# USAspending Federal Award Search — Contracts, Grants & Loans (`aitoolsempire/usaspending-award-search`) Actor Search US federal contracts, grants, loans and other awards by keyword, category, date and amount. Structured JSON with recipient, amount, agency and award links. - **URL**: https://apify.com/aitoolsempire/usaspending-award-search.md - **Developed by:** [Kenneth Bonnet](https://apify.com/aitoolsempire) (community) - **Categories:** Lead generation, Developer tools, News - **Stats:** 2 total users, 1 monthly users, 75.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## USAspending Federal Award Search — Contracts, Grants & Loans Search **US federal awards** — contracts, grants, loans, direct payments — by keyword, category, date range, and dollar amount. Get clean, structured JSON with the recipient, amount, awarding agency, and a **direct link to the award page** — no browser, no API key, no rate-limit headaches. > Point it at `"artificial intelligence"`, `"cybersecurity"`, a competitor's name, > or an agency's spending, filter by amount and date, and get a ready-to-use feed > of who won what. --- ### What it does This Actor queries the **official USAspending.gov API v2** (`api.usaspending.gov`) — the U.S. government's system of record for federal spending — and flattens the results into a tidy dataset. Coverage spans **FY2008 (Oct 2007) to today**. Each result row includes: | Field | Description | |---|---| | `award_id` | Federal award / contract number (PIID, FAIN, etc.) | | `recipient_name` | The company or entity that received the award | | `award_amount` | Total obligated/awarded amount in USD | | `awarding_agency` | Federal agency that made the award | | `awarding_sub_agency` | Sub-agency (when available) | | `award_type` | Award type descriptor | | `start_date` / `end_date` | Period of performance | | `description` | Award description | | `agency_slug` | Agency identifier | | `award_url` | **Direct link to the USAspending award page** | | `internal_id` | USAspending internal award id | | `scraped_at` | UTC timestamp of extraction | ### Example use cases - **GovCon sales & lead gen** — find every contract mentioning your product area, who won it, for how much, and which agency bought it. - **Competitive intelligence** — track a competitor's federal award wins over time. - **Market sizing** — total federal spend on a topic (AI, cyber, cloud) by agency. - **Journalism & research** — surface awards by keyword with citable links. - **Grant discovery** — filter to `grants` and a keyword to map funding. ### Input | Field | Type | Description | |---|---|---| | `keywords` | array | Words/phrases to match | | `award_categories` | array | `contracts`, `grants`, `direct_payments`, `loans`, `other` | | `start_date` / `end_date` | string | Date range (`YYYY-MM-DD`) | | `min_amount` / `max_amount` | integer | Award amount bounds (USD) | | `max_results` | integer | Cap on records returned | Example input: ```json { "keywords": ["artificial intelligence"], "award_categories": ["contracts"], "start_date": "2026-01-01", "min_amount": 1000000, "max_results": 200 } ```` ### Output A dataset of federal award records (see the table above), sorted by award amount (largest first). Export to JSON, CSV, or Excel, or read it via the dataset API. ### Pricing **Pay per result** — you are charged only for award records actually delivered. No monthly rental, no charge for empty searches. ### Notes - Data comes directly from USAspending.gov, whose contents are in the **public domain**. This Actor does not alter award data. - The Actor sends a descriptive `User-Agent` and paces requests politely. ### Disclaimer This tool retrieves publicly available U.S. federal spending data from USAspending.gov. It is not affiliated with or endorsed by the U.S. Department of the Treasury. Provided for informational purposes only. # Actor input Schema ## `keywords` (type: `array`): Words or phrases to match in award descriptions, recipients, and identifiers. Example: \["artificial intelligence"], \["cybersecurity"]. Leave empty to search all awards in the filters below. ## `award_categories` (type: `array`): Which kinds of federal awards to include. ## `start_date` (type: `string`): Only awards with an action/period starting on or after this date. USAspending data covers FY2008 (2007-10-01) onward. ## `end_date` (type: `string`): Only awards on or before this date. Defaults to today. ## `min_amount` (type: `integer`): Only awards at or above this dollar amount. ## `max_amount` (type: `integer`): Only awards at or below this dollar amount. ## `max_results` (type: `integer`): Maximum number of award records to return. ## `user_agent` (type: `string`): A descriptive User-Agent with contact info. A sensible default is provided. ## Actor input object example ```json { "keywords": [ "artificial intelligence" ], "award_categories": [ "contracts" ], "start_date": "2026-01-01", "end_date": "2026-06-30", "min_amount": 1000000, "max_results": 100 } ``` # 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 = { "keywords": [ "artificial intelligence" ], "max_results": 100 }; // Run the Actor and wait for it to finish const run = await client.actor("aitoolsempire/usaspending-award-search").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 = { "keywords": ["artificial intelligence"], "max_results": 100, } # Run the Actor and wait for it to finish run = client.actor("aitoolsempire/usaspending-award-search").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 '{ "keywords": [ "artificial intelligence" ], "max_results": 100 }' | apify call aitoolsempire/usaspending-award-search --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=aitoolsempire/usaspending-award-search", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "USAspending Federal Award Search — Contracts, Grants & Loans", "description": "Search US federal contracts, grants, loans and other awards by keyword, category, date and amount. Structured JSON with recipient, amount, agency and award links.", "version": "0.1", "x-build-id": "BvdGJ1DeZDE7zquyH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/aitoolsempire~usaspending-award-search/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-aitoolsempire-usaspending-award-search", "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/aitoolsempire~usaspending-award-search/runs": { "post": { "operationId": "runs-sync-aitoolsempire-usaspending-award-search", "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/aitoolsempire~usaspending-award-search/run-sync": { "post": { "operationId": "run-sync-aitoolsempire-usaspending-award-search", "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": { "keywords": { "title": "Keywords", "uniqueItems": true, "type": "array", "description": "Words or phrases to match in award descriptions, recipients, and identifiers. Example: [\"artificial intelligence\"], [\"cybersecurity\"]. Leave empty to search all awards in the filters below.", "items": { "type": "string" } }, "award_categories": { "title": "Award categories", "uniqueItems": true, "type": "array", "description": "Which kinds of federal awards to include.", "items": { "type": "string", "enum": [ "contracts", "grants", "direct_payments", "loans", "other" ] }, "default": [ "contracts" ] }, "start_date": { "title": "Awarded on or after (YYYY-MM-DD)", "type": "string", "description": "Only awards with an action/period starting on or after this date. USAspending data covers FY2008 (2007-10-01) onward." }, "end_date": { "title": "Awarded on or before (YYYY-MM-DD)", "type": "string", "description": "Only awards on or before this date. Defaults to today." }, "min_amount": { "title": "Minimum award amount (USD)", "type": "integer", "description": "Only awards at or above this dollar amount." }, "max_amount": { "title": "Maximum award amount (USD)", "type": "integer", "description": "Only awards at or below this dollar amount." }, "max_results": { "title": "Maximum results", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of award records to return." }, "user_agent": { "title": "Custom User-Agent (optional)", "type": "string", "description": "A descriptive User-Agent with contact info. A sensible default is provided." } } }, "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 } } } } } } } } } } ```