# FRED Economic Data Scraper (`parseforge/fred-scraper`) Actor Scrape economic data from the Federal Reserve’s FRED API, including series details, observations, categories, and metadata. Access indicators like CPI, GDP, unemployment rates, and thousands more. Ideal for economists, researchers, and analysts needing automated, up-to-date economic intelligence. - **URL**: https://apify.com/parseforge/fred-scraper.md - **Developed by:** [ParseForge](https://apify.com/parseforge) (community) - **Categories:** Automation, Other - **Stats:** 13 total users, 0 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.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 ![ParseForge Banner](https://github.com/ParseForge/apify-assets/blob/ad35ccc13ddd068b9d6cba33f323962e39aed5b2/banner.jpg?raw=true) ## πŸ“Š FRED Scraper > πŸš€ **Collect economic data from the Federal Reserve (FRED) in minutes.** Search by keyword or category. Filter by frequency, units, and seasonal adjustment. Export time series with observations. No coding, no FRED API key required. > πŸ•’ **Last updated:** 2026-04-23 Β· **πŸ“Š 20+ fields** per series Β· **πŸ” 6 filters** Β· **πŸ“ˆ Time series observations** Β· **🚫 No auth** required The **FRED Scraper** collects economic data series from the Federal Reserve Economic Data (FRED) database, returning **20+ fields per series**: series ID, title, frequency, units, seasonal adjustment, observation start/end dates, popularity, and optionally full time series observations (date + value pairs). Runs support up to 1,000,000 series on a paid plan. FRED hosts over 800,000 economic time series from 100+ sources. The Actor supports keyword search with category, frequency, units, seasonal adjustment, and sort filters. | 🎯 Target Audience | πŸ’‘ Primary Use Cases | |---|---| | Economists, data scientists, financial analysts, policy researchers, journalists, BI teams | Economic research, financial modeling, trend analysis, policy research, data journalism | --- ### πŸ“‹ What the FRED Scraper does Keyword search with 6 filters: - πŸ” **Keyword search.** Free-text search across series titles and descriptions. - πŸ“‚ **Category filter.** Browse by FRED category ID. - πŸ“… **Frequency filter.** Daily, weekly, monthly, quarterly, annual. - πŸ“Š **Units filter.** Levels, change, percent change, etc. - 🌑️ **Seasonal adjustment.** Seasonally adjusted or not. - πŸ“ˆ **Observations toggle.** Optionally fetch full time series data points. Each series record includes ID, title, frequency, units, seasonal adjustment, observation dates, popularity, and (when enabled) array of date-value observation pairs. > πŸ’‘ **Why it matters:** downloading FRED data manually means clicking through the website series by series. This Actor exports structured economic data at scale, ready for your financial models, research databases, or BI dashboards. --- ### 🎬 Full Demo _🚧 Coming soon: a 3-minute walkthrough showing how to go from sign-up to a downloaded dataset._ --- ### βš™οΈ Input
InputTypeDefaultBehavior
maxItemsinteger10Max series. Free: limited. Paid: up to 1,000,000.
searchTextstring""Keyword search across series.
categoryIdstring""FRED category ID.
frequencystring""Daily, weekly, monthly, quarterly, annual.
unitsstring""Levels, change, percent change.
seasonalAdjustmentstring""Seasonally adjusted or not.
includeObservationsbooleanfalseFetch full time series observations.
sortOrderstring""Sort by popularity, title, or date.
**Example: GDP data with observations.** ```json { "searchText": "GDP", "frequency": "quarterly", "includeObservations": true, "maxItems": 10 } ```` **Example: monthly unemployment rate series.** ```json { "searchText": "unemployment rate", "frequency": "monthly", "seasonalAdjustment": "sa", "maxItems": 20 } ``` > ⚠️ **Good to Know:** FRED is maintained by the Federal Reserve Bank of St. Louis and hosts data from 100+ government and international sources. Enabling includeObservations adds full time series data but increases processing time. *** ### πŸ“Š Output Each series record contains **20+ fields**. Download the dataset as CSV, Excel, JSON, or XML. #### 🧾 Schema | Field | Type | Example | |---|---|---| | πŸ†” seriesId | string | `"GDP"` | | πŸ“ title | string | `"Gross Domestic Product"` | | πŸ“… frequency | string | `"Quarterly"` | | πŸ“Š units | string | `"Billions of Dollars"` | | 🌑️ seasonalAdjustment | string | `"Seasonally Adjusted Annual Rate"` | | πŸ“… observationStart | string | `"1947-01-01"` | | πŸ“… observationEnd | string | `"2026-01-01"` | | ⭐ popularity | number | `95` | | πŸ“ notes | string | `"BEA Account Code: A191RC"` | | πŸ“ˆ observations | array | null | `[{ "date": "2025-10-01", "value": 28900.5 }]` | | πŸ”— fredUrl | string | `"https://fred.stlouisfed.org/series/GDP"` | | πŸ•’ scrapedAt | ISO 8601 | `"2026-04-16T00:00:00.000Z"` | #### πŸ“¦ Sample records
πŸ“ˆ GDP with observations ```json { "seriesId": "GDP", "title": "Gross Domestic Product", "frequency": "Quarterly", "units": "Billions of Dollars", "seasonalAdjustment": "Seasonally Adjusted Annual Rate", "observationStart": "1947-01-01", "observationEnd": "2026-01-01", "popularity": 95, "notes": "BEA Account Code: A191RC", "observations": [ { "date": "2025-10-01", "value": 28900.5 }, { "date": "2025-07-01", "value": 28650.2 } ], "fredUrl": "https://fred.stlouisfed.org/series/GDP", "scrapedAt": "2026-04-16T00:00:00.000Z" } ```
πŸ“Š Unemployment rate series ```json { "seriesId": "UNRATE", "title": "Unemployment Rate", "frequency": "Monthly", "units": "Percent", "seasonalAdjustment": "Seasonally Adjusted", "observationStart": "1948-01-01", "observationEnd": "2026-03-01", "popularity": 92, "notes": "The unemployment rate represents the number of unemployed...", "observations": null, "fredUrl": "https://fred.stlouisfed.org/series/UNRATE", "scrapedAt": "2026-04-16T00:00:00.000Z" } ```
πŸ“… Niche series with sparse data ```json { "seriesId": "MORTGAGE30US", "title": "30-Year Fixed Rate Mortgage Average in the United States", "frequency": "Weekly", "units": "Percent", "seasonalAdjustment": "Not Seasonally Adjusted", "observationStart": "1971-04-02", "observationEnd": "2026-04-10", "popularity": 85, "notes": null, "observations": null, "fredUrl": "https://fred.stlouisfed.org/series/MORTGAGE30US", "scrapedAt": "2026-04-16T00:00:00.000Z" } ```
*** ### ✨ Why choose this Actor | | Capability | |---|---| | πŸ“Š | **800,000+ series.** Full FRED database from 100+ sources. | | πŸ” | **6 filters.** Keyword, category, frequency, units, seasonal adjustment, sort. | | πŸ“ˆ | **Time series observations.** Optional full date-value pairs per series. | | πŸ“… | **Frequency control.** Daily, weekly, monthly, quarterly, annual. | | ⭐ | **Popularity ranking.** Sort by how popular each series is on FRED. | | ⚑ | **Scalable.** From single series lookups to full category sweeps. | | 🚫 | **No authentication.** No FRED API key needed. | > πŸ“Š FRED is the most widely used source of economic data in the world. Structured access powers every financial model, policy analysis, and economic research workflow. *** ### πŸ“ˆ How it compares to alternatives | Approach | Cost | Coverage | Refresh | Observations | Setup | |---|---|---|---|---|---| | **⭐ FRED Scraper** *(this Actor)* | $5 free credit, then pay-per-use | Full FRED | **Live per run** | Optional per series | ⚑ 2 min | | FRED API (direct) | Free with rate limits | Full | Real-time | Yes | ⏳ Hours (API key + client) | | Manual FRED website | Free | One series at a time | Manual | Manual CSV export | πŸ•’ Hours | | Paid economic data platforms | $500-50,000/year | Multi-source | Varies | Yes | 🐒 Weeks | Pick this Actor when you want FRED data on demand, with category and frequency filters, without writing API client code. *** ### πŸš€ How to use 1. πŸ“ **Sign up.** [Create a free account with $5 credit](https://console.apify.com/sign-up?fpr=vmoqkp) (takes 2 minutes). 2. 🌐 **Open the Actor.** Go to the FRED Scraper page on the Apify Store. 3. 🎯 **Set input.** Enter a keyword, pick frequency and units, toggle observations. 4. πŸš€ **Run it.** Click **Start** and let the Actor collect your data. 5. πŸ“₯ **Download.** Grab your results in the **Dataset** tab as CSV, Excel, JSON, or XML. > ⏱️ Total time from signup to downloaded dataset: **3-5 minutes.** No coding required. *** ### πŸ’Ό Business use cases
#### πŸ“Š Financial Modeling - Pull GDP, CPI, and interest rate series - Build macro indicator dashboards - Track Fed policy metrics in real time - Power quantitative models with fresh data #### πŸ“ˆ Economic Research - Analyze employment trends over decades - Compare regional economic indicators - Study monetary policy impact - Build longitudinal datasets
#### πŸ“° Data Journalism - Track inflation and cost-of-living metrics - Visualize housing market trends - Report on labor market changes - Build interactive economic charts #### 🏒 Business Intelligence - Monitor consumer confidence and spending - Track industry-specific indicators - Build automated economic briefings - Feed dashboards with fresh macro data
*** *** ### 🌟 Beyond business use cases Data like this powers more than commercial workflows. The same structured records support research, education, civic projects, and personal initiatives.
#### πŸŽ“ Research and academia - Empirical datasets for papers, thesis work, and coursework - Longitudinal studies tracking changes across snapshots - Reproducible research with cited, versioned data pulls - Classroom exercises on data analysis and ethical scraping #### 🎨 Personal and creative - Side projects, portfolio demos, and indie app launches - Data visualizations, dashboards, and infographics - Content research for bloggers, YouTubers, and podcasters - Hobbyist collections and personal trackers
#### 🀝 Non-profit and civic - Transparency reporting and accountability projects - Advocacy campaigns backed by public-interest data - Community-run databases for local issues - Investigative journalism on public records #### πŸ§ͺ Experimentation - Prototype AI and machine-learning pipelines with real data - Validate product-market hypotheses before engineering spend - Train small domain-specific models on niche corpora - Test dashboard concepts with live input
### πŸ€– Ask an AI assistant about this scraper Open a ready-to-send prompt about this ParseForge actor in the AI of your choice: - πŸ’¬ [**ChatGPT**](https://chat.openai.com/?q=How%20do%20I%20use%20the%20FRED%20Economic%20Data%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - 🧠 [**Claude**](https://claude.ai/new?q=How%20do%20I%20use%20the%20FRED%20Economic%20Data%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - πŸ” [**Perplexity**](https://perplexity.ai/search?q=How%20do%20I%20use%20the%20FRED%20Economic%20Data%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - πŸ…’ [**Copilot**](https://copilot.microsoft.com/?q=How%20do%20I%20use%20the%20FRED%20Economic%20Data%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) ### ❓ Frequently Asked Questions
πŸ’³ Do I need a paid Apify plan to run this actor? No. You can start right now on the free Apify plan, which includes **$5 in free monthly credit**. That is enough to run this actor several times and explore the output before committing to anything. Paid plans unlock higher limits, more concurrent runs, and larger datasets. [Create a free Apify account here](https://console.apify.com/sign-up?fpr=vmoqkp) to get started.
🚨 What happens if my run fails or returns no results? Failed runs are not charged. If the source site changes, proxies get rate-limited, or a specific input matches nothing, re-run the actor or open our [contact form](https://tally.so/r/BzdKgA) and we will investigate. You can also check the run log in the Apify console to see why the run stopped.
πŸ“ How many items can I scrape per run? Free users are limited to **10 items per run** so you can preview the output and confirm the actor works for your use case. Paid users can raise maxItems up to **1,000,000** per run. [Upgrade here](https://console.apify.com/sign-up?fpr=vmoqkp) if you need full scale.
πŸ•’ How fresh is the data? Every run fetches live data at the moment of execution. There is no cache or delay: the records you get reflect what the source returned at that moment. Schedule the actor to maintain a rolling snapshot of the data you need.
πŸ§‘β€πŸ’» Can I call this actor from my own code? Yes. Apify exposes every actor as a REST endpoint and ships first-class SDKs for [Node.js](https://docs.apify.com/sdk/js) and [Python](https://docs.apify.com/sdk/python). You can start a run, read the dataset, and handle webhooks from your own app in a few lines. All you need is your Apify API token.
πŸ“€ How do I export the data? Every Apify dataset can be downloaded in one click from the console as CSV, JSON, JSONL, Excel, HTML, XML, or RSS. You can also pull results programmatically via the [Apify API](https://docs.apify.com/api/v2) or stream them into BigQuery, S3, and other destinations through built-in integrations.
πŸ“… Can I schedule the actor to run automatically? Yes. Use the Apify scheduler to run the actor on any cadence, from hourly to monthly. Results are saved to your dataset and can be delivered to webhooks, email, Slack, cloud storage, or automation tools such as Zapier and Make. ***
### πŸ”Œ Automating FRED Scraper Control the scraper programmatically for scheduled runs and pipeline integrations: - 🟒 **Node.js.** Install the apify-client NPM package. - 🐍 **Python.** Use the apify-client PyPI package. - πŸ“š See the [Apify API documentation](https://docs.apify.com/api/v2) for full details. The [Apify Schedules feature](https://docs.apify.com/platform/schedules) lets you trigger this Actor on any cron interval. Weekly pulls keep your economic data pipeline in sync. ### πŸ”Œ Integrate with any app FRED Scraper connects to any cloud service via [Apify integrations](https://apify.com/integrations): - [**Make**](https://docs.apify.com/platform/integrations/make) - Automate multi-step workflows - [**Zapier**](https://docs.apify.com/platform/integrations/zapier) - Connect with 5,000+ apps - [**Slack**](https://docs.apify.com/platform/integrations/slack) - Get run notifications - [**Airbyte**](https://docs.apify.com/platform/integrations/airbyte) - Pipe economic data into your warehouse - [**GitHub**](https://docs.apify.com/platform/integrations/github) - Trigger runs from commits - [**Google Drive**](https://docs.apify.com/platform/integrations/drive) - Export datasets straight to Sheets You can also use webhooks to trigger downstream actions when a run finishes. Push fresh economic data into your models, or alert your team in Slack. *** ### πŸ”— Recommended Actors - [**πŸ“Š USAspending Scraper**](https://apify.com/parseforge/usaspending-scraper) - Federal spending data - [**πŸ“Š Indexmundi Scraper**](https://apify.com/parseforge/indexmundi-scraper) - Global economic indicators - [**🏦 FDIC Bank Scraper**](https://apify.com/parseforge/fdic-bank-scraper) - Bank financial data - [**🏦 FINRA BrokerCheck Scraper**](https://apify.com/parseforge/finra-brokercheck-scraper) - Broker regulatory data - [**πŸ“‹ GSA eLibrary Scraper**](https://apify.com/parseforge/gsa-elibrary-scraper) - Government contract data > πŸ’‘ **Pro Tip:** browse the complete [ParseForge collection](https://apify.com/parseforge) for more financial and government data scrapers. *** **πŸ†˜ Need Help?** [**Open our contact form**](https://tally.so/r/BzdKgA) to request a new scraper, propose a custom data project, or report an issue. *** > **⚠️ Disclaimer:** this Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by the Federal Reserve Bank of St. Louis or the Federal Reserve System. All trademarks mentioned are the property of their respective owners. Only publicly available FRED data is collected. # Actor input Schema ## `maxItems` (type: `integer`): Maximum number of series to collect. Free users: Limited to 100. Paid users: Optional, max 1,000,000. Leave empty for unlimited (paid users only). ## `searchText` (type: `string`): Full-text search query to find economic data series. Searches across series titles, units, frequency, and tags. Example: 'cpi', 'unemployment', 'gdp' ## `categoryId` (type: `integer`): Filter results by FRED category ID. Example: 10 (National Accounts), 1 (Production & Business Activity) ## `frequency` (type: `string`): Filter by data frequency ## `units` (type: `string`): Filter by units of measurement ## `seasonalAdjustment` (type: `string`): Filter by seasonal adjustment ## `includeObservations` (type: `boolean`): Whether to include observation data (date-value pairs) for each series. ⚠️ WARNING: This will significantly increase processing time and cost. The scraper fetches all historical observations for each series, which requires additional API requests and may take several minutes for large datasets. ## `sortOrder` (type: `string`): Field to sort results by ## `orderBy` (type: `string`): Sort order direction ## Actor input object example ```json { "maxItems": 10, "searchText": "cpi", "includeObservations": false, "sortOrder": "search_rank", "orderBy": "desc" } ``` # Actor output Schema ## `results` (type: `string`): Complete dataset with all scraped FRED series including metadata and observations ## `overview` (type: `string`): Overview view of FRED series with key fields displayed in a table format # 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 = { "maxItems": 10, "searchText": "cpi" }; // Run the Actor and wait for it to finish const run = await client.actor("parseforge/fred-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "maxItems": 10, "searchText": "cpi", } # Run the Actor and wait for it to finish run = client.actor("parseforge/fred-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "maxItems": 10, "searchText": "cpi" }' | apify call parseforge/fred-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=parseforge/fred-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "FRED Economic Data Scraper", "description": "Scrape economic data from the Federal Reserve’s FRED API, including series details, observations, categories, and metadata. Access indicators like CPI, GDP, unemployment rates, and thousands more. Ideal for economists, researchers, and analysts needing automated, up-to-date economic intelligence.", "version": "1.0", "x-build-id": "fEw464aY8JLvBoLg0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/parseforge~fred-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-parseforge-fred-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/parseforge~fred-scraper/runs": { "post": { "operationId": "runs-sync-parseforge-fred-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/parseforge~fred-scraper/run-sync": { "post": { "operationId": "run-sync-parseforge-fred-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 1000000, "type": "integer", "description": "Maximum number of series to collect. Free users: Limited to 100. Paid users: Optional, max 1,000,000. Leave empty for unlimited (paid users only)." }, "searchText": { "title": "Search Text", "type": "string", "description": "Full-text search query to find economic data series. Searches across series titles, units, frequency, and tags. Example: 'cpi', 'unemployment', 'gdp'" }, "categoryId": { "title": "Category ID", "type": "integer", "description": "Filter results by FRED category ID. Example: 10 (National Accounts), 1 (Production & Business Activity)" }, "frequency": { "title": "Frequency", "enum": [ "Daily", "Weekly", "Biweekly", "Monthly", "Quarterly", "Semiannual", "Annual" ], "type": "string", "description": "Filter by data frequency" }, "units": { "title": "Units", "enum": [ "Percent", "Index", "Dollars", "Thousands", "Millions", "Billions", "Ratio", "Number", "Rate", "Index 1982-1984=100", "Index 2012=100", "Index 2015=100", "Index 2017=100" ], "type": "string", "description": "Filter by units of measurement" }, "seasonalAdjustment": { "title": "Seasonal Adjustment", "enum": [ "Seasonally Adjusted", "Not Seasonally Adjusted" ], "type": "string", "description": "Filter by seasonal adjustment" }, "includeObservations": { "title": "Include Observations", "type": "boolean", "description": "Whether to include observation data (date-value pairs) for each series. ⚠️ WARNING: This will significantly increase processing time and cost. The scraper fetches all historical observations for each series, which requires additional API requests and may take several minutes for large datasets.", "default": false }, "sortOrder": { "title": "Sort Order", "enum": [ "search_rank", "series_id", "title", "units", "frequency", "seasonal_adjustment", "realtime_start", "realtime_end", "last_updated", "observation_start", "observation_end", "popularity", "group_popularity" ], "type": "string", "description": "Field to sort results by", "default": "search_rank" }, "orderBy": { "title": "Order By", "enum": [ "asc", "desc" ], "type": "string", "description": "Sort order direction", "default": "desc" } } }, "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 } } } } } } } } } } ```