Camelot PDF Table Extractor
Extracts structured tables from PDF files into pandas DataFrames using either lattice mode (ruled lines) or stream mode (whitespace), enabling programmatic access to tabular data embedded in PDFs.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Processes files locally with no network calls; Ghostscript dependency has a history of CVEs and should be kept patched
⚡ Reliability
Best When
Your agent needs to programmatically extract tabular data from machine-generated PDFs with clear ruled lines or consistent column spacing.
Avoid When
PDFs are scanned images without a text layer, contain highly irregular table layouts, or require real-time extraction at high throughput.
Use Cases
- • Extract financial tables from PDF reports so an agent can analyze revenue, cost, or balance sheet data without manual copy-paste
- • Parse government or regulatory PDF documents to retrieve structured datasets for downstream agent reasoning
- • Pull invoice line-item tables from PDF invoices into structured records for automated accounts-payable workflows
- • Batch-process a directory of PDF research papers and extract all numeric data tables for statistical aggregation
- • Convert scanned or machine-generated PDF forms into tabular CSV output that an agent can query with SQL
Not For
- • Scanned image-only PDFs with no embedded text layer — Camelot requires a text layer; use an OCR tool first for image PDFs
- • Extracting non-tabular content such as body text, headers, or images from PDFs — use PyMuPDF or pdfplumber for general text extraction
- • Production-scale distributed PDF processing pipelines — Camelot is a single-process library without built-in concurrency or job queuing
Interface
Authentication
Self-hosted Python library; no authentication required
Pricing
MIT licensed open-source library; only costs are compute and Ghostscript system dependency
Agent Metadata
Known Gotchas
- ⚠ Ghostscript must be installed as a system dependency; its absence raises a confusing ImportError or FileNotFoundError rather than a clear dependency message
- ⚠ Lattice mode silently returns empty tables for PDFs without visible ruling lines; agents must check table.df.empty before trusting results
- ⚠ Stream mode accuracy degrades significantly on multi-column layouts where whitespace gaps between columns are inconsistent
- ⚠ camelot.read_pdf() is synchronous and blocking; large multi-page PDFs can freeze an agent event loop if called without threading or subprocess isolation
- ⚠ The accuracy score in TableList is a heuristic, not a guarantee — a 99% score can still contain incorrectly merged or split cells that require downstream validation
Alternatives
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Scores are editorial opinions as of 2026-03-06.