# Court Records Search - Federal & State Opinions (`pink_comic/courtlistener-legal-opinions`) Actor Search court records and legal opinions across federal and state courts via CourtListener. Find case law, court rulings, and judicial opinions by keyword, court, or date range. For law firms, legal researchers, compliance teams, and litigation support. Pay per result. - **URL**: https://apify.com/pink\_comic/courtlistener-legal-opinions.md - **Developed by:** [Ava Torres](https://apify.com/pink_comic) (community) - **Categories:** Automation, Developer tools, AI - **Stats:** 4 total users, 3 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 results 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 ## CourtListener Court Opinions Search CourtListener for court opinions, case law, and legal decisions from federal and state courts across the United States. ### Use Cases - **Legal Research**: Find case law on any topic across all federal and state courts - **Litigation Analysis**: Track how often cases are cited, find related opinions by court or judge - **Compliance Monitoring**: Monitor new opinions in specific courts or on specific regulatory topics - **Journalism**: Research court decisions on public interest topics with full citation data - **Academic Research**: Build datasets of court opinions filtered by date, court, and subject matter ### Input | Field | Description | Example | |-------|-------------|---------| | query | Full-text search query | "artificial intelligence" | | court | Court ID filter | "scotus", "ca9", "dcd" | | dateFrom | Filed after (YYYY-MM-DD) | "2023-01-01" | | dateTo | Filed before (YYYY-MM-DD) | "2024-12-31" | | status | Publication status | "Published", "Unpublished" | | maxResults | Max results (1-10000) | 100 | ### Output Each result includes: - **caseName** - Case name (e.g. "Gonzalez v. Google LLC") - **court** - Full court name - **courtId** - Court identifier (e.g. "scotus") - **dateFiled** - Date the opinion was filed - **docketNumber** - Docket number - **judge** - Judge or panel - **citations** - Array of case citations (e.g. "598 U.S. 617") - **citeCount** - Number of times cited by other opinions - **status** - Published or Unpublished - **opinionUrl** - Full URL to the opinion on CourtListener - **snippet** - Text excerpt from the opinion - **downloadUrl** - Direct link to download the opinion PDF ### Data Source Data comes from the [CourtListener](https://www.courtlistener.com/) free public API maintained by the Free Law Project. No API key required. # Actor input Schema ## `query` (type: `string`): Full-text search query (e.g. "artificial intelligence", "patent infringement", "First Amendment"). ## `court` (type: `string`): Filter by court ID (e.g. "scotus" for Supreme Court, "ca9" for Ninth Circuit, "dcd" for D.C. District Court). Leave empty for all courts. ## `dateFrom` (type: `string`): Filed after date in YYYY-MM-DD format (e.g. "2023-01-01"). ## `dateTo` (type: `string`): Filed before date in YYYY-MM-DD format (e.g. "2024-12-31"). ## `status` (type: `string`): Filter by opinion status: "Published" or "Unpublished". Leave empty for all. ## `maxResults` (type: `integer`): Maximum number of results to return. ## Actor input object example ```json { "query": "artificial intelligence", "court": "", "dateFrom": "", "dateTo": "", "status": "", "maxResults": 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 = { "query": "artificial intelligence", "court": "", "dateFrom": "", "dateTo": "", "status": "", "maxResults": 100 }; // Run the Actor and wait for it to finish const run = await client.actor("pink_comic/courtlistener-legal-opinions").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 = { "query": "artificial intelligence", "court": "", "dateFrom": "", "dateTo": "", "status": "", "maxResults": 100, } # Run the Actor and wait for it to finish run = client.actor("pink_comic/courtlistener-legal-opinions").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 '{ "query": "artificial intelligence", "court": "", "dateFrom": "", "dateTo": "", "status": "", "maxResults": 100 }' | apify call pink_comic/courtlistener-legal-opinions --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=pink_comic/courtlistener-legal-opinions", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Court Records Search - Federal & State Opinions", "description": "Search court records and legal opinions across federal and state courts via CourtListener. Find case law, court rulings, and judicial opinions by keyword, court, or date range. For law firms, legal researchers, compliance teams, and litigation support. Pay per result.", "version": "0.1", "x-build-id": "iIf82NU39xRJ8Hwyb" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/pink_comic~courtlistener-legal-opinions/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-pink_comic-courtlistener-legal-opinions", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/pink_comic~courtlistener-legal-opinions/runs": { "post": { "operationId": "runs-sync-pink_comic-courtlistener-legal-opinions", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/pink_comic~courtlistener-legal-opinions/run-sync": { "post": { "operationId": "run-sync-pink_comic-courtlistener-legal-opinions", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "query": { "title": "Search Query", "type": "string", "description": "Full-text search query (e.g. \"artificial intelligence\", \"patent infringement\", \"First Amendment\").", "default": "artificial intelligence" }, "court": { "title": "Court", "type": "string", "description": "Filter by court ID (e.g. \"scotus\" for Supreme Court, \"ca9\" for Ninth Circuit, \"dcd\" for D.C. District Court). Leave empty for all courts.", "default": "" }, "dateFrom": { "title": "Date From", "type": "string", "description": "Filed after date in YYYY-MM-DD format (e.g. \"2023-01-01\").", "default": "" }, "dateTo": { "title": "Date To", "type": "string", "description": "Filed before date in YYYY-MM-DD format (e.g. \"2024-12-31\").", "default": "" }, "status": { "title": "Status", "type": "string", "description": "Filter by opinion status: \"Published\" or \"Unpublished\". Leave empty for all.", "default": "" }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of results to return.", "default": 100 } } }, "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 } } } } } } } } } } ```