EdgarTools — Python Library for SEC EDGAR Filings

EdgarTools is a Python library for accessing SEC EDGAR filings as structured data. Parse financial statements, insider trades, fund holdings, proxy statements, and 20+ other filing types with a consistent Python API — in a few lines of code. Free and open source.

!EdgarTools SEC filing data extraction demo

Why EdgarTools?

SEC EDGAR has every filing back to 1994, free — and almost none of it is ready to use. EdgarTools turns any filing into a typed Python object, so a 10-K's revenue is one line instead of an afternoon of XBRL parsing.

# Apple's latest income statement — rendered, standardized, done
from edgar import Company
Company("AAPL").get_financials().income_statement()

Financial Statements Income, balance sheet, cash flow in one call XBRL-standardized for cross-company comparison

Every Filing Type 13F holdings, Form 4 insiders, 8-K events, funds, proxies Typed objects + pandas DataFrames for 20+ forms

Built for Pipelines & AI Rate-limit aware, smart caching, enterprise mirrors Built-in MCP server + LLM-ready text for RAG

How It Works

Everything starts with a Company or a Filing. Call .obj() and you get a typed object built for that form — its data ready as pandas DataFrames and clean text.

The same typed output that reads cleanly in a notebook drops straight into a pipeline: DataFrames for your warehouse, LLM-ready text and an MCP server for your AI stack, rate-limit and enterprise-mirror aware for scale.

Quick Start

1. Install

pip install edgartools

2. Identify yourself to the SEC — EDGAR requires an email with every request. No key, no signup, no rate-limit tier; set it once:

from edgar import *
set_identity("[email protected]")

3. Get data — every filing is now a few lines away:

# Standardized financial statements, straight from XBRL
Company("AAPL").get_financials().income_statement()
The latest insider Form 4 as a structured object
Company("AAPL").get_filings(form="4").latest().obj()

!Apple SEC Form 4 insider transactions parsed into a structured Python object

Next: explore the Use Cases below, or dive into the documentation and Quick Guide.

Use Cases

Financial statements from 10-K and 10-Q filings

financials = Company("MSFT").get_financials()
financials.balance_sheet()     # all line items
financials.income_statement()  # revenue, net income, EPS

Insider trading from SEC Form 4

form4 = Company("TSLA").get_filings(form="4").latest().obj()
form4.to_dataframe()  # insider buy/sell transactions

13F institutional holdings & hedge fund portfolios

thirteenf = get_filings(form="13F-HR").latest().obj()
thirteenf.holdings  # every portfolio position as a DataFrame

8-K current reports & corporate events

eightk = get_filings(form="8-K").latest().obj()
eightk.items  # reported event items

XBRL financial data across companies

facts = Company("AAPL").get_facts()
facts.query().by_concept("Revenue").to_dataframe()  # revenue history as a DataFrame

Key Features

EdgarTools supports all SEC form types including 10-K annual reports, 10-Q quarterly filings, 8-K current reports, 13F institutional holdings, Form 4 insider transactions, proxy statements (DEF 14A), S-1 registration statements, N-CSR fund reports, N-MFP money market data, N-PORT fund portfolios, Schedule 13D/G ownership, Form D offerings, Form C crowdfunding, and Form 144 restricted stock. Parse XBRL financial data, extract text sections, and convert filings to pandas DataFrames.

Comparison with Alternatives

EdgarTools is a Python library that talks directly to SEC EDGAR. sec-api is the best-known hosted API that returns JSON. Both parse filings — the difference is how you work with the data, and what it costs you.

| | EdgarTools | sec-api | |---|------------|---------| | Cost | Free, MIT | $49+/mo | | Data format | Typed Python objects → DataFrames | JSON you parse yourself | | Where it runs | In your process — no key, no quotas, no vendor lock-in | Hosted API — key + rate tiers | | Filing coverage | 20+ typed forms (10-K, 8-K, 13F, N-PORT, proxy…) | 15+ structured endpoints | | AI / MCP | Built in | | | Open source | Inspect, fork, self-host | Proprietary |

Bottom line: in Python, EdgarTools gives you typed objects, AI-native output, and the full SEC corpus — free, open, and inspectable, with no keys or bills. pip install edgartools and you're querying filings in two lines.

Library or hosted?

EdgarTools is the open-source library — SEC-filing primitives you compose in your own code, free and self-run.

edgar.tools is the hosted platform built on that same open engine: the full SEC corpus as a managed service, so your team gets the data without running the pipeline — and without the black box of a closed API.

Reach for the library when you want control in your own stack; reach for edgar.tools when you'd rather not operate it yourself.

AI Integration

Use EdgarTools with Claude Code & Claude Desktop

EdgarTools includes an MCP server and AI skills for Claude Desktop and Claude Code. Ask questions in natural language and get answers backed by real SEC data.

• "Compare Apple and Microsoft's revenue growth rates over the past 3 years"
• "Which Tesla executives sold more than $1 million in stock in the past 6 months?"

pip install "edgartools[ai]"
python -c "from edgar.ai import install_skill; install_skill()"

{
  "mcpServers": {
    "edgartools": {
      "command": "uvx",
      "args": ["--from", "edgartools[ai]", "edgartools-mcp"],
      "env": {
        "EDGAR_IDENTITY": "Your Name [email protected]"
      }
    }
  }
}

❤️ Support This Project

EdgarTools runs in production at hedge funds, fintechs, and research desks — MIT-licensed, no keys, no subscriptions, and maintained by one person.

The SEC amends filing formats every quarter and ships a new XBRL taxonomy every year. Sponsorship is what keeps 20+ parsers current and funds new extractors as fresh disclosure types appear.

_{Recurring sponsorship + corporate tiers via GitHub · One-time thanks via Buy Me a Coffee}

For teams running EdgarTools in production

If EdgarTools is in your data pipeline, GitHub Sponsors offers corporate tiers from $250 to $1,500/mo with:

• Response SLAs (24h–48h first response on critical issues)
• Quarterly strategy calls and roadmap input
• Logo placement in this README
• 7-day early access for internal regression testing
• Annual invoicing through GitHub — procurement-friendly

Community & Support

Documentation & Resources

• Documentation
• Notebooks / Examples
• Quick Guide
• EdgarTools Blog

Get Help & Connect

• GitHub Issues - Bug reports and feature requests
• Discussions - Questions and community discussions

Contributing

Contributions welcome:

• Code: Fix bugs, add features, improve documentation
• Examples: Share interesting use cases and examples
• Feedback: Report issues or suggest improvements
• Spread the Word: Star the repo, share with colleagues

Professional Services

Need help building production SEC data infrastructure? The creator of EdgarTools offers consulting for teams building financial AI products:

• SEC Data Sprint (1–3 days) — Working prototype on your data
• Architecture Review (1–2 weeks) — Pipeline audit with prioritized fixes
• Pipeline Build (2–4 weeks) — Production-ready code, tests, and handoff

EdgarTools is distributed under the MIT License

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