EdgarTools

EdgarTools — Python Library for SEC EDGAR Filings

EdgarTools is a Python library for SEC EDGAR filings as structured data: financial statements, insider trades, fund holdings, proxy statements and dozens of other filing types, through one consistent Python API. SEC publishes every filing since 1994 for free, and almost none of it is ready to use; EdgarTools turns a filing into a typed Python object. Free and open source, MIT-licensed.

[Docs](https://edgartools.readthedocs.io/en/latest/) · [Cheat sheet](https://edgartools.readthedocs.io/en/latest/quick-guide/) · Agents: [Skill](https://edgartools.readthedocs.io/en/latest/ai/skills/) · [MCP server](https://edgartools.readthedocs.io/en/latest/ai/mcp-setup/) · Hosted: [edgar.tools](https://www.edgar.tools)

Quick Start

1. Install. The package is edgartools; the module you import is edgar. Python 3.11+.

uv add edgartools        # or: pip install edgartools

2. Identify yourself to the SEC. EDGAR requires a contact email on every request. There is no key, signup or rate-limit tier.

from edgar import Company, set_identity

set_identity("your.name@example.com")

3. Get data.

# Five fiscal years of standardized income statement, from XBRL

Company("NVDA").income_statement(periods=5) #> Total Revenue $215.9B $130.5B $60.9B

Why not just call SEC's APIs?

data.sec.gov gives you JSON facts and filing indexes. Everything past that is your code. EdgarTools does that part:

• Section text and markdown. Item 1A, Item 7 or any Item of a 10-K or 10-Q as text, and whole filings as markdown for RAG. Raw HTML is where hand-rolled parsers break.
• Parsed forms, not XML. 13F holdings, Form 4 transactions, N-PORT portfolios and 8-K items as typed objects and DataFrames.
• Statements that line up. Rows standardized across filers, so "Total Revenue" means the same thing for a bank and a chipmaker, plus discrete quarters (Q4 derived from the 10-K) and trailing twelve months.
• Search and plumbing. Full-text search over filing content, the live filing feed, rate limiting, caching and offline storage.

It runs in your own process, MIT-licensed, with no API key and no vendor quota; SEC's fair-access limit of 10 requests a second still applies, and EdgarTools keeps to it.

What you can do

Full-text search across SEC filings

Search the text of every filing since 2001, not just form types and dates.

from edgar import search_filings

Every 10-K filed since 2025 that mentions tariffs

results = search_filings("tariff", forms="10-K", start_date="2025-01-01") hit = results[0] hit.accession_number, hit.form, hit.filed, hit.company # a hit's fields; filed is the filing date hit.get_filing() # the Filing behind a hit, ready for .obj() or .markdown()

Structured: 8-Ks that reported a cybersecurity incident (Item 1.05) in 2025

search_filings(forms="8-K", items="1.05", start_date="2025-01-01", end_date="2025-12-31") #> 19 total

[Full-text search guide →](https://edgartools.readthedocs.io/en/latest/guides/full-text-search/)

Financial statements over several years

Company("MSFT").income_statement(periods=10)  # also .balance_sheet() and .cash_flow_statement()

Financial Statements guide →

One metric across companies

Pick rows by their standardized label, never by position: the first row of an income statement is not always total revenue.

import pandas as pd

revenue = { ticker: Company(ticker).income_statement(periods=3, as_dataframe=True) .set_index("label").filter(like="FY").loc["Total Revenue"] / 1e9 for ticker in ["JPM", "PFE", "CAT"] } pd.DataFrame(revenue).round(1) # $ billions #> FY 2025 182.4 62.6 67.6

Quarterly figures and trailing twelve months

nvda = Company("NVDA")

nvda.get_ttm_revenue() # the last four quarters, summed nvda.income_statement(period="quarterly", periods=8) # discrete quarters; Q4 is derived from the 10-K

Sections and markdown for RAG

filing = Company("NVDA").latest("10-K")

filing.markdown() # the whole filing as markdown

tenk = filing.obj() tenk.risk_factors # Item 1A as text tenk["Item 1C"] # any Item by number

[Sections guide →](https://edgartools.readthedocs.io/en/latest/concepts/data-objects/)

Find filings, by anything

from edgar import find, get_current_filings, get_filings

get_filings(2025, 4, form="10-K") # every 10-K filed in Q4 2025 get_current_filings() # the live feed, as SEC posts it find("0000320193-25-000079") # an accession number, CIK or ticker

[Finding filings guide →](https://edgartools.readthedocs.io/en/latest/guides/searching-filings/)

13F institutional holdings

thirteenf = Company("BRK.A").get_filings(form="13F-HR").latest().obj()

thirteenf.holdings # one row per security: Issuer, Cusip, Ticker, SharesPrnAmount, Value, ...

[Institutional Holdings guide →](https://edgartools.readthedocs.io/en/latest/guides/thirteenf-data-object-guide/)

Insider trades from Form 4

form4 = Company("TSLA").get_filings(form="4").latest().obj()

form4.to_dataframe() # buys, sells, grants and exercises

[Insider Trades guide →](https://edgartools.readthedocs.io/en/latest/insider-filings/)

Fund holdings from N-PORT

from edgar import Fund

Fund("VOO").get_portfolio() # the latest N-PORT holdings as a DataFrame

[Fund Portfolios guide →](https://edgartools.readthedocs.io/en/latest/guides/nport-data-object-guide/)

8-K events and earnings releases

eightk = find("0001045810-26-000073").obj()  # NVIDIA, 2026-08-26

eightk.items # ['Item 2.02', 'Item 9.01'] eightk.earnings # the EX-99.1 press release tables, parsed

[Current Events guide →](https://edgartools.readthedocs.io/en/latest/eightk-filings/)

Library or hosted?

EdgarTools is this library: free, MIT-licensed, running in your own Python process. [edgar.tools](https://www.edgar.tools) is the hosted platform built on it: the same parsing, kept running and current, with every answer cited to its filing. You are not paying for the parsing; you are paying for it to be run and answerable. No feature is ever removed from the library to sell the platform.

Both have an MCP server: the library's runs locally (see AI Integration below), and edgar.tools' is hosted at app.edgar.tools/mcp. Attach one per client, not both: they overlap and split your assistant's routing.

AI integration

EdgarTools ships an agent skill and a local MCP server, both in the ai extra. For an assistant that cannot run Python, use the hosted connector from the section above instead.

pip install "edgartools[ai]"

python -c "from edgar.ai import install_skill; install_skill()" # the skill, for Claude Code and Claude Desktop

The MCP server works with any MCP client. For Claude Desktop, add it to claude_desktop_config.json:

{

"mcpServers": { "edgartools": { "command": "uvx", "args": ["--from", "edgartools[ai]", "edgartools-mcp"], "env": { "EDGAR_IDENTITY": "Your Name your.email@example.com" } } } }

uvx needs uv; without it, pip install "edgartools[ai]" and run python -m edgar.ai. Setup for other clients: MCP server guide · Skill guide.

Support the project

The SEC changes filing formats and ships a new XBRL taxonomy every year. Sponsorship is what keeps the parsers current and pays for new ones as new disclosures appear.

• [GitHub Sponsors](https://github.com/sponsors/dgunning): monthly or one-time. Companies can sponsor on their existing GitHub bill.
• [Buy Me a Coffee](https://www.buymeacoffee.com/edgartools): a one-time thank-you.
• [Consulting](https://www.edgar.tools/consulting): help from the library's author with SEC data pipelines and financial AI built on it.

Docs, help and contributing

• Documentation · Cheat sheet · Notebooks · Blog
• Questions go to Discussions; bugs and feature requests to Issues.
• Contributions are welcome, including AI-assisted ones. Read the contributing guide and point your assistant at AGENTS.md.

EdgarTools is distributed under