OpenClaw can turn a PDF into structured data in one tool call, then scale that same process into a reusable skill for invoices, contracts, forms, and Office files. The reliable pattern is: send the source document with a narrowly defined extraction prompt, request schema-conforming JSON, validate every response, retain the original and page references, and route partial or low-confidence results to review.
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What OpenClaw document parsing automates
OpenClaw’s PDF tool accepts one PDF path or URL, multiple paths or URLs, an analysis prompt, and an optional page filter. That is enough for a focused extraction job: identify the fields you need, ask for a strict output contract, and limit processing to the pages that contain those fields.
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OpenClaw does not guarantee that every document is complete or machine-readable. Provider support, embedded text, scan quality, page selection, file size, and image-rendering limits all affect the result. Treat the model response as an extraction candidate that must be validated, not as an unquestioned database write.
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Start with a single PDF
1. Make the document available
Provide a local PDF path, a reachable PDF URL, or an inbound document reference managed by OpenClaw. Keep the original file identifier in your job record so the extracted values can be traced back to the source.
2. Ask for fields and failure behavior explicitly
A vague request such as “summarize this invoice” invites prose and makes downstream processing fragile. State the fields, the required types, the treatment of missing values, and the need for page references.
Extract invoice number, supplier, invoice date, currency, subtotal, tax, and total.
Return only JSON matching the requested schema.
Use null when a field is missing or unreadable.
Include the source page for every extracted field.
Do not calculate values that are not printed in the document.
When your runtime accepts several PDF inputs, send them as a batch only when the documents share the same extraction contract. Separate prompts and records are safer when different document types require different fields.
3. Select relevant pages
Use the PDF tool’s page filter when the document is long and you know where the required information appears. Restricting pages reduces unnecessary extraction and makes it easier to detect whether a required field was actually in scope. If you omit a page that contains a total, signature, or definition, the output can be internally consistent but still incomplete.
4. Validate before storing
Parse the response as JSON, check required keys and data types, reject extra prose when your contract disallows it, and apply domain checks such as “tax plus subtotal should agree with the printed total” only when your business rules permit that comparison. A failed check should create a review task or a controlled retry, not silently overwrite the source record.
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5. Preserve provenance
Store the original PDF, the exact extracted JSON, the prompt or schema version, selected pages, and any partial-document notice together. Page references let a reviewer verify a value quickly and make corrections auditable.
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A schema makes missing information visible and gives your application a stable interface. The following example is a conceptual contract; implement it with the JSON Schema validator used by your application.
{
"type": "object",
"additionalProperties": false,
"required": ["invoice_number", "supplier", "invoice_date", "currency", "subtotal", "tax", "total", "field_sources"],
"properties": {
"invoice_number": {"type": ["string", "null"]},
"supplier": {"type": ["string", "null"]},
"invoice_date": {"type": ["string", "null"]},
"currency": {"type": ["string", "null"]},
"subtotal": {"type": ["number", "null"]},
"tax": {"type": ["number", "null"]},
"total": {"type": ["number", "null"]},
"field_sources": {
"type": "object",
"additionalProperties": {
"type": ["string", "null"]
}
}
}
}
Keep the document’s printed representation separate from normalized values when precision matters. For example, retain the raw date string and currency symbol alongside a normalized ISO date or numeric amount. Do not ask the extractor to invent a value for a missing field; null is more useful than a plausible guess.
Understand native PDF input and fallback extraction
OpenClaw uses native document input for providers that declare native PDF support. In that mode, the provider receives the PDF as a document rather than as text assembled by OpenClaw. For providers without native support, the bundled document-extract plugin uses the clawpdf package, based on PDFium WebAssembly, to extract text and images.
The fallback has an important scan-detection behavior: pages with fewer than 200 extracted characters are rendered to PNG. Image analysis or OCR can then be applied to those rendered pages. The shared render budget is 4,000,000 pixels, so a document with many large pages can become partial even when its file size is modest.
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| Decision point | What to check | Operational consequence |
|---|---|---|
| Provider capability | Does the selected provider declare native PDF support? | If yes, OpenClaw can pass the document directly; otherwise fallback extraction is used. |
| Text layer | Do pages contain meaningful embedded text? | Born-digital pages can usually follow a text-first path; sparse pages may be rendered for image analysis. |
| Scan quality | Is the page a clear image, skewed scan, handwriting, or a complex form? | OCR and visual review may be required, and recognition quality will vary by document. |
| Page and file limits | Are the requested pages within configured limits? | Exceeding limits produces a partial result rather than a complete extraction. |
| Output contract | Does the consumer need prose or validated records? | Use a schema and a validator for database or workflow integration. |
Know the default PDF limits
When no custom configuration is set, OpenClaw documents a maximum of 20 pages and 10 MB per PDF through agents.defaults.pdfMaxPages and agents.defaults.pdfMaxMb. These are per-PDF defaults, not a promise that a multi-file request can contain unlimited pages or bytes.
If page, text, or image limits prevent complete processing, OpenClaw adds a partial-document notice to the analysis context and to the tool result. Your pipeline should persist that notice and prevent an apparently valid record from being marked complete without review.
| Limit or signal | Documented behavior | What your workflow should do |
|---|---|---|
agents.defaults.pdfMaxPages |
20 pages when unset | Filter pages or raise the configured limit deliberately after checking workload and provider constraints. |
agents.defaults.pdfMaxMb |
10 MB per PDF when unset | Reject, split, or preprocess oversized files; never assume truncation is harmless. |
| Low extracted text | Pages with fewer than 200 characters can be rendered to PNG in fallback mode | Expect an image/OCR path and verify fields visually when the page is important. |
| Shared render budget | 4,000,000 pixels | Reduce selected pages or image dimensions when rendering becomes partial. |
| Partial-document notice | Added when page, text, or image limits make extraction incomplete | Propagate the notice to storage and send the item to review or remediation. |
Handle scanned PDFs with an OCR branch
A scanned PDF may have no usable text layer. In that case, text extraction alone cannot recover the fields. OpenClaw’s fallback can render sparse pages to PNG, but OCR or image-capable analysis is still needed to read those images.
- Detect the condition. Check whether extracted text is empty or unusually short on pages that visibly contain content.
- Render only the needed pages. Use the page filter first so the render budget is reserved for pages containing fields, signatures, or totals.
- Run OCR or image analysis. Keep the page number with every recognized value and record that the value came from OCR.
- Validate aggressively. Compare recognized dates, amounts, and identifiers against expected formats; route handwriting, stamps, tables, and low-confidence characters to a human.
- Retain the image evidence. Store the source PDF and, where policy allows, the rendered page or OCR text used for the decision.
OCR quality is workload-dependent. A clean typed scan, a skewed fax, a multilingual form, and a handwritten signature should not share the same automatic-approval threshold. Verify representative documents before assigning an unattended workflow.
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Extend parsing to Office files with a skill
The community OpenClaw Office Assistant skill covers PDF, DOCX, DOC, XLSX, XLS, PPTX, and PPT. Its documented approach uses deterministic Python libraries for modern Office files, falls back to OCR when a scanned PDF lacks embedded text, returns stable JSON for post-processing, and supports summaries, field extraction, rough structure recovery, and multi-document comparison.
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| Package or utility | Role in the Office workflow |
|---|---|
pypdf |
PDF text and document handling |
python-docx |
DOCX extraction |
openpyxl |
XLSX workbook extraction |
python-pptx |
PPTX slide extraction |
poppler-utils |
Additional PDF conversion and rendering support |
tesseract-ocr |
OCR for image-based pages |
tesseract-ocr-chi-sim |
Chinese/English OCR support recommended by the project |
libreoffice |
Broader Office conversion coverage |
antiword and catdoc |
Legacy DOC conversion support |
The skill is text-first. It does not promise perfect preservation of layout, chart semantics, or complete visual structure. Use page, slide, sheet, and cell references where possible, and require review for charts, merged cells, diagrams, and documents whose meaning depends on visual placement.
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 useful skill adds the extraction contract and its operational guardrails.
Define the skill frontmatter
---
name: invoice-document-parser
description: Extract invoice fields from PDFs and Office documents into validated JSON with page references.
---
Specify the workflow in the instructions
- List accepted formats and how inbound references are resolved.
- State the exact fields, data types, units, date format, and null behavior.
- Require page, slide, sheet, or cell references for every populated field.
- Tell the agent to distinguish printed values from calculated values.
- Define the JSON-schema validation step and the response to invalid JSON.
- Set retry limits and a human-review route for missing, conflicting, or low-confidence fields.
- Specify where the source file, extracted record, validation report, and partial notice are saved.
Add readiness checks
Skill readiness can depend on required binaries, environment variables, configuration paths, and operating-system constraints. A host may have OCR or Office conversion utilities installed while a sandbox cannot access them. Check dependencies before processing and fail with a clear setup message that names the missing capability.
Readiness checks:
- confirm the input path or URL is readable;
- confirm the configured PDF and file-size limits;
- check required OCR or conversion binaries when the selected format needs them;
- verify the output location is writable;
- record the skill version and schema version.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose between a tool call, a skill, and a plugin
OpenClaw’s architecture separates three purposes: tools let an agent act, skills teach a reusable workflow, and plugins add integrations or runtime surfaces.
| Approach | Best fit | What you must provide | Main limitation |
|---|---|---|---|
| One-off PDF tool call | A single document or an exploratory extraction | Source path or URL, focused prompt, optional page filter | Consistency, validation, and storage rules must be handled by the calling process. |
| Reusable skill | Repeated invoices, contracts, forms, or multi-format jobs | Markdown instructions, schema, dependency checks, retry/review behavior, retention paths | It still relies on the available tools and provider capabilities. |
| Plugin integration | A new provider, channel, hook, conversion layer, or external system | Runtime integration and a defined interface to the workflow | More deployment and maintenance surface than a skill. |
Start with a tool call while you learn the document’s variation. Promote the process to a skill when the fields and review rules repeat. Add a plugin only when the missing capability is an integration or runtime surface rather than instructions.
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Design a production-safe processing loop
- Ingest. Assign a document ID and retain the original bytes or an immutable reference.
- Classify. Select the extraction contract for an invoice, contract, form, spreadsheet, or presentation.
- Scope. Choose pages, slides, sheets, or ranges; record the selection.
- Extract. Run native PDF input when supported and fallback/OCR paths when required.
- Validate. Check JSON syntax, schema, required fields, types, and document-specific consistency rules.
- Assess completeness. Treat a partial-document notice, missing source references, or unreadable fields as review conditions.
- Persist. Store source, output, validation results, notices, and version metadata together.
- Review or publish. Send exceptions to a queue; only publish records that meet your completeness policy.
For retries, preserve the first response and reason for retry. A second extraction should be distinguishable from an overwrite, especially when a provider, page selection, or OCR path changes.
Troubleshoot common failures
| Symptom | Likely cause | Correction |
|---|---|---|
| Only the first portion of a long PDF is represented | The default 20-page limit or a configured page filter | Select the required pages explicitly or adjust the limit, then verify completeness. |
| The file is rejected or the result is partial | The PDF exceeds the 10 MB default or another configured size boundary | Split or preprocess the source, or change configuration deliberately and record the change. |
| Scanned pages return empty fields | No usable embedded text | Use rendered-page OCR or image analysis and send poor-quality pages to review. |
| Images disappear from fallback extraction | The shared 4,000,000-pixel render budget was exceeded | Reduce selected pages or rendering dimensions and process the remainder separately. |
| The response contains prose around JSON | The prompt did not impose an output contract or the provider did not follow it | Parse defensively, reject nonconforming output, and retry with an explicit JSON-only instruction. |
| Values look plausible but cannot be verified | No page or source references were required | Make references mandatory and retain the original document with the record. |
| Office conversion works on one machine only | Required binaries are absent or inaccessible in the execution sandbox | Run readiness checks and provide a setup error instead of silently switching extraction behavior. |
| Tables or charts are structurally wrong | The Office skill is text-first and does not preserve full visual semantics | Use a visual review path and treat recovered structure as approximate. |
Set realistic accuracy expectations
The documented workflow does not publish validated accuracy, latency, or cost benchmarks for document parsing. OCR quality, layout recovery, chart interpretation, and provider behavior are workload-dependent. Before production use, test a representative set that includes clean born-digital PDFs, scans, rotated pages, tables, multilingual text, long files, and deliberately incomplete documents.
Measure the outcomes that matter to your process: field-level correctness, missing-field rate, false extraction rate, percentage of records requiring review, and frequency of partial-document notices. Keep test documents and expected answers versioned so changes to prompts, providers, skills, or OCR dependencies can be compared.
Implementation checklist
- Use a focused prompt with an explicit schema and null policy.
- Apply page filters to large documents only when the selected pages are known to contain every required field.
- Record whether extraction used native PDF input, fallback text extraction, or OCR.
- Validate JSON before any database or workflow update.
- Require page, slide, sheet, or cell references for populated fields.
- Persist the original source and every partial-document notice.
- Check OCR and Office-conversion dependencies inside the actual execution environment.
- Route low-confidence, visually complex, conflicting, or incomplete results to a human queue.
- Promote repeated work from a one-off tool call to a skill; use a plugin only for a new integration or runtime surface.
How to automate document parsing with OpenClaw
For a single PDF, the PDF tool plus a strict prompt and schema validation is the smallest dependable solution. For scans and Office formats, add the appropriate OCR and conversion path through a skill, document its dependencies, and preserve source references. The automation becomes production-ready when partial results are explicit, every value is traceable, and exceptions have a defined review route.
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