# CourtListener Federal Court Docket Monitor – Court Data API (`peepee/courtlistener-docket-monitor`) Actor Structured CourtListener/RECAP feed of federal court dockets, opinions and new case filings by court, party and nature-of-suit. For litigation intel, law firms and legal-analytics products. - **URL**: https://apify.com/peepee/courtlistener-docket-monitor.md - **Developed by:** [K O](https://apify.com/peepee) (community) - **Categories:** Developer tools, Business - **Stats:** 2 total users, 1 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. 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 Federal Court Docket Monitor – PACER / Court Data API A **CourtListener API** wrapper and federal-court monitor that pulls dockets, opinions and RECAP case filings and normalizes each into a clean record: case name, court, docket number, nature of suit, filing date and a direct link. Use it to build a **litigation-intelligence feed**, a new-case monitor for a court or party, a docket-watch tool, or legal-analytics datasets — a free, structured alternative to raw PACER. ### Who uses it Law firms and litigation-intelligence teams, legal-analytics and docket-alert products, corporate legal departments, journalists, and academic legal researchers. ### Why this is worth charging for PACER is expensive and unstructured; CourtListener/RECAP mirrors much of it via a clean API but paginates with cursors and mixes result shapes across search types. This actor normalizes dockets and opinions into a consistent, deduplicated feed you can filter by court, party or nature of suit and monitor on a schedule. ### Output Each dataset item is one normalized record: ```json { "caseName": "Doe v. Acme Corp", "court": "cand", "docketNumber": "3:24-cv-01234", "dateFiled": "2024-04-01", "natureOfSuit": "830 Patent", "docketId": 65432100, "url": "https://www.courtlistener.com/docket/65432100/doe-v-acme-corp/", "source": "CourtListener" } ```` ### Input | Field | Description | |---|---| | `apiToken` | Optional free token for higher rate limits (Authorization: Token …). | | `searchType` | Which CourtListener index to search. | | `query` | Full-text query, e.g. a party name or keyword. | | `court` | Court identifier, e.g. cand (N.D. Cal.), scotus. | | `filedAfter` | Only cases filed on or after this date. | | `maxPagesToScan` | Cursor pages to follow per run. | | `onlyNewSinceLastRun` | Recommended for schedules — skips records already returned by a previous run using persisted state, so you're only charged for genuinely new items. | | `maxItems` | Stop after this many normalized records. | ### Pricing (Pay-Per-Event) - **`query`** — $0.02, charged once per run for the API query/poll. - **`result-record`** — $0.02 per normalized record pushed. This is the primary event. - `apify-actor-start` (Apify-managed) — covers baseline compute per run. A new-case monitor for a court or party returns tens of results per run for cents. Add a free CourtListener token to raise rate limits for broad scans. ### Source & reliability Data comes from the official `courtlistener.com/api/rest/v4` service; a free token is optional but recommended. No proxies required. Run `npm test` for the offline self-test covering the normalizer, including edge cases and missing-field handling. ### FAQ **Is this a free PACER alternative?** It wraps CourtListener/RECAP, which mirrors a large share of PACER content for free in structured form — ideal for monitoring and analytics. **Can I watch a specific court for new cases?** Yes. Set court and filedAfter (or a party query) and schedule with onlyNewSinceLastRun for new-case alerts. *** *SEO keywords: CourtListener API, PACER alternative API, court docket scraper, litigation monitor, legal case data feed, new lawsuit alerts* # Actor input Schema ## `apiToken` (type: `string`): Optional free token for higher rate limits (Authorization: Token …). ## `searchType` (type: `string`): Which CourtListener index to search. ## `query` (type: `string`): Full-text query, e.g. a party name or keyword. ## `court` (type: `string`): Court identifier, e.g. cand (N.D. Cal.), scotus. ## `filedAfter` (type: `string`): Only cases filed on or after this date. ## `maxPagesToScan` (type: `integer`): Cursor pages to follow per run. ## `maxItems` (type: `integer`): Stop after this many normalized records have been pushed. Leave blank for no limit (bounded by pagination settings). ## `onlyNewSinceLastRun` (type: `boolean`): Recommended for scheduled runs. Uses persisted state to skip records already returned earlier, so a recurring schedule only charges for genuinely new items. ## Actor input object example ```json { "searchType": "r", "maxPagesToScan": 3, "onlyNewSinceLastRun": true } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("peepee/courtlistener-docket-monitor").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 = {} # Run the Actor and wait for it to finish run = client.actor("peepee/courtlistener-docket-monitor").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 '{}' | apify call peepee/courtlistener-docket-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=peepee/courtlistener-docket-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "CourtListener Federal Court Docket Monitor – Court Data API", "description": "Structured CourtListener/RECAP feed of federal court dockets, opinions and new case filings by court, party and nature-of-suit. For litigation intel, law firms and legal-analytics products.", "version": "1.0", "x-build-id": "FYN4DeZo9WyKYfC5r" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/peepee~courtlistener-docket-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-peepee-courtlistener-docket-monitor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/peepee~courtlistener-docket-monitor/runs": { "post": { "operationId": "runs-sync-peepee-courtlistener-docket-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/peepee~courtlistener-docket-monitor/run-sync": { "post": { "operationId": "run-sync-peepee-courtlistener-docket-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "apiToken": { "title": "CourtListener API token", "type": "string", "description": "Optional free token for higher rate limits (Authorization: Token …)." }, "searchType": { "title": "Search type", "enum": [ "r", "o", "d" ], "type": "string", "description": "Which CourtListener index to search.", "default": "r" }, "query": { "title": "Search query", "type": "string", "description": "Full-text query, e.g. a party name or keyword." }, "court": { "title": "Court ID", "type": "string", "description": "Court identifier, e.g. cand (N.D. Cal.), scotus." }, "filedAfter": { "title": "Filed after (YYYY-MM-DD)", "type": "string", "description": "Only cases filed on or after this date." }, "maxPagesToScan": { "title": "Max pages to scan", "minimum": 1, "maximum": 100, "type": "integer", "description": "Cursor pages to follow per run.", "default": 3 }, "maxItems": { "title": "Maximum records to return", "minimum": 1, "type": "integer", "description": "Stop after this many normalized records have been pushed. Leave blank for no limit (bounded by pagination settings)." }, "onlyNewSinceLastRun": { "title": "Only return records not seen in a previous run", "type": "boolean", "description": "Recommended for scheduled runs. Uses persisted state to skip records already returned earlier, so a recurring schedule only charges for genuinely new items.", "default": true } } }, "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 } } } } } } } } } } ```