# Grant & Foundation Funding Intel - Grants.gov + 990-PF (`seibs.co/grant-funding-intel`) Actor Federal grant opportunities (Grants.gov) + private-foundation giving (IRS 990-PF via ProPublica) in one schema. Funder, eligibility/recipient, amount, focus area, deadline. Funder-to-recipient enrichment, giving history, funder-match scoring, monitor mode. For grant writers and nonprofits. - **URL**: https://apify.com/seibs.co/grant-funding-intel.md - **Developed by:** [Seibs.co](https://apify.com/seibs.co) (community) - **Categories:** Business, Developer tools, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $6.00 / 1,000 funding records 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 ## Grant & Foundation Funding Intel **Federal grant opportunities and private-foundation giving in one normalized schema.** Candid's Foundation Directory Online paywalls the funder graph; the underlying IRS Form 990-PF data is public but fragmented and hard to assemble. This actor unifies two public sources - **Grants.gov** (federal opportunities) and **IRS Form 990 / 990-PF via ProPublica Nonprofit Explorer** (foundation giving) - into one queryable feed of funders, eligibility, amounts, focus areas, and deadlines. Built for **grant writers, nonprofits, and fiscal sponsors** hunting non-dilutive funding. --- ### What it does | Mode | What you get | Source | |---|---|---| | `grant_search` | Open federal grant opportunities by keyword, agency, eligibility, or funding category - with award range, eligibility, and deadline. | Grants.gov Search2 API | | `foundation_search` | Private foundations by name, state, focus area, or size - optionally with multi-year giving history. | IRS 990-PF via ProPublica | | `funder_profile` | Deep profile of specific funders (foundation EINs and/or federal agency codes): multi-year giving trend, payout vs the 5% minimum, asset trend, and recipient-level grant awards. | ProPublica + Grants.gov + 990-PF XML | | `funder_match` | The funders most likely to fund your cause - federal opportunities and foundations scored against a cause / focus area / region / size, with transparent match reasons. | Both, scored | All four emit the **same normalized record**: `funder`, `recipient_or_eligibility`, `amount` / `amount_min` / `amount_max`, `focus_area`, `deadline`, `source`, `source_url`. --- ### Quick start **Find open federal education grants for nonprofits:** ```json { "mode": "grant_search", "keywords": ["literacy"], "funding_categories": ["education"], "eligibilities": ["nonprofits_with_501c3"], "opp_statuses": "posted", "max_results_per_query": 25 } ```` **Profile a foundation's giving history:** ```json { "mode": "funder_profile", "eins": ["56-2618866"], "include_giving_history": true } ``` **Find foundations likely to fund your project:** ```json { "mode": "funder_match", "cause": "after-school literacy in rural Ohio", "focus_area": "Education", "states": ["OH"], "min_assets": 5000000, "include_giving_history": true } ``` **Search private foundations by focus + state:** ```json { "mode": "foundation_search", "keywords": ["community foundation"], "states": ["CA"], "ntee_major": ["2"], "include_giving_history": true } ``` *** ### Output schema (normalized) | Field | Meaning | |---|---| | `record_type` | `grant_opportunity` | `funder` | `grant_award` | `funder_match` | | `funder_name` / `funder_type` | Federal agency or foundation; `federal_agency` | `private_foundation` | | `funder_ein` / `funder_state` | Foundation identity | | `title` | Opportunity title or grant purpose | | `recipient_or_eligibility` | Who can apply (opportunity) or who received the grant (award) | | `amount` / `amount_min` / `amount_max` / `amount_label` | Award floor/ceiling, grant amount, or annual giving | | `focus_area` / `focus_categories` | Funding category (opportunity) or NTEE focus (foundation) | | `deadline` | Application close date (opportunities) | | `giving_history` | Multi-year grants paid, payout ratio, asset trend (foundations) | | `match_score` / `match_reasons` | Funder-match ranking + transparent reasons | | `source` / `source_url` | Provenance, back to the official record | Dataset **views**: `overview` (AI-agent skim), `opportunities` (open federal grants), `funders` (foundation funders). *** ### Recipient-level grant awards (990-PF Part XV) The list of *who a foundation actually funded* (recipient, place, purpose, amount) lives in **Part XV of Form 990-PF**. ProPublica exposes the giving *totals*; the per-recipient lines live in the e-file XML. The IRS now serves machine-readable 990s only as **per-year bulk ZIPs** (the old AWS S3 per-filing bucket is frozen at 2021), so live per-filing recipient extraction is gated behind the bulk corpus - that gating is exactly the value. In `funder_profile` mode, supply `irs_xml_url` pointing at a specific 990-PF e-file XML and the actor parses its Part XV lines into `grant_award` records. Without it, you still get the funder's full giving history and a note explaining the gate. *** ### Pricing (pay-per-event) | Event | Price | When | |---|---|---| | `funder_record` | $0.006 | Per normalized record (opportunity, funder, or award) | | `grant_history_enrichment` | $0.012 | Per foundation enriched with multi-year giving history, or per Part XV award extraction | | `funder_match` | $0.015 | Per scored funder-match result | | `scheduled_delta_run` | $0.050 | Per scheduled monitor run (new-opportunity / new-grant digest) | A focused `grant_search` for 25 opportunities costs ~$0.15. A `funder_profile` of one foundation with giving history costs ~$0.018. *** ### Monitor mode (new-opportunity alerts) Save this actor as a task and put it on a schedule. On scheduled runs it computes the delta against the previous run (new opportunities, changed deadlines, new grants) and, if you set `slack_webhook_url`, posts a digest to Slack. Grant cycles are recurring - this is the recurring value. *** ### Sources & access - **Grants.gov Search2 / fetchOpportunity** - public REST API, no key, logged-out. Covers every federal grant-making agency. - **IRS Form 990 / 990-PF via ProPublica Nonprofit Explorer API** - public REST API, no key, logged-out. Structured 990-PF financials (grants paid, assets, qualifying distributions) for 501(c)(3) nonprofits and private foundations. - **IRS 990-PF e-file XML** - public; recipient-level Part XV lines, parsed when an `irs_xml_url` is supplied. **Legal & responsible use.** Public filings and public feeds only - logged-out, no accounts, no API keys, no paywalls. PII is minimized: agency contacts and 990-PF recipient/related-person names are public record; the actor keeps name + place + purpose + amount and never enriches into private contact data. Always verify deadlines and eligibility against the official source before applying. *** ### AI agents & MCP A paired **MCP server** (`mcp-grant-funding-intel`) exposes these capabilities as AI-agent tools (`search_grants`, `find_funders`, `profile_funder`, `match_funders`), and is x402 (USDC on Base) and Skyfire agentic-payment ready. The dataset's `overview` view and the MCP-compatible output link are built for direct LLM tool-call wiring. # Actor input Schema ## `mode` (type: `string`): grant\_search = federal grant opportunities (Grants.gov). foundation\_search = private foundations (990-PF) by name/state/focus/size. funder\_profile = deep profile of specific funders (EINs + agency codes) with multi-year giving history. funder\_match = surface + score funders most likely to fund a cause/region. ## `keywords` (type: `array`): Search terms. grant\_search: matches opportunity title/description (e.g. 'workforce development'). foundation\_search: matches foundation name. Hard cap of 20. ## `agencies` (type: `string`): Pipe-separated Grants.gov agency codes for grant\_search / funder\_profile, e.g. 'NSF' or 'HHS|ED|USDA'. Leave blank for all agencies. ## `eligibilities` (type: `array`): Who can apply (grant\_search). Plain tokens or Grants.gov codes. Common: nonprofits\_with\_501c3, public\_higher\_education, state\_governments, city\_or\_township\_governments, individuals, small\_businesses, indian\_tribal\_governments. Hard cap of 20. ## `funding_categories` (type: `array`): Funding activity category (grant\_search / funder\_match). Plain tokens or Grants.gov codes. Common: education, health, housing, environment, arts, community\_development, employment\_labor\_and\_training, food\_and\_nutrition, science\_and\_technology. Hard cap of 20. ## `opp_statuses` (type: `string`): Pipe-separated Grants.gov statuses for grant\_search: posted, forecasted, closed, archived. Default 'posted' (open opportunities). ## `states` (type: `array`): Two-letter state codes. foundation\_search/funder\_match: filter foundations by state of domicile (e.g. \['CA','NY']). Hard cap of 51 (50 states + DC). ## `ntee_major` (type: `array`): ProPublica NTEE major-group ids for foundation\_search (1=Arts, 2=Education, 3=Environment/Animals, 4=Health, 5=Human Services, 6=International, 7=Public/Societal Benefit, 8=Religion). Numbers only. ## `min_assets` (type: `integer`): Only surface foundations whose latest fair-market asset value meets this floor (foundation\_search/funder\_match). Requires include\_giving\_history to filter on enriched assets. Leave 0 for no floor. ## `eins` (type: `array`): Employer Identification Numbers of foundations to deep-profile (e.g. \['56-2618866']). Each yields a funder record + multi-year giving history. Hard cap of 50. ## `cause` (type: `string`): Free-text description of what you need funded (funder\_match), e.g. 'after-school literacy program in rural Ohio'. Drives federal-opportunity keyword + foundation matching. ## `focus_area` (type: `string`): Target focus area for funder\_match, e.g. 'Education', 'Health', 'Human Services', 'Environment'. Maps to NTEE buckets for foundation matching. ## `irs_xml_url` (type: `string`): Optional. URL to a specific 990-PF e-file XML document. When supplied, recipient-level Part XV grant lines are parsed into grant\_award records. The IRS serves machine-readable 990s only as per-year bulk ZIPs, so live per-filing extraction is gated; this lets you point at one filing. ## `include_giving_history` (type: `boolean`): For foundations, fetch the full 990-PF filing series and compute year-over-year grants paid, payout ratio vs the 5% minimum, asset trend, and giving direction. Adds grant\_history\_enrichment PPE per foundation. On by default in funder\_profile. ## `include_opportunity_detail` (type: `boolean`): Fetch each opportunity's detail page for award floor/ceiling, eligibility text, funding categories, and deadlines. Off = faster, metadata-only rows. ## `max_results_per_query` (type: `integer`): Hard cap on records returned per keyword/search. Default 25. ## `contact_email` (type: `string`): Optional contact email added to the outbound User-Agent so the public APIs can reach you. Not required; not stored. ## `slack_webhook_url` (type: `string`): Optional. When this actor runs on a schedule, post a new-opportunity / new-grant digest to this Slack incoming webhook. Adds scheduled\_delta\_run PPE on scheduled runs only. ## `use_apify_proxy` (type: `boolean`): Route requests through Apify Proxy. DATACENTER is sufficient - Grants.gov and ProPublica are public APIs that do not block datacenter IPs. ## `apify_proxy_groups` (type: `array`): Apify Proxy groups. DATACENTER is fine for both upstreams. ## `concurrency` (type: `integer`): Parallel requests to the upstream public APIs. Capped at 8 to stay polite. ## Actor input object example ```json { "mode": "grant_search", "keywords": [ "education" ], "agencies": "", "eligibilities": [], "funding_categories": [], "opp_statuses": "posted", "states": [], "ntee_major": [], "min_assets": 0, "eins": [], "cause": "youth literacy", "focus_area": "Education", "irs_xml_url": "", "include_giving_history": false, "include_opportunity_detail": true, "max_results_per_query": 10, "contact_email": "", "slack_webhook_url": "", "use_apify_proxy": true, "apify_proxy_groups": [ "DATACENTER" ], "concurrency": 5 } ``` # Actor output Schema ## `datasetItems` (type: `string`): Narrow, token-efficient slice of every record. Consumer: LLM agents (Claude, GPT, LangChain tools), MCP hosts, dashboards. Fields: type, funder, title/purpose, eligibility/recipient, amount, focus, deadline, source, URL. ## `datasetItemsOpportunities` (type: `string`): Federal grant opportunities with award range, eligibility, and deadline. Consumer: grant writers, nonprofits, fiscal sponsors building a pipeline. ## `datasetItemsFunders` (type: `string`): Private foundations with latest grants paid, payout ratio, and focus area. Consumer: prospect-research teams building a funder list. ## `datasetItemsMcp` (type: `string`): First 50 overview records as a clean JSON array. Wrap on the agent side in an MCP tool-call response envelope, e.g. `{ "ok": true, "data": , "meta": { "actor": "grant-funding-intel", "count": , "view": "overview" } }`. Consumer: MCP servers, Claude Desktop, Cursor, OpenAI Assistants tool calls. ## `datasetItemsCsv` (type: `string`): Spreadsheet-friendly export of the overview view. Consumer: humans, grant-ops teams, Excel / Google Sheets users. # 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 = { "mode": "grant_search", "keywords": [ "education" ], "opp_statuses": "posted", "cause": "youth literacy", "focus_area": "Education", "max_results_per_query": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("seibs.co/grant-funding-intel").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 = { "mode": "grant_search", "keywords": ["education"], "opp_statuses": "posted", "cause": "youth literacy", "focus_area": "Education", "max_results_per_query": 10, } # Run the Actor and wait for it to finish run = client.actor("seibs.co/grant-funding-intel").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 '{ "mode": "grant_search", "keywords": [ "education" ], "opp_statuses": "posted", "cause": "youth literacy", "focus_area": "Education", "max_results_per_query": 10 }' | apify call seibs.co/grant-funding-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seibs.co/grant-funding-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Grant & Foundation Funding Intel - Grants.gov + 990-PF", "description": "Federal grant opportunities (Grants.gov) + private-foundation giving (IRS 990-PF via ProPublica) in one schema. Funder, eligibility/recipient, amount, focus area, deadline. Funder-to-recipient enrichment, giving history, funder-match scoring, monitor mode. For grant writers and nonprofits.", "version": "0.1", "x-build-id": "HbkzjEFoGylX8oNKc" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seibs.co~grant-funding-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seibs.co-grant-funding-intel", "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/seibs.co~grant-funding-intel/runs": { "post": { "operationId": "runs-sync-seibs.co-grant-funding-intel", "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/seibs.co~grant-funding-intel/run-sync": { "post": { "operationId": "run-sync-seibs.co-grant-funding-intel", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "grant_search", "foundation_search", "funder_profile", "funder_match" ], "type": "string", "description": "grant_search = federal grant opportunities (Grants.gov). foundation_search = private foundations (990-PF) by name/state/focus/size. funder_profile = deep profile of specific funders (EINs + agency codes) with multi-year giving history. funder_match = surface + score funders most likely to fund a cause/region.", "default": "grant_search" }, "keywords": { "title": "Keywords", "maxItems": 20, "type": "array", "description": "Search terms. grant_search: matches opportunity title/description (e.g. 'workforce development'). foundation_search: matches foundation name. Hard cap of 20.", "default": [], "items": { "type": "string" } }, "agencies": { "title": "Federal agency codes (Grants.gov)", "type": "string", "description": "Pipe-separated Grants.gov agency codes for grant_search / funder_profile, e.g. 'NSF' or 'HHS|ED|USDA'. Leave blank for all agencies.", "default": "" }, "eligibilities": { "title": "Eligibility filter", "maxItems": 20, "type": "array", "description": "Who can apply (grant_search). Plain tokens or Grants.gov codes. Common: nonprofits_with_501c3, public_higher_education, state_governments, city_or_township_governments, individuals, small_businesses, indian_tribal_governments. Hard cap of 20.", "default": [], "items": { "type": "string" } }, "funding_categories": { "title": "Funding category filter", "maxItems": 20, "type": "array", "description": "Funding activity category (grant_search / funder_match). Plain tokens or Grants.gov codes. Common: education, health, housing, environment, arts, community_development, employment_labor_and_training, food_and_nutrition, science_and_technology. Hard cap of 20.", "default": [], "items": { "type": "string" } }, "opp_statuses": { "title": "Opportunity statuses (Grants.gov)", "type": "string", "description": "Pipe-separated Grants.gov statuses for grant_search: posted, forecasted, closed, archived. Default 'posted' (open opportunities).", "default": "posted" }, "states": { "title": "States", "maxItems": 51, "type": "array", "description": "Two-letter state codes. foundation_search/funder_match: filter foundations by state of domicile (e.g. ['CA','NY']). Hard cap of 51 (50 states + DC).", "default": [], "items": { "type": "string" } }, "ntee_major": { "title": "NTEE major group (foundation focus)", "type": "array", "description": "ProPublica NTEE major-group ids for foundation_search (1=Arts, 2=Education, 3=Environment/Animals, 4=Health, 5=Human Services, 6=International, 7=Public/Societal Benefit, 8=Religion). Numbers only.", "default": [], "items": { "type": "string" } }, "min_assets": { "title": "Minimum foundation assets (USD)", "minimum": 0, "type": "integer", "description": "Only surface foundations whose latest fair-market asset value meets this floor (foundation_search/funder_match). Requires include_giving_history to filter on enriched assets. Leave 0 for no floor.", "default": 0 }, "eins": { "title": "Foundation EINs (funder_profile)", "maxItems": 50, "type": "array", "description": "Employer Identification Numbers of foundations to deep-profile (e.g. ['56-2618866']). Each yields a funder record + multi-year giving history. Hard cap of 50.", "default": [], "items": { "type": "string" } }, "cause": { "title": "Cause / project (funder_match)", "type": "string", "description": "Free-text description of what you need funded (funder_match), e.g. 'after-school literacy program in rural Ohio'. Drives federal-opportunity keyword + foundation matching.", "default": "" }, "focus_area": { "title": "Focus area (funder_match)", "type": "string", "description": "Target focus area for funder_match, e.g. 'Education', 'Health', 'Human Services', 'Environment'. Maps to NTEE buckets for foundation matching.", "default": "" }, "irs_xml_url": { "title": "990-PF e-file XML URL (funder_profile, optional)", "type": "string", "description": "Optional. URL to a specific 990-PF e-file XML document. When supplied, recipient-level Part XV grant lines are parsed into grant_award records. The IRS serves machine-readable 990s only as per-year bulk ZIPs, so live per-filing extraction is gated; this lets you point at one filing.", "default": "" }, "include_giving_history": { "title": "Include multi-year giving history", "type": "boolean", "description": "For foundations, fetch the full 990-PF filing series and compute year-over-year grants paid, payout ratio vs the 5% minimum, asset trend, and giving direction. Adds grant_history_enrichment PPE per foundation. On by default in funder_profile.", "default": false }, "include_opportunity_detail": { "title": "Fetch full opportunity detail (Grants.gov)", "type": "boolean", "description": "Fetch each opportunity's detail page for award floor/ceiling, eligibility text, funding categories, and deadlines. Off = faster, metadata-only rows.", "default": true }, "max_results_per_query": { "title": "Max results per query", "minimum": 1, "maximum": 100, "type": "integer", "description": "Hard cap on records returned per keyword/search. Default 25.", "default": 25 }, "contact_email": { "title": "Contact email (optional, polite User-Agent)", "type": "string", "description": "Optional contact email added to the outbound User-Agent so the public APIs can reach you. Not required; not stored.", "default": "" }, "slack_webhook_url": { "title": "Slack webhook (monitor mode)", "type": "string", "description": "Optional. When this actor runs on a schedule, post a new-opportunity / new-grant digest to this Slack incoming webhook. Adds scheduled_delta_run PPE on scheduled runs only.", "default": "" }, "use_apify_proxy": { "title": "Use Apify Proxy", "type": "boolean", "description": "Route requests through Apify Proxy. DATACENTER is sufficient - Grants.gov and ProPublica are public APIs that do not block datacenter IPs.", "default": true }, "apify_proxy_groups": { "title": "Proxy groups", "type": "array", "description": "Apify Proxy groups. DATACENTER is fine for both upstreams.", "default": [ "DATACENTER" ], "items": { "type": "string" } }, "concurrency": { "title": "Max concurrent requests", "minimum": 1, "maximum": 8, "type": "integer", "description": "Parallel requests to the upstream public APIs. Capped at 8 to stay polite.", "default": 5 } } }, "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 } } } } } } } } } } ```