PDF To JSON API

[ PDF To JSON API ]

Turn PDFs into Structured JSON Instantly with PDF To JSON API

Use LlamaParse to capture tables, layout, and visuals accurately, then output clean, verifiable JSON.

Parse PDFs into Structured JSON with LlamaParse

LlamaParse turns messy PDFs into clean, structured JSON your API can trust, using agentic document parsing that understands layout, tables, and embedded visuals. You get verifiable fields with confidence signals and fewer manual fixes, so downstream workflows run reliably as volume and document formats change.

Best-in-Class Accuracy

PDF to JSON API for Every Industry

Venture-Backed Startups

Ship customer-facing document workflows (uploads, onboarding, summaries) without building brittle PDF parsing code by using LlamaParse to turn messy decks, invoices, and contracts into clean Markdown/JSON. Natural-language parsing instructions let your team iterate extraction and schema changes in hours, while tier-based agentic processing keeps accuracy high without blowing up unit economics.

Banking and Lending Operations

Automate intake for loan packages by extracting financial statements, pay stubs, and tax forms into auditable JSON with page-level citations and confidence scores for faster underwriting. Layout-aware table extraction prevents scrambled numbers in multi-column statements, reducing manual rekeying and exceptions that stall approvals.

Insurance Claims and Underwriting

Parse adjuster reports, photos, repair estimates, and loss runs so claim systems can reconcile totals and coverage details even when key data lives in tables, images, or scanned PDFs. Auto-correction loops and multimodal parsing reduce supplement churn by catching mismatched line items and interpreting visual evidence without forcing teams into slow human review.

Manufacturing and Supply Chain

Convert POs, invoices, packing slips, and spec sheets into structured records that match ERP fields, even when vendors change layouts or embed tables inside PDFs. JSON mode with granular metadata enables deterministic validation and matching across documents, improving three-way match accuracy and speeding up AP and receiving.

The Solution

OCR Features Built for a Reliable Document Processing API

01

REST API + SDKs

Use LlamaParse via a simple REST API with maintained Python and TypeScript SDKs, so document ingestion fits cleanly into your existing services. This lets you ship a document processing API quickly without hand-rolling file handling, retries, and parsing plumbing.

02

Layout-Aware Parsing

LlamaParse understands page structure (sections, columns, headers/footers) so extracted content keeps the right reading order. That means your API returns consistent, usable text even when inputs vary wildly in layout.

03

Table Extraction to Markdown

It accurately captures complex tables and nested cells and reconstructs them into clean Markdown or structured representations. Your API consumers get data that’s ready for downstream ETL or LLM workflows instead of scrambled rows and brittle post-processing.

04

JSON Output + Metadata

Return structured JSON with rich metadata like page numbers, element types, and spatial coordinates for traceability. This makes it straightforward to build an API that supports auditing, precise lookups, and human-in-the-loop verification when needed.

Technical OCR documentation

Agentic OCR, documented for builders.

Explore our developer guides to easily connect your document pipelines to LlamaParse.

Explore the documentation

Eliminate Human Error

Our AI catches the typos that tired eyes miss.

Format Flexibility

Export to Excel, JSON, XML, or directly via API.

Enterprise-Grade Security

SOC2 Type II compliant with end-to-end encryption.

No-Code Templates

Train the tool on your specific forms in minutes, not days.

Lightning Speed

Average processing time of <3 seconds per page.

LlamaParse’s support of a wide variety of filetypes and its accuracy of parsing made it the best tool we tested in our evaluations. The LlamaIndex team was very responsive and we were off to the races within a day.

Turn data chaos into data clarity.

Parse your documents free. 10,000 credits to start.

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Common FAQs

How Does it Work?

01

How quickly can we integrate the Document Processing API into our product?

You can start parsing documents in minutes via the REST API, or use the maintained Python and TypeScript SDKs to integrate directly into your existing services. The SDKs handle common plumbing like file upload patterns and retries so your team can ship faster with less custom code.

02

Will the output keep the correct reading order for multi-column or complex layouts?

Yes—layout-aware parsing preserves page structure like sections, columns, and headers/footers so text comes back in a reliable reading order. This reduces downstream cleanup and prevents “scrambled” content when documents vary widely in formatting.

03

How does table extraction work, and can I use it for ETL or LLM workflows?

Tables are extracted with support for complex structures, including nested cells, and can be returned as clean Markdown or structured representations. That makes the data immediately usable for ETL pipelines, analytics, or LLM applications without fragile post-processing.

04

Can I get structured JSON output with metadata for auditing and traceability?

Yes—the API can return JSON enriched with metadata such as page numbers, element types, and spatial coordinates. This makes it easy to build audit trails, power precise lookups, and support human-in-the-loop review when accuracy is critical.

05

What happens if a document is messy, inconsistent, or changes format over time?

The parser is designed to handle real-world variability by understanding layout rather than relying on brittle assumptions. You’ll get more consistent results across different templates, scans, and formatting changes—reducing maintenance as your document sources evolve.

06

How do we validate results and debug issues when something looks off?

Rich metadata (like page references and coordinates) helps you trace any extracted field back to where it came from in the original document. This makes it straightforward to verify outputs, spot edge cases, and improve your workflow with targeted rules or review steps.