If your agent reads documents in production, the extraction API you pick determines how much your team owns versus how much comes out of the box. Some APIs return raw block primitives and leave the schema mapping, evaluation, and review to you. Others handle all of that. Here's how the five best options stack up on the things that actually matter for agent workloads. The comparison covers structured output quality, extraction accuracy on real documents, SDK availability, latency and cost control, and compliance coverage. Each entry covers what it delivers out of the box versus what your team builds on top.
TLDR:
- A document extraction API runs two steps: parsing converts the raw document into clean, structured, LLM-ready content, then extraction pulls your defined fields into schema-defined JSON with confidence scores and citations.
- Rank APIs on structured output quality, per-field confidence scores, multifile support, and compliance coverage.
- AWS Textract returns Block primitives you must map yourself.
- Unstructured outputs typed elements suited for RAG chunking, not schema-defined JSON for agent workflows.
- Extend MAX scores 99.2% mean accuracy on LongArray-Extract (extraction benchmark), with three extraction modes, multifile support across 50 files, and zero data retention.
What Is a Document Extraction API?
A document extraction API is a service that takes an unstructured document, a PDF invoice, a scanned form, a bank statement, and returns structured data your code can act on. The pipeline has two steps. Parsing converts the raw file into clean, structured, LLM-ready content. Extraction pulls the specific fields you define into schema-mapped JSON with confidence scores and citations.
For agents, this is the ingestion layer. An agent reasoning over a loan packet or a clinical record can't act on raw pixels or a wall of OCR text. It needs discrete values tied to labels, tables preserved as rows, and confidence signals it can check before acting.
The API sits between the messy source document and the agent, turning files into inputs a model can reliably use.
How We Ranked These Document Extraction APIs
These are the criteria engineers should weigh when comparing extraction APIs for agent workloads.
- Structured output quality: Does it return schema-defined JSON with per-field confidence scores and source citations, or raw block primitives you have to map yourself?
- Extraction accuracy on real documents: How does it hold up on dense forms, large arrays, complex tables, and mixed document types, not clean PDFs alone?
- API-first design: Clean REST endpoints with SDKs in Python, TypeScript, Java, and Go that drop into an agent runtime.
- Latency and cost control: Can you tune speed and spend per workflow, or is there one mode for everything?
- Agent runtime compatibility: Multifile extraction, large-array handling, and confidence scoring an agent can check.
- Enterprise readiness: SOC 2, HIPAA, GDPR, zero data retention, and flexible deployment.
LlamaParse was also evaluated. On LongArray-Extract, LlamaParse completes 35 of 45 documents at 34.7% mean accuracy. It is a strong parsing layer for teams building LlamaIndex-native RAG stacks, but it does not ship with built-in evaluation, schema versioning, a human review UI, or multifile extraction. For agent workloads that need schema-defined JSON with confidence scores and production controls, those gaps require substantial custom engineering. See the full Extend vs. LlamaParse breakdown for details.
Best Overall Document Extraction API: Extend
Engineers are building agents that can take on increasingly complex work, but those agents can't act on critical data trapped in messy, unstructured documents. Extend delivers a production-ready document ingestion layer that turns those documents into structured output that agents can reliably use, freeing up engineers to focus on building their core product. It provides APIs that cover parsing, extraction, splitting, classification, form filling, and Workflows, along with a complete toolkit to support deployment and controls over accuracy, latency, and cost.
Two open benchmarks cover parsing and extraction separately. On parsing, RealDoc-Bench shows Extend Parse 2.0 at 95.7% Q&A accuracy, leading every other provider including LlamaParse Agentic (92.1%) and Reducto Agentic (91.1%). On extraction, LongArray-Extract shows Extend MAX at 99.2% mean accuracy with 100% run completion on 45 PDFs, running nearly 3x faster than the closest peer.
What makes it fit agent runtimes:
- Extraction modes: Three agentic document extraction modes (light, performance, and agentic) to tune cost, latency, and accuracy per workflow.
- Multifile extraction: Up to 50 related files per run, built for loan packets and claim files.
- Schema-defined JSON: Per-field confidence scores and source citations, plus SDKs in Python, TypeScript, Java, and Go, an MCP server, a CLI agent skill, and open-source templates.
- Zero data retention: SOC 2, HIPAA, and GDPR compliance, with flexible hosting for sensitive, high-volume workloads.
Reducto
Reducto is a document parsing and extraction API for developer teams that need fast OCR and parsing as a foundational layer. The API turns PDFs and other files into structured output.
What They Offer
- Parsing API: Strong baseline performance on standard documents.
- Deep Extract: Complex extractions, with a V3 alpha released July 2026.
- Classify endpoint: Routes documents before extraction.
- Batch Queue: High-volume async workloads at discounted credits.
Good for: teams that need a focused parsing API for straightforward document types and will build their own schema versioning, evaluation, and quality tooling on top.
Limitation: Reducto offers Standard, Agentic, and Deep Extract modes, but has no schema versioning and no built-in evaluation on standard plans. Evaluation is gated behind higher-tier annual plans. There is no human review UI and no native support for multi-step document workflow orchestration. Teams must build QA, routing, and accuracy monitoring on top.
Bottom line: Extend vs. Reducto. Extend covers multi-mode processing, built-in evaluation, schema versioning, and agentic optimization, so teams ship without building evaluation, versioning, or QA tooling from scratch.
AWS Textract
AWS Textract is a managed AWS ML service that extracts text, key-value pairs, tables, layout elements, queries, and signatures from documents. It returns a graph of Block objects your application must map to its own data model.
What They Offer
- Document detection: Text, table, form, query, layout, and signature types via API.
- Async batch processing: Multi-page documents through S3.
- Amazon A2I integration: Low-confidence human review routing.
- Pre-built APIs: Specific document types including AnalyzeExpense, AnalyzeID, and Analyze Lending.
Good for: AWS-native teams that need low-cost extraction from clean documents and can own schema mapping, evaluation, and orchestration using S3, Lambda, Step Functions, and A2I.
The ExtractBench structured extraction benchmark covers field-level accuracy across credit agreements, research papers, resumes, and SEC filings.
Limitation: Textract returns Block primitives, so teams build all schema mapping, evaluation, and review as custom code. See Extend vs. AWS Textract for a full accuracy and pricing breakdown.
Bottom line: Extend delivers schema-defined JSON, over 25 points higher accuracy on real-world documents, and a production layer that removes the need to build evaluation, orchestration, and review from scratch.
Google Document AI
Google Document AI is a managed GCP service that extracts structured data through a processor-based API. Built on Vertex AI, it organizes OCR, classification, splitting, and extraction around pre-trained and custom processors.
What They Offer
- Enterprise Document OCR: Supports 200+ languages.
- Pre-trained processors: Invoices, receipts, W-2s, bank statements, and identity documents.
- Custom Extractor: Generative AI for zero-shot and few-shot extraction.
- Layout parser: Image and table annotations (including table recognition and context-aware chunking) went GA May 2026; Gemini-powered layout parser models remain in Preview (for a deeper look at how a Parse API powers AI document agents, see our infrastructure breakdown).
- Native integrations: Cloud Storage, BigQuery, and Vertex AI.
Good for: GCP-native teams processing document types in Google's pre-trained catalog.
Limitation: Document AI is cloud-only and requires a GCP project, service account, and SDK setup first. There is no built-in evaluation, schema versioning, or review UI, so teams build all quality tooling themselves. Document types outside the catalog can use the generative Custom Extractor for zero-shot or few-shot extraction, or traditional custom processor training with labeled data.
Bottom line: Extend vs. Google Cloud Document AI. Extend adds deployment flexibility, built-in evaluation and schema versioning, and higher accuracy on the complex layouts pre-trained processors struggle with.
Unstructured
Unstructured is an open-source document ETL library and enterprise API that partitions documents into typed element objects (titles, paragraphs, tables, images) built for RAG pipelines and AI data ingestion. It covers 60+ file types across a self-hosted library and a managed API.
What They Offer
- Partitioning: 60+ file types with auto, fast, hi-res, and OCR-only strategies.
- Chunking and enrichment: Embedding generation and image/table enrichment via the enterprise API.
- Data connectors: 40+ connectors for moving content to vector databases and cloud storage.
- MCP server: Unstructured Transform for agent integration.
- SDKs: Python and JavaScript, with SaaS and in-VPC deployment.
Good for: teams building RAG pipelines that need broad file coverage and vector database connectors.
Limitation: output is typed elements, not schema-defined JSON tied to your business fields. No per-field confidence scores with citations, schema versioning, review UI, or field-level accuracy evaluation.
Bottom line: Unstructured fits chunking and embedding for RAG. For a direct comparison, see Extend vs. Unstructured on schema-defined extraction, per-field accuracy, and production controls.
Feature Comparison Table of Document Extraction APIs
Here is how the five ranked options map against the criteria from our ranking framework.
| Capability | Extend | Reducto | AWS Textract | Google Document AI | Unstructured |
|---|---|---|---|---|---|
| Schema-defined JSON output | Yes | Yes | No (Block graph) | Partial (processor-dependent) | No (typed elements) |
| Document processing confidence scores with citations | Yes | No | No | No | No |
| Multiple extraction modes (light/performance/agentic) | Yes | No | No | No | No |
| Multifile extraction (up to 50 files) | Yes | No | No | No | No |
| Large-array extraction (1,000+ rows) | Yes | In beta | No | No | No |
| Built-in schema versioning | Yes | No | No | No | No |
| Built-in evaluation framework | Yes | No | No | No | No |
| Human-in-the-loop review UI | Yes | No | Via Amazon A2I (custom) | No | No |
| SDKs (Python, TypeScript, Java, Go) | Yes | Partial | Yes | Yes | Partial |
| Self-hosted / on-premises deployment | Yes | Limited | No | No | Yes |
| SOC 2, HIPAA, GDPR compliance | Yes | SOC 2, HIPAA | Yes | Yes | SOC 2 |
| Zero data retention policy | Yes | Only available with platform fee | No | No | No |
| Tool calling for document-processing agents / MCP server | Yes | Yes | No | No | Yes |
Why Extend Is the Best Document Extraction API for AI Agents
Agents can only act on data they trust: schema-faithful JSON, accuracy you can measure on the documents that matter, and control over latency and cost on every call. Use those criteria to pick your document parser for AI agents. On extraction, Extend MAX delivers 99.2% mean accuracy with full run completion on LongArray-Extract, running nearly 3x faster than the closest peer.
No other tested system pairs that accuracy with three extraction modes, multifile extraction, built-in evaluation, and schema versioning. That combination ships with a zero data retention policy in one API-first product built for agents, so if you are shipping agents that read documents in production, it removes the guesswork.
Final Thoughts on the Best Document Extraction APIs for AI Agents
Every API in this list handles documents, but they diverge quickly on what your agents get back and how much you build on top. If your team can own schema mapping, evaluation, and quality tooling, lower-level options like Textract or Reducto are worth a look. If you want that layer handled for you, Extend is built around exactly that. Start with the output your agents need and work backward to the API that gets you there.
FAQ
How do I choose the right document extraction API for my AI agent from this list?
Start with your output requirements. If your agent needs schema-defined JSON with per-field confidence scores it can check before acting, only Extend delivers that out of the box. If you're building RAG pipelines and need broad file coverage, Unstructured fits that use case. If you're already deep in AWS or GCP, Textract and Google Document AI reduce integration friction but require you to own schema mapping and quality tooling yourself.
Is Extend more accurate than Reducto and LlamaParse on real-world documents?
Yes, on open benchmarks using real production documents. LongArray-Extract measures extraction quality: Extend MAX achieves 99.2% mean accuracy with 100% run completion across 45 PDFs, running nearly 3x faster than the closest peer.
When should I use AWS Textract or Google Document AI instead of Extend?
Use Textract if you process clean, simple documents inside an existing AWS infrastructure and your team can own schema mapping with S3, Lambda, and Step Functions. Use Google Document AI if your document types fall within Google's pre-trained processor catalog and you're building on GCP. For complex layouts, large arrays, or agent workloads that need built-in evaluation and schema versioning, neither cloud provider delivers those controls without substantial custom engineering.
Can Extend handle the volume and document complexity required for enterprise agent pipelines?
Yes. Extend supports documents up to 2,000 pages per file, multifile extraction across up to 50 related files per run, and batch processing for high-volume workloads. Customers including Flatiron Health process 100M+ pages, and Checkr reports 95-100% accuracy on high-volume background screening documents with 60-100% reduction in manual review depending on document type.
What separates Extend from Unstructured for agent use cases?
Unstructured returns typed element objects suited for chunking and embedding in RAG pipelines. Extend returns schema-defined JSON with per-field confidence scores and source citations that an agent can act on directly. For agents that need to read a loan packet, extract 50 specific fields, and check confidence before writing to a downstream system, Unstructured's output structure requires extensive additional mapping work that Extend's extraction API handles natively.
