# SCOTUS Oyez Oral Arguments Scraper (`jungle_synthesizer/scotus-oyez-oral-arguments-scraper`) Actor Scrape Supreme Court oral argument transcripts, case metadata, and speaker-attributed text from Oyez.org. Extract 5,000+ cases dating back to 1955 with full transcripts (justice/advocate speech turns), audio links, decisions, dissents, and concurrences. - **URL**: https://apify.com/jungle\_synthesizer/scotus-oyez-oral-arguments-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Education, Other, Automation - **Stats:** 2 total users, 1 monthly users, 81.8% 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 ## SCOTUS Oyez Oral Arguments Scraper Scrape Supreme Court oral argument transcripts, case metadata, and speaker-attributed speech from [Oyez](https://www.oyez.org) (the IIT Chicago-Kent canonical SCOTUS index). Covers 5,000+ cases dating back to 1955 with full transcripts showing justice and advocate turns, audio links, decisions, votes, and advocate details. ### What You Get Each output record contains: - **Case metadata**: docket number, case name, parties, term year, lower court, manner of jurisdiction, citation - **Dates**: date argued and date decided (ISO 8601) - **People**: which justices heard the case, which decided it, and all advocates with their roles - **Decisions**: majority/dissenting vote counts, winning party, decision type - **Oral argument sessions**: title, audio MP3 URL, duration (when fetchTranscripts is enabled) - **Full transcripts**: speaker-attributed segments with justice/advocate name, role, spoken text, and start/end timestamps in seconds - **Opinion announcements**: post-decision audio and transcripts - **Written opinions**: author, type (majority, concurring, dissenting), Justia link - **Case summaries**: facts of the case, conclusion (HTML-stripped) - **Cross-references**: Justia URL, Oyez canonical URL, Oyez API URL ### Input Options | Field | Description | Default | |-------|-------------|---------| | `termStart` | First SCOTUS term year to scrape (e.g. 2020) | 2020 | | `termEnd` | Last SCOTUS term year (inclusive). Omit to scrape only termStart | 2023 | | `docketNumber` | Scrape a single case by docket number (e.g. `19-1392`). Requires `termStart`. | — | | `caseName` | Filter cases whose name contains this string (case-insensitive, e.g. `Dobbs`) | — | | `fetchTranscripts` | Fetch full speaker-attributed transcripts for each oral argument | true | | `maxItems` | Maximum case records to return (0 = unlimited) | 15 | ### Example: Single Case with Transcript ```json { "termStart": 2021, "docketNumber": "19-1392", "fetchTranscripts": true, "maxItems": 1 } ```` Returns Dobbs v. Jackson Women's Health Organization with the full Oyez-normalized transcript, audio link, advocate list, and decision metadata. ### Example: Full Recent Term (No Transcripts) ```json { "termStart": 2022, "termEnd": 2022, "fetchTranscripts": false, "maxItems": 0 } ``` Returns all ~62 October 2022 Term cases with metadata, advocate lists, and decision records — no transcript audio fetches. ### Example: Dobbs + Bruen (Named Case Search) ```json { "termStart": 2021, "termEnd": 2021, "caseName": "New York", "fetchTranscripts": true, "maxItems": 5 } ``` ### Transcript Format When `fetchTranscripts` is enabled, `oral_argument_transcript` contains a JSON-serialized array: ```json [ { "speaker": "John G. Roberts, Jr.", "role": "Chief Justice of the United States", "text": "We will hear argument this morning in Case 19-1392...", "start_sec": 0.08, "end_sec": 9.2, "section": 0 }, { "speaker": "Scott Stewart", "role": "Counsel for Petitioner", "text": "Mr. Chief Justice, and may it please the Court...", "start_sec": 9.2, "end_sec": 45.1, "section": 0 } ] ``` Audio MP3 links are in `oral_argument_sessions[].media_url`. ### Data Source & Attribution Data is sourced from the [Oyez Project](https://www.oyez.org), operated by IIT Chicago-Kent College of Law. Oyez's normalized case metadata and annotation work (justice attribution, summaries) is licensed [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — commercial redistribution of Oyez's annotated content requires attribution. The underlying oral argument audio and verbatim transcripts are US federal government works and are public domain. ### Performance - Rate-limited to 2 requests/second out of respect for Oyez's non-profit infrastructure - Memory: 256 MB (no browser) - Typical run: ~1-2 seconds per case without transcripts; ~3-5 seconds per case with transcripts (one additional API call per oral argument session) ### Why Oyez? Oyez is the only public source with speaker-attributed SCOTUS oral argument transcripts. `supremecourt.gov` publishes raw transcript PDFs but without justice attribution or audio alignment. Oyez normalizes these into a structured JSON API with justice/advocate identification, making it the canonical source for constitutional law research, legal-tech RAG pipelines, and AI training datasets. # 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. ## `termStart` (type: `integer`): First SCOTUS term year to scrape (e.g. 2020 for the October 2020 term). Leave empty for all terms. ## `termEnd` (type: `integer`): Last SCOTUS term year to scrape (inclusive). Leave empty to scrape only the start term. ## `docketNumber` (type: `string`): Scrape a single case by docket number (e.g. '19-1392'). When set, termStart/termEnd filter to a single term. ## `caseName` (type: `string`): Filter cases whose name contains this string (case-insensitive, e.g. 'Dobbs'). ## `fetchTranscripts` (type: `boolean`): When enabled, fetches and normalizes the full speaker-attributed transcript for each oral argument. Increases run time and cost. ## `maxItems` (type: `integer`): Maximum number of case records to return. Default 15 for preview; set higher or 0 for unlimited. ## 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...", "termStart": 2020, "termEnd": 2023, "fetchTranscripts": true, "maxItems": 15 } ``` # 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...", "termStart": 2020, "termEnd": 2023, "docketNumber": "", "caseName": "", "fetchTranscripts": true, "maxItems": 15 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/scotus-oyez-oral-arguments-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...", "termStart": 2020, "termEnd": 2023, "docketNumber": "", "caseName": "", "fetchTranscripts": True, "maxItems": 15, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/scotus-oyez-oral-arguments-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...", "termStart": 2020, "termEnd": 2023, "docketNumber": "", "caseName": "", "fetchTranscripts": true, "maxItems": 15 }' | apify call jungle_synthesizer/scotus-oyez-oral-arguments-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/scotus-oyez-oral-arguments-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SCOTUS Oyez Oral Arguments Scraper", "description": "Scrape Supreme Court oral argument transcripts, case metadata, and speaker-attributed text from Oyez.org. Extract 5,000+ cases dating back to 1955 with full transcripts (justice/advocate speech turns), audio links, decisions, dissents, and concurrences.", "version": "1.0", "x-build-id": "ammdIMzfA8Qz9lEoi" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~scotus-oyez-oral-arguments-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-scotus-oyez-oral-arguments-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~scotus-oyez-oral-arguments-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-scotus-oyez-oral-arguments-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~scotus-oyez-oral-arguments-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-scotus-oyez-oral-arguments-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": { "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." }, "termStart": { "title": "Term Start Year", "type": "integer", "description": "First SCOTUS term year to scrape (e.g. 2020 for the October 2020 term). Leave empty for all terms." }, "termEnd": { "title": "Term End Year", "type": "integer", "description": "Last SCOTUS term year to scrape (inclusive). Leave empty to scrape only the start term." }, "docketNumber": { "title": "Specific Docket Number", "type": "string", "description": "Scrape a single case by docket number (e.g. '19-1392'). When set, termStart/termEnd filter to a single term." }, "caseName": { "title": "Case Name Search", "type": "string", "description": "Filter cases whose name contains this string (case-insensitive, e.g. 'Dobbs')." }, "fetchTranscripts": { "title": "Fetch Full Transcripts", "type": "boolean", "description": "When enabled, fetches and normalizes the full speaker-attributed transcript for each oral argument. Increases run time and cost.", "default": true }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of case records to return. Default 15 for preview; set higher or 0 for unlimited.", "default": 15 } } }, "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 } } } } } } } } } } ```