docs: fix links

sources/platform/actors/development/quick-start/build_with_ai.md

@@ -21,7 +21,7 @@ This guide provides best practices for building new Actors or improving existing
 
 ## AI coding assistant instructions
 
-Use the following prompt in your AI coding assistant such as [Cursor](https://www.cursor.com/), [Claude Code](https://www.claude.com/product/claude-code) or [GitHub Copilot](https://github.com/features/copilot):
+Use the following prompt in your AI coding assistant such as [Cursor](https://cursor.com/), [Claude Code](https://claude.com/product/claude-code) or [GitHub Copilot](https://github.com/features/copilot):
 
 <PromptButton prompt={AGENTS_PROMPT} title="Use pre-built prompt for your AI coding assistant" />
 

sources/platform/actors/publishing/monetize/index.mdx

@@ -77,14 +77,14 @@ All other changes (such as decreasing prices, adjusting descriptions, or removin
 
 :::important Frequency of major monetization adjustments
 
-You can make major monetization changes to each Actor only **once per month**. After making a major change, you must wait until it takes effect (14 days) plus an additional period before making another major change. For further information & guidelines, please refer to our [Terms & Conditions](https://apify.com/store-terms-and-conditions)
+You can make major monetization changes to each Actor only **once per month**. After making a major change, you must wait until it takes effect (14 days) plus an additional period before making another major change. For further information & guidelines, please refer to our [Terms & Conditions](/legal/store-publishing-terms-and-conditions)
 
 :::
 
 ## Monthly payouts and analytics
 
 Payout invoices are automatically generated on the 11th of each month, summarizing the profits from all your Actors for the previous month.
-In accordance with our [Terms & Conditions](https://apify.com/store-terms-and-conditions), only funds from legitimate users who have already paid are included in the payout invoice.
+In accordance with our [Terms & Conditions](/legal/store-publishing-terms-and-conditions), only funds from legitimate users who have already paid are included in the payout invoice.
 
 :::note How negative profits are handled
 
@@ -98,7 +98,7 @@ If no action is taken, the payout will be automatically approved on the 14th, wi
 - $20 for PayPal
 - $100 for other payout methods
 
-If the monthly profit does not meet these thresholds, as per our [Terms & Conditions](https://apify.com/store-terms-and-conditions), the funds will roll over to the next month until the threshold is reached.
+If the monthly profit does not meet these thresholds, as per our [Terms & Conditions](/legal/store-publishing-terms-and-conditions), the funds will roll over to the next month until the threshold is reached.
 
 ## Handle free users
 

sources/platform/console/two-factor-authentication.md

@@ -26,7 +26,7 @@ If it's not enabled, click on the **Enable** button. You should see the two-fact
 
 ![Apify Console setup two-factor authentication - app](./images/console-two-factor-app-setup.png)
 
-In this view, you can use your favorite authenticator app to scan the QR code. We recommend using Google Authenticator ([Google Play Store](https://play.google.com/store/apps/details?id=com.google.android.apps.authenticator2&hl=en_US)/[Apple App Store](https://apps.apple.com/us/app/google-authenticator/id388497605)) or [Authy](https://authy.com/)([Google Play Store](https://play.google.com/store/apps/details?id=com.authy.authy)/[Apple App Store](https://apps.apple.com/us/app/twilio-authy/id494168017) but any other authenticator app should work as well.
+In this view, you can use your favorite authenticator app to scan the QR code. We recommend using Google Authenticator ([Google Play Store](https://play.google.com/store/apps/details?id=com.google.android.apps.authenticator2&hl=en_US)/[Apple App Store](https://apps.apple.com/us/app/google-authenticator/id388497605)) or [Authy](https://www.authy.com/)([Google Play Store](https://play.google.com/store/apps/details?id=com.authy.authy)/[Apple App Store](https://apps.apple.com/us/app/twilio-authy/id494168017) but any other authenticator app should work as well.
 
 You can also set up your app/browser extension manually without the QR code. To do that, click on the **Setup key** link below the QR code. This view with the key will pop up:
 

sources/platform/integrations/ai/agno.md

@@ -140,6 +140,6 @@ Agno supports any Apify Actor via the ApifyTools class. You can specify a single
 - [How to build an AI Agent](https://blog.apify.com/how-to-build-an-ai-agent/)
 - [Agno Framework Documentation](https://docs.agno.com)
 - [Apify Platform Documentation](https://docs.apify.com)
-- [Apify Actor Documentation](https://docs.apify.com/actors)
+- [Apify Actor Documentation](/platform/actors)
 - [Apify Store - Browse available Actors](https://apify.com/store)
 - [Agno Apify Toolkit Documentation](https://docs.agno.com/tools/toolkits/others/apify#apify)

sources/platform/integrations/ai/aws_bedrock.md

@@ -26,7 +26,7 @@ Before getting started, ensure you have:
 
 - An active AWS Account.
 - An Apify account and an [API token](https://docs.apify.com/platform/integrations/api#api-token).
-- Granted access to any Large Language Model from Amazon Bedrock. To add access to a LLM, follow this [guide](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access-modify.html). We'll use **Anthropic Claude 3.5 Sonnet** in this example.
+- Granted access to any Large Language Model from Amazon Bedrock. To add access to a LLM, follow this [guide](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html). We'll use **Anthropic Claude 3.5 Sonnet** in this example.
 
 The overall process for creating an agent includes the following [steps](https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html):
 

sources/platform/integrations/ai/langchain.md

@@ -10,13 +10,13 @@ slug: /integrations/langchain
 
 ---
 
-> For more information on LangChain visit its [documentation](https://python.langchain.com/docs/).
+> 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.
 Then we feed the documents into a vector index and answer questions from it.
 
 This example demonstrates how to integrate Apify with LangChain using the Python language.
-If you prefer to use JavaScript, you can follow the  [JavaScript LangChain documentation](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/apify_dataset/).
+If you prefer to use JavaScript, you can follow the  [JavaScript LangChain documentation](https://docs.langchain.com/oss/javascript/integrations/document_loaders/web_loaders/apify_dataset).
 
 Before we start with the integration, we need to install all dependencies:
 
@@ -54,7 +54,7 @@ llm = ChatOpenAI(model="gpt-4o-mini")
 
 loader = apify.call_actor(
     actor_id="apify/website-content-crawler",
-    run_input={"startUrls": [{"url": "https://python.langchain.com/docs/get_started/introduction"}], "maxCrawlPages": 10, "crawlerType": "cheerio"},
+    run_input={"startUrls": [{"url": "https://docs.langchain.com/oss/python/langchain/overviewget_started/introduction"}], "maxCrawlPages": 10, "crawlerType": "cheerio"},
     dataset_mapping_function=lambda item: Document(
         page_content=item["text"] or "", metadata={"source": item["url"]}
     ),
@@ -107,7 +107,7 @@ llm = ChatOpenAI(model="gpt-4o-mini")
 print("Call website content crawler ...")
 loader = apify.call_actor(
     actor_id="apify/website-content-crawler",
-    run_input={"startUrls": [{"url": "https://python.langchain.com/docs/get_started/introduction"}], "maxCrawlPages": 10, "crawlerType": "cheerio"},
+    run_input={"startUrls": [{"url": "https://docs.langchain.com/oss/python/langchain/overviewget_started/introduction"}], "maxCrawlPages": 10, "crawlerType": "cheerio"},
     dataset_mapping_function=lambda item: Document(page_content=item["text"] or "", metadata={"source": item["url"]}),
 )
 print("Compute embeddings...")
@@ -130,7 +130,7 @@ After running the code, you should see the following output:
 answer: LangChain is a framework designed for developing applications powered by large language models (LLMs). It simplifies the
  entire application lifecycle, from development to productionization and deployment. LangChain provides open-source components and integrates with various third-party tools, making it easier to build and optimize applications using language models.
 
-source: https://python.langchain.com/docs/get_started/introduction
+source: https://docs.langchain.com/oss/python/langchain/overviewget_started/introduction
 ```
 
 LangChain is a standard interface through which you can interact with a variety of large language models (LLMs).
@@ -154,6 +154,6 @@ Similarly, you can use other Apify Actors to load data into LangChain and query
 
 ## Resources
 
-- [LangChain introduction](https://python.langchain.com/docs/get_started/introduction)
-- [Apify Dataset loader](https://python.langchain.com/docs/integrations/document_loaders/apify_dataset)
-- [LangChain Apify Provider](https://python.langchain.com/docs/integrations/providers/apify)
+- [LangChain introduction](https://docs.langchain.com/oss/python/langchain/overviewget_started/introduction)
+- [Apify Dataset loader](https://docs.langchain.com/oss/python/langchain/overviewintegrations/document_loaders/apify_dataset)
+- [LangChain Apify Provider](https://docs.langchain.com/oss/python/langchain/overviewintegrations/providers/apify)

sources/platform/integrations/ai/langflow.md

@@ -12,7 +12,7 @@ slug: /integrations/langflow
 
 ## What is Langflow
 
-[Langflow](https://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.
+[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.
 
 :::note Explore Langflow
 
@@ -37,7 +37,7 @@ This guide will demonstrate two different ways to use Apify Actors with Langflow
 
 :::note Cloud vs local setup
 
-Langflow can either be installed locally or used in the cloud. The cloud version is available on the [Langflow](http://langflow.org/) website. If you are using the cloud version, you can skip the installation step, and go straight to [Creating a new flow](#creating-a-new-flow)
+Langflow can either be installed locally or used in the cloud. The cloud version is available on the [Langflow](https://www.langflow.org/) website. If you are using the cloud version, you can skip the installation step, and go straight to [Creating a new flow](#creating-a-new-flow)
 
 :::
 

sources/platform/integrations/ai/llama.md

@@ -10,7 +10,7 @@ slug: /integrations/llama-index
 
 ---
 
-> For more information on LlamaIndex, visit its [documentation](https://docs.llamaindex.ai/en/stable/).
+> For more information on LlamaIndex, visit its [documentation](https://developers.llamaindex.ai/python/framework/).
 
 ## What is LlamaIndex?
 
@@ -76,4 +76,4 @@ documents = reader.load_data(
 ## Resources
 
 * [Apify loaders](https://llamahub.ai/l/readers/llama-index-readers-apify)
-* [LlamaIndex documentation](https://docs.llamaindex.ai/en/stable/)
+* [LlamaIndex documentation](https://developers.llamaindex.ai/python/framework/)

sources/platform/integrations/ai/mastra.md

@@ -31,7 +31,7 @@ This guide demonstrates how to integrate Apify Actors with Mastra by building an
 ### Prerequisites
 
 - _Apify API token_: To use Apify Actors, you need an Apify API token. Learn how to obtain it in the [Apify documentation](https://docs.apify.com/platform/integrations/api).
-- _LLM provider API key_: To power the agents, you need an LLM provider API key. For example, get one from the [OpenAI](https://platform.openai.com/account/api-keys) or [Anthropic](https://console.anthropic.com/settings/keys).
+- _LLM provider API key_: To power the agents, you need an LLM provider API key. For example, get one from the [OpenAI](https://platform.openai.com/account/api-keys) or [Anthropic](https://platform.claude.com/settings/keys).
 - _Node.js_: Ensure you have Node.js installed.
 - _Packages_: Install the following packages:
 

sources/platform/integrations/ai/mcp.md

@@ -13,7 +13,7 @@ import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
 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/). The server enables AI agents to
+using [Model Context Protocol](https://modelcontextprotocol.io/docs/getting-started/intro). The server enables AI agents to
 discover and run Actors from [Apify Store](https://apify.com/store), access storages and results,
 and enabled AI coding assistants to access Apify documentation and tutorials.
 

sources/platform/integrations/ai/openai_agents.md

@@ -283,4 +283,4 @@ For a comprehensive example with error handling and reporting, refer to the [Ope
 - [OpenAI Agent MCP Tester GitHub repository](https://github.com/apify/openai-agent-mcp-tester) - Source code for the MCP tester Actor
 - [Apify MCP server](https://mcp.apify.com) - Interactive configuration tool for the Apify MCP server
 - [Apify MCP documentation](/platform/integrations/mcp) - Complete guide to using the Apify MCP server
-- [Model Context Protocol specification](https://modelcontextprotocol.io/) - Learn about the MCP specification
+- [Model Context Protocol specification](https://modelcontextprotocol.io/docs/getting-started/intro) - Learn about the MCP specification

sources/platform/integrations/ai/skyfire.md

@@ -33,7 +33,7 @@ The [Apify MCP server](https://docs.apify.com/platform/integrations/mcp) provide
 
 Before using agentic payments through MCP, you need:
 
-1. _A Skyfire account_ with a funded wallet - [sign up at Skyfire](https://app.skyfire.xyz/)
+1. _A Skyfire account_ with a funded wallet - [sign up at Skyfire](https://app.skyfire.xyz/auth)
 1. _An MCP client_ that supports multiple server connections, such as [OpenCode](https://opencode.ai/), [Claude Desktop](https://claude.com/download) with MCP support, or other compatible clients
 1. _Both MCP servers configured_: Skyfire's MCP server and Apify's MCP server
 
@@ -103,7 +103,7 @@ If you're using [Claude Desktop](https://claude.com/download), add this configur
 </TabItem>
 </Tabs>
 
-Replace `YOUR_SKYFIRE_API_KEY` with Skyfire buyer API key, which you can obtain from your [Skyfire dashboard](https://app.skyfire.xyz/).
+Replace `YOUR_SKYFIRE_API_KEY` with Skyfire buyer API key, which you can obtain from your [Skyfire dashboard](https://app.skyfire.xyz/auth).
 
 ### How it works
 

sources/platform/integrations/data-storage/airbyte.md

@@ -39,4 +39,4 @@ To find your Apify API token, you need to navigate to the **Settings** tab and s
 
 And that's it! You now have Apify datasets set up as a Source, and you can use Airbyte to transfer your datasets to one of the available destinations.
 
-To learn more about how to setup a Connection, visit [Airbyte's documentation](https://docs.airbyte.com/using-airbyte/getting-started/set-up-a-connection)
+To learn more about how to setup a Connection, visit [Airbyte's documentation](https://docs.airbyte.com/platform/using-airbyte/getting-started/set-up-a-connection)

sources/platform/integrations/workflows-and-notifications/kestra.md

@@ -19,33 +19,33 @@ This guide shows you how to set up the integration, configure authentication, an
 Before you begin, make sure you have:
 
 - An [Apify account](https://console.apify.com/)
-- A [Kestra instance](https://kestra.io/docs/getting-started/quickstart) (self‑hosted or cloud)
+- A [Kestra instance](https://kestra.io/docs/quickstart) (self‑hosted or cloud)
 
 ## Authentication
 
 The Apify plugin uses API key authentication. Store your API key in [Kestra Secrets](https://kestra.io/docs/concepts/secret) through the UI or environment variables. In the open-source version, manage Secrets using base64-encoded environment variables. You can also use [Kestra's KV Store](https://kestra.io/docs/concepts/kv-store) to persist API keys across executions and workflows.
 
 To add your Apify API token, go to the Secrets section in the Kestra UI and create a new secret with the key `APIFY_API_KEY` and your token as the value.
 
-## Use Apify Tasks as an action
+## Use Apify tasks as an action
 
 Tasks allow you to perform operations like running an Actor within a workflow.
 
 1. Create a new flow.
-1. Inside the **Flow code** tab change the hello task's type to be **io.kestra.plugin.apify.actor.Run**.
-1. Change the task's id to be **run_apify_actor**
+1. Inside the **Flow code** tab change the hello task's type to be `io.kestra.plugin.apify.actor.Run`.
+1. Change the task's id to be `run_apify_actor`
 1. Remove the message property.
-1. Configure the **run_apify_actor** task by adding your required values for the properties listed below:
-    - **actorId**: Actor ID or a tilde-separated owner's username and Actor name.
-    - **apiToken**: A reference to the secret value you set up earlier. For example "\{\{secret(namespace=flow.namespace, key='APIFY_API_KEY')\}\}"
-1. Add a new task below the **run_apify_actor** with an ID of **get_dataset** and a type of **io.kestra.plugin.apify.dataset.Get**.:
-1. Configure the **get_dataset** to fetch the dataset generated by the **run_apify_actor** task by configuring the following values:
-    - **datasetId**: The ID of the dataset to fetch. You can use the value from the previous task using the following syntax: "\{\{secret(namespace=flow.namespace, key='APIFY_API_KEY')\}\}"
-    - **input**: Input for the Actor run. The input is optional and can be used to pass data to the Actor. For our example we will add 'hashtags: ["fyp"]'
-    - **maxItems**: The maximum number of items to fetch from the dataset. For our example we will set this to 5.
-1. Now add the final task to log the output of the dataset. Add a new task below the **log_output** with an ID of **log_output** and a type of **io.kestra.plugin.core.log.Log**.
-1. Configure the **log_output** task to log the output of the dataset by configuring the following values:
-    - **message**: The message to log. You can use the value from the previous task using the following syntax: '\{\{outputs.get_dataset.dataset\}\}'
+1. Configure the `run_apify_actor` task by adding your required values for the properties listed below:
+    - `actorId`: Actor ID or a tilde-separated owner's username and Actor name.
+    - `apiToken`: A reference to the secret value you set up earlier. For example "\{\{secret(namespace=flow.namespace, key='APIFY_API_KEY')\}\}"
+1. Add a new task below the `run_apify_actor` with an ID of `get_dataset` and a type of `io.kestra.plugin.apify.dataset.Get`.:
+1. Configure the `get_dataset` to fetch the dataset generated by the `run_apify_actor` task by configuring the following values:
+    - `datasetId`: The ID of the dataset to fetch. You can use the value from the previous task using the following syntax: "\{\{secret(namespace=flow.namespace, key='APIFY_API_KEY')\}\}"
+    - `input`: Input for the Actor run. The input is optional and can be used to pass data to the Actor. For our example we will add 'hashtags: ["fyp"]'
+    - `maxItems`: The maximum number of items to fetch from the dataset. For our example we will set this to 5.
+1. Now add the final task to log the output of the dataset. Add a new task below the `log_output` with an ID of `log_output` and a type of `io.kestra.plugin.core.log.Log`.
+1. Configure the `log_output` task to log the output of the dataset by configuring the following values:
+    - `message`: The message to log. You can use the value from the previous task using the following syntax: '\{\{outputs.get_dataset.dataset\}\}'
 1. Now save and run your flow.
 
 Your completed template should match the template below.

sources/platform/integrations/workflows-and-notifications/n8n/index.md

@@ -201,7 +201,7 @@ Automatically start an n8n workflow when an Actor or task run finishes:
 
 ## Resources
 
-- [n8n Community Nodes Documentation](https://docs.n8n.io/integrations/community-nodes/)
+- [n8n Community Nodes Documentation](https://docs.n8n.io/integrations/)
 - [Apify API Documentation](https://docs.apify.com)
 - [n8n Documentation](https://docs.n8n.io)