data, where data.folder carries folderId, folderStatus, folderDocumentIds, and documentsList. Verify authenticity against the HMAC-SHA-256 digest Foxit computes over the raw request body with your Webhook Secret and appends to the callback URL as a signature query parameter. Compare against the raw body bytes, not a re-serialized payload.

On completion, download the executed document and archive it. A GET to https://na1.foxitesign.foxit.com/api/folders/download?folderId={id} with the bearer token returns the signed PDF as application/pdf. Route that binary to a storage node such as Google Drive, an S3 bucket, or an internal file-system node, then fire any downstream update.

Not every send completes, so handle the abandoned-signer path. If the completion webhook never fires, use an n8n Wait node with a timeout, or poll GET /api/folders/viewActivityHistory?folderId={id} (GET only, and it responds once the folder is shared) to read the audit trail, then branch to a follow-up rather than hanging.

The Vercel Workflows Alternative

If your stack already runs on Vercel, you can build the same pipeline as a durable function instead of a visual graph. Vercel Workflows is built on the open-source Workflow SDK. Scaffold a Next.js app, add the SDK, and wrap the config so the build compiles your workflow functions into durable routes:

npm create next-app@latest foxit-vercel-workflow
cd foxit-vercel-workflow
npm i workflow
// next.config.ts
import { withWorkflow } from "workflow/next";
import type { NextConfig } from "next";

const nextConfig: NextConfig = {};

export default withWorkflow(nextConfig);

A CRM would kick off a run by POSTing to an API route, which starts the workflow with start() and returns immediately while the durable function runs in the background:

// app/api/trigger/route.ts
import { NextResponse } from "next/server";
import { start } from "workflow/api";
import { contractWorkflow, type Deal } from "@/workflows/contract";

export async function POST(req: Request) {
  const deal = (await req.json()) as Deal;
  await start(contractWorkflow, [deal]);
  return NextResponse.json({ started: true });
}

The orchestration function carries the 'use workflow' directive, which makes it resumable and able to survive deploys and crashes through deterministic replay. It generates the contract, sends it for signature, then awaits a hook keyed by the folderId until the signing completes:

// workflows/contract.ts
import { signatureHook } from "./hooks";

export async function contractWorkflow(deal: Deal) {
  "use workflow";

  const pdfBase64 = await generateContract(deal);
  const folderId = await sendForSignature(pdfBase64, deal);

  // Pause here, consuming no compute, until the eSign webhook resumes it.
  const completion = await signatureHook.create({ token: String(folderId) });

  const archivedBytes = await archiveSigned(folderId);
  return { folderId, event: completion.event_name, archivedBytes };
}

Each Foxit call becomes a step. A function with the 'use step' directive runs a unit of durable work and gets built-in retries on transient failures like network errors, so a flaky call is retried without re-running the rest of the pipeline.

// app/steps/generate-contract.ts
async function generateContract(deal: Deal) {
  'use step';

  const res = await fetch(
    'https://na1.fusion.foxit.com/document-generation/api/GenerateDocumentBase64',
    {
      method: 'POST',
      headers: {
        client_id: process.env.FOXIT_DOCGEN_CLIENT_ID!,
        client_secret: process.env.FOXIT_DOCGEN_CLIENT_SECRET!,
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        base64FileString: TEMPLATE_B64,
        documentValues: deal,
        outputFormat: 'pdf',
      }),
    },
  );
  const { base64FileString } = await res.json();
  return base64FileString;
}

The eSign wait is a hook, not a poll. Define a hook, create it keyed by the folderId, and resume it from an API route when Foxit POSTs the completion webhook. The workflow pauses without consuming compute and resumes exactly where it left off.

// workflows/hooks.ts
import { defineHook } from "workflow";

export type FoxitCompletion = {
  event_name: string;
  data: { folder: { folderId: number; folderStatus: string } };
};

export const signatureHook = defineHook<FoxitCompletion>();
// app/api/foxit-webhook/route.ts
import { signatureHook } from "@/workflows/hooks";

export async function POST(req: Request) {
  const payload = await req.json();
  const folderId = payload?.data?.folder?.folderId;
  // Resume only on the terminal event, so earlier lifecycle events
  // (viewed, signed, completed) do not consume the single-use hook.
  if (folderId && payload?.event_name === "folder_executed") {
    await signatureHook.resume(String(folderId), payload);
  }
  return new Response("OK");
}

In this code, the workflow calls two steps and then waits on signatureHook. When Foxit’s folder_executed webhook hits /api/foxit-webhook, the route calls hook.resume with the folderId as the key, which wakes the paused workflow and hands it the payload so it can archive the signed document. The event_name check matters, because the same account webhook also delivers folder_viewed, folder_signed, and folder_completed, and any of those would otherwise consume the single-use hook early. Use sleep() from the workflow package for a timeout fallback if the signer never acts. The credential and endpoint facts are identical to the n8n version, since only the orchestration layer changed. The Workflow Concepts page documents the directives, sleep, and hooks in full, and the full runnable project is in the demo repo.

Deployed to Vercel and triggered with a sample deal, the run shows up under Observability, where you can watch runs pause and resume in real time.

Vercel Workflows dashboard run list

The Workflows runs list in the Vercel dashboard. A run stays Active while it is paused on the signing hook, then flips to Completed once the eSign webhook resumes it.

Opening a completed run shows the whole durable pipeline as a trace. The two Foxit steps run in seconds, the hook span (named by the folderId) accounts for the multi-minute wait while the document sat unsigned, and archiveSigned runs the instant the real folder_executed webhook resumes the workflow.

Vercel Workflows run trace with signing wait

A completed run trace. generateContract (1.77s) and sendForSignature (1.09s) are the real Foxit calls, the 34624119 span is the hook paused for about three minutes until the document was signed, and archiveSigned (680ms) downloads the executed PDF once the webhook fires.

CRM-Triggered Document Pipeline FAQ

Yes. Set outputFormat to "docx" to get a merged Word document back instead of a PDF. That’s useful for contracts that need further edits or approvals before going to eSign.

Yes. createfolder accepts a PDF from any source, such as a publicly accessible URL, multipart form data, or a base64 string you’ve encoded from an existing file. DocGen and eSign are independent APIs that pair well together because DocGen’s base64 output is exactly what createfolder’s inputType: “base64” mode expects, but neither requires the other.

The folder stays in SHARED or PARTIALLY_SIGNED status indefinitely with no automatic expiry. You’re responsible for detecting stalled folders. In n8n, use a Wait node with a timeout. In Vercel Workflows, use sleep() inside a Promise.race. Both paths let you branch to a follow-up action rather than letting the workflow hang.

Add multiple objects to the parties array with sequential sequence values (1, 2, 3…). The eSign API dispatches to party 2 only after party 1 has signed. Make sure each Text Tag in your template references the correct party number (${signfield:2:y} for party 2’s signature field) and that parties includes a matching entry for every number you use.

The token response includes expires_in in seconds. Cache the token with a timestamp, check before each call whether you’re within a safe margin of expiry (60 seconds is reasonable), and re-fetch from POST /api/oauth2/access_token when needed. Fetching a new token on every API call adds unnecessary latency and wastes credits.

Yes. base64FileString is an array. Pass multiple base64-encoded PDFs alongside their corresponding fileNames, and all documents will be included in the same folder for the signer to complete in one session.

A CRM-triggered document pipeline is an automated workflow that listens for a CRM event, such as a deal moving to “closed won” in HubSpot, and automatically generates, sends for signature, and archives a contract without human intervention. It matters because manual document handling after a deal closes introduces delays, missed steps, and no audit trail when something fails silently mid-process.

Test End-to-End and Harden for Production

Prove the pipeline by firing a sample deal at the n8n Webhook Test URL with the cURL command from Step 2. Confirm the Document Generation call returns a base64 PDF, the eSign folder is created with a folder.folderId, and the signing email reaches your test address. Running the middle by hand first means you catch a bad credential or a tag mismatch before any nodes downstream depend on it.

Harden the credentials next. Store client_id, client_secret, and the eSign keys as n8n credentials or Vercel environment variables, never as plaintext in node config, and add an n8n error workflow that alerts on any 4xx or 5xx from either Foxit API. Distinguish a 4xx (fix the payload) from a 5xx (retry with backoff).

Finally, size for credits. Each Document Generation call and each eSign send consumes credits, so read the Foxit API credits reference before going to production. The free Developer plan covers 500 credits per year, which is plenty to build and validate the whole pipeline.

Create a free Foxit developer account with no credit card required, grab your Client ID and Secret from the APIs Dashboard, and run your first Document Generation call against the live endpoint before wiring it into a workflow. Create your free account to get started.

Build a CRM-to-eSign Document Pipeline in n8n Using Foxit’s REST APIs

Illustration of a CRM deal moving through Foxit API steps to produce a signed PDF

This tutorial shows how to wire the Foxit API n8n integration into a single automated chain. You’ll learn how to generate a PDF, send it for signature, and archive the signed copy without anyone touching a file.

A deal moves to closed-won in the CRM, and then a person takes over. Someone exports the deal fields, pastes them into a contract template, saves a PDF, uploads it to a signing tool, types in the counterparty’s email, and hits send. Days later they check whether it was signed, download the executed copy, and drop it into a shared drive so finance can find it. Every one of those steps is a place where data gets retyped, a wrong template gets attached, or a signature request stalls because nobody was watching.

The failure is structural, not human. The deal data lives in the CRM, the contract template lives in a content folder, the signature lives in a separate vendor, and the archive lives somewhere else again. Nothing connects them, so a person becomes the integration layer.

The target state removes that person from the loop. A CRM event fires, an automation runs a short chain of API calls, and a signed PDF lands in storage with no manual touch. This tutorial builds exactly that using n8n, a source-available workflow tool, as the orchestrator. You will build it step by step, and you do not need to be an n8n expert to follow along. The chain is a CRM webhook, then document generation, then send-for-signature, then an archive step, wired together with n8n’s HTTP Request node.

A Continuous Chain of REST Calls

The pipeline has three stages, and each is a REST call the previous stage feeds directly.

  1. Generate. A CRM webhook delivers deal data into n8n. The workflow merges that data into a Word template and renders a PDF through the Foxit Document Generation API, a cloud REST endpoint with no SDK to install.
  2. Sign. The rendered PDF goes straight to the Foxit eSign API, which creates a signing folder, routes it to the parties, and manages the signature lifecycle.
  3. Archive. When signing completes, the workflow downloads the executed PDF and writes it to storage, then updates the CRM record.

The data handoff between stages is what makes the chain clean. Document Generation returns the finished PDF as a base64 string in its JSON response, and that same base64 string is exactly what the eSign folder-create call accepts as its file payload. No temporary files, no disk writes, and no format juggling between the two calls. The eSign completion event then carries the folder reference the archive stage uses to pull the signed document.

One detail belongs up front, since it shapes every node. The two APIs live on two different hosts. Document Generation runs on https://na1.fusion.foxit.com, and eSign runs on https://na1.foxitesign.foxit.com. They also authenticate differently, which the next section covers. There is no dedicated Foxit node in n8n and none is needed, because both are well-formed REST endpoints that the HTTP Request node handles with custom headers and JSON bodies.

Run n8n Locally and Get Foxit Credentials

The whole tutorial runs on free, self-hosted infrastructure. There is no n8n Cloud plan to sign up for and no paid Foxit tier involved. You run n8n yourself in a container, and the only account you create is Foxit’s free Developer plan.

Install Docker

You need Docker to run n8n. On macOS or Windows, install Docker Desktop; on Linux, install Docker Engine. Both are covered on the Get Docker page. After installing, confirm it works by running docker --version in a terminal.

Start n8n and create your local account

Run the two commands below, taken from n8n’s Docker docs:

docker volume create n8n_data
docker run -it --rm --name n8n -p 5678:5678 \
  -v n8n_data:/home/node/.n8n \
  docker.n8n.io/n8nio/n8n

The n8n_data volume persists your workflows and credentials across restarts, since n8n stores them in a local SQLite database by default. The -p 5678:5678 flag maps the editor to a local port. Now do these steps in order:

  1. Open http://localhost:5678 in your browser.
  2. On first launch, n8n asks you to create an owner account. This is a local account for your own instance, not an n8n Cloud login, so use any email and password you like.
  3. You land on an empty workflow canvas. This is where you will build the pipeline.

Swap -it --rm for -d in the run command when you later want n8n running in the background.

Get your Foxit credentials

Register a Foxit account at account.foxit.com/site/sign-up, verify your email, and activate the free Developer plan. From the APIs Dashboard, copy your Document Generation Client ID and Client Secret. The eSign API is provisioned separately, so once eSign is active on your account, retrieve its Client ID and Client Secret as a distinct pair. You now have two credential sets, and they are not interchangeable.

They differ because the two APIs authenticate differently, and this is the single thing that trips up most first builds. Document Generation takes the credentials as two request headers, lowercase client_id and client_secret, on every call. eSign instead runs an OAuth2 exchange first, trading its credentials for a short-lived bearer token that you then send as an Authorization: Bearer <token> header.

Store the DocGen credentials once, reuse them everywhere

Rather than pasting your Client ID and Secret into every node, store them once in n8n’s credential store. Because Foxit needs two custom headers, use the Custom Auth credential type (the same type Foxit’s own published n8n template uses). In n8n, go to Credentials, click Create credential, search for Custom Auth, click Continue, and paste this JSON:

{
  "headers": {
    "client_id": "YOUR_FOXIT_CLIENT_ID",
    "client_secret": "YOUR_FOXIT_CLIENT_SECRET"
  }
}
n8n Custom Auth credential form with Foxit client_id and client_secret JSON fields

The Custom Auth credential holds both Foxit headers in one place. Any HTTP Request node can attach it, so your Client ID and Secret never appear in a node field or an exported workflow.

In this credential, the headers object lists the two headers Foxit expects, with your real Client ID and Secret as the values. Save it, and every Document Generation call in this tutorial can attach it instead of carrying the raw keys.

Import the Finished Workflow (Optional Shortcut)

If you would rather see the whole pipeline first and study it, import the finished version and then follow the step-by-step build below to understand each node. On any workflow canvas, open the ⋯ (Actions) menu in the top right and choose Import from URL….

n8n workflow actions menu with Import from URL option highlighted

The Actions menu on the workflow canvas. “Import from URL…” pulls a workflow straight from a link, so there is nothing to download.

Paste the raw URL of the ready-made pipeline JSON and click Import:

https://github.com/lucienchemaly/foxit-demo-templates/raw/main/n8n/foxit-crm-to-esign-pipeline.json
n8n Import Workflow from URL dialog with GitHub link pasted in

The Import from URL dialog. n8n fetches the JSON and renders the full node graph on the canvas, ready for you to attach your own credentials.

The imported graph arrives with placeholder credentials, so you still create the Custom Auth credential above and select it on the two Foxit nodes. Whether you import or build from scratch, the sections below explain exactly what each node does.

Step 1: Trigger the Workflow and Generate the PDF

Add the trigger and fire it with a test request

Start the workflow with a Webhook node, which is how a CRM will eventually call your pipeline when a deal closes. Click the + on the canvas, search for Webhook, and add it. Set HTTP Method to POST and Path to crm-deal-closed. n8n gives every Webhook node two addresses, a Test URL for building and a Production URL for live traffic.

The Webhook node’s Test URL and “Listen for test event” button. Clicking Listen puts the node in a one-shot capture mode so you can send it a sample request while building.

Because you are working locally and do not have a real CRM pointed at your laptop yet, fire the webhook yourself. Click Listen for test event, copy the Test URL, and send it a sample deal payload with cURL:

curl -X POST "http://localhost:5678/webhook-test/crm-deal-closed" \
  -H "Content-Type: application/json" \
  -d '{
    "company_name": "Acme Robotics",
    "deal_id": "INV-2026-0042",
    "amount": "1875.50"
  }'

In this request, you POST a small JSON object that mimics what a CRM would send when a deal closes. The Webhook node captures it, and those three fields (company_name, deal_id, amount) become available to every downstream node as {{ $json.body.company_name }} and so on. When you later move to production, switch to the node’s Production URL and register it in your CRM’s webhook settings. To expose your local machine to the internet for that, run a tunnel such as ngrok (ngrok http 5678) and register the tunnel address.

Prepare the Word template

The template is an ordinary Word document with Foxit DocGen merge tags where the deal data should land. A tag is a field name in double braces, and you can add Word-style format switches for dates and currency. Download the ready-to-use sample so you do not have to author one, invoice_simple.docx. It carries {{ companyName }}, {{ invoiceNumber }}, {{ invoiceDate \@ MM/dd/yyyy }}, and {{ totalDue \# "$#,##0.00" }}. For repeating rows such as line items, DocGen supports a loop with {{TableStart:lineItems}} and {{TableEnd:lineItems}} markers placed in the same table row, shown in the companion invoice_table.docx. If you want to confirm a template’s tags before wiring it in, POST it once to https://na1.fusion.foxit.com/document-generation/api/AnalyzeDocumentBase64, which returns the full list of tags it detected. The Foxit DocGen quickstart walks through the request shape in more detail.

Add and configure the Generate PDF node

Click + to add an HTTP Request node after the Webhook, and configure it field by field.

  • Method : set to POST.
  • URL : https://na1.fusion.foxit.com/document-generation/api/GenerateDocumentBase64
  • Authentication : choose Generic Credential Type, then Custom Auth, and select the credential you created earlier.
  • Send Body : turn this on, set the body type to JSON, and paste the payload below.
n8n HTTP Request node configured with the Foxit Document Generation API URL

The HTTP Request node config panel. Every Foxit call in this tutorial is built here by setting the method and URL, attaching the credential, and toggling Send Body on for the JSON payload.

{
  "base64FileString": "<base64-encoded invoice_simple.docx>",
  "documentValues": {
    "companyName": "{{ $json.body.company_name }}",
    "invoiceNumber": "{{ $json.body.deal_id }}",
    "invoiceDate": "2026-07-01",
    "totalDue": "{{ $json.body.amount }}"
  },
  "outputFormat": "pdf"
}

In this body, base64FileString is the Word template encoded as base64, documentValues is a flat object whose keys match the template tags exactly and whose values are pulled from the webhook payload using n8n expressions, and outputFormat is the lowercase string "pdf". The keys in documentValues must match the tag names in the template character for character, since a mismatch renders that field empty rather than raising an error. To turn the template file into the base64 string, fetch it with an HTTP Request node set to return the file, then convert it in a Code node with items[0].binary.data.data, or paste a pre-encoded string while you are testing.

One limit is worth planning for. Document Generation rejects a .docx payload larger than 4 MB measured after base64 encoding, which is roughly a 3 MB raw file, and the rejection comes back as an HTTP 500 rather than a friendly validation message. If your template is heavy, compress its images through Word’s Picture Format pane, drop embedded fonts and OLE objects, and split an oversized template into parts you merge later.

The endpoint is synchronous, so there is no polling. The response is a JSON object with message, fileExtension, and base64FileString, where base64FileString holds the rendered PDF as base64. That value flows straight into Step 2.

Step 2: Exchange an eSign Token and Send for Signature

Get a bearer token

The eSign leg starts by turning your eSign credentials into a bearer token. Add another HTTP Request node and configure it:

  • Method : POST
  • URL : https://na1.foxitesign.foxit.com/api/oauth2/access_token
  • Send Body : on, body type Form Urlencoded, with four fields, grant_type set to client_credentials, client_id set to your eSign Client ID, client_secret set to your eSign Client Secret, and scope set to read-write.

The response is JSON with access_token, token_type (the string bearer), expires_in, and instance_url. This is a standard OAuth2 client-credentials grant as defined in RFC 6749. The token is long-lived but not permanent, so in production cache it and refresh when expires_in is close to elapsing rather than minting a new one on every run.

The token node uses a Form URL Encoded body with the four OAuth fields, not JSON. This is the one eSign call that sends the raw credentials rather than a bearer header.

Click Execute step on this node to confirm it works before wiring the rest. A successful run returns the token in the output panel.

A successful token call. The green check and the access_token / token_type values confirm the credentials and endpoint are correct before you build the next node.

Place signature fields with Text Tags

Signature placement is handled inside the document using Foxit eSign Text Tags, which you type into the Word template while authoring it. The syntax is ${fieldtype:party:mandatory}, so ${signfield:1:y} is a required signature for party 1 and ${datefield:2:n} is an optional date for party 2. If a tag needs to contain a space, replace it with an underscore, because a literal space breaks tag recognition. To keep the tags invisible in the finished document, set their text color to match the page background. On upload you set processTextTags: true, and Foxit converts each tag into a real signing field automatically, which removes any manual drag-and-drop field placement.

There is a party-mapping rule that fails silently, so check it before every send. Every party number referenced by a Text Tag must have a matching entry in the parties array of the create call. If a tag points at party 2 but your parties array only defines party 1, a sendNow: true create still returns success while quietly dropping party 2’s fields, so nobody is ever asked to sign them. Line up the token party numbers with the parties entries first.

Add the Create Signature Folder node

Add a third HTTP Request node for the send-for-signature call:

  • Method : POST
  • URL : https://na1.foxitesign.foxit.com/api/folders/createfolder
  • Send Headers : on, add one header named Authorization with the value Bearer {{ $json.access_token }}, which references the token from the previous node.
  • Send Body : on, body type JSON, with the payload below.
{
  "folderName": "Contract - {{ $('CRM Webhook').item.json.body.company_name }}",
  "inputType": "base64",
  "base64FileString": ["{{ $('Generate PDF').item.json.base64FileString }}"],
  "fileNames": ["contract.pdf"],
  "processTextTags": true,
  "sendNow": true,
  "parties": [
    {
      "firstName": "Alex",
      "lastName": "Rivera",
      "emailId": "[email protected]",
      "permission": "FILL_FIELDS_AND_SIGN",
      "sequence": 1,
      "workflowSequence": 1
    }
  ]
}

In this call, inputType is set to "base64" and base64FileString is an array holding the PDF that Step 1 produced, which is the direct handoff between the two APIs. The parties array lists each signer with their name, email, and order, processTextTags: true turns the embedded tags into fields, and sendNow: true dispatches the folder for signature in the same request. The response nests the identifier at folder.folderId, which you keep for status tracking and archival. Foxit’s API uses folder terminology throughout rather than envelope. If your process needs a review gate before anything is sent, set sendNow: false to create a draft, then dispatch it later with a POST /api/folders/sendDraftFolder call carrying the returned folderId.

n8n HTTP Request node with Authorization bearer header for the Foxit createfolder API

The create-folder node. The Authorization header value Bearer {{ $json.access_token }} pulls the token straight from the previous node, and the input panel on the left shows that token flowing in.

n8n canvas showing the CRM webhook, Generate PDF, eSign token, and Create Signature Folder nodes connected

The finished chain in the local n8n editor. Each stage is a plain HTTP Request node, and the base64 PDF from the generation node flows directly into the eSign create-folder node.

Once the folder is sent, the signer receives the document with the Text Tags already converted into interactive fields routed to them.

What the recipient sees after dispatch. This is the visible proof that processTextTags placed the fields correctly and routed them to the right party.

Step 3: Capture Signing Status and Archive the Document

With the folder sent, the workflow needs to know when it is signed so it can archive the result. The folder moves through a defined lifecycle, DRAFT, then SHARED, then PARTIALLY SIGNED, then COMPLETED, then EXECUTED, where the digital signature is applied to the PDF a few seconds after COMPLETED. Archive on the COMPLETED or EXECUTED event so you capture the finalized, digitally signed file.

Foxit eSign can push a webhook when the folder completes, configured in the eSign portal’s owner-only API and Webhooks settings, and you would receive it with a second n8n Webhook node. For a first local build, the simpler path is to poll. Add a Schedule Trigger node paired with an HTTP Request to https://na1.foxitesign.foxit.com/api/folders/viewActivityHistory?folderId={id}, which reads the folder’s audit trail (Created, Invitation Sent, Opened, Viewed, Signed, Folder Executed). This endpoint is GET-only and returns data only once the folder has been shared or sent, so it will not respond for a draft. Gate the next step with an IF node that checks whether the status has reached COMPLETED.

On completion, download the signed PDF through the eSign API and route the binary to a storage node, whether that is Google Drive, an S3 bucket, or an internal file-system node, then fire any downstream updates such as a Slack message or a CRM field write. Because not every send completes, add an error branch as well. If a party declines or the folder expires, route to a separate path that updates the CRM record to reflect the outcome and alerts the deal owner, so a human steps in only on the exceptions.

Common Mistakes and Troubleshooting

A few pitfalls account for most failed first runs.

  • Wrong host. Document Generation is on na1.fusion.foxit.com; eSign is on na1.foxitesign.foxit.com. Sending a DocGen body to the eSign host (or the reverse) returns an auth or not-found error.
  • Credential not attached. If a Foxit call comes back 401, the most common cause is an HTTP Request node with Authentication left on None. Attach the Custom Auth credential (DocGen) or add the Authorization: Bearer header (eSign).
  • Token expired or missing. The eSign bearer token is not permanent. If createfolder returns 401 after the pipeline has been idle, re-run the token node so a fresh access_token flows into the header.
  • Tag names do not match. A documentValues key that does not exactly match a {{ }} tag renders that field blank with no error. Cross-check the names, or run AnalyzeDocumentBase64 to list the real tags.
  • Base64 payload too big. An HTTP 500 with a “cannot be larger than 4 MB” body means the encoded .docx exceeded the cap. Slim the template per Step 1.
  • Party numbers do not line up. A Text Tag pointing at a party not present in the parties array is silently dropped, so that signer is never asked to sign. Match token party numbers to parties entries.

Foxit API n8n FAQ

The free Foxit Developer plan gives you 500 credits per year with instant activation and no credit card required. n8n runs as the source-available Community Edition in Docker with no license key. Both are fully functional for building and testing the complete pipeline described here.

No. Each API uses its own Client ID and Secret. You will store two separate credential sets in n8n, one for the Document Generation header-based auth and one for the eSign OAuth2 token exchange. They come from separate sections of the Foxit APIs Dashboard.

Use an n8n Schedule node as your trigger. Add an HTTP Request node after it that polls your CRM’s REST API for deals matching your target stage criteria. HubSpot’s Deal Search API and Salesforce’s SOQL query endpoint both support this pattern. The rest of the workflow runs identically from that point forward.

Yes. If you prefer not to embed Text Tags in the DOCX, omit processTextTags: true from your createfolder call and place fields manually using the Foxit eSign web editor after the document is uploaded. The manual approach works fine for low-volume workflows but does not scale as cleanly as tag-based automation when the template changes frequently.

A folder moves through five states in order:

  • DRAFT : created but not sent
  • SHARED : sent to all parties
  • PARTIALLY SIGNED : at least one party has signed
  • COMPLETED : all parties have signed
  • EXECUTED : the digital signature stamp is applied

Your n8n archival step should trigger on COMPLETED or EXECUTED. If you archive on COMPLETED and need the final stamp, wait for the EXECUTED event instead.

The expires_in field in the token response tells you how long the bearer token is valid. For high-frequency workflows, add a Code node before the createfolder call that checks whether the stored token is still valid and re-requests it if not. You can also request a fresh token at the start of each pipeline execution. The client-credentials grant is stateless, so there is no session overhead to worry about.

Run your template through the Analyze endpoint at POST https://na1.fusion.foxit.com/document-generation/api/AnalyzeDocumentBase64 before executing the full pipeline. It returns every tag the API detects, making it straightforward to catch case mismatches or typos. The merge API performs a case-sensitive lookup, so a template field named {{First_Name}} will not match a JSON key of first_name.

What to Build Next

The fastest way to prove the pipeline is to run the middle of it by hand first. Spin up the local n8n container, activate the free Foxit Developer plan, pull your Client ID and Secret from the APIs Dashboard, and fire the Document Generation POST against invoice_simple.docx from a REST client like Postman or cURL. Once that returns a base64 PDF, you know the hardest call works before any nodes are wired.

From there, two durable extensions are worth building. Save your signing setup as a reusable eSign template with POST /api/templates/createtemplate so future sends skip the field setup, and version the n8n workflow itself by exporting its JSON, so the whole chain can be redeployed or shared across the team.

Building Agentic Document Workflows: How LLM Agents Use PDF APIs to Convert, Extract, and Sign at Scale

Diagram of agentic document workflows showing an LLM agent calling Foxit MCP tools to convert, extract, and sign PDFs

This guide walks through building agentic document workflows by exposing Foxit’s PDF and eSign APIs as callable MCP tools, so any compatible agent host can run the full document lifecycle in one automated pipeline.

Agentic document workflows go beyond retrieval, since they convert, transform, merge, and sign documents without human intervention. This guide shows how to expose Foxit’s production PDF API as callable MCP tools so any LLM agent can execute the full document lifecycle (OCR, extraction, generation, and legally binding signatures) in a single automated pipeline.

Most LLM-powered applications have solved the retrieval problem. The harder part of agentic document workflows is action, the moment your agent needs to convert a scanned invoice to searchable text, merge a dozen contract pages into a package, and route it for a legally binding signature without a human in the loop.

RAG gets text into a context window, which is useful for reading. Once you need to produce, transform, or sign a document, you’ve moved into document operations territory. A plain text API won’t close that delta, and bolting together a dozen bespoke REST wrappers every time you need a new pipeline quickly becomes the bottleneck.

The Model Context Protocol (MCP) gives agents a standard way to discover and call tools. Document workflows have been missing a tool surface that exposes real PDF operations as callable MCP tools, backed by a production-ready API. This guide walks through exactly how to build that.

What You Need Before You Start

Five prerequisites are required to follow this guide. You need a Foxit developer account, the open-source MCP server, an MCP-compatible host, three environment variables, and a Python workspace for the signing example.

A Foxit developer account. Sign up at account.foxit.com/site/sign-up (no credit card required for the free Developer plan). The Foxit Developer Portal issues your Client ID and Client Secret, gives you access to the API Playground, and tracks usage in real time.

The open-source MCP server. Clone github.com/foxitsoftware/foxit-pdf-api-mcp-server. The repo ships two active implementations, a Python build (using FastMCP, Python 3.11+, and the uv package manager) and a TypeScript build (Node.js 18+, pnpm). The original stdio-python variant is deprecated, so use the current Python or TypeScript implementation.

An MCP-compatible host. You need somewhere to run the agent. Claude Desktop, Cursor, or VS Code with GitHub Copilot all work, and any MCP-compliant custom agent framework will also connect to the server.

A Python workspace for the signing example. The eSign walkthrough later in this guide runs a short Python script, so you need Python 3.8+ and the requests library. That walkthrough also uses a separate set of eSign credentials, which you set up in its own section rather than here. Scaffold an isolated workspace in one shot:

mkdir agentic-docs && cd agentic-docs
python3 -m venv .venv && source .venv/bin/activate
pip install requests

Three environment variables. Before launching your MCP host process, export these:

export FOXIT_CLOUD_API_HOST="https://na1.fusion.foxit.com/pdf-services"
export FOXIT_CLOUD_API_CLIENT_ID="your_client_id"
export FOXIT_CLOUD_API_CLIENT_SECRET="your_client_secret"

Never hardcode credentials in config files. The MCP server reads these at startup and uses them to authenticate every request to the PDF Services API.

What “Agentic” Actually Means for Document Workflows

An agentic document workflow executes operations on documents (converting formats, applying OCR, merging pages, routing for signature) rather than simply retrieving text from them. The tool surface required is fundamentally different from a RAG setup.

Retrieval-augmented generation pulls text from a document and injects it into a prompt. An agentic document workflow does something to a document, whether it converts a format, applies OCR to make a scanned image searchable, merges pages from multiple sources, or routes the result for signature.

In a tool-use architecture, the LLM doesn’t call the API directly. It picks the right operation from a catalog of tools based on the task, calls it with structured inputs, processes the result, and decides whether to continue the chain or hand off to the next step. If you’ve worked with web-search or code-execution tools in LangChain or AutoGen, the pattern is identical. The model reasons about which tool to invoke, not about how the underlying HTTP request works.

A REST API is an HTTP surface. An MCP tool is a named, typed function with an input schema, an output contract, and a description the model uses to decide when and whether to call it. MCP standardizes that interface so any compliant host can discover the full tool catalog, call individual operations, and chain results without custom adapter code.

A well-designed MCP server eliminates the bespoke integration layer. Without one, every document-heavy agent pipeline requires someone to write and maintain that plumbing from scratch.

Architecture Overview: Two Modes for Agent-Driven PDF Processing

The Foxit PDF API MCP Server wraps Foxit’s cloud PDF Services API as 30+ callable MCP tools, covering every stage of a document lifecycle. Foxit PDF Editor is the first PDF editor in the industry to act as an MCP Host, connecting outward to external MCP Servers and acting on open documents.

Those two facts define two distinct architectural modes.

Mode 1: Programmatic pipeline. Your MCP host (Claude Desktop, Cursor, VS Code with GitHub Copilot, or a custom agent) registers the Foxit PDF API MCP Server. The agent calls PDF tools directly, the server translates those calls into Foxit PDF Services REST requests, and structured results return to the agent. The agent never writes REST plumbing. This is the right model for automated pipelines running without a human in the loop.

Mode 2: In-app orchestration. Foxit PDF Editor acts as the MCP Host. Its embedded AI Assistant connects to external MCP Servers (Jira, Salesforce, Gmail, Notion, GitHub, Google Workspace) and acts on the open document. You could extract fields from a contract PDF and open a Jira ticket without leaving the editor. This is the right model when a knowledge worker needs AI assistance during document review.

Mode 1 is what the rest of this guide builds. Its data flow runs like this:

Foxit MCP server architecture diagram for agentic document workflows

The agent calls tools, the MCP server handles the REST layer against PDF Services, and a prepared document hands off to eSign at the end of the chain.

One detail to understand before you build is that each successful Foxit PDF Services API call consumes one credit from your plan. Failed requests (4xx or 5xx) do not consume credits. The Developer Dashboard shows real-time usage, so you can see exactly what a pipeline costs per document before scaling it up.

Setting Up the Foxit MCP Server

The server exposes tools across six categories (document lifecycle, creation, conversion, manipulation, security, and forms) plus OCR and document compare.

The full tool catalog breaks down as follows:

  • Document lifecycle : upload, download, delete
  • Creation : Word, Excel, PowerPoint, HTML, URL, plain text, and image to PDF
  • Conversion : PDF to Word, Excel, PowerPoint, HTML, plain text, and image
  • Manipulation : merge, split, extract pages, compress, flatten, linearize, watermark, and page operations
  • Security : add and remove passwords, set permissions
  • Forms : export and import form data as JSON

OCR and document compare are also in the catalog. Signing lives in the eSign API covered in Section 6.

Mode 1: Programmatic Pipeline Setup

Clone the repo and pick your implementation. For the Python version with VS Code and GitHub Copilot, create or update your .vscode/mcp.json with the following:

{
  "servers": {
    "foxit-pdf": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/foxit-pdf-api-mcp-server",
        "run",
        "foxit-pdf-api-mcp-server"
      ],
      "env": {
        "FOXIT_CLOUD_API_HOST": "${env:FOXIT_CLOUD_API_HOST}",
        "FOXIT_CLOUD_API_CLIENT_ID": "${env:FOXIT_CLOUD_API_CLIENT_ID}",
        "FOXIT_CLOUD_API_CLIENT_SECRET": "${env:FOXIT_CLOUD_API_CLIENT_SECRET}"
      }
    }
  }
}

VS Code launches the MCP server as a subprocess through uv, which runs the cloned Python build from its directory. The three ${env:...} references pull credentials from the shell environment instead of hardcoding them in the file, so replace /absolute/path/to/foxit-pdf-api-mcp-server with the path where you cloned the repo and restart VS Code to load the server. Claude Desktop uses the same shape under an mcpServers key and can run the published npm package directly with "command": "npx" and "args": ["-y", "@foxitsoftware/foxit-pdf-api-mcp-server"], so you do not have to clone anything for that host.

Mode 2: In-App Orchestration Setup

Open Foxit PDF Editor, click the AI Assistant tab in the Ribbon, and launch AI Chat to open the right-hand panel. In the bottom left of that panel, click MCP Tools, then click Add MCP Server to configure a new MCP service. Fill in the required fields and save. Once configured, the server appears in the MCP Tools list and its tools activate inside AI Chat.

In both modes, the server reads your Client ID and Client Secret from the environment variables set at startup. No additional gateway configuration is required.

Core Document Operations Agents Can Execute

With the server running, your agent has 30+ callable PDF operations available. Five categories do the heaviest lifting in production pipelines, namely conversion, OCR, structural extraction, merge/split, and document generation via DocGen.

Conversion. An agent receiving an uploaded DOCX triggers the Word-to-PDF creation tool before any downstream step. The output is a standards-compliant PDF that every subsequent operation (OCR, extraction, merge) can work with consistently, eliminating manual conversion and format ambiguity downstream.

OCR. When an agent ingests a scanned or image-only PDF, an OCR call should precede any extraction step. Calling the OCR tool makes the document searchable and text-extractable, which is required for invoice and contract pipelines where key fields sit inside scanned images. The agent calls OCR, waits for the result, and proceeds.

Structural extraction. After OCR, an agent extracts text, tables, and form-field data as structured JSON. Foxit’s structural extraction returns per-page content plus images, giving you a payload that routes cleanly into a BI tool or a second LLM step for analysis or classification. For the full response schema, refer to the Foxit MCP Server developer blog.

Merge and split. An agent assembling a contract package from multiple source documents calls the merge tool with an ordered list of PDFs. An agent pre-processing a large compliance document for parallel LLM analysis calls the split tool to divide it into per-section chunks. Both operations are synchronous and safe to retry on failure.

Document generation via DocGen. For dynamically generated contracts, invoices, or reports, the Foxit Document Generation API accepts a DOCX template with {{dynamic_tags}} and a JSON data payload from a CRM, database, or form response. It returns a finished PDF via POST /document-generation/api/GenerateDocumentBase64. A ready-to-use template lives in the Foxit demos repo if you want to test the call without authoring one. When you upload a DOCX template directly, the 4 MB post-base64 encoding cap applies, so slim templates down by stripping embedded fonts and large images before encoding. The agent supplies the JSON payload at runtime, so a single template can produce thousands of unique documents.

A representative end-to-end pipeline runs like this. An agent ingests a purchase order scan, calls OCR to make it searchable, extracts the structured fields as JSON, merges that data into a contract template via DocGen, and hands the finished PDF off to eSign. Every step is a tool call. The agent reasons about sequencing while the MCP server handles the REST layer.

Agent-Triggered Signing Workflows via the eSign API

Document signing uses a separate REST service, the Foxit eSign API, which has its own credentials and completes the pipeline in three calls. The agent exchanges its credentials for a token, creates a signing folder from the prepared PDF, and dispatches it to the signer.

The eSign API runs on its own host and issues its own Client ID and Client Secret from the eSign portal, separate from the PDF Services credentials the MCP server uses. Export the three eSign variables the script reads before running it:

export FOXIT_ESIGN_BASE_URL="https://na1.foxitesign.foxit.com"
export FOXIT_ESIGN_CLIENT_ID="your_esign_client_id"
export FOXIT_ESIGN_CLIENT_SECRET="your_esign_client_secret"

The folder can only be sent if the signer has a signature field, and the simplest way to place one is with Foxit eSign text tags embedded in the document. Download the ready-to-sign sample, agent_agreement.pdf, into your workspace as agreement.pdf. It already carries the tag that maps a signature field to the first party, so the folder is sendable as is. Then run:

import base64
import os
import requests

BASE_URL = os.environ["FOXIT_ESIGN_BASE_URL"]      # https://na1.foxitesign.foxit.com
CLIENT_ID = os.environ["FOXIT_ESIGN_CLIENT_ID"]
CLIENT_SECRET = os.environ["FOXIT_ESIGN_CLIENT_SECRET"]


def get_access_token():
    resp = requests.post(
        f"{BASE_URL}/api/oauth2/access_token",
        data={
            "client_id": CLIENT_ID,
            "client_secret": CLIENT_SECRET,
            "grant_type": "client_credentials",
            "scope": "read-write",
        },
        timeout=30,
    )
    resp.raise_for_status()
    return resp.json()["access_token"]


def route_for_signature(pdf_path, signer):
    token = get_access_token()
    headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}

    with open(pdf_path, "rb") as fh:
        encoded = base64.b64encode(fh.read()).decode()

    folder = requests.post(
        f"{BASE_URL}/api/folders/createfolder",
        headers=headers,
        json={
            "folderName": "Agent Service Agreement",
            "inputType": "base64",
            "base64FileString": [encoded],
            "fileNames": ["agreement.pdf"],
            "processTextTags": True,
            "sendNow": False,
            "parties": [
                {
                    "permission": "FILL_FIELDS_AND_SIGN",
                    "firstName": signer["first_name"],
                    "lastName": signer["last_name"],
                    "emailId": signer["email"],
                    "sequence": 1,
                }
            ],
        },
        timeout=60,
    )
    folder.raise_for_status()
    folder_id = folder.json()["folder"]["folderId"]

    sent = requests.post(
        f"{BASE_URL}/api/folders/sendDraftFolder",
        headers=headers,
        json={"folderId": folder_id},
        timeout=60,
    )
    sent.raise_for_status()
    return folder_id


if __name__ == "__main__":
    fid = route_for_signature(
        "agreement.pdf",
        {"first_name": "Jordan", "last_name": "Lee", "email": "[email protected]"},
    )
    print(f"Folder {fid} sent for signature")

In this code, you read the eSign credentials from the environment and exchange them for a bearer token at the access_token endpoint, where the request is form-encoded rather than JSON (sending JSON returns a 415). You then base64-encode the local PDF and post it to createfolder with inputType set to base64 so the API reads the base64FileString array, with processTextTags set to True so the document’s text tags become a real signature field, and with sendNow set to False so the folder is created as a draft instead of emailing anyone immediately. The parties array names the signer with FILL_FIELDS_AND_SIGN permission, you read the new id at folder.folderId, and you pass it to sendDraftFolder, which dispatches the draft to the signer. To confirm the result, call GET /api/folders/viewActivityHistory?folderId={id}, which returns the activity log once the folder has been shared (a draft returns logs of a non-shared folder can not be viewed). Foxit uses “folder” throughout the eSign API, never “envelope.”

Compliance is built into the API layer. The Foxit eSign API supports eIDAS, the ESIGN Act, UETA, HIPAA, and GDPR, covering the requirements for agents operating in legal, healthcare, and finance contexts. No separate compliance infrastructure is required.

Production Considerations: Compliance, Cost, and Error Handling

The Foxit PDF Services and eSign APIs are SOC 2 Type II certified, with HIPAA BAA support, GDPR compliance, and CCPA coverage built in. Three additional concerns (credit consumption, idempotency, and async polling) determine whether a pipeline runs reliably at scale.

Credit consumption. The free Developer plan includes 500 credits per year (annual reset, no rollover). The Startup plan is $1,750/year for 3,500 credits. The Business plan is $4,500/year for 150,000 credits. Each successful API call consumes one credit; 4xx and 5xx responses do not. A pipeline processing 50 documents per day burns through those allocations quickly. Monitor real-time usage in the Developer Dashboard before moving to production and size your plan accordingly.

Idempotency. Merge, flatten, and convert calls are safe to retry on failure. Signing-folder creation is not, so gate createfolder behind a state check so the agent doesn’t create duplicate folders and dispatch duplicate signing requests to the same signers.

Async operations. Translation and batch conversion operations are asynchronous, so they return a job ID immediately, and your agent should poll the job status endpoint roughly every three seconds until the status is COMPLETED or FAILED before proceeding to the next step in the chain. Handling these concerns at build time separates a working pipeline from one that generates support tickets.

Common Mistakes

  • Dropping the /api/ prefix on eSign calls : Every eSign path lives under /api/, as in /api/folders/createfolder. Omitting it returns a 404 against a docs-style path that does not exist.
  • Sending the token request as JSON : The access_token endpoint is form-encoded. A JSON body returns 415 Unsupported Media Type, so pass the credentials as form data.
  • Forgetting inputType: base64 : When you send a base64 PDF without it, createfolder rejects the request with fileUrls or base64FileString cannot be empty. URL mode uses fileUrls and fileNames instead.
  • Sending a signer with no signature field : A FILL_FIELDS_AND_SIGN party needs a field. If the document has no text tag like ${s:1:______} and you skip processTextTags, sendDraftFolder returns Please assign a signature field. Use underscores in the tag placeholder, since an empty placeholder does not create a field.
  • Expecting a signing tool in the MCP server : The 30+ MCP tools cover PDF operations, not signatures. Signing is the eSign API, a separate service with separate credentials.

Agentic Document Workflows FAQ

An agentic document workflow is an automated pipeline in which an LLM agent executes operations on documents (conversion, OCR, extraction, merging, generation, and signing) without human intervention. Unlike retrieval-augmented generation, which only reads documents, an agentic workflow produces and transforms them using callable tools exposed through a protocol like MCP.

MCP defines a standard interface for exposing named, typed functions, called tools, that an LLM agent can discover and invoke. A PDF API MCP server wraps REST endpoints as MCP tools with input schemas and output contracts. The agent selects the right tool based on task context, calls it with structured parameters, and processes the result, without writing any HTTP request logic.

The Foxit PDF API MCP Server exposes 30+ tools covering document lifecycle (upload, download, delete), creation (Word, Excel, PowerPoint, HTML, image to PDF), conversion (PDF to multiple formats), manipulation (merge, split, compress, OCR, watermark), security (password management), and forms (JSON import/export).

No. Only successful Foxit PDF Services API calls consume credits. Requests that return 4xx or 5xx status codes do not count against your plan. The Developer Dashboard provides real-time usage tracking so you can measure pipeline cost per document before scaling.

After preparing a document, your agent calls the Foxit eSign API directly. It authenticates with client_credentials, POSTs to /api/folders/createfolder with the base64 PDF and a parties entry for the signer, then POSTs to /api/folders/sendDraftFolder with the returned folderId. The signing email workflow triggers automatically, and the agent can poll /api/folders/viewActivityHistory for the audit trail.

The Foxit eSign API supports eIDAS, the U.S. ESIGN Act, UETA, HIPAA, GDPR, and CCPA. The PDF Services API is SOC 2 Type II certified with HIPAA BAA support. No separate compliance wrappers are required for agents operating in legal, healthcare, or financial contexts.

Mode 1 is a programmatic pipeline where an external LLM agent (Claude Desktop, Cursor, VS Code with GitHub Copilot, or a custom framework) registers the Foxit MCP Server and calls PDF tools automatically. Mode 2 is in-app orchestration where Foxit PDF Editor acts as the MCP Host, connecting to external services like Jira or Salesforce while a knowledge worker reviews the open document.

The /api/folders/createfolder endpoint is not idempotent. If an agent retries on failure without a state check, it will create duplicate folders and send duplicate signing requests to the same signers. Merge, flatten, and convert operations are safe to retry; folder creation requires the agent to verify no existing folder was created before calling the endpoint again.

Start Building: Free Developer Access in Minutes

The full pipeline in this guide is available to test today. Activate a free Foxit Developer plan to get your Client ID, Client Secret, access to the API Playground, and 500 credits for real requests. No credit card required.

Clone the open-source MCP server, export the three environment variables, and register the server in Claude Desktop, Cursor, or VS Code with GitHub Copilot. At that point, 30+ PDF tools are callable from your agent with no local SDK to install and no REST plumbing to write.

Once a document is prepared, extend the pipeline into legally binding signatures with the eSign API. The architecture in this guide covers the full document lifecycle from conversion and OCR through extraction, generation, merging, and signing, all in a single agentic document workflow.

Create your free developer account to clone the open-source MCP server and make 30+ PDF tools callable from your agent in minutes, no credit card required.

Create Custom Invoices with Word Templates and Foxit Document Generation

Word invoice template with double-bracket tokens used by a generate invoice pdf API

This tutorial shows how to design a Word invoice template with dynamic tokens and use Foxit’s Document Generation API and a short Python script to turn JSON data into polished, ready-to-send PDF invoices.

Manual invoice processing costs between $18 and $26 per invoice, while automated workflows bring that down to $2.50 to $4, according to industry accounts-payable benchmarks. Foxit’s Document Generation API generates well-formatted, dynamic PDF invoices from your existing data, which eliminates manual data entry, formatting errors, and the overhead of per-invoice handling. This tutorial shows you how to build that workflow end to end.

Before You Start

To follow along, grab your free credentials on the developer portal and read the introductory guide to automated document pipelines, which covers the basics of working with the API.

The API works with Microsoft Word templates containing tokens wrapped in double brackets. You pass the template and your data to the API, it replaces those tokens with your data values, and returns a PDF (or Word file, if you prefer).

Prerequisites

This tutorial runs a short Python script, so set up the following before you start:

  • Python 3.12 or newer. The script uses an f-string syntax introduced in Python 3.12.

  • pip for installing packages, plus the built-in venv module for an isolated environment.

  • The requests library for the HTTP call.

  • A code editor such as VS Code with the Python extension. PyCharm, WebStorm, or Sublime Text work too.

  • curl if you want to hit the endpoint from the command line first.

  • A Foxit developer account. Sign up for free to get your Client ID and Client Secret.

Scaffold the workspace in one shot:

mkdir foxit-invoices && cd foxit-invoices
python3 -m venv .venv
source .venv/bin/activate
pip install requests

Then export your credentials and host so the script can read them from the environment:

export CLIENT_ID="your_client_id"
export CLIENT_SECRET="your_client_secret"
export HOST="https://na1.fusion.foxit.com"

Designing Your Invoice Word Template

Template design in Word is your starting point. An invoice typically includes:

  • The customer receiving the invoice

  • The invoice number and issue date

  • The payment due date

  • A line-item table with product name, quantity, price, and a running total

The Document Generation API places no constraints on template design. Size, alignment, and styling can match your corporate standards, whether that means a simple layout or a polished, branded one. Consider the template below (download link at the end of this article):

MS Word template

The Word template with double-bracket tokens that the Document Generation API replaces at runtime.

Working from the top of the template:

  • {{ invoiceNum }} is the invoice number for the customer.

  • {{ today \@ MM/dd/yyyy }} combines a built-in value (today, which represents the current date at API call time) with a date mask that controls the display format. The API docs list all available masks.

  • {{ accountName }} is a standard token mapped directly from your data.

  • {{ paymentDueDate \@ MM/dd/yyyy }} demonstrates date masks applied to dates from your own data, not just built-ins.

  • The line-item table uses one header row and one data row. The dynamic row opens with {{TableStart:lineItems}} (where lineItems is the name of an array in your JSON) and closes with {{TableEnd:lineItems}}. Between those markers sit product, qty, price, totalPrice, and ROW_NUMBER (a built-in that auto-increments from 1). The \# Currency format applied to totalPrice renders it as a formatted currency value.

  • A final table row uses SUM(ABOVE) with currency formatting to total the column.

Structuring Your Invoice Data as JSON

In a production system, invoice data typically comes from a database or e-commerce API. For this demo, it comes from a JSON file:

[
  {
    "invoiceNum": 100,
    "accountName": "Customer Alpha",
    "accountNumber": 1,
    "paymentDueDate": "August 15, 2025",
    "lineItems": [
      { "product": "Product 1", "qty": 5, "price": 2, "totalPrice": 10 },
      { "product": "Product 5", "qty": 3, "price": 9, "totalPrice": 18 },
      { "product": "Product 4", "qty": 1, "price": 50, "totalPrice": 50 },
      { "product": "Product X", "qty": 2, "price": 15, "totalPrice": 30 }
    ]
  },
  {
    "invoiceNum": 25,
    "accountName": "Customer Beta",
    "accountNumber": 2,
    "paymentDueDate": "August 15, 2025",
    "lineItems": [
      { "product": "Product 2", "qty": 9, "price": 2, "totalPrice": 18 },
      { "product": "Product 4", "qty": 1, "price": 8, "totalPrice": 8 },
      { "product": "Product 3", "qty": 10, "price": 25, "totalPrice": 250 },
      { "product": "Product YY", "qty": 3, "price": 15, "totalPrice": 45 },
      { "product": "Product AA", "qty": 2, "price": 100, "totalPrice": 200 }
    ]
  },
  {
    "invoiceNum": 51,
    "accountName": "Customer Gamma",
    "accountNumber": 3,
    "paymentDueDate": "August 15, 2025",
    "lineItems": [
      { "product": "Product 9", "qty": 1, "price": 2, "totalPrice": 2 },
      { "product": "Product 23", "qty": 30, "price": 9, "totalPrice": 270 },
      { "product": "Product ZZ", "qty": 6, "price": 15, "totalPrice": 90 }
    ]
  }
]

The array holds three invoice objects, each matching the structure of the Word template. The accountNumber field has no matching template token, and that’s intentional. Your data can contain fields the template doesn’t use, and accountNumber is used in the Python script to build unique output filenames.

Calling the Document Generation API with Python

The Generate Document endpoint requires your credentials, a base64-encoded version of the template, and your data. The complete demo runs in just over 50 lines of Python:

import os
import requests
import sys
from time import sleep
import base64
import json
from datetime import datetime

CLIENT_ID = os.environ.get('CLIENT_ID')
CLIENT_SECRET = os.environ.get('CLIENT_SECRET')
HOST = os.environ.get('HOST')

def docGen(doc, data, id, secret):

	headers = {
		"client_id":id,
		"client_secret":secret
	}

	body = {
		"outputFormat":"pdf",
		"documentValues": data,
		"base64FileString":doc
	}

	request = requests.post(f"{HOST}/document-generation/api/GenerateDocumentBase64", json=body, headers=headers)

	return request.json()

with open('invoice.docx', 'rb') as file:
	bd = file.read()
	b64 = base64.b64encode(bd).decode('utf-8')

with open('invoicedata.json', 'r') as file:
	data = json.load(file)

for invoiceData in data:
	result = docGen(b64, invoiceData, CLIENT_ID, CLIENT_SECRET)

	if result["base64FileString"] == None:
		print("Something went wrong.")
		print(result)
		sys.exit()

	b64_bytes = result["base64FileString"].encode('ascii')
	binary_data = base64.b64decode(b64_bytes)

	filename = f"invoice_account_{invoiceData["accountNumber"]}.pdf"

	with open(filename, 'wb') as file:
		file.write(binary_data)
		print(f"Done and stored to {filename}")

After importing modules and loading credentials from environment variables, the docGen function takes the template, data, and credentials, then posts to the API endpoint. The API returns the rendered PDF as a base64 string.

The main loop reads and base64-encodes the template, loads the JSON file, iterates over each invoice object, calls the API, and writes the result to a uniquely named file using accountNumber. You don’t have to write results to disk at all, since the raw binary data can go straight to an email attachment or a document storage system.

Keep one limit in mind, since the GenerateDocumentBase64 endpoint rejects a .docx payload larger than 4 MB after base64 encoding. If your template approaches that ceiling, compress its images through Word’s Picture Format tools, drop embedded fonts and OLE objects, and split oversized templates into smaller ones.

Example PDF result

The rendered PDF invoice the script produces, with all tokens replaced by values from the JSON payload.

Generate Invoice PDF API FAQ

The API returns either a PDF or a DOCX file. You specify the format in the outputFormat field of the request body, as shown in the Python example above.

Yes. The documentValues parameter accepts any JSON-serializable object, so you can pull data from Salesforce, a SQL database, a REST endpoint, or any other source and pass it directly to the API. The JSON file in this tutorial is a stand-in for whatever data source you use in production.

No. You design templates in standard Microsoft Word. The API reads the token syntax ({{ }}) from any valid .docx file, with no Foxit desktop software or proprietary editor required.

Foxit’s API platform is SOC 2 Type II certified and supports GDPR and HIPAA compliance, which makes it suitable for invoices that include customer personally identifiable information or regulated financial data. See Foxit’s API security and compliance page for the full list of frameworks.

The free Developer plan gives you instant access. It covers this tutorial and experimentation with your own templates before committing to a paid tier.

Next Steps

Get free credentials and download the template, Python script, and sample output from the GitHub repository to run this demo yourself.

Two natural extensions follow from here. Foxit Connectors provides 40+ pre-built integrations with platforms like Salesforce, SharePoint, and Google Drive, so you can pull invoice data directly from your CRM or ERP rather than a local JSON file. You can also chain document generation with an eSign step, sending the invoice for client acknowledgment immediately after creation, by passing the generated PDF to the Foxit eSign API.

DOCX to PDF via the Foxit PDF Services API: Python and cURL Walkthrough

Foxit docx to pdf api four-step conversion flow in Python and cURL.

This walkthrough covers the full DOCX-to-PDF flow on the Foxit PDF Services API with runnable Python and cURL for each call.

Automated document pipelines demand conversion tooling that accepts a file, queues a job, and returns clear status at every step. The Foxit PDF Services API gives you exactly that: a four-endpoint async flow covering upload, convert, poll, and download. Each step returns a typed payload, the task model exposes four explicit states with a numeric progress field, and error codes map cleanly to distinct recovery paths.

This tutorial walks through every step of that flow in Python 3 with the requests library, plus cURL equivalents for each call. You’ll have a runnable convert.py script you can drop into a pipeline today.

Prerequisites

Before you run a single line of this tutorial, get the following in place. Each item links to its canonical install or setup guide.

  • Python 3.8 or newer — verify with python3 --version. The script uses only standard library modules plus one external package, so any modern 3.x will do.
  • pip — bundled with Python 3.4+. Verify with python3 -m pip --version.
  • A virtual environment — isolates project dependencies so they don’t collide with system Python or other projects. See the venv tutorial for platform-specific activation commands.
  • The requests library — the only third-party dependency in this walkthrough. Installed inside the venv below.
  • A code editor — Visual Studio Code with the Python extension is a solid default, but PyCharm, Sublime Text, or any editor you like will work.
  • cURL — pre-installed on macOS and most Linux distros. Windows users can install from the official site or use WSL.
  • A Foxit Developer account — register for free (no credit card required). The Foxit Developer Portal provisions a default application with your CLIENT_ID and CLIENT_SECRET immediately after signup.

Set up the project workspace:

mkdir foxit-docx-to-pdf && cd foxit-docx-to-pdf
python3 -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install requests

Export your credentials as environment variables so the script never sees them as hardcoded strings:

export CLIENT_ID=your_client_id_here
export CLIENT_SECRET=your_client_secret_here
Your Python script reads them with `os.environ.get()`:
import os

CLIENT_ID = os.environ.get("CLIENT_ID")
CLIENT_SECRET = os.environ.get("CLIENT_SECRET")

BASE_URL = "https://na1.fusion.foxit.com"

All four API calls go to https://na1.fusion.foxit.com. The developer portal also offers a live sandbox and pre-built Postman collections if you want to verify calls in a GUI before scripting.

For a sample DOCX to work with right away, download input.docx directly from the foxitsoftware/developerapidemos GitHub repository and save it to your working directory.

How the Auth Model Works

The Foxit PDF Services API authenticates through named request headers. Pass client_id and client_secret directly on every call:

headers = {
    "client_id": CLIENT_ID,
    "client_secret": CLIENT_SECRET,
}
That single dict covers upload (multipart POST), polling (GET), and download (GET). The convert endpoint takes a JSON body, so it requires `Content-Type: application/json` as well:
json_headers = {
    "client_id": CLIENT_ID,
    "client_secret": CLIENT_SECRET,
    "Content-Type": "application/json",
}

The API expects the raw key/secret pair in those named headers. Wrapping credentials in an Authorization: Bearer header instead returns 400, since the required client_id and client_secret headers are missing.

Step 1 and Step 2: Upload the DOCX and Initiate Conversion

Step 1: Upload the DOCX File

POST /pdf-services/api/documents/upload accepts the file as multipart/form-data and returns a documentId that every subsequent call needs.

import requests

def upload_doc(file_path: str) -> str:
    url = f"{BASE_URL}/pdf-services/api/documents/upload"
    headers = {
        "client_id": CLIENT_ID,
        "client_secret": CLIENT_SECRET,
    }
    with open(file_path, "rb") as f:
        files = {"file": (os.path.basename(file_path), f)}
        response = requests.post(url, headers=headers, files=files)
    response.raise_for_status()
    return response.json()["documentId"]

cURL equivalent:

curl -X POST "https://na1.fusion.foxit.com/pdf-services/api/documents/upload" \
  -H "client_id: $CLIENT_ID" \
  -H "client_secret: $CLIENT_SECRET" \
  -F "[email protected]"

Uploaded files carry a 100 MB cap and are automatically deleted after 24 hours. A documentId scopes to the current upload session and expires with the source file, so treat it as ephemeral.

Step 2: Initiate the PDF Conversion

POST /pdf-services/api/documents/create/pdf-from-word accepts a JSON body with the documentId and returns a taskId. The API handles 10 to 10,000+ conversions per day across production pipelines, queuing jobs asynchronously to avoid blocking the connection until the PDF is ready.

import json

def convert_to_pdf(document_id: str) -> str:
    url = f"{BASE_URL}/pdf-services/api/documents/create/pdf-from-word"
    headers = {
        "client_id": CLIENT_ID,
        "client_secret": CLIENT_SECRET,
        "Content-Type": "application/json",
    }
    payload = {"documentId": document_id}
    response = requests.post(url, headers=headers, data=json.dumps(payload))
    response.raise_for_status()
    return response.json()["taskId"]

cURL equivalent:

curl -X POST "https://na1.fusion.foxit.com/pdf-services/api/documents/create/pdf-from-word" \
  -H "client_id: $CLIENT_ID" \
  -H "client_secret: $CLIENT_SECRET" \
  -H "Content-Type: application/json" \
  -d '{"documentId": "<your_document_id>"}'

The endpoint returns 202 Accepted, confirming the job is queued. It also accepts .doc, .rtf, .dot, .dotx, .docm, .dotm, and .wpd files through the same documentId input, so legacy Word formats work through the same pipeline.

Step 3: Polling the Task Status

GET /pdf-services/api/tasks/{task-id} returns four fields you need to act on in your polling loop:

  • status: one of PENDING, IN_PROGRESS, COMPLETED, or FAILED
  • progress: int32, 0 to 100
  • resultDocumentId: populated when status reaches COMPLETED
  • error: populated when status reaches FAILED

The task state machine advances in one direction: PENDING to IN_PROGRESS, then to either COMPLETED or FAILED.

Foxit DOCX to PDF API task state machine: PENDING to IN_PROGRESS, then COMPLETED with resultDocumentId or FAILED with an error object.

import time

def poll_task(task_id: str, max_attempts: int = 30) -> str:
    url = f"{BASE_URL}/pdf-services/api/tasks/{task_id}"
    headers = {
        "client_id": CLIENT_ID,
        "client_secret": CLIENT_SECRET,
    }
    for attempt in range(max_attempts):
        response = requests.get(url, headers=headers)
        response.raise_for_status()
        data = response.json()
        status = data.get("status")
        progress = data.get("progress", 0)
        print(f"Attempt {attempt + 1}: status={status}, progress={progress}%")
        if status == "COMPLETED":
            return data["resultDocumentId"]
        if status == "FAILED":
            raise RuntimeError(f"Conversion failed: {data.get('error')}")
        time.sleep(2)
    raise TimeoutError(f"Task {task_id} did not complete in {max_attempts} attempts")

Two-second polling intervals work across a wide range of document sizes, and polling more aggressively only consumes rate limit budget without affecting conversion time.

Step 4: Downloading the Converted PDF

GET /pdf-services/api/documents/{documentId}/download fetches the finished PDF. The path parameter in the API reference reads {documentId}, but the value you pass here is the resultDocumentId from the completed poll response. The server assigns that ID to the generated PDF output at conversion time, making it the correct identifier to use at this step.

Stream the response to disk with stream=True and iter_content(chunk_size=8192). Buffering a large PDF fully into memory before writing it causes problems on high-volume pipelines.

def download_result(result_document_id: str, output_path: str) -> None:
    url = f"{BASE_URL}/pdf-services/api/documents/{result_document_id}/download"
    headers = {
        "client_id": CLIENT_ID,
        "client_secret": CLIENT_SECRET,
    }
    with requests.get(url, headers=headers, stream=True) as response:
        response.raise_for_status()
        with open(output_path, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                if chunk:
                    f.write(chunk)

The cURL equivalent uses the --output flag to write directly to disk:

curl -X GET "https://na1.fusion.foxit.com/pdf-services/api/documents/<result_document_id>/download" \
  -H "client_id: $CLIENT_ID" \
  -H "client_secret: $CLIENT_SECRET" \
  --output output.pdf

To verify the output, check response.headers.get("Content-Type") for application/pdf, or inspect the first four bytes of the written file for the %PDF magic bytes if your pipeline requires format validation.

Error Handling for Production

The Foxit PDF Services API documentation covers 400, 404, 413, and 500 across the four endpoints. The 401 appears on authentication failures as a practical case even though it’s absent from the documented example responses. Each status code points to a specific root cause with a concrete recovery path:

  • 400: malformed request body or unsupported file type. Validate the input file path and extension before calling upload_doc().
  • 401: credential misconfiguration. Verify that CLIENT_ID and CLIENT_SECRET are exported in your shell and that the header names are lowercase client_id and client_secret.
  • 404: the documentId has expired. The server deletes uploaded files after 24 hours, so the convert and download endpoints return 404 for any documentId past that window. Re-upload the source file and restart from the upload step. An expired or unknown taskId on the poll endpoint behaves differently: it returns HTTP 200 with status: "FAILED" and an error object whose message reads "task is not exist". The poll loop’s FAILED branch already catches that case.
  • 413: file exceeds the 100 MB upload cap. Pre-check with os.path.getsize() before uploading, or split the document.
  • 500: transient server error. Apply exponential backoff with a ceiling of 3 retries (wait times of 1s, 2s, and 4s).
def call_with_retry(fn, *args, max_retries: int = 3, **kwargs):
    for attempt in range(max_retries + 1):
        try:
            return fn(*args, **kwargs)
        except requests.HTTPError as e:
            code = e.response.status_code
            if code == 400:
                raise ValueError(
                    "Bad request. Confirm the input is a supported Word format."
                ) from e
            if code == 401:
                raise PermissionError(
                    "Authentication failed. Check CLIENT_ID and CLIENT_SECRET env vars."
                ) from e
            if code == 404:
                raise FileNotFoundError(
                    "Document or task expired (24h TTL). Re-upload and retry."
                ) from e
            if code == 413:
                raise OverflowError(
                    "File too large. The upload cap is 100 MB."
                ) from e
            if code == 500 and attempt < max_retries:
                wait = 2 ** attempt  # 1s, 2s, 4s
                print(f"Server error. Retrying in {wait}s ({attempt + 1}/{max_retries})")
                import time
                time.sleep(wait)
                continue
            raise

Pipeline authors should treat documentId values as ephemeral: each one expires with its source file after 24 hours, so pipeline code that caches documentId values between sessions will see 404s on every convert call, and re-uploading is always the correct recovery path.

The Complete Script

Set your environment variables, then run python convert.py input.docx output.pdf:

import os
import json
import time
import sys
import requests

CLIENT_ID = os.environ.get("CLIENT_ID")
CLIENT_SECRET = os.environ.get("CLIENT_SECRET")
BASE_URL = "https://na1.fusion.foxit.com"


def call_with_retry(fn, *args, max_retries: int = 3, **kwargs):
    for attempt in range(max_retries + 1):
        try:
            return fn(*args, **kwargs)
        except requests.HTTPError as e:
            code = e.response.status_code
            if code == 400:
                raise ValueError(
                    "Bad request. Confirm the input is a supported Word format."
                ) from e
            if code == 401:
                raise PermissionError(
                    "Authentication failed. Check CLIENT_ID and CLIENT_SECRET env vars."
                ) from e
            if code == 404:
                raise FileNotFoundError(
                    "Document or task expired (24h TTL). Re-upload and retry."
                ) from e
            if code == 413:
                raise OverflowError(
                    "File too large. The upload cap is 100 MB."
                ) from e
            if code == 500 and attempt < max_retries:
                wait = 2 ** attempt  # 1s, 2s, 4s
                print(f"Server error. Retrying in {wait}s ({attempt + 1}/{max_retries})")
                time.sleep(wait)
                continue
            raise


def upload_doc(file_path: str) -> str:
    url = f"{BASE_URL}/pdf-services/api/documents/upload"
    headers = {"client_id": CLIENT_ID, "client_secret": CLIENT_SECRET}
    with open(file_path, "rb") as f:
        files = {"file": (os.path.basename(file_path), f)}
        r = requests.post(url, headers=headers, files=files)
    r.raise_for_status()
    return r.json()["documentId"]


def convert_to_pdf(document_id: str) -> str:
    url = f"{BASE_URL}/pdf-services/api/documents/create/pdf-from-word"
    headers = {
        "client_id": CLIENT_ID,
        "client_secret": CLIENT_SECRET,
        "Content-Type": "application/json",
    }
    r = requests.post(url, headers=headers, data=json.dumps({"documentId": document_id}))
    r.raise_for_status()
    return r.json()["taskId"]


def poll_task(task_id: str, max_attempts: int = 30) -> str:
    url = f"{BASE_URL}/pdf-services/api/tasks/{task_id}"
    headers = {"client_id": CLIENT_ID, "client_secret": CLIENT_SECRET}
    for attempt in range(max_attempts):
        r = requests.get(url, headers=headers)
        r.raise_for_status()
        data = r.json()
        status = data.get("status")
        print(f"[{attempt + 1}/{max_attempts}] status={status}, progress={data.get('progress', 0)}%")
        if status == "COMPLETED":
            return data["resultDocumentId"]
        if status == "FAILED":
            raise RuntimeError(f"Conversion failed: {data.get('error')}")
        time.sleep(2)
    raise TimeoutError(f"Task {task_id} did not complete after {max_attempts} attempts")


def download_result(result_document_id: str, output_path: str) -> None:
    url = f"{BASE_URL}/pdf-services/api/documents/{result_document_id}/download"
    headers = {"client_id": CLIENT_ID, "client_secret": CLIENT_SECRET}
    with requests.get(url, headers=headers, stream=True) as r:
        r.raise_for_status()
        with open(output_path, "wb") as f:
            for chunk in r.iter_content(chunk_size=8192):
                if chunk:
                    f.write(chunk)


def convert_docx_to_pdf(input_path: str, output_path: str) -> None:
    print(f"Uploading {input_path}...")
    document_id = call_with_retry(upload_doc, input_path)
    print(f"Uploaded. documentId={document_id}")

    print("Initiating conversion...")
    task_id = call_with_retry(convert_to_pdf, document_id)
    print(f"Queued. taskId={task_id}")

    print("Polling for completion...")
    result_document_id = call_with_retry(poll_task, task_id)
    print(f"Completed. resultDocumentId={result_document_id}")

    print(f"Downloading to {output_path}...")
    call_with_retry(download_result, result_document_id, output_path)
    print("Done.")


if __name__ == "__main__":
    if len(sys.argv) != 3:
        print("Usage: python convert.py <input.docx> <output.pdf>")
        sys.exit(1)
    convert_docx_to_pdf(sys.argv[1], sys.argv[2])

The Foxit PDF Services API also supports merging, compression, linearization, and OCR through additional endpoints. All of them share the same host and header-based auth pattern, so the functions you’ve built here extend naturally as your pipeline grows.

Create your free Foxit developer account and run your first conversion in under five minutes, with no credit card required at signup.

DOCX to PDF API FAQ

Yes. The /pdf-services/api/documents/create/pdf-from-word endpoint accepts .doc, .docx, .rtf, .dot, .dotx, .docm, .dotm, and .wpd. The same four-step flow applies for all of them.

Uploaded documents are automatically deleted after 24 hours. Treat documentId values as ephemeral and re-upload whenever you need to convert a file after that window.

Faster polling consumes rate limit budget without affecting conversion speed. The server determines conversion time based on document complexity and queue load, so polling intervals below 2 seconds add no throughput benefit.

Yes. Each upload returns an independent documentId and each conversion returns an independent taskId. Run concurrent conversions by launching multiple threads or async tasks, with each one tracking its own taskId. Python’s concurrent.futures.ThreadPoolExecutor is a straightforward way to manage this.

From the Foxit Developer Portal dashboard, under the default application created at signup. Both values are available immediately after account creation.

The API authenticates through named request headers. Pass client_id and client_secret directly on every request, and the server reads those credentials on each call.