# FOMC Meeting Transcripts & Minutes Scraper (`jungle_synthesizer/fed-fomc-transcripts-scraper`) Actor Scrapes the Federal Reserve FOMC historical archive (1936-present). Extracts transcripts, minutes, Tealbooks, Beige Books, and statements for every FOMC meeting. Optionally extracts PDF plain-text with participant lists and topic tags. - **URL**: https://apify.com/jungle\_synthesizer/fed-fomc-transcripts-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Business, Education - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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. 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 ## FOMC Meeting Transcripts & Minutes Scraper Extracts FOMC meeting artifacts from the [Federal Reserve's historical materials archive](https://www.federalreserve.gov/monetarypolicy/fomc_historical.htm) — the official public record of every Federal Open Market Committee meeting since 1936. Collects transcript PDFs, minutes, Tealbooks, Beige Books, policy statements, and press conference links for every FOMC meeting in the embargo-cleared corpus (currently 1936–2020). Optionally extracts full plain-text from PDFs with participant lists and topic tags. ### What This Scraper Collects - **Transcripts** — verbatim PDFs of FOMC meeting proceedings (released under the 5-year embargo rule) - **Minutes** — official summary of each meeting, released approximately 3 weeks after the meeting - **Tealbooks A & B** — staff economic forecasts and analysis prepared before each meeting - **Beige Book** — regional economic conditions summary from all 12 Federal Reserve Districts - **Agendas** — formal meeting agenda PDFs - **Policy Statements** — press release HTML links for post-meeting rate decisions - **Press Conferences** — chair press conference page links (post-2011) Each record includes: meeting date, meeting type (regular or conference call), artifact type, artifact URL, Fed Chair name at time of the meeting, minutes release date, statement URL, press conference URL, embargo status, and scraped timestamp. With `extractPdfText: true`, also includes plain text, semicolon-separated participant names, and heuristic topic tags. ### Features - Covers the full historical archive from 1936 to 2020 (85 years, 800+ artifacts) - Filter by year range with `startYear` / `endYear` — run only the years you care about - Filter by artifact type — transcripts only, minutes only, or any combination - Identifies Fed Chair by meeting date using a built-in tenure map (Volcker, Greenspan, Bernanke, Yellen, Powell) - Optional PDF text extraction — extracts participant list from the PRESENT section and heuristic topic tags (inflation, employment, interest rates, balance sheet, GDP, credit, financial stability, international) - Detects conference call meetings separately from regular scheduled meetings - Runs on 512 MB memory, no proxy required — federalreserve.gov is fully public ### Who Uses a FOMC Transcript Dataset? - **Macroeconomic research desks** — build time-series analysis of Fed language, voting patterns, and policy signals across chair eras - **AI training shops** — primary-source central-bank verbatim is high-value training data for finance-aware LLMs and monetary policy models - **Academic researchers** — automates what was previously a hand-download task for papers citing FOMC transcripts - **Quantitative analysts** — run NLP models over FOMC text to extract sentiment, policy stance, and forward guidance signals - **Journalists and financial writers** — search the full historical record for specific topics or speeches ### How the Scraper Works 1. Fetches the [Historical Materials by Year](https://www.federalreserve.gov/monetarypolicy/fomc_historical_year.htm) index page to enumerate all available year pages. 2. Filters to years within `startYear`–`endYear` and crawls each per-year page. 3. Parses every meeting panel, classifying each link by artifact type. 4. Emits one record per artifact link, enriched with meeting metadata and chair name. 5. If `extractPdfText: true`, downloads each PDF and extracts plain text, participants, and topic tags before saving. ### Input ```json { "startYear": 2015, "endYear": 2020, "artifactTypes": ["transcript", "minutes"], "maxItems": 0, "extractPdfText": false } ```` | Field | Type | Default | Description | |-------|------|---------|-------------| | `startYear` | Integer | `2015` | Earliest FOMC year to include (1936–2020). | | `endYear` | Integer | `2020` | Latest FOMC year to include (1936–2020). | | `artifactTypes` | Array | `["transcript", "minutes"]` | Types to collect: `transcript`, `minutes`, `tealbook_a`, `tealbook_b`, `beige_book`, `agenda`, `statement`, `press_conference`. | | `maxItems` | Integer | `0` | Maximum artifact records to return. `0` = unlimited. | | `extractPdfText` | Boolean | `false` | Download each PDF and extract plain text. Significantly increases runtime. | #### Collect Only Transcripts, 2010–2020 ```json { "startYear": 2010, "endYear": 2020, "artifactTypes": ["transcript"] } ``` #### Extract PDF Text for NLP Analysis ```json { "startYear": 2015, "endYear": 2020, "artifactTypes": ["transcript"], "extractPdfText": true, "maxItems": 20 } ``` ### Output Schema | Field | Description | |-------|-------------| | `meeting_date` | Meeting date in YYYY-MM-DD (last day for multi-day meetings) | | `meeting_type` | `regular` or `conference_call` | | `year` | Meeting year as integer | | `artifact_type` | `transcript`, `minutes`, `tealbook_a`, `tealbook_b`, `beige_book`, `agenda`, `statement`, or `press_conference` | | `artifact_url` | Full URL to the PDF or HTML artifact | | `artifact_filename` | Filename from the URL | | `artifact_text` | Plain text from PDF (only when `extractPdfText: true`) | | `participants` | Semicolon-separated participant names from the transcript PRESENT section | | `chair_name` | Fed Chair at the time of the meeting | | `minutes_release_date` | Date the minutes were publicly released | | `statement_url` | Policy statement URL (post-2008 meetings) | | `press_conference_url` | Chair press conference URL (post-2011) | | `canonical_url` | Year-index source page URL | | `embargo_status` | `public` for all artifacts in the archive | | `extracted_topics` | Semicolon-separated topic tags from PDF text (when `extractPdfText: true`) | | `scraped_at` | ISO 8601 timestamp | ### Notes - The 5-year embargo means transcripts are only available for meetings that occurred at least 5 years ago. As of 2026, the archive covers through 2020. - Conference call meetings (emergency sessions) are labeled `meeting_type: conference_call`. They were common during the 2008 financial crisis. - PDF text extraction works well for transcripts from 1990 onwards (searchable PDFs). Pre-1990 transcripts may be image-only scans; the extractor returns an empty `artifact_text` for those rather than failing. - All data is public domain (U.S. federal government publication). # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of artifact records to return. 0 = unlimited (full corpus). ## `startYear` (type: `integer`): Earliest FOMC meeting year to include (1936-2020). Defaults to 2015. ## `endYear` (type: `integer`): Latest FOMC meeting year to include (1936-2020). Defaults to 2020. ## `artifactTypes` (type: `array`): Artifact types to collect: transcript, minutes, tealbook\_a, tealbook\_b, beige\_book, agenda, statement, press\_conference. Default: transcript and minutes. ## `extractPdfText` (type: `boolean`): Download each PDF and extract plain text. Increases runtime significantly. Disabled by default. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "startYear": 2015, "endYear": 2020, "artifactTypes": [ "transcript", "minutes" ], "extractPdfText": false } ``` # Actor output Schema ## `results` (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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "startYear": 2015, "endYear": 2020, "artifactTypes": [ "transcript", "minutes" ], "extractPdfText": false }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/fed-fomc-transcripts-scraper").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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "startYear": 2015, "endYear": 2020, "artifactTypes": [ "transcript", "minutes", ], "extractPdfText": False, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/fed-fomc-transcripts-scraper").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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "startYear": 2015, "endYear": 2020, "artifactTypes": [ "transcript", "minutes" ], "extractPdfText": false }' | apify call jungle_synthesizer/fed-fomc-transcripts-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/fed-fomc-transcripts-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "FOMC Meeting Transcripts & Minutes Scraper", "description": "Scrapes the Federal Reserve FOMC historical archive (1936-present). Extracts transcripts, minutes, Tealbooks, Beige Books, and statements for every FOMC meeting. Optionally extracts PDF plain-text with participant lists and topic tags.", "version": "0.1", "x-build-id": "A5FmHr2NVhIcybSot" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~fed-fomc-transcripts-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-fed-fomc-transcripts-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/jungle_synthesizer~fed-fomc-transcripts-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-fed-fomc-transcripts-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/jungle_synthesizer~fed-fomc-transcripts-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-fed-fomc-transcripts-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": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of artifact records to return. 0 = unlimited (full corpus).", "default": 10 }, "startYear": { "title": "Start Year", "type": "integer", "description": "Earliest FOMC meeting year to include (1936-2020). Defaults to 2015.", "default": 2015 }, "endYear": { "title": "End Year", "type": "integer", "description": "Latest FOMC meeting year to include (1936-2020). Defaults to 2020.", "default": 2020 }, "artifactTypes": { "title": "Artifact Types", "type": "array", "description": "Artifact types to collect: transcript, minutes, tealbook_a, tealbook_b, beige_book, agenda, statement, press_conference. Default: transcript and minutes.", "default": [ "transcript", "minutes" ], "items": { "type": "string" } }, "extractPdfText": { "title": "Extract PDF Text", "type": "boolean", "description": "Download each PDF and extract plain text. Increases runtime significantly. Disabled by default.", "default": false } } }, "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 } } } } } } } } } } ```