# Education & EdTech Intelligence (AI-Powered) (`visita/education-edtech-intelligence`) Actor This actor is a powerful data-gathering tool that transforms raw news from top RSS feeds (focused on Education, EdTech, and Learning Trends) into structured, actionable intelligence. It uses DuckDuckGo News Search to gather real-time context and an LLM (OpenAI) to perform advanced analysis. - **URL**: https://apify.com/visita/education-edtech-intelligence.md - **Developed by:** [Visita Intelligence](https://apify.com/visita) (community) - **Categories:** AI, News, Agents - **Stats:** 5 total users, 0 monthly users, 0.0% runs succeeded, 1 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## 📚 Education & EdTech Intelligence Pipeline This actor is a powerful data-gathering tool that transforms raw news from top RSS feeds (focused on **Education, EdTech, and Learning Trends**) into structured, actionable intelligence. It uses DuckDuckGo News Search to gather real-time context and an LLM (OpenAI) to perform advanced analysis. This actor is designed to run on a schedule, continuously gathering fresh intelligence. The structured data it produces is intended to be consumed by other systems, such as our flagship actor, **Content Blueprint AI**, which can use this stream of data to generate reports, sector briefings, analysis, and more. *** ### How to Use You have two primary ways to use this actor: 1. **As a Data Source for Downstream Systems:** This is the primary intended use. Run this actor on a schedule to build a dataset of fresh intelligence. Other tools, including the `Content Blueprint AI` actor, can then be pointed to this actor's dataset to generate final content and reports. 2. **As a Standalone Tool:** Run this actor to generate high-quality, structured news intelligence. You are free to download the resulting dataset for your own sector analysis, research, or to feed into other custom education workflows. --- ### Features - **Comprehensive Source Aggregation**: Gathers news from a curated list of top-tier Education and EdTech feeds (e.g., Education Week, EdSurge, eLearning Industry). - **Real-Time Grounding (DuckDuckGo)**: Uses the **DuckDuckGo News Search** to find fresh, corroborating snippets for each article, enriching the context before analysis. This ensures the summary is based on current, cross-validated information. - **Advanced AI Analysis**: Leverages an LLM to analyze each article for sector dynamic/sentiment (e.g., `Innovation/Adoption`, `Policy/Regulation`, `Funding/Investment`), categorize the topic (e.g., `K-12 Policy/Funding`, `Higher Education Trends`, `AI in Education`), and extract key entities. - **Resilient Processing**: The pipeline is designed to **fall back** to the original RSS summary if the DuckDuckGo search fails, ensuring the run completes without crashing. - **Duplicate Prevention**: Intelligently tracks processed articles across runs to ensure you only process and pay for new information. - **Cost-Saving Test Mode**: Includes a test mode to run the full workflow with dummy data, allowing for development and testing without incurring API costs. --- ### Setup and Configuration Before running the actor, you only need to provide an API key for the LLM service. 1. **OpenAI API Key**: * You will need an API key from your OpenAI account. #### Add Keys to Apify Secrets For security, add this key as a **secret environment variable** in your Apify Actor settings: - `OPENAI_API_KEY`: Your OpenAI API Key. --- ### Cost of Usage 💸 **Important Note:** The costs listed below are for **this actor only**. Using this data with any other actor will incur its own separate API and platform costs. #### Costs for This Actor 1. **Apify Platform Usage**: Standard platform costs for running the actor, which depends on the duration of the run. 2. **DuckDuckGo Search**: This service is **free** and is handled internally by the actor, replacing the need for paid search APIs. The actor performs **one search query for every article** it processes. 3. **OpenAI API**: This is the primary cost. The actor makes **two LLM calls for every article**: one to summarize the DuckDuckGo Search snippets and another to perform the final analysis (sentiment, category, etc.). --- ### Input | Field | Type | Default | Description | | :--- | :--- | :--- | :--- | | `source` | String | `all` | The news source category to use (e.g., 'all', 'edweek', 'edsurge'). | | `customFeedUrl` | String | `null` | A custom RSS feed URL to use if `source` is set to `custom`. | | `maxArticles` | Integer | `20` | The maximum number of new articles to fetch and process in a single run. | | `region` | String | `wt-wt` | Region to limit search results by (e.g., 'us-en' for US, 'wt-wt' for World). | | `timeLimit` | String | `w` | Limit search results by time (e.g., 'd' for day, 'w' for week). | | `runTestMode` | Boolean | `false` | Bypasses all external API calls for zero-cost testing. **Do not enable in production.**. | --- ### Output The actor saves its results in the dataset. Each item is a structured JSON object with the following fields: | Field | Type | Description | | :--- | :--- | :--- | | `source` | String | The name of the news source (e.g., 'Education Week'). | | `title` | String | The original title of the news article. | | `url` | String | The URL of the original article. | | `published` | String | The publication date string from the RSS feed. | | `summary` | String | The AI-generated summary of the article's content. | | `sentiment` | String | The AI-analyzed sector dynamic (e.g., `Innovation/Adoption`, `Policy/Regulation`). | | `category` | String | The AI-assigned category (e.g., `K-12 Policy/Funding`, `AI in Education`). | | `key_entities` | Array of Strings | A list of key entities like companies, topics, policies, or institutions. # Actor input Schema ## `source` (type: `string`): Select the news feed to collect from. ## `customFeedUrl` (type: `string`): Provide a custom RSS feed URL (if 'Custom' selected). ## `maxArticles` (type: `integer`): Provide a maximum number of articles to fetch ## `region` (type: `string`): Select the region for DuckDuckGo news search results. ## `timeLimit` (type: `string`): Filter search results by time period. ## `runTestMode` (type: `boolean`): Setting this to true bypasses external API calls for zero-cost testing. **Do not enable in production.** ## Actor input object example ```json { "source": "all", "maxArticles": 10, "region": "wt-wt", "timeLimit": "w", "runTestMode": false } ```` # 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("visita/education-edtech-intelligence").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("visita/education-edtech-intelligence").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 visita/education-edtech-intelligence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=visita/education-edtech-intelligence", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Education & EdTech Intelligence (AI-Powered)", "description": "This actor is a powerful data-gathering tool that transforms raw news from top RSS feeds (focused on Education, EdTech, and Learning Trends) into structured, actionable intelligence. It uses DuckDuckGo News Search to gather real-time context and an LLM (OpenAI) to perform advanced analysis.", "version": "0.0", "x-build-id": "MhCucY3dbit7xKxft" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/visita~education-edtech-intelligence/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-visita-education-edtech-intelligence", "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/visita~education-edtech-intelligence/runs": { "post": { "operationId": "runs-sync-visita-education-edtech-intelligence", "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/visita~education-edtech-intelligence/run-sync": { "post": { "operationId": "run-sync-visita-education-edtech-intelligence", "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", "required": [ "source" ], "properties": { "source": { "title": "News Source", "enum": [ "all", "brainscape", "edweek", "edsurge", "stevehargadon", "classwork", "kqed", "edcircuit", "ednewsdaily", "theedublogger", "edtechdigest", "edtechreview", "fortelabs", "fenews", "charteredcollege", "elearningindustry", "custom" ], "type": "string", "description": "Select the news feed to collect from.", "default": "all" }, "customFeedUrl": { "title": "Custom RSS Feed URL", "type": "string", "description": "Provide a custom RSS feed URL (if 'Custom' selected)." }, "maxArticles": { "title": "Maximum Articles", "type": "integer", "description": "Provide a maximum number of articles to fetch", "default": 10 }, "region": { "title": "Search Region", "enum": [ "wt-wt", "us-en", "uk-en", "za-en", "au-en", "ca-en", "de-de", "fr-fr" ], "type": "string", "description": "Select the region for DuckDuckGo news search results.", "default": "wt-wt" }, "timeLimit": { "title": "Time Limit", "enum": [ "any", "d", "w", "m" ], "type": "string", "description": "Filter search results by time period.", "default": "w" }, "runTestMode": { "title": "Admin Test Mode (Bypasses ALL External API Costs)", "type": "boolean", "description": "Setting this to true bypasses external API calls for zero-cost testing. **Do not enable in production.**", "default": false } } }, "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 } } } } } } } } } } ```