sec2md 0.1.21

Convert SEC EDGAR filings to LLM-ready Markdown for AI agents and agentic RAG

sec2md

Transform messy SEC filings into clean, structured Markdown. Built for AI. Optimized for retrieval. Traceable to the source.

Apple 10-K: Raw SEC HTML (left) vs. sec2md output (right)

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The Problem

SEC filings are the worst documents you'll ever feed to an LLM — 200 pages of nested HTML, XBRL tags, invisible elements, and tables-within-tables.

When you throw this at a standard parser:

• Tables break — Financial statements become garbled text. Your model hallucinates numbers.
• Pages vanish — Can't cite sources. Can't trace answers back. Compliance says no.
• Sections blur — Risk Factors and MD&A become one wall of text. Retrieval pulls the wrong context.
• Structure is lost — Headers, emphasis, lists — the cues LLMs use to reason — gone.

And even the converters that handle the HTML well still throw away provenance. You get clean text with no way to trace it back to where it came from in the original filing. For production RAG on regulated documents, that's a dealbreaker.

The Solution

import sec2md

parser = sec2md.Parser(filing_html)
pages = parser.get_pages()

# 60 pages | 293 citable elements | 46,238 tokens
# Tables intact. Pages tracked. Sections detected. Every element traceable.

sec2md rebuilds SEC filings as clean, semantic Markdown — preserving the structure, tables, and pagination that make retrieval possible. But unlike generic converters, it also preserves the full citation chain from every piece of output back to the source HTML.

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Traceability

Every paragraph, table, and heading gets a stable element ID that maps directly to a DOM node in the original filing HTML. From chunk to element to source — the chain is unbroken.

chunks = sec2md.chunk_pages(pages, chunk_size=512)

chunk = chunks[5]
print(chunk.element_ids)
# ['sec2md-p12-p0-a1b2c3d4', 'sec2md-p12-t0-e5f6a7b8', ...]
print(chunk.page_range)          # (12, 13)
print(chunk.display_page_range)  # (45, 46) — as printed in the filing

# Open the original filing in your browser — scrolls to the source, highlights in yellow
chunk.visualize(parser.html())

chunk.visualize() opens the original filing HTML, scrolls to the chunk's source elements, and highlights them.

Every Chunk carries page numbers (both sequential and the original display page from the filing footer), element IDs for citation, and a direct link back to the source HTML. Every Element can do the same:

element = chunk.elements[0]
element.visualize(parser.html())  # Highlights just this element

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What You Can Do

Extract sections

Don't process 200 pages when you only need Risk Factors:

from sec2md import Item10K

sections = sec2md.extract_sections(pages, filing_type="10-K")
risk = sec2md.get_section(sections, Item10K.RISK_FACTORS)

print(risk.page_range)  # (7, 19)
print(risk.tokens)       # 11,474

Works across 10-K, 10-Q, 8-K, and 20-F.

Filing Type	Section Extraction	Notes
10-K	18 items (ITEM 1-16)	Full PART/ITEM detection
10-Q	11 items (Parts I & II)	Including financial statements
8-K	41 items (1.01-9.01)	With exhibit parsing from 9.01
20-F	Items 1-19, 16A-16I	Foreign private issuers
DEF 14A, Exhibits	--	Parsed as clean Markdown

Handle complex tables

SEC tables are notoriously complex — rowspans, colspans, merged cells, currency symbols in separate columns. Some filings don't even use <table> tags, building tables from absolutely-positioned CSS divs instead. sec2md handles both, and large tables are automatically split across chunks with headers preserved.

Works with edgartools

Pair with edgartools for end-to-end filing pipelines:

from edgar import Company

company = Company("AAPL")
filing = company.get_filings(form="10-K").latest()
pages = sec2md.parse_filing(filing.html())

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Installation

pip install sec2md

Getting Started

Try the Getting Started notebook — parse a real 10-K, extract sections, chunk for RAG, and visualize traceability in under a minute.

Documentation

Full documentation: sec2md.readthedocs.io

• Quickstart Guide
• Convert Filings
• Extract Sections
• Chunking for RAG
• EdgarTools Integration
• API Reference

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Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

MIT © 2025