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OpenClaw can turn a PDF into structured data by combining its PDF tool with a precise extraction prompt, a defined JSON schema, validation, and a review path for uncertain results. The dependable pattern is not “send a file and trust the summary.” It is: retain the original, extract only the required pages, require field-level JSON, detect partial input, validate the result, and send exceptions to a human.
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For born-digital PDFs, OpenClaw can use a provider’s native PDF input. When native support is unavailable, its bundled document-extract plugin uses the clawpdf PDFium WebAssembly package to extract text and images. Scanned PDFs and Office files need a broader workflow, usually a reusable skill that adds OCR, format-specific parsers, normalization, and review rules.
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What an automated OpenClaw document workflow actually does
Document parsing is the conversion of an unstructured file into fields that another system can use. An invoice workflow might need an invoice number, supplier, date, currency, subtotal, tax, total, and page references. A contract workflow might extract parties, effective dates, renewal terms, governing law, and notice periods. A useful automation therefore has five separate responsibilities:
- Ingest: receive a local PDF path, a URL, or an OpenClaw-managed inbound reference.
- Extract: pass one or more documents, an analysis prompt, and, when useful, a page filter to the PDF tool.
- Structure: require a strict JSON object rather than a prose summary.
- Check: validate types, required fields, allowed values, and source-page references.
- Route: accept clean records automatically and send missing, contradictory, low-confidence, or partial results to review.
Keep the source file, the extracted object, and any partial-document notice together. That package gives an operator a way to verify a value and lets you rerun the same document after changing a prompt or parser.
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Start with a single PDF and a strict extraction prompt
The smallest useful implementation is a single-document call. Locate the PDF, identify the pages that contain the target fields, and ask OpenClaw for only those fields. A prompt should define the output contract, missing-value behavior, and evidence requirements.
Example invoice prompt
Extract these invoice fields from the supplied PDF: invoice_number, supplier_name, invoice_date, currency, subtotal, tax, and total.Return one JSON object matching this contract. Use null when a field is absent or cannot be read. Do not infer values from arithmetic or surrounding business context. Include a page_references object whose keys are the field names and whose values are arrays of source page numbers. If the document is incomplete, say so in a partial_document field and identify the affected fields.The wording matters. “Summarize the invoice” permits an answer that sounds plausible but omits a required value. Naming every field, requiring null for absence, and asking for page references makes omissions visible to the next stage.
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If the total is on page 1 and the line-item details are on pages 2–3, select those pages rather than sending a 100-page attachment. Page selection reduces irrelevant context and helps stay inside the PDF tool’s limits. Do not assume that the first page contains every field: identify the pages from a quick inspection, an index, or a preliminary extraction pass.
Process multiple PDFs deliberately
The PDF tool accepts multiple PDF paths or URLs. Use that capability for a defined batch, such as a set of invoices from one supplier, but preserve document identity in every result. Add a source-document identifier and page references to the requested output so that a value cannot be mistaken for one from another file. If documents have different layouts, process them by template or supplier instead of silently combining unrelated extraction rules.
Define and validate the JSON output
Free-form model output is not a database record. Validation should happen immediately after the PDF tool returns a result and before the record is stored or forwarded to accounting, search, or another automation.
A practical invoice schema
{
"type": "object",
"required": [
"invoice_number",
"supplier_name",
"invoice_date",
"currency",
"subtotal",
"tax",
"total",
"page_references",
"partial_document"
],
"properties": {
"invoice_number": { "type": ["string", "null"] },
"supplier_name": { "type": ["string", "null"] },
"invoice_date": { "type": ["string", "null"] },
"currency": { "type": ["string", "null"] },
"subtotal": { "type": ["number", "null"] },
"tax": { "type": ["number", "null"] },
"total": { "type": ["number", "null"] },
"page_references": {
"type": "object",
"additionalProperties": {
"type": "array",
"items": { "type": "integer", "minimum": 1 }
}
},
"partial_document": { "type": "boolean" }
},
"additionalProperties": false
}Choose conventions before production. Decide whether amounts are numbers or decimal strings, which date format is accepted, whether currency must be an ISO code, and whether negative values are legal. A schema can reject malformed data; it cannot prove that a visually misread number is correct, so semantic checks are also needed.
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Apply semantic checks after schema validation
- Check that a required identifier is not an empty string.
- Check that dates parse to the agreed format and fall within a sensible range for the business process.
- Check that currency is present when an amount is present.
- For invoices, compare subtotal plus tax with total within the rounding policy; flag a mismatch rather than changing a value.
- Require at least one page reference for every non-null extracted field.
- Reject or review a result marked
partial_document: true.
When validation fails, save the raw response and the validation errors. A retry can use a narrower prompt, selected pages, or a different extraction path, but it should not overwrite the original response.
Understand native PDF input versus fallback extraction
OpenClaw chooses between two broad paths. A provider that declares native PDF support receives the document directly as a PDF input. For providers without that capability, the bundled document-extract plugin invokes clawpdf, which uses PDFium WebAssembly to extract text and images.
| Path | How the file is handled | Best fit | Important caveat |
|---|---|---|---|
| Native PDF mode | The selected provider receives the PDF as a document input and analyzes it. | PDFs with selectable text, or providers whose document vision can interpret the supplied pages. | Behavior depends on the provider’s declared PDF support and limits. |
| Fallback extraction | document-extract calls clawpdf to extract text and images; sparse pages can be rendered to PNG. |
Providers that cannot accept a PDF directly. | Scans, unusual fonts, complex layouts, and visual elements may require OCR or human review. |
This distinction is operational, not merely technical. A text-first extraction can preserve words while losing the relationship between a label and a value, the reading order of columns, or the meaning of a chart. Treat layout-sensitive fields as review candidates unless representative tests show reliable recovery.
Plan for OpenClaw’s PDF limits and partial results
When configuration is unset, the documented defaults are agents.defaults.pdfMaxPages: 20 and agents.defaults.pdfMaxMb: 10 per PDF. A request can also be constrained by the selected page range and by text or image limits. If those boundaries prevent complete extraction, OpenClaw adds a partial-document notice to the analysis context and to the tool result.
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| Boundary | Default or behavior | What to do |
|---|---|---|
| Pages | 20 pages per PDF when no other configuration is set. | Select the pages containing the required fields or adjust configuration deliberately. |
| File size | 10 MB per PDF when no other configuration is set. | Reduce unnecessary pages, split the document under a documented policy, or change the configured limit. |
| Extracted text | Text limits can make a result partial. | Use page filtering and require the partial flag to be surfaced downstream. |
| Rendered images | Fallback rendering has a shared budget of 4,000,000 pixels. | Render only relevant pages and expect image-heavy documents to need review. |
Never hide the partial notice from an operator or downstream system. A partial invoice may contain the header but omit the totals page; a partial contract may contain definitions but not the signature or renewal clause. Store the notice alongside the JSON and make it a routing condition, not an informational footnote.
Parse scanned PDFs with an OCR-aware workflow
A scanned PDF is a collection of page images rather than a reliable text layer. Native PDF handling or ordinary text extraction may return little or no usable text. In the fallback path, pages with fewer than 200 extracted characters are rendered to PNG. Those images can then be interpreted by an OCR-capable or image-capable stage, subject to the shared 4,000,000-pixel render budget.
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Separate extraction from OCR
- Detect pages with no text or fewer than 200 extracted characters.
- Render only those pages, keeping the total rendered area within the available budget.
- Run OCR or image analysis on the rendered pages.
- Normalize OCR output into the same field model used for born-digital documents.
- Attach page references and mark fields that need visual confirmation.
OCR is especially vulnerable to decimal points, currency symbols, handwritten marks, stamps, skew, faint scans, and multi-column reading order. Do not “repair” a doubtful amount by calculating what the total ought to be. Preserve the extracted value, record the uncertainty, and route it for review.
Use confidence and review gates even when the OCR engine supplies no score
If your OCR or provider does not expose a calibrated confidence value, create rule-based review triggers instead. Examples include a missing invoice number, a total that fails the arithmetic check, a page with no recognized text, conflicting values across pages, or a page reference that does not exist. These gates are safer than treating every syntactically valid JSON object as correct.
Extend beyond PDFs with the OpenClaw Office Assistant skill
The community OpenClaw Office Assistant skill covers PDF, DOCX, DOC, XLSX, XLS, PPTX, and PPT. Its documented approach is text-first: deterministic Python libraries handle modern Office files, and scanned PDFs can fall back to OCR when embedded text is missing. It returns stable JSON for post-processing and is suited to summaries, field extraction, rough structure recovery, and multi-document comparison.
Dependencies listed by the skill
pypdffor PDF work.python-docxfor DOCX files.openpyxlfor XLSX files.python-pptxfor PPTX files.poppler-utilsfor PDF rendering utilities.tesseract-ocrfor OCR, withtesseract-ocr-chi-simrecommended for Chinese/English PDFs.libreoffice,antiword, andcatdocfor broader format coverage.
Installations must be checked in the environment where the skill runs. A utility available on a host may be unavailable inside a sandbox or container. The skill should fail with a clear setup message that names the missing binary, rather than returning an empty extraction that looks successful.
Know what “text-first” does not promise
The Office Assistant skill does not try to perfectly preserve layout, chart semantics, or full visual structure. It is therefore a good fit for paragraphs, tables that can be read as text, and stable field extraction, but a poor fit for workflows where the exact visual arrangement is the data. Slides with diagrams, spreadsheets whose meaning depends on formatting, and contracts with complex side-by-side clauses deserve a visual review path.
Build a reusable OpenClaw parsing skill
OpenClaw skills are Markdown instruction packs. The official skills documentation requires, verbatim, “Every skill needs at minimum a name and description in the frontmatter.” A document skill should add more than that minimum so that the workflow is repeatable across agents and operators.
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Minimal frontmatter and workflow outline
---
name: invoice-document-parser
description: Extract and validate invoice fields from PDFs and scanned documents.
---
# Inputs
- PDF path, URL, or inbound document reference
- Optional page selection
# Output
- JSON matching the invoice schema
- Page references for every non-null field
- Partial-document status
# Rules
- Use null for missing values
- Never infer an amount
- Send schema failures, conflicts, and partial documents to reviewThe field names and frontmatter values above are an example of organization, not a promise of a particular installation command. Adapt the skill to the schema and runtime used by your OpenClaw deployment.
Include readiness checks
At startup, check required binaries, Python packages, environment variables, configuration paths, and operating-system constraints. A readiness check should distinguish “OCR is unavailable” from “the document has no text.” That distinction tells an operator whether to fix the environment or review the source file.
Make retries deterministic
Define when to retry and what changes on each attempt. A sensible sequence is: first extract the selected pages; if required fields are missing, retry with a more explicit field prompt; if the pages are image-only, invoke OCR; if validation still fails, create a review task. Keep each attempt, prompt version, and parser path with the source record.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose between a one-off call, a skill, and a plugin
OpenClaw’s architecture separates tools, skills, and plugins. Tools let an agent act, skills teach a reusable workflow, and plugins add integrations or runtime surfaces.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →| Approach | Use it when | What you define | Trade-off |
|---|---|---|---|
| One-off PDF tool call | You need a focused extraction from one or a few PDFs. | Source file, page filter, prompt, output contract, and validation step. | Fast to start, but rules and review handling are easy to apply inconsistently. |
| Reusable skill | The same document type arrives repeatedly or multiple formats need one workflow. | Input rules, extraction prompts, schema, dependency checks, retries, review behavior, and storage locations. | Requires design and maintenance, but makes results more consistent. |
| Plugin-backed integration | You need a new provider, channel, hook, or runtime surface. | Integration behavior in addition to the parsing workflow. | More engineering overhead; unnecessary for a narrowly scoped extraction. |
A plugin is not automatically a better parser. Use one when the missing capability is an integration or runtime surface. Put document-specific instructions and validation in a skill, and keep the actual focused extraction in the PDF tool or parser it calls.
Package the workflow for traceability
A production record should be reproducible without relying on the model’s memory. Store these items together:
- The original file or a durable reference to it.
- The selected page range and the extraction path used.
- The exact prompt and schema version.
- The raw tool response.
- The normalized JSON object.
- Validation errors, semantic checks, and review decisions.
- Any partial-document notice and the reason it was triggered.
Keep page or slide references in the normalized output. For a contract, references might point to the clause page; for a spreadsheet, they may need sheet and cell coordinates rather than PDF page numbers. The principle is the same: every important value should lead an operator back to source evidence.
Troubleshoot by symptom
The result says the document is partial
Check the page count, file size, selected pages, extracted-text limits, and rendered-image budget. Narrow the page selection to the required fields, split the document under a documented naming scheme, or change the relevant configuration. Do not clear the flag merely because the returned JSON is well formed.
Text fields are empty on a scanned PDF
Confirm whether the pages contain an embedded text layer. If not, route sparse pages through OCR or image analysis, then rerun the same schema validation. If OCR is unavailable, report the dependency failure explicitly rather than returning null for every field without explanation.
Columns or tables are in the wrong order
Switch from a text-only assumption to a layout-aware or visual review path. Ask for page references and preserve the raw page image where permitted. A parser that captures every word but associates each value with the wrong label has produced a failed extraction.
Numbers disagree across pages
Retain each candidate with its page reference, apply the arithmetic and duplicate-field checks, and send the conflict to review. Never silently choose the last occurrence or overwrite one value with a calculated result.
The skill works on the host but not in its sandbox
Run the skill’s readiness checks inside the actual execution environment. Install or expose the required Python packages and binaries there, or change the workflow to a parser that is available. Return a setup message naming the unavailable dependency.
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No validated accuracy, latency, or cost benchmarks are published for this document-parsing workflow. OCR quality, layout recovery, chart interpretation, and provider behavior depend on the workload. Test with documents that include selectable text, scans, skew, multiple columns, tables, unusual fonts, missing fields, long page counts, and deliberately incomplete files.
Measure the outcomes that matter to your process: required-field completeness, arithmetic-check failures, page-reference correctness, partial-document detection, and the percentage of records sent to human review. Keep a labeled sample and rerun it whenever you change the provider, prompt, skill instructions, OCR package, or PDF limits. This gives you evidence for your own documents without implying a universal benchmark.
A practical implementation checklist
- Define the fields, types, missing-value policy, and source-reference format.
- Choose native PDF input when the provider supports it; otherwise plan for fallback extraction.
- Set or confirm page and file-size limits instead of relying on accidental defaults.
- Use page filters for large documents and preserve the selected range.
- Detect image-only or sparse pages and provide an OCR path.
- Validate JSON before storage or downstream automation.
- Run semantic checks for dates, amounts, currencies, duplicates, and cross-field arithmetic.
- Make partial results and dependency failures visible.
- Retain the source, raw response, normalized output, prompt version, and review history.
- Use a skill for repeatability and a plugin only when an integration or runtime surface is required.
- Test representative documents and maintain a human review queue for uncertain cases.
When OpenClaw is the right fit
OpenClaw is a practical fit when an agent needs to extract known fields from PDFs or Office files, return stable JSON, and hand exceptions to a review process. It is less suitable as a promise of perfect layout reconstruction or unattended interpretation of charts, complex visual documents, or poor-quality scans. The safest design treats extraction as one stage in a controlled pipeline: source in, evidence-linked JSON out, and an explicit decision whenever the input or result is incomplete.
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