# SEC EDGAR Filings & Fundamentals API — RAG-Ready (`commonelements/sec-edgar-ai`) Actor Turn any ticker into clean, citation-tagged RAG chunks of SEC filings (10-K, 10-Q, 8-K, Form 4, 13F) plus normalized XBRL fundamentals. Official SEC EDGAR data, no API key. Full-text search, watch mode for new filings. Built for finance AI agents and RAG. - **URL**: https://apify.com/commonelements/sec-edgar-ai.md - **Developed by:** [Harry Schoeller](https://apify.com/commonelements) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $2.00 / 1,000 dataset item scrapeds 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 & Fundamentals API — RAG-Ready Turn the U.S. SEC EDGAR database into **embeddings-ready, citation-rich data for AI / RAG pipelines and finance agents**. Give it tickers (or CIKs, or a full-text query); get back clean Markdown chunks of every **10-K, 10-Q, and 8-K** — split by SEC **Item** section, token-budgeted, deep-linked — plus **XBRL fundamentals** (Revenues, Net Income, EPS, Assets, cash flow) attached to each filing. No API keys. Keyless EDGAR endpoints only, with a polite 10 req/s token-bucket limiter and a descriptive `User-Agent`, exactly as the SEC requires. ### What it does 1. **Ticker → CIK** resolution via the canonical `company_tickers.json`. 2. **Filing listing & filtering** by form type and date window from the EDGAR submissions API. 3. **Full-text search** across all of EDGAR via `efts.sec.gov` (optional `fullTextQuery`). 4. **Primary document fetch** + clean **HTML → Markdown** (Readability + Turndown/GFM — financial tables survive intact). 5. **SEC Item segmentation** (`Item 1A. Risk Factors`, `Item 7. MD&A`, 8-K `Item 2.02`, …) so every chunk carries an Item anchor. 6. **Structure-aware, token-budgeted chunking** with overlap and `headingsPath`. 7. **XBRL fundamentals** per company via the `companyconcept` / `companyfacts` API. ### Why use it (SEO) - **SEC EDGAR API** for AI without writing a scraper. - **10-K / 10-Q / 8-K** filings as **RAG chunks** with working **citations** and **deep links**. - **XBRL fundamentals** — Revenues, Net Income, EPS, Total Assets, operating/investing/financing cash flow. - Drop-in formats for **LangChain**, **pgvector / vector DB** bulk upsert, or generic JSONL. - Built for **financial analysis agents**, equity research copilots, and compliance/risk tooling. ### Input | Field | Type | Default | Notes | |---|---|---|---| | `tickers` | string[] | — | e.g. `["AAPL","MSFT"]` | | `ciks` | string[] | — | raw CIKs, zero-padded internally | | `filingTypes` | string[] | `["10-K","10-Q","8-K"]` | EDGAR form types | | `since` / `until` | string | — | ISO date window on filingDate | | `fullTextQuery` | string | — | phrase search across all EDGAR | | `maxFilingsPerCompany` | int | 25 | per-company cap | | `maxFilingsTotal` | int | 0 | global cap (0 = unlimited) | | `extractChunks` | bool | true | fetch + chunk primary docs | | `chunkSize` / `chunkOverlap` | int | 512 / 64 | token budget | | `splitByItem` | bool | true | segment on SEC Item boundaries | | `includeFundamentals` | bool | true | attach XBRL facts | | `xbrlConcepts` | string[] | built-in ~20 | override the us-gaap allowlist | | `xbrlSource` | enum | `companyconcept` | `companyconcept` or `companyfacts` | | `outputFormat` | enum | `chunks-jsonl` | `chunks-jsonl`, `langchain`, `jsonl-bulk`, `filings-only` | | `userAgentEmail` | string | — | contact email for the SEC User-Agent | At least one of `tickers`, `ciks`, or `fullTextQuery` is required. ### Output Two record kinds land in the dataset: - **`recordType: "chunk"`** — the primary RAG artifact. Markdown text + `headingsPath` + a citation block (ticker, CIK, form, accession, filing date, Item code/title, deep link, and a human-readable `citation` string). - **`recordType: "filing"`** — one per filing, carrying `fundamentals` (latest observation per GAAP concept) and provenance. `outputFormat: "filings-only"` emits only these. #### Default GAAP concepts (us-gaap) `Revenues`, `RevenueFromContractWithCustomerExcludingAssessedTax`, `CostOfRevenue`, `GrossProfit`, `OperatingIncomeLoss`, `NetIncomeLoss`, `EarningsPerShareBasic`, `EarningsPerShareDiluted`, `ResearchAndDevelopmentExpense`, `Assets`, `AssetsCurrent`, `Liabilities`, `LiabilitiesCurrent`, `StockholdersEquity`, `CashAndCashEquivalentsAtCarryingValue`, `LongTermDebtNoncurrent`, `NetCashProvidedByUsedInOperatingActivities`, `NetCashProvidedByUsedInInvestingActivities`, `NetCashProvidedByUsedInFinancingActivities`, `PaymentsToAcquirePropertyPlantAndEquipment`. ### Endpoints used (all keyless) - `https://www.sec.gov/files/company_tickers.json` - `https://data.sec.gov/submissions/CIK##########.json` - `https://efts.sec.gov/LATEST/search-index` - `https://www.sec.gov/Archives/edgar/data/.../index.json` + primary documents - `https://data.sec.gov/api/xbrl/companyconcept/...` and `.../companyfacts/...` ### Local development ```bash npm install npm run build apify run # uses .actor/INPUT.json ```` # Actor input Schema ## `tickers` (type: `array`): Ticker symbols, e.g. AAPL, MSFT. Resolved to CIK via company\_tickers.json. ## `ciks` (type: `array`): Raw CIK numbers (any length; zero-padded internally). Use instead of/with tickers. ## `filingTypes` (type: `array`): EDGAR form types to include. e.g. 10-K, 10-Q, 8-K, S-1, DEF 14A, 20-F. ## `since` (type: `string`): Only filings with filingDate >= this ISO date (YYYY-MM-DD). ## `until` (type: `string`): Only filings with filingDate <= this ISO date (YYYY-MM-DD). Optional. ## `fullTextQuery` (type: `string`): Optional. Phrase search across ALL EDGAR filings via efts.sec.gov. Runs in addition to (or instead of) per-company listing. ## `maxFilingsPerCompany` (type: `integer`): Maximum number of filings to list per company before applying the global total cap. ## `maxFilingsTotal` (type: `integer`): Hard cap across all companies/searches. 0 = unlimited. ## `extractChunks` (type: `boolean`): Fetch each filing's primary document, convert to Markdown, split by Item, and chunk. ## `chunkSize` (type: `integer`): Target tokens per RAG chunk. Larger = fewer, longer chunks. ## `chunkOverlap` (type: `integer`): Token overlap between adjacent chunks to preserve context across boundaries. ## `splitByItem` (type: `boolean`): Pre-segment the filing on SEC Item boundaries before chunking, so every chunk carries its Item anchor. ## `includeFundamentals` (type: `boolean`): Attach top GAAP concept facts per company via the XBRL companyconcept/companyfacts API. ## `xbrlConcepts` (type: `array`): Override the default ~20-concept allowlist (us-gaap taxonomy tag names). Empty = built-in list. ## `xbrlSource` (type: `string`): companyconcept = N small requests (one per concept, exact allowlist). companyfacts = 1 big request, filtered client-side. ## `outputFormat` (type: `string`): RAG record shape. 'filings-only' emits filing metadata + fundamentals with no chunking. ## `userAgentEmail` (type: `string`): SEC requires a descriptive User-Agent with a contact. Sent as 'CommonElements sec-edgar-ai/1.0 '. ## Actor input object example ```json { "tickers": [ "AAPL", "MSFT" ], "ciks": [ "320193" ], "filingTypes": [ "10-K", "10-Q", "8-K" ], "since": "2023-01-01", "fullTextQuery": "material weakness", "maxFilingsPerCompany": 25, "maxFilingsTotal": 0, "extractChunks": true, "chunkSize": 512, "chunkOverlap": 64, "splitByItem": true, "includeFundamentals": true, "xbrlConcepts": [ "Revenues", "NetIncomeLoss" ], "xbrlSource": "companyconcept", "outputFormat": "chunks-jsonl", "userAgentEmail": "admin@commonelements.com" } ``` # Actor output Schema ## `results` (type: `string`): No description ## `runSummary` (type: `string`): No description # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("commonelements/sec-edgar-ai").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 = {} # Run the Actor and wait for it to finish run = client.actor("commonelements/sec-edgar-ai").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 '{}' | apify call commonelements/sec-edgar-ai --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=commonelements/sec-edgar-ai", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SEC EDGAR Filings & Fundamentals API — RAG-Ready", "description": "Turn any ticker into clean, citation-tagged RAG chunks of SEC filings (10-K, 10-Q, 8-K, Form 4, 13F) plus normalized XBRL fundamentals. Official SEC EDGAR data, no API key. Full-text search, watch mode for new filings. Built for finance AI agents and RAG.", "version": "1.0", "x-build-id": "YaaqKihpDgGsX1jS2" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/commonelements~sec-edgar-ai/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-commonelements-sec-edgar-ai", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/commonelements~sec-edgar-ai/runs": { "post": { "operationId": "runs-sync-commonelements-sec-edgar-ai", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/commonelements~sec-edgar-ai/run-sync": { "post": { "operationId": "run-sync-commonelements-sec-edgar-ai", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "tickers": { "title": "Tickers", "type": "array", "description": "Ticker symbols, e.g. AAPL, MSFT. Resolved to CIK via company_tickers.json.", "items": { "type": "string" } }, "ciks": { "title": "CIKs", "type": "array", "description": "Raw CIK numbers (any length; zero-padded internally). Use instead of/with tickers.", "items": { "type": "string" } }, "filingTypes": { "title": "Filing types", "type": "array", "description": "EDGAR form types to include. e.g. 10-K, 10-Q, 8-K, S-1, DEF 14A, 20-F.", "default": [ "10-K", "10-Q", "8-K" ], "items": { "type": "string" } }, "since": { "title": "Since date", "type": "string", "description": "Only filings with filingDate >= this ISO date (YYYY-MM-DD)." }, "until": { "title": "Until date", "type": "string", "description": "Only filings with filingDate <= this ISO date (YYYY-MM-DD). Optional." }, "fullTextQuery": { "title": "Full-text search", "type": "string", "description": "Optional. Phrase search across ALL EDGAR filings via efts.sec.gov. Runs in addition to (or instead of) per-company listing." }, "maxFilingsPerCompany": { "title": "Max filings per company", "minimum": 1, "type": "integer", "description": "Maximum number of filings to list per company before applying the global total cap.", "default": 25 }, "maxFilingsTotal": { "title": "Max filings total", "minimum": 0, "type": "integer", "description": "Hard cap across all companies/searches. 0 = unlimited.", "default": 0 }, "extractChunks": { "title": "Extract RAG chunks", "type": "boolean", "description": "Fetch each filing's primary document, convert to Markdown, split by Item, and chunk.", "default": true }, "chunkSize": { "title": "Chunk size (tokens)", "minimum": 64, "type": "integer", "description": "Target tokens per RAG chunk. Larger = fewer, longer chunks.", "default": 512 }, "chunkOverlap": { "title": "Chunk overlap (tokens)", "minimum": 0, "type": "integer", "description": "Token overlap between adjacent chunks to preserve context across boundaries.", "default": 64 }, "splitByItem": { "title": "Split by Item section", "type": "boolean", "description": "Pre-segment the filing on SEC Item boundaries before chunking, so every chunk carries its Item anchor.", "default": true }, "includeFundamentals": { "title": "Include XBRL fundamentals", "type": "boolean", "description": "Attach top GAAP concept facts per company via the XBRL companyconcept/companyfacts API.", "default": true }, "xbrlConcepts": { "title": "GAAP concepts", "type": "array", "description": "Override the default ~20-concept allowlist (us-gaap taxonomy tag names). Empty = built-in list.", "items": { "type": "string" } }, "xbrlSource": { "title": "XBRL fetch strategy", "enum": [ "companyconcept", "companyfacts" ], "type": "string", "description": "companyconcept = N small requests (one per concept, exact allowlist). companyfacts = 1 big request, filtered client-side.", "default": "companyconcept" }, "outputFormat": { "title": "Output format", "enum": [ "chunks-jsonl", "langchain", "jsonl-bulk", "filings-only" ], "type": "string", "description": "RAG record shape. 'filings-only' emits filing metadata + fundamentals with no chunking.", "default": "chunks-jsonl" }, "userAgentEmail": { "title": "Contact email for User-Agent", "type": "string", "description": "SEC requires a descriptive User-Agent with a contact. Sent as 'CommonElements sec-edgar-ai/1.0 '.", "default": "admin@commonelements.com" } } }, "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 } } } } } } } } } } ```