docs: add third-party integration disclaimer to integration pages

sources/_partials/_third-party-integration.mdx

@@ -0,0 +1,5 @@
+:::info Found obsolete content?
+
+This integration uses a third-party service. If you find outdated content, please [submit an issue on GitHub](https://github.com/apify/apify-docs/issues).
+
+:::

sources/platform/integrations/ai/agno.md

@@ -6,6 +6,10 @@ sidebar_position: 19
 slug: /integrations/agno
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
+<ThirdPartyDisclaimer />
+
 ## What is Agno?
 
 [Agno](https://docs.agno.com/) is an open-source framework for building intelligent AI agents. It provides a flexible architecture to create agents with custom tools, enabling seamless integration with external services like Apify for tasks such as web scraping, data extraction and automation.

sources/platform/integrations/ai/aws_bedrock.md

@@ -6,6 +6,8 @@ sidebar_position: 15
 slug: /integrations/aws_bedrock
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
 [Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that provides access to large language models (LLMs), allowing users to create and manage retrieval-augmented generative (RAG) pipelines, and create AI agents to plan and perform actions.
 AWS Bedrock supports a wide range of models from providers such as A21 Labs, Anthropic, Cohere, Meta, and Mistral AI.
 These models are designed to handle complex, multistep tasks across systems, knowledge bases, and APIs, making them versatile for various use cases.
@@ -14,6 +16,8 @@ In this tutorial, we’ll demonstrate how to create and use AWS Bedrock AI agent
 The AI agent will be configured to either answer questions from an internal LLM knowledge or to leverage the [RAG Web Browser](https://apify.com/apify/rag-web-browser) to perform internet searches for relevant information.
 This approach enables the agent to provide more comprehensive and accurate responses by combining internal knowledge with real-time data from the web.
 
+<ThirdPartyDisclaimer />
+
 ## AWS Bedrock AI agents
 
 Amazon Bedrock allows you to create AI agents powered by large language models to analyze user input and determine the required data sources, and execute actions needed to fulfill the user requests.

sources/platform/integrations/ai/chatgpt.md

@@ -6,6 +6,8 @@ sidebar_position: 12
 slug: /integrations/chatgpt
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
 The _ChatGPT_ integration enables you to connect ChatGPT to Apify's extensive library of [Actors](https://apify.com/store) through the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/docs/getting-started/intro).
 This allows ChatGPT to access real-time web data and automation capabilities by using Apify tools directly in conversations.
 By default, the Apify MCP server exposes a set of tools that let you search and run any Actor you have access to, including all public Actors and rental Actors you have rented.
@@ -14,6 +16,8 @@ _Example query_: "Find and run an Actor that scrapes Instagram profiles and gets
 
 In this tutorial, you'll learn how to connect _ChatGPT_ to the _Apify MCP server_ using a custom connector.
 
+<ThirdPartyDisclaimer />
+
 ## Prerequisites
 
 Before connecting ChatGPT to Apify, you'll need:

sources/platform/integrations/ai/crewai.md

@@ -6,6 +6,10 @@ sidebar_position: 3
 slug: /integrations/crewai
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
+<ThirdPartyDisclaimer />
+
 ## What is CrewAI
 
 [CrewAI](https://www.crewai.com/) is an open-source Python framework designed to orchestrate autonomous, role-playing AI agents that collaborate as a "crew" to tackle complex tasks. It enables developers to define agents with specific roles, assign tasks, and integrate tools - like Apify Actors - for real-world data retrieval and automation.
@@ -36,6 +40,7 @@ First, import all required packages:
 
 ```python
 import os
+
 from crewai import Agent, Task, Crew
 from crewai_tools import ApifyActorsTool
 from langchain_openai import ChatOpenAI

sources/platform/integrations/ai/flowise.md

@@ -6,6 +6,10 @@ sidebar_position: 10
 slug: /integrations/flowise
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
+<ThirdPartyDisclaimer />
+
 ## What is Flowise?
 
 Flowise is an open-source UI visual tool to build your customized LLM flow using Langchain.

sources/platform/integrations/ai/google-adk.md

@@ -6,6 +6,10 @@ sidebar_position: 2
 slug: /integrations/google-adk
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
+<ThirdPartyDisclaimer />
+
 ## What is the Google ADK
 
 [Google Agent Development Kit](https://github.com/google/adk-python) is a framework for developing and deploying AI agents.

sources/platform/integrations/ai/haystack.md

@@ -6,6 +6,8 @@ sidebar_position: 4
 slug: /integrations/haystack
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
 [Haystack](https://haystack.deepset.ai/) is an open source framework for building production-ready LLM applications, agents, advanced retrieval-augmented generative pipelines, and state-of-the-art search systems that work intelligently over large document collections. For more information on Haystack, visit its [documentation](https://docs.haystack.deepset.ai/docs/intro).
 
 In this example, we'll use the [Website Content Crawler](https://apify.com/apify/website-content-crawler) Actor, which can deeply crawl websites such as documentation sites, knowledge bases, or blogs, and extract text content from the web pages.
@@ -175,6 +177,8 @@ for doc in results["retriever"]["documents"]:
 
 To run it, you can use the following command: `python apify_integration.py`
 
+<ThirdPartyDisclaimer />
+
 ## Resources
 
 - [Apify-haystack integration documentation](https://haystack.deepset.ai/integrations/apify)

sources/platform/integrations/ai/langchain.md

@@ -6,6 +6,8 @@ sidebar_position: 5
 slug: /integrations/langchain
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
 > For more information on LangChain visit its [documentation](https://docs.langchain.com/oss/python/langchain/overview).
 
 In this example, we'll use the [Website Content Crawler](https://apify.com/apify/website-content-crawler) Actor, which can deeply crawl websites such as documentation, knowledge bases, help centers, or blogs and extract text content from the web pages.
@@ -148,6 +150,8 @@ print("Documents:", loader.load())
 
 Similarly, you can use other Apify Actors to load data into LangChain and query the vector index.
 
+<ThirdPartyDisclaimer />
+
 ## Resources
 
 - [LangChain quickstart](https://docs.langchain.com/oss/python/langchain/quickstart)

sources/platform/integrations/ai/langflow.md

@@ -6,6 +6,10 @@ sidebar_position: 6
 slug: /integrations/langflow
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
+<ThirdPartyDisclaimer />
+
 ## What is Langflow
 
 [Langflow](https://www.langflow.org/) is a low-code, visual tool that enables developers to build powerful AI agents and workflows that can use any API, models, or databases.

sources/platform/integrations/ai/langgraph.md

@@ -6,6 +6,10 @@ sidebar_position: 8
 slug: /integrations/langgraph
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
+<ThirdPartyDisclaimer />
+
 ## What is LangGraph
 
 [LangGraph](https://www.langchain.com/langgraph) is a framework designed for constructing stateful, multi-agent applications with Large Language Models (LLMs), allowing developers to build complex AI agent workflows that can leverage tools, APIs, and databases.

sources/platform/integrations/ai/lindy.md

@@ -6,8 +6,12 @@ sidebar_position: 9
 slug: /integrations/lindy
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
 [Lindy](https://www.lindy.ai/) is an AI-powered automation platform that lets you create intelligent workflows and automate complex tasks. By integrating Apify with Lindy, you can leverage Apify's web scraping capabilities within Lindy's AI-driven automation workflows to extract data, monitor websites, and trigger actions based on scraped information.
 
+<ThirdPartyDisclaimer />
+
 ## Prerequisites
 
 To use the Apify integration with Lindy, you need:

sources/platform/integrations/ai/llama.md

@@ -6,8 +6,12 @@ sidebar_position: 7
 slug: /integrations/llama-index
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
 > For more information on LlamaIndex, visit its [documentation](https://developers.llamaindex.ai/python/framework/).
 
+<ThirdPartyDisclaimer />
+
 ## What is LlamaIndex?
 
 LlamaIndex is a platform that allows you to create and manage vector databases and LLMs.

sources/platform/integrations/ai/mastra.md

@@ -6,6 +6,10 @@ sidebar_position: 11
 slug: /integrations/mastra
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
+<ThirdPartyDisclaimer />
+
 ## What is Mastra
 
 [Mastra](https://mastra.ai) is an open-source TypeScript framework for building AI applications efficiently. It provides essential tools like agents, workflows, retrieval-augmented generation (RAG), integrations, and evaluations. Supporting any LLM (e.g., GPT-4, Claude, Gemini). You can run it locally or deploy it to a serverless cloud like [Apify](https://apify.com).
@@ -43,6 +47,7 @@ First, import all required packages:
 import { Agent } from '@mastra/core/agent';
 import { MastraMCPClient } from '@mastra/mcp';
 import { openai } from '@ai-sdk/openai';
+
 // For Anthropic use
 // import { anthropic } from '@ai-sdk/anthropic';
 ```

sources/platform/integrations/ai/mcp.md

@@ -11,6 +11,7 @@ toc_max_heading_level: 4
 
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
 
 The Apify's MCP server ([mcp.apify.com](https://mcp.apify.com)) allows AI applications and agents to interact with the Apify platform
 using [Model Context Protocol](https://modelcontextprotocol.io/docs/getting-started/intro). The server enables AI agents to
@@ -19,6 +20,8 @@ and enables AI coding assistants to access Apify documentation and tutorials.
 
 ![Apify MCP server](../../images/apify_mcp_server.png)
 
+<ThirdPartyDisclaimer />
+
 ## Prerequisites
 
 Before connecting your AI to Apify, you'll need three things:
@@ -303,7 +306,6 @@ Use the UI configurator `https://mcp.apify.com/` to select your tools visually,
 | `add-actor`* | experimental | ❔ | Add an Actor as a new tool for the user to call |
 | `get-actor-output`* | - | ✅ | Retrieve the output from an Actor call which is not included in the output preview of the Actor tool. |
 
-
 :::note Retrieving full output
 
 The `get-actor-output` tool is automatically included with any Actor-related tool, such as `call-actor`, `add-actor`, or specific Actor tools like `apify-slash-rag-web-browser`. When you call an Actor, you receive an output preview. Depending on the output format and length, the preview may contain the complete output or only a limited version to avoid overwhelming the LLM. To retrieve the full output, use the `get-actor-output` tool with the `datasetId` from the Actor call. This tool supports limit, offset, and field filtering.
@@ -318,7 +320,6 @@ It can search Apify Store for relevant Actors using the `search-actors` tool, in
 This dynamic discovery means your AI can adapt to new tasks without manual configuration.
 Each discovered Actor becomes immediately available for future use in the conversation.
 
-
 :::note Dynamic tool discovery
 
 When you use the `actors` tool category, clients that support dynamic tool discovery (such as Claude.ai web and VS Code) will automatically receive the `add-actor` tool instead of `call-actor` for enhanced Actor discovery capabilities.

sources/platform/integrations/ai/milvus.md

@@ -8,12 +8,16 @@ toc_min_heading_level: 2
 toc_max_heading_level: 4
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
 [Milvus](https://milvus.io/) is an open-source vector database optimized for performing similarity searches on large datasets of high-dimensional vectors.
 Its focus on efficient vector similarity search allows for the creation of powerful and scalable retrieval systems.
 
 The Apify integration for Milvus allows exporting results from Apify Actors and Dataset items into a Milvus collection.
 It can also be connected to a managed Milvus instance on [Zilliz Cloud](https://cloud.zilliz.com).
 
+<ThirdPartyDisclaimer />
+
 ## Prerequisites
 
 Before you begin, ensure that you have the following:

sources/platform/integrations/ai/openai_agents.md

@@ -6,11 +6,15 @@ sidebar_position: 13
 slug: /integrations/openai-agents
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
 The _OpenAI Agents Python SDK_ enables you to build AI agents powered by OpenAI's language models that can use tools, manage context, and interact with external systems through the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/docs/getting-started/intro).
 By connecting to the Apify MCP server, your agents can access Apify's extensive library of Actors to perform web scraping, data extraction, and automation tasks in real time.
 
 For more details about the OpenAI Agents SDK, refer to the [official documentation](https://openai.github.io/openai-agents-python/).
 
+<ThirdPartyDisclaimer />
+
 ## Prerequisites
 
 Before integrating Apify with OpenAI Agents SDK, you'll need:
@@ -38,7 +42,6 @@ from agents.mcp import MCPServerStreamableHttp
 os.environ["APIFY_TOKEN"] = "Your Apify API token"
 os.environ["OPENAI_API_KEY"] = "Your OpenAI API key"
 
-
 async def main() -> None:
     # Create MCP server connection with Bearer token
     async with MCPServerStreamableHttp(
@@ -62,7 +65,6 @@ async def main() -> None:
         result = await Runner.run(agent, "Search the web and summarize recent trends in AI agents")
         print(result.final_output)
 
-
 if __name__ == "__main__":
     asyncio.run(main())
 ```
@@ -133,7 +135,6 @@ from agents.mcp import MCPServerStreamableHttp
 os.environ["APIFY_TOKEN"] = "Your Apify API token"
 os.environ["OPENAI_API_KEY"] = "Your OpenAI API key"
 
-
 async def main() -> None:
     # Create MCP server connection
     async with MCPServerStreamableHttp(
@@ -157,7 +158,6 @@ async def main() -> None:
         result = await Runner.run(agent, "Search the web and summarize recent trends in AI agents")
         print(result.final_output)
 
-
 if __name__ == "__main__":
     asyncio.run(main())
 ```
@@ -176,7 +176,6 @@ from agents.mcp import MCPServerStreamableHttp
 os.environ["APIFY_TOKEN"] = "Your Apify API token"
 os.environ["OPENAI_API_KEY"] = "Your OpenAI API key"
 
-
 async def main() -> None:
     # Create MCP server connection with Instagram scraper
     async with MCPServerStreamableHttp(
@@ -202,7 +201,6 @@ async def main() -> None:
         )
         print(result.final_output)
 
-
 if __name__ == "__main__":
     asyncio.run(main())
 ```
@@ -221,7 +219,6 @@ from agents.mcp import MCPServerStreamableHttp
 os.environ["APIFY_TOKEN"] = "Your Apify API token"
 os.environ["OPENAI_API_KEY"] = "Your OpenAI API key"
 
-
 async def main() -> None:
     # Connect to Apify MCP server for testing
     async with MCPServerStreamableHttp(
@@ -251,7 +248,6 @@ async def main() -> None:
         )
         print(result.final_output)
 
-
 if __name__ == "__main__":
     asyncio.run(main())
 ```

sources/platform/integrations/ai/openai_assistants.md

@@ -6,6 +6,8 @@ sidebar_position: 14
 slug: /integrations/openai-assistants
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
 [OpenAI Assistants API](https://platform.openai.com/docs/assistants/overview) allows you to build your own AI applications such as chatbots, virtual assistants, and more.
 The OpenAI Assistants can access OpenAI knowledge base ([vector store](https://platform.openai.com/docs/api-reference/vector-stores)) via file search and use function calling for dynamic interaction and data retrieval.
 
@@ -14,6 +16,8 @@ Unlike Custom GPT, OpenAI Assistants are available via API, enabling integration
 In this tutorial, we’ll start by demonstrating how to create an assistant and integrate real-time data using function calling with the [RAG Web Browser](https://apify.com/apify/rag-web-browser).
 Next, we’ll show how to save data from Apify Actors into the OpenAI Vector Store for easy retrieval through [file-search](https://platform.openai.com/docs/assistants/tools/file-search).
 
+<ThirdPartyDisclaimer />
+
 ## Real-time search data for OpenAI Assistant
 
 We'll use the [RAG Web Browser](https://apify.com/apify/rag-web-browser) Actor to fetch the latest information from the web and provide it to the OpenAI Assistant through [function calling](https://platform.openai.com/docs/assistants/tools/function-calling?context=without-streaming).

sources/platform/integrations/ai/pinecone.md

@@ -8,10 +8,14 @@ toc_min_heading_level: 2
 toc_max_heading_level: 4
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
 [Pinecone](https://www.pinecone.io) is a managed vector database that allows users to store and query dense vectors for AI applications such as recommendation systems, semantic search, and retrieval augmented generation (RAG).
 
 The Apify integration for Pinecone enables you to export results from Apify Actors and Dataset items into a specific Pinecone vector index.
 
+<ThirdPartyDisclaimer />
+
 ## Prerequisites
 
 Before you begin, ensure that you have the following:

sources/platform/integrations/ai/qdrant.md

@@ -8,10 +8,14 @@ toc_min_heading_level: 2
 toc_max_heading_level: 4
 ---
 
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
+
 [Qdrant](https://qdrant.tech) is a high performance managed vector database that allows users to store and query dense vectors for next generation AI applications such as recommendation systems, semantic search, and retrieval augmented generation (RAG).
 
 The Apify integration for Qdrant enables you to export results from Apify Actors and Dataset items into a specific Qdrant collection.
 
+<ThirdPartyDisclaimer />
+
 ## Prerequisites
 
 Before you begin, ensure that you have the following:

sources/platform/integrations/ai/skyfire.md

@@ -8,6 +8,7 @@ slug: /integrations/skyfire
 
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
+import ThirdPartyDisclaimer from '@site/sources/_partials/_third-party-integration.mdx';
 
 Agentic payments enable AI agents to autonomously run Apify Actors using third-party payment providers, without requiring traditional Apify user accounts. This allows agents to discover, execute, and pay for web scraping and automation tasks independently.
 
@@ -19,6 +20,8 @@ Keep in mind that agentic payments are an experimental feature and may undergo s
 
 :::
 
+<ThirdPartyDisclaimer />
+
 ## What is Skyfire?
 
 [Skyfire](https://skyfire.xyz/) is a payment network built specifically for AI agents, enabling autonomous transactions with digital wallets and spending controls. It provides the infrastructure necessary for agents to make payments on behalf of users, allowing autonomous AI-driven workflows.