Reducto

Document parsing API designed specifically for AI/RAG pipelines. Converts complex PDFs (tables, figures, forms, scanned documents) to structured Markdown or JSON optimized for LLM ingestion. Handles multi-column layouts, complex tables, math formulas, and mixed content that generic PDF parsers fail on. Built with agent RAG pipelines as the primary use case.

Evaluated Mar 07, 2026 (0d ago) vv1
Homepage ↗ AI & Machine Learning pdf document-parsing extraction ocr tables markdown rag agents
⚙ Agent Friendliness
60
/ 100
Can an agent use this?
🔒 Security
79
/ 100
Is it safe for agents?
⚡ Reliability
74
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
--
Documentation
82
Error Messages
76
Auth Simplicity
88
Rate Limits
72

🔒 Security

TLS Enforcement
100
Auth Strength
75
Scope Granularity
65
Dep. Hygiene
80
Secret Handling
78

HTTPS enforced. Documents uploaded to Reducto servers — data retention and processing policies should be reviewed for sensitive documents. No SOC2 publicly confirmed.

⚡ Reliability

Uptime/SLA
75
Version Stability
75
Breaking Changes
72
Error Recovery
75
AF Security Reliability

Best When

You need high-quality PDF parsing for agent RAG pipelines, especially documents with complex layouts (tables, figures, math) where other parsers lose structure.

Avoid When

You're processing simple, text-only PDFs — cheaper tools like pdfplumber or AWS Textract will do fine at lower cost.

Use Cases

  • Parse complex PDF documents (financial reports, research papers, legal contracts) into clean Markdown for agent RAG pipelines
  • Extract structured table data from PDFs and financial documents into JSON with column headers preserved for agent data analysis
  • Process scanned or low-quality PDFs with OCR enhancement specifically tuned for high-quality LLM context extraction
  • Convert academic papers with math formulas, charts, and multi-column layouts into agent-consumable text without layout artifacts
  • Batch process large document collections for agent knowledge base ingestion with structured, chunking-ready output

Not For

  • Simple text extraction from clean PDFs — pdfplumber or PyMuPDF are cheaper and faster for straightforward PDFs
  • Real-time document processing at millisecond latency — Reducto prioritizes quality over speed for complex documents
  • Non-document file types (images, audio, spreadsheets as .xlsx) — Reducto focuses on PDF and document formats

Interface

REST API
Yes
GraphQL
No
gRPC
No
MCP Server
No
SDK
Yes
Webhooks
Yes

Authentication

Methods: api_key
OAuth: No Scopes: No

API key passed in Authorization header as Bearer token. Keys provisioned in Reducto dashboard. No scope granularity — single key grants full API access.

Pricing

Model: usage_based
Free tier: Yes
Requires CC: No

Pricing is per-page with volume discounts. Complex documents (tables, figures) may cost more than simple text pages. Free credits available for evaluation without credit card.

Agent Metadata

Pagination
none
Idempotent
Full
Retry Guidance
Documented

Known Gotchas

  • Large document processing is asynchronous — agents must handle async job submission, polling, and completion webhooks rather than synchronous responses
  • Output quality varies significantly by document type — financial PDFs with tables parse much better than scanned handwritten notes
  • Markdown output may contain formatting artifacts for very complex layouts — validate output quality for your document type before production use
  • Page counts for billing are based on parsed pages, not input pages — some preprocessing may alter page count
  • Document confidentiality: PDFs are uploaded to Reducto's servers for processing — sensitive documents require reviewing data handling policy

Alternatives

Full Evaluation Report

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$99

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$3

Score Monitoring

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Continuous monitoring

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Scores are editorial opinions as of 2026-03-07.

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Packages Evaluated
26150
Need Evaluation
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