# FEC Campaign Finance Scraper (`automation-lab/fec-campaign-finance-scraper`) Actor Download official FEC campaign-finance bulk data for candidates, committees, contributions, and disbursements. Filter by cycle, IDs, state, and party. - **URL**: https://apify.com/automation-lab/fec-campaign-finance-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## FEC Campaign Finance Scraper Download searchable Federal Election Commission campaign-finance rows from public bulk data files. The actor streams official FEC ZIP files, filters rows locally, and returns normalized JSON for candidates, committees, contributions, and disbursements. It does not require an OpenFEC API key. ### What does FEC Campaign Finance Scraper do? FEC Campaign Finance Scraper helps you collect campaign-finance data from official Federal Election Commission bulk downloads. It can extract: - ๐Ÿง‘โ€๐Ÿ’ผ Candidate master records - ๐Ÿ›๏ธ Committee master records - ๐Ÿ’ต Individual contribution records - ๐Ÿ” Committee-to-candidate contribution records - ๐Ÿงพ Operating disbursement records Each output row includes normalized IDs, names, dates, amounts, source URL, row number, raw FEC fields, and `scrapedAt`. ### Who is it for? This scraper is useful for: - ๐Ÿ—ณ๏ธ Political compliance teams monitoring committees and candidate filings - ๐Ÿ“ฐ Journalists investigating federal campaign money flows - ๐Ÿ”Ž Donor researchers looking for contribution patterns - ๐Ÿ“Š Campaign-intelligence vendors building recurring data pipelines - ๐Ÿงช Civic data analysts who need reproducible FEC extracts - ๐Ÿงพ Legal and audit teams checking finance records by cycle ### Why use this actor? The FEC provides excellent public bulk data, but it is packaged as large pipe-delimited ZIP files. This actor makes those files easier to use in automation: - No browser automation - No login - No OpenFEC API key - Bounded `maxItems` for cost control - Filters for candidate IDs, committee IDs, states, and parties - Normalized JSON output ready for export ### Data sources The actor downloads files from official FEC bulk-download URLs such as: - `https://www.fec.gov/files/bulk-downloads/2024/cn24.zip` - `https://www.fec.gov/files/bulk-downloads/2024/cm24.zip` - `https://www.fec.gov/files/bulk-downloads/2024/indiv24.zip` - `https://www.fec.gov/files/bulk-downloads/2024/pas224.zip` - `https://www.fec.gov/files/bulk-downloads/2024/oppexp24.zip` ### Supported datasets | Input `dataType` | FEC file family | Typical use | | --- | --- | --- | | `candidates` | Candidate master | Candidate IDs, names, party, office, state | | `committees` | Committee master | Committee IDs, names, treasurer, connected org | | `individual_contributions` | Individual contributions | Donor names, employers, dates, amounts | | `committee_contributions` | Committee contributions | PAC/committee support to candidates | | `disbursements` | Operating expenditures | Payees, purposes, dates, amounts | ### How much does it cost to extract FEC campaign finance data? This actor uses pay-per-event pricing: a small start fee plus a per-row charge for each saved dataset item. Use a small `maxItems` value for tests. Increase `maxItems` for production exports after confirming your filters. ### Input options | Field | Type | Description | | --- | --- | --- | | `dataType` | string | FEC dataset to download and parse | | `cycle` | integer | Even-numbered election cycle, for example `2024` | | `maxItems` | integer | Maximum matching rows to save | | `candidateIds` | string[] | Optional FEC candidate ID filters | | `committeeIds` | string[] | Optional FEC committee ID filters | | `states` | string[] | Optional two-letter state filters | | `parties` | string[] | Optional party filters such as `DEM` or `REP` | ### Example input ```json { "dataType": "committees", "cycle": 2024, "states": ["NY"], "maxItems": 100 } ```` ### Output data Each item can include the following fields: | Field | Description | | --- | --- | | `dataType` | Dataset selected in the input | | `cycle` | FEC election cycle year | | `candidateId` | FEC candidate ID when available | | `candidateName` | Candidate or recipient name when available | | `committeeId` | FEC committee ID when available | | `committeeName` | Committee name when available | | `contributorName` | Individual or entity contributor name | | `recipientName` | Recipient candidate name | | `payeeName` | Disbursement payee name | | `transactionDate` | Normalized transaction date | | `amount` | Contribution or disbursement amount | | `state` | State from the FEC row | | `party` | Party from master records | | `office` | Candidate office when available | | `rowNumber` | Row number in the bulk file | | `sourceUrl` | Official FEC ZIP URL | | `scrapedAt` | ISO timestamp for collection | | `raw` | Original parsed FEC columns | ### Output example ```json { "dataType": "committees", "cycle": 2024, "committeeId": "C00001743", "committeeName": "LOCAL 1814 ILA POLITICAL ACTION AND EDUCATION FUND", "state": "NY", "sourceUrl": "https://www.fec.gov/files/bulk-downloads/2024/cm24.zip", "scrapedAt": "2026-07-04T03:00:15.705Z", "raw": { "committeeId": "C00001743" } } ``` ### How to run 1. Choose a `dataType`. 2. Choose an even-numbered `cycle`. 3. Add filters if you know candidate IDs, committee IDs, states, or parties. 4. Set `maxItems`. 5. Run the actor and export the dataset as JSON, CSV, Excel, or via API. ### Tips for better results - Start with `committees` or `candidates` to find IDs. - Use `candidateIds` or `committeeIds` before running large transaction exports. - Use `states` to reduce broad candidate, committee, and transaction pulls. - Keep `maxItems` low for exploratory work. - For historical analysis, run one cycle at a time. ### Integrations You can connect the scraper to: - Google Sheets or Airtable for campaign monitoring dashboards - BI tools for political spending analysis - Databases for recurring compliance archives - Alerting workflows that watch for new donor or committee patterns - LLM research agents that summarize campaign-finance activity ### API usage: Node.js ```js import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); const run = await client.actor('automation-lab/fec-campaign-finance-scraper').call({ dataType: 'committees', cycle: 2024, states: ['NY'], maxItems: 100, }); console.log(run.defaultDatasetId); ``` ### API usage: Python ```python from apify_client import ApifyClient import os client = ApifyClient(os.environ['APIFY_TOKEN']) run = client.actor('automation-lab/fec-campaign-finance-scraper').call(run_input={ 'dataType': 'candidates', 'cycle': 2024, 'states': ['CA'], 'parties': ['DEM'], 'maxItems': 100, }) print(run['defaultDatasetId']) ``` ### API usage: cURL ```bash curl -X POST "https://api.apify.com/v2/acts/automation-lab~fec-campaign-finance-scraper/runs?token=$APIFY_TOKEN" \ -H 'Content-Type: application/json' \ -d '{"dataType":"committees","cycle":2024,"states":["NY"],"maxItems":100}' ``` ### MCP usage Use this actor from Claude Desktop or Claude Code through Apify MCP: `https://mcp.apify.com/?tools=automation-lab/fec-campaign-finance-scraper` Add the MCP server in Claude Code: ```bash claude mcp add apify-fec-campaign-finance "https://mcp.apify.com/?tools=automation-lab/fec-campaign-finance-scraper" ``` Claude Desktop JSON configuration: ```json { "mcpServers": { "apify-fec-campaign-finance": { "url": "https://mcp.apify.com/?tools=automation-lab/fec-campaign-finance-scraper" } } } ``` Example prompts: - "Run the FEC campaign finance scraper for 2024 NY committees and summarize labor PACs." - "Extract 2024 candidate master rows for California Democrats." - "Pull committee contribution rows for a candidate ID and prepare a CSV." ### Legality This actor collects public government data published by the Federal Election Commission. Users are responsible for complying with Apify terms, FEC data-use expectations, and any rules that apply to their analysis, publication, or compliance workflow. ### FAQ #### Do I need an OpenFEC API key? No. The actor uses public FEC bulk downloads, not authenticated OpenFEC API endpoints. ### Troubleshooting #### Why did I get fewer rows than `maxItems`? Your filters may be narrow for that cycle and dataset. Remove filters or try a different data type. #### Why are some fields empty? FEC bulk files have different columns per dataset. The actor keeps unavailable fields empty and preserves parsed original values in `raw`. ### Related scrapers Explore other automation-lab actors for government, compliance, and public-data workflows: - https://apify.com/automation-lab/fec-electronic-filing-parser - https://apify.com/automation-lab/website-contact-finder - https://apify.com/automation-lab/company-email-finder ### Changelog - Initial version: HTTP-only FEC bulk-data streamer with filters and normalized output. ### Notes For very broad transaction pulls, FEC files can be large. The actor streams ZIP contents and stops at `maxItems`, but targeted filters are still recommended for faster runs. # Actor input Schema ## `dataType` (type: `string`): Public FEC bulk dataset to stream and normalize. ## `cycle` (type: `integer`): Even-numbered FEC election cycle year, such as 2024 or 2022. ## `maxItems` (type: `integer`): Stop after this many matching FEC rows. Keep low for tests; increase for research exports. ## `candidateIds` (type: `array`): Optional FEC candidate IDs such as H4NY03099. Leave empty for all candidates. ## `committeeIds` (type: `array`): Optional FEC committee IDs such as C00828541. Leave empty for all committees. ## `states` (type: `array`): Optional two-letter state filters such as NY, CA, or TX. ## `parties` (type: `array`): Optional party filters from FEC master records, such as DEM, REP, IND, or LIB. ## Actor input object example ```json { "dataType": "committees", "cycle": 2024, "maxItems": 20, "candidateIds": [], "committeeIds": [], "states": [], "parties": [] } ``` # Actor output Schema ## `overview` (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 = { "dataType": "committees", "cycle": 2024, "maxItems": 20, "candidateIds": [], "committeeIds": [], "states": [], "parties": [] }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/fec-campaign-finance-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "dataType": "committees", "cycle": 2024, "maxItems": 20, "candidateIds": [], "committeeIds": [], "states": [], "parties": [], } # Run the Actor and wait for it to finish run = client.actor("automation-lab/fec-campaign-finance-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "dataType": "committees", "cycle": 2024, "maxItems": 20, "candidateIds": [], "committeeIds": [], "states": [], "parties": [] }' | apify call automation-lab/fec-campaign-finance-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/fec-campaign-finance-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "FEC Campaign Finance Scraper", "description": "Download official FEC campaign-finance bulk data for candidates, committees, contributions, and disbursements. Filter by cycle, IDs, state, and party.", "version": "0.1", "x-build-id": "wLMS4P4Nlp1F3ckH6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~fec-campaign-finance-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-fec-campaign-finance-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~fec-campaign-finance-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-fec-campaign-finance-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~fec-campaign-finance-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-fec-campaign-finance-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "dataType", "cycle", "maxItems" ], "properties": { "dataType": { "title": "Data type", "enum": [ "candidates", "committees", "individual_contributions", "committee_contributions", "disbursements" ], "type": "string", "description": "Public FEC bulk dataset to stream and normalize.", "default": "committees" }, "cycle": { "title": "Election cycle", "minimum": 1978, "maximum": 2030, "type": "integer", "description": "Even-numbered FEC election cycle year, such as 2024 or 2022.", "default": 2024 }, "maxItems": { "title": "Maximum rows", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Stop after this many matching FEC rows. Keep low for tests; increase for research exports.", "default": 20 }, "candidateIds": { "title": "Candidate IDs", "type": "array", "description": "Optional FEC candidate IDs such as H4NY03099. Leave empty for all candidates.", "items": { "type": "string" } }, "committeeIds": { "title": "Committee IDs", "type": "array", "description": "Optional FEC committee IDs such as C00828541. Leave empty for all committees.", "items": { "type": "string" } }, "states": { "title": "States", "type": "array", "description": "Optional two-letter state filters such as NY, CA, or TX.", "items": { "type": "string" } }, "parties": { "title": "Parties", "type": "array", "description": "Optional party filters from FEC master records, such as DEM, REP, IND, or LIB.", "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 } } } } } } } } } } ```