# SEC EDGAR Filings — Clean Markdown + Diffs (`lizaraco/sec-filings-to-markdown`) Actor Search SEC EDGAR, convert 10-K/10-Q/8-K filings to clean sectioned markdown for LLMs and agents, and diff risk factors year-over-year. Official SEC APIs. - **URL**: https://apify.com/lizaraco/sec-filings-to-markdown.md - **Developed by:** [Shawn Downs](https://apify.com/lizaraco) (community) - **Categories:** AI, Automation, Agents - **Stats:** 2 total users, 1 monthly users, 84.2% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $20.00 / 1,000 filing to markdowns This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## SEC EDGAR Filings — Clean Markdown + Diffs Search SEC EDGAR, convert filings (10-K, 10-Q, 8-K, S-1, …) into **clean, sectioned markdown** ready for LLMs, RAG pipelines, and AI agents — and **diff two filings section by section** to see exactly what a company changed year over year. Built on the SEC's official data APIs (public domain, fair-access compliant), so nothing here breaks when a website redesigns. Unofficial tool; not affiliated with the U.S. SEC. Data only — not investment advice. ### Search SEC filings by full text `mode: "search"` runs EDGAR full-text search with form-type and date filters and returns structured rows: company, CIK, form, filing date, accession number, and a direct document URL. Feed the URLs to the converter below or into your own pipeline. ### Convert a 10-K to markdown for your LLM `mode: "filing-md"` with `company` (ticker like `AAPL` or a CIK) fetches the latest filing(s) and returns clean markdown with inline-XBRL stripped, boilerplate removed, and an index of detected sections (Item 1A Risk Factors, Item 7 MD&A, …). One call, one LLM-ready document. ### Diff risk factors year over year `mode: "diff"` pulls the two most recent filings of a form and compares them **section by section**: which items were added, removed, or changed, with line counts, a change ratio, and a unified-diff excerpt. The fastest way to spot new risk language before reading 300 pages — nobody wants to eyeball two 10-Ks side by side. ### Output schema Search rows: `company, cik, form, filed, accession, doc_url`. Filing rows add `markdown, sections, char_count`. Diff rows: `section, status (added/removed/changed/unchanged), lines_added, lines_removed, change_ratio, diff_excerpt, old_filed, new_filed`. ### Why this actor - Official SEC endpoints only — stable, legal, fast (no scraping arms race). - Fair-access compliant client (rate-paced, contact-identified User-Agent — set `contactEmail` to yours). - Sectioned output built for agent consumption; diffs nobody else on the Store offers. - Errors surface as clear messages, not silent empty datasets. ### Use with AI agents Works through the Apify MCP server: an agent can search filings, pull one as markdown, or ask "what changed in this year's risk factors" and get structured JSON back. Pay-per-event pricing — cents per filing, no subscription. # Actor input Schema ## `mode` (type: `string`): What to do. ## `query` (type: `string`): Full-text search query (search mode), e.g. "artificial intelligence" risk. ## `company` (type: `string`): Ticker (AAPL) or CIK number — used by filing-md and diff modes. ## `forms` (type: `array`): e.g. 10-K, 10-Q, 8-K, S-1. Default 10-K. ## `count` (type: `integer`): filing-md mode: how many latest filings to convert. ## `maxResults` (type: `integer`): search mode row cap. ## `dateFrom` (type: `string`): search mode: only filings on/after this date. ## `dateTo` (type: `string`): search mode: only filings on/before this date. ## `contactEmail` (type: `string`): SEC requires a contact in the User-Agent of automated clients. Set yours; runs still work with the default. ## `proxyGroups` (type: `array`): Optional Apify Proxy groups (e.g. RESIDENTIAL) if SEC blocks the default datacenter pool. ## Actor input object example ```json { "mode": "search", "query": "\"artificial intelligence\" risk factors", "count": 1, "maxResults": 50 } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "query": "\"artificial intelligence\" risk factors" }; // Run the Actor and wait for it to finish const run = await client.actor("lizaraco/sec-filings-to-markdown").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "query": "\"artificial intelligence\" risk factors" } # Run the Actor and wait for it to finish run = client.actor("lizaraco/sec-filings-to-markdown").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "query": "\\"artificial intelligence\\" risk factors" }' | apify call lizaraco/sec-filings-to-markdown --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lizaraco/sec-filings-to-markdown", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SEC EDGAR Filings — Clean Markdown + Diffs", "description": "Search SEC EDGAR, convert 10-K/10-Q/8-K filings to clean sectioned markdown for LLMs and agents, and diff risk factors year-over-year. Official SEC APIs.", "version": "0.1", "x-build-id": "gm7DnxlYZgBY4wrOK" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lizaraco~sec-filings-to-markdown/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lizaraco-sec-filings-to-markdown", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lizaraco~sec-filings-to-markdown/runs": { "post": { "operationId": "runs-sync-lizaraco-sec-filings-to-markdown", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lizaraco~sec-filings-to-markdown/run-sync": { "post": { "operationId": "run-sync-lizaraco-sec-filings-to-markdown", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "search", "filing-md", "diff" ], "type": "string", "description": "What to do.", "default": "search" }, "query": { "title": "Search query", "type": "string", "description": "Full-text search query (search mode), e.g. \"artificial intelligence\" risk." }, "company": { "title": "Company (ticker or CIK)", "type": "string", "description": "Ticker (AAPL) or CIK number — used by filing-md and diff modes." }, "forms": { "title": "Form types", "type": "array", "description": "e.g. 10-K, 10-Q, 8-K, S-1. Default 10-K.", "items": { "type": "string" } }, "count": { "title": "Number of filings", "minimum": 1, "maximum": 20, "type": "integer", "description": "filing-md mode: how many latest filings to convert.", "default": 1 }, "maxResults": { "title": "Max search results", "minimum": 1, "maximum": 500, "type": "integer", "description": "search mode row cap.", "default": 50 }, "dateFrom": { "title": "Filed after (YYYY-MM-DD)", "type": "string", "description": "search mode: only filings on/after this date." }, "dateTo": { "title": "Filed before (YYYY-MM-DD)", "type": "string", "description": "search mode: only filings on/before this date." }, "contactEmail": { "title": "Your contact email (SEC fair-access)", "type": "string", "description": "SEC requires a contact in the User-Agent of automated clients. Set yours; runs still work with the default." }, "proxyGroups": { "title": "Apify Proxy groups", "type": "array", "description": "Optional Apify Proxy groups (e.g. RESIDENTIAL) if SEC blocks the default datacenter pool.", "items": { "type": "string" } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```