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 | ------------------------------------- | ---------------------------------------------------------------------------------------------------- |
 | [Airbyte](./airbyte/)                 | Data integration platform specialising in ELT pipelines.                                             |
 | [Airflow](./airflow/)                 | Platform designed for developing, scheduling, and monitoring batch-oriented workflows.               |
+| [Apify](./apify/)                     | Platform to build web scrapers and automate web browser tasks.                                       |
 | [AutoGen](./autogen/)                 | Framework from Microsoft building LLM applications using multiple conversational agents.             |
 | [Bubble](./bubble)                    | Development platform for application development with a no-code interface                            |
 | [Canopy](./canopy/)                   | Framework from Pinecone for building RAG applications using LLMs and knowledge bases.                |
diff --git a/qdrant-landing/content/documentation/frameworks/apify.md b/qdrant-landing/content/documentation/frameworks/apify.md
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+---
+title: Apify 
+weight: 3600
+---
+
+# Apify
+
+[Apify](https://apify.com/) is a web scraping and browser automation platform featuring an [app store](https://apify.com/store) with over 1,500 pre-built micro-apps known as Actors. These serverless cloud programs, which are essentially dockers under the hood, are designed for various web automation applications, including data collection.
+
+One such Actor, built especially for AI and RAG applications, is [Website Content Crawler](https://apify.com/apify/website-content-crawler).  
+
+It's ideal for this purpose because it has built-in HTML processing and data-cleaning functions. That means you can easily remove fluff, duplicates, and other things on a web page that aren't relevant, and provide only the necessary data to the language model.
+
+The Markdown can then be used to feed Qdrant to train AI models or supply them with fresh web content.
+
+Qdrant is available as an [official integration](https://apify.com/apify/qdrant-integration) to load Apify datasets into a collection.
+
+You can refer to the [Apify documentation](https://docs.apify.com/platform/integrations/qdrant) to set up the integration via the Apify UI.
+
+## Programmatic Usage
+
+Apify also supports programmatic access to integrations via the [Apify Python SDK](https://docs.apify.com/sdk/python/).
+
+1. Install the Apify Python SDK by running the following command:
+
+    ```sh
+    pip install apify-client
+    ```
+
+2. Create a Python script and import all the necessary modules:
+
+    ```python
+    from apify_client import ApifyClient
+
+    APIFY_API_TOKEN = "YOUR-APIFY-TOKEN"
+    OPENAI_API_KEY = "YOUR-OPENAI-API-KEY"
+    # COHERE_API_KEY = "YOUR-COHERE-API-KEY"
+
+    QDRANT_URL = "YOUR-QDRANT-URL"
+    QDRANT_API_KEY = "YOUR-QDRANT-API-KEY"
+
+    client = ApifyClient(APIFY_API_TOKEN)
+    ```
+
+3. Call the [Website Content Crawler](https://apify.com/apify/website-content-crawler) Actor to crawl the Qdrant documentation and extract text content from the web pages:
+
+    ```python
+    actor_call = client.actor("apify/website-content-crawler").call(
+        run_input={"startUrls": [{"url": "https://qdrant.tech/documentation/"}]}
+    )
+    ```
+
+4. Call the Qdrant integration and store all data in the Qdrant Vector Database:
+
+    ```python
+    qdrant_integration_inputs = {
+        "qdrantUrl": QDRANT_URL,
+        "qdrantApiKey": QDRANT_API_KEY,
+        "qdrantCollectionName": "apify",
+        "qdrantAutoCreateCollection": True,
+        "datasetId": actor_call["defaultDatasetId"],
+        "datasetFields": ["text"],
+        "enableDeltaUpdates": True,
+        "deltaUpdatesPrimaryDatasetFields": ["url"],
+        "expiredObjectDeletionPeriodDays": 30,
+        "embeddingsProvider": "OpenAI", # "Cohere"
+        "embeddingsApiKey": OPENAI_API_KEY,
+        "performChunking": True,
+        "chunkSize": 1000,
+        "chunkOverlap": 0,
+    }
+    actor_call = client.actor("apify/qdrant-integration").call(run_input=qdrant_integration_inputs)
+
+    ```
+
+Upon running the script, the data from <https://qdrant.tech/documentation/> will be scraped, transformed into vector embeddings and stored in the Qdrant collection.
+
+## Further Reading
+
+- Apify [Documentation](https://docs.apify.com/)
+- Apify [Templates](https://apify.com/templates)
+- Integration [Source Code](https://github.com/apify/actor-vector-database-integrations)
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