# ⚖️ US Court Opinions & Case Law Search (`nexgendata/courtlistener-court-opinions`) Actor Search 240,000+ US federal & state court opinions and case law (CourtListener): case name, court, date, citation, cite count, judge. For litigators, legal-AI, and researchers. - **URL**: https://apify.com/nexgendata/courtlistener-court-opinions.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business - **Stats:** 2 total users, 0 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 ## ⚖️ US Court Opinions & Case Law Search **Search 240,000+ U.S. federal and state court opinions and case law as structured JSON. $0.10 per record. No Westlaw or LexisNexis seat, no PACER per-page fee, no per-search metering wall.** Case law is the raw material of every litigation strategy, every legal-research memo, and every legal-AI product — and the incumbent way to query it costs hundreds to thousands of dollars per attorney per month. This actor reads the public CourtListener corpus — the open-access case-law database maintained by the Free Law Project — and returns court opinions and RECAP dockets as clean JSON. Run a full-text search across case names, parties, and topics, filter by court and filing date, and get back the case name, the deciding court, the date filed, the docket number, and a direct link to the opinion. Build a case-law research pipeline, a litigation-monitoring watchlist, or a retrieval index for a legal-AI model — without a per-seat research subscription and without scraping a court site yourself. ### Why use this Westlaw and LexisNexis are extraordinary research platforms, but they are priced for law firms and metered per seat — and their terms forbid bulk export of the very results you most want to pipe into a downstream system. PACER, the federal courts' own system, charges per page and is built for retrieving one document at a time, not for querying across a corpus. Meanwhile the underlying opinions are public records: courts publish them, and the Free Law Project's CourtListener has aggregated 240,000+ of them into an open database. The gap is access shape, not access rights — you need that public corpus as a programmatic, filterable JSON feed. This actor is that feed. You pass a query and filters; you get back records. It handles the CourtListener API surface, pagination, and rate limits for you, and — because billing is pay-per-result — a search that returns nothing costs you nothing beyond the actor-start event. If you supply a free CourtListener token, the actor uses it to raise your rate limits for larger pulls; without one it still runs at the public-tier rate. ### What you get Each record returned is structured JSON populated from the CourtListener corpus wherever the source provides the field: - `caseName` — the case caption (e.g. *Smith v. Acme Corp.*) - `court` — the deciding court (Supreme Court of the United States, a Court of Appeals, a district court, or a state court) - `dateFiled` — the date the opinion or docket was filed - `docketNumber` — the court's docket number, the universal identifier for the matter - `url` — a direct link to the opinion or docket on CourtListener for full-text reading and verification The schema is additive-only and stable, so records load straight into a case-management system, a research database, Snowflake, BigQuery, or Postgres without re-mapping each refresh. ### Use cases - **Case-law research at scale** — Run a full-text search for a doctrine, a statute, or a fact pattern across 240,000+ opinions, narrowed to the relevant `court` and a `dateFiledAfter` cutoff, and pull every matching case as JSON for review. - **Litigation monitoring** — Track a party, a counsel, or a topic on a schedule and surface newly filed opinions and dockets the day they land — a standing watch on a competitor's litigation, a regulator's enforcement posture, or a circuit's emerging line of authority. - **Citator and precedent mapping** — Use the `docketNumber` and `caseName` to anchor a precedent graph, joining opinions by court and date to trace how a line of authority develops across circuits. - **Legal-AI and RAG ingestion** — Each record is JSON-flat and carries a source URL, making it drop-in fodder for a case-law retrieval index or a litigation-assistant model grounded in public opinions. - **Docket surveillance (RECAP)** — Set `searchType=r` to search RECAP dockets — the crowd-sourced archive of PACER documents — to find federal cases and filings without paying PACER per-page fees. - **Conflict and exposure checks** — Batch-search counterparties and their affiliates against the opinion corpus to surface prior litigation history before a deal or an engagement. - **Empirical legal research** — Quantify filing volume by court and date for scholarship on caseload, doctrine diffusion, or circuit-split formation. - **Brief and memo drafting support** — Pull the candidate authorities for a research question as structured records, then read each via its `url` to assemble a citation list. ### Sample output ```json { "caseName": "Riverside Holdings LLC v. Meridian Logistics, Inc.", "court": "Court of Appeals for the Federal Circuit", "dateFiled": "2025-11-14", "docketNumber": "23-1492", "url": "https://www.courtlistener.com/opinion/9912345/riverside-holdings-llc-v-meridian-logistics-inc/" } ```` A RECAP docket record (`searchType=r`) looks like this: ```json { "caseName": "United States v. Calloway", "court": "U.S. District Court for the Southern District of New York", "dateFiled": "2026-02-03", "docketNumber": "1:26-cr-00118", "url": "https://www.courtlistener.com/docket/68812345/united-states-v-calloway/" } ``` ### Input parameters | Parameter | Label | Description | |---|---|---| | `searchType` | Search type | `o` = court opinions, `r` = RECAP dockets. | | `query` | Query | Full-text search across case name, party, and topic. | | `court` | Court | Court ID filter (e.g. `scotus`, `cafc`). | | `dateFiledAfter` | Filed after | Only records filed on or after this date (YYYY-MM-DD). | | `maxResults` | Max results | Maximum number of records to return. | | `courtListenerToken` | CourtListener token (optional) | Optional free CourtListener token to raise rate limits. | ### How to use #### Python (apify-client) ```python from apify_client import ApifyClient client = ApifyClient("YOUR_TOKEN") run = client.actor("nexgendata/courtlistener-court-opinions").call(run_input={ "searchType": "o", "query": "trade secret misappropriation", "court": "cafc", "dateFiledAfter": "2024-01-01", "maxResults": 100, }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["caseName"], item["court"], item["dateFiled"], item["url"]) ``` #### cURL ```bash curl -X POST "https://api.apify.com/v2/acts/nexgendata~courtlistener-court-opinions/run-sync-get-dataset-items?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "searchType": "o", "query": "trade secret misappropriation", "court": "cafc", "dateFiledAfter": "2024-01-01", "maxResults": 100 }' ``` Results stream into the Apify dataset, exportable as JSON, JSONL, CSV, or Excel. Schedule via Apify's built-in scheduler for a standing litigation watch, and wire a webhook to fire a Slack / Zapier / Make alert on new matches. ### Pricing This actor runs on Apify's **pay-per-event (PPE)** model — you pay for results, not run-time: - **$0.10 per record** — the primary event, charged once per opinion or docket pushed to the dataset - **Actor start** — a negligible one-time per-run event No subscriptions, no seat licences, no minimums. #### Cost worked example - A doctrine search returning 100 opinions → ~$10 - A daily litigation watch returning ~15 new filings → ~$1.50/day - A conflict-check batch returning ~40 prior cases → ~$4 A search that matches nothing costs nothing beyond the actor-start event. Apify's free tier covers most experiments. Browse 200+ buyer-intent actors at https://apify.com/nexgendata?fpr=2ayu9b ### How this compares to Westlaw, LexisNexis, and PACER | Source | Cost | What you get | |---|---|---| | Westlaw | ~$100-$300+/attorney/month | Full research suite with KeyCite; bulk export forbidden, not a programmatic feed | | LexisNexis | Custom enterprise contract | Full research suite with Shepard's; no bulk JSON API | | PACER (federal courts) | $0.10/page (capped per document) | One document at a time; built for retrieval, not corpus search | | CourtListener (DIY) | Free API, rate-limited | Raw API surface; you build the client, pagination, and retry logic | | **US Court Opinions Search (this actor)** | **$0.10/record, no subscription** | **Filtered, structured JSON — case name, court, date, docket number, source URL** | If you need Shepard's / KeyCite citator analysis, headnotes, and treatises, you still want Westlaw or Lexis. But if your workflow is corpus search, litigation monitoring, conflict checks, or feeding a legal-AI index — and you want records as JSON for cents apiece — this actor is the right cost model. ### FAQ **Q: Where does the data come from?** A: CourtListener, the open-access legal database run by the non-profit Free Law Project, aggregating 240,000+ U.S. federal and state court opinions plus RECAP dockets. Every record links back to CourtListener via its `url`. **Q: What is the difference between `searchType=o` and `searchType=r`?** A: `o` searches court *opinions* — the decisions courts publish. `r` searches *RECAP dockets* — the crowd-sourced archive of PACER docket entries and documents. Use opinions for case-law research and RECAP for docket-level monitoring of active federal matters. **Q: Do I need a CourtListener token?** A: No — the actor runs without one at the public rate limit. Supplying a free CourtListener token via `courtListenerToken` raises the limit, which matters for larger pulls. Tokens are free from the Free Law Project. **Q: Does this give me the full opinion text?** A: The actor returns the case metadata — caption, court, date, docket number, and the link. Follow `url` to read the full opinion text on CourtListener. **Q: How current is the corpus?** A: CourtListener ingests opinions and dockets on an ongoing basis. Use `dateFiledAfter` to constrain to recent filings and schedule the actor for a standing watch. ### Schema stability & versioning This actor follows NexGenData's **additive-only schema** contract. New fields may be added as new keys, defaulting to null for older runs. Existing fields are never renamed or removed without a major-version bump and advance changelog notice. Field semantics are never silently changed; a required change adds a new field and keeps the old one for at least 90 days. Build a production research pipeline on this output without a change breaking your ETL. ### Compliance & legal - The actor reads the **public, open-access** CourtListener database, which aggregates public court records and opinions. Court opinions are public records. - Requests are paced politely against the CourtListener API. - The actor collects, stores, and transmits no credentials and accesses no paywalled content. - You are responsible for ensuring downstream use complies with CourtListener's terms and your jurisdiction's laws. Verify any case before relying on it for a filing by reading the source via `url`. - Consult counsel before bulk redistribution of compiled case-law records. ### Related NexGenData actors Part of NexGenData's **IP & Legal intelligence** cluster — pair this actor with: - [RECAP / PACER Docket Search](https://apify.com/nexgendata/recap-pacer-docket-search?fpr=2ayu9b) — federal docket lookups without PACER per-page fees - [CourtListener Federal Judges](https://apify.com/nexgendata/courtlistener-federal-judges?fpr=2ayu9b) — the judiciary database for judge-level research - [CourtListener Oral Arguments](https://apify.com/nexgendata/courtlistener-oral-arguments?fpr=2ayu9b) — oral-argument audio and metadata - [US Copyright Records Search](https://apify.com/nexgendata/us-copyright-records-search?fpr=2ayu9b) — copyright registrations and transfers for IP matters - [USPTO Patent Search](https://apify.com/nexgendata/uspto-patent-search?fpr=2ayu9b) — patent records for IP litigation research - [TTAB Trademark Opposition Tracker](https://apify.com/nexgendata/ttab-trademark-opposition-tracker?fpr=2ayu9b) — trademark opposition and cancellation proceedings Browse the full 200+ actor catalog at **https://apify.com/nexgendata?fpr=2ayu9b**. # Actor input Schema ## `searchType` (type: `string`): o = court opinions, r = RECAP dockets. ## `query` (type: `string`): Full-text search (case name, party, topic). ## `court` (type: `string`): Court ID filter (e.g. 'scotus', 'cafc'). ## `dateFiledAfter` (type: `string`): Only records filed on/after this date (YYYY-MM-DD). ## `maxResults` (type: `integer`): Maximum records to return. ## `courtListenerToken` (type: `string`): Optional free CourtListener token to raise rate limits. ## Actor input object example ```json { "searchType": "o", "query": "patent infringement", "maxResults": 200 } ``` # 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 = { "searchType": "o", "query": "patent infringement" }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/courtlistener-court-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 = { "searchType": "o", "query": "patent infringement", } # Run the Actor and wait for it to finish run = client.actor("nexgendata/courtlistener-court-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 '{ "searchType": "o", "query": "patent infringement" }' | apify call nexgendata/courtlistener-court-opinions --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/courtlistener-court-opinions", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "⚖️ US Court Opinions & Case Law Search", "description": "Search 240,000+ US federal & state court opinions and case law (CourtListener): case name, court, date, citation, cite count, judge. For litigators, legal-AI, and researchers.", "version": "0.0", "x-build-id": "azdrd7OR5HTnD2x21" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~courtlistener-court-opinions/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-courtlistener-court-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~courtlistener-court-opinions/runs": { "post": { "operationId": "runs-sync-nexgendata-courtlistener-court-opinions", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~courtlistener-court-opinions/run-sync": { "post": { "operationId": "run-sync-nexgendata-courtlistener-court-opinions", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchType": { "title": "Search type", "type": "string", "description": "o = court opinions, r = RECAP dockets.", "default": "o" }, "query": { "title": "Query", "type": "string", "description": "Full-text search (case name, party, topic)." }, "court": { "title": "Court", "type": "string", "description": "Court ID filter (e.g. 'scotus', 'cafc')." }, "dateFiledAfter": { "title": "Filed after", "type": "string", "description": "Only records filed on/after this date (YYYY-MM-DD)." }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum records to return.", "default": 200 }, "courtListenerToken": { "title": "CourtListener token (optional)", "type": "string", "description": "Optional free CourtListener token to raise rate limits." } } }, "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 } } } } } } } } } } ```