docs: Fix broken links in site (#82)

Author: katmayb

src/langgraph-platform/INVALID-LICENSE.mdx

@@ -2,9 +2,9 @@
 title: Connect an authentication provider
 sidebarTitle: Connect an authentication provider
 ---
-In [the last tutorial](resource-auth), you added resource authorization to give users private conversations. However, you are still using hard-coded tokens for authentication, which is not secure. Now you'll replace those tokens with real user accounts using [OAuth2](getting-started).
+In [the last tutorial](/langgraph-platform/resource-auth), you added resource authorization to give users private conversations. However, you are still using hard-coded tokens for authentication, which is not secure. Now you'll replace those tokens with real user accounts using [OAuth2](/langgraph-platform/deployment-quickstart).
 
-You'll keep the same [`Auth`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python-sdk_ref#langgraph_sdk.auth.Auth) object and [resource-level access control](auth#single-owner-resources), but upgrade authentication to use Supabase as your identity provider. While Supabase is used in this tutorial, the concepts apply to any OAuth2 provider. You'll learn how to:
+You'll keep the same [`Auth`](/langgraph-platform/python-sdk#langgraph_sdk.auth.Auth) object and [resource-level access control](/langgraph-platform/auth#single-owner-resources), but upgrade authentication to use Supabase as your identity provider. While Supabase is used in this tutorial, the concepts apply to any OAuth2 provider. You'll learn how to:
 
 1. Replace test tokens with real JWT tokens
 2. Integrate with OAuth2 providers for secure user authentication
@@ -40,7 +40,7 @@ sequenceDiagram
 
 Before you start this tutorial, ensure you have:
 
-* The [bot from the second tutorial](resource-auth) running without errors.
+* The [bot from the second tutorial](/langgraph-platform/resource-auth) running without errors.
 * A [Supabase project](https://supabase.com/dashboard) to use its authentication server.
 
 ## 1. Install dependencies
@@ -75,9 +75,9 @@ Since you're using Supabase for this, you can do this in the Supabase dashboard:
 
 ## 3. Implement token validation
 
-In the previous tutorials, you used the [`Auth`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python-sdk_ref#langgraph_sdk.auth.Auth) object to [validate hard-coded tokens](set-up-custom-auth) and [add resource ownership](resource-auth).
+In the previous tutorials, you used the [`Auth`](/langgraph-platform/python-sdk#langgraph_sdk.auth.Auth) object to [validate hard-coded tokens](/langgraph-platform/set-up-custom-auth) and [add resource ownership](/langgraph-platform/resource-auth).
 
-Now you'll upgrade your authentication to validate real JWT tokens from Supabase. The main changes will all be in the [`@auth.authenticate`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python-sdk_ref#langgraph_sdk.auth.Auth.authenticate) decorated function:
+Now you'll upgrade your authentication to validate real JWT tokens from Supabase. The main changes will all be in the [`@auth.authenticate`](/langgraph-platform/python-sdk#langgraph_sdk.auth.Auth.authenticate) decorated function:
 
 * Instead of checking against a hard-coded list of tokens, you'll make an HTTP request to Supabase to validate the token.
 * You'll extract real user information (ID, email) from the validated token.
@@ -270,5 +270,5 @@ You've successfully built a production-ready authentication system for your Lang
 Now that you have production authentication, consider:
 
 1. Building a web UI with your preferred framework (see the [Custom Auth](https://github.com/langchain-ai/custom-auth) template for an example)
-2. Learn more about the other aspects of authentication and authorization in the [conceptual guide on authentication](auth).
-3. Customize your handlers and setup further after reading the [reference docs](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python-sdk_ref#langgraph_sdk.auth.Auth).
+2. Learn more about the other aspects of authentication and authorization in the [conceptual guide on authentication](/langgraph-platform/auth).
+3. Customize your handlers and setup further after reading the [reference docs](/langgraph-platform/python-sdk#langgraph_sdk.auth.Auth).

src/langgraph-platform/add-auth-server.mdx

@@ -69,27 +69,27 @@ To review, edit, and approve tool calls in an agent or workflow, use LangGraph's
     const result = await client.runs.wait(
       threadID,
       assistantID,
-      { input: { "some_text": "original text" } }   // (1)!
+      { input: { "some_text": "original text" } }   # (1)!
     );
 
-    console.log(result['__interrupt__']); // (2)!
+    console.log(result['__interrupt__']); # (2)!
     // > [
-    // >     {
-    // >         'value': {'text_to_revise': 'original text'},
-    // >         'resumable': True,
-    // >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
-    // >         'when': 'during'
-    // >     }
-    // > ]
+    # >     {
+    # >         'value': {'text_to_revise': 'original text'},
+    # >         'resumable': True,
+    # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
+    # >         'when': 'during'
+    # >     }
+    # > ]
 
     // Resume the graph
     console.log(await client.runs.wait(
         threadID,
         assistantID,
-        // highlight-next-line
-        { command: { resume: "Edited text" }}   // (3)!
+        # highlight-next-line
+        { command: { resume: "Edited text" }}   # (3)!
     ));
-    // > {'some_text': 'Edited text'}
+    # > {'some_text': 'Edited text'}
     ```
 
     1. The graph is invoked with some initial state.
@@ -136,7 +136,7 @@ To review, edit, and approve tool calls in an agent or workflow, use LangGraph's
 
 <Accordion title="Extended example: using `interrupt`">
   This is an example graph you can run in the LangGraph API server.
-  See [LangGraph Platform quickstart](deployment-quickstart) for more details.
+  See [LangGraph Platform quickstart](/langgraph-platform/deployment-quickstart) for more details.
 
   ```python
   from typing import TypedDict
@@ -176,7 +176,7 @@ To review, edit, and approve tool calls in an agent or workflow, use LangGraph's
   3. Once resumed, the return value of `interrupt(...)` is the human-provided input, which is used to update the state.
 
   Once you have a running LangGraph API server, you can interact with it using
-  [LangGraph SDK](../cloud/reference/sdk/python-sdk_ref/)
+  [LangGraph SDK](/langgraph-platform/python-sdk)
 
   <Tabs>
     <Tab title="Python">
@@ -241,27 +241,27 @@ To review, edit, and approve tool calls in an agent or workflow, use LangGraph's
       const result = await client.runs.wait(
         threadID,
         assistantID,
-        { input: { "some_text": "original text" } }   // (1)!
+        { input: { "some_text": "original text" } }   # (1)!
       );
 
-      console.log(result['__interrupt__']); // (2)!
-      // > [
-      // >     {
-      // >         'value': {'text_to_revise': 'original text'},
-      // >         'resumable': True,
-      // >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
-      // >         'when': 'during'
-      // >     }
-      // > ]
+      console.log(result['__interrupt__']); # (2)!
+      # > [
+      # >     {
+      # >         'value': {'text_to_revise': 'original text'},
+      # >         'resumable': True,
+      # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
+      # >         'when': 'during'
+      # >     }
+      # > ]
 
       // Resume the graph
       console.log(await client.runs.wait(
           threadID,
           assistantID,
-          // highlight-next-line
-          { command: { resume: "Edited text" }}   // (3)!
+          # highlight-next-line
+          { command: { resume: "Edited text" }}   # (3)!
       ));
-      // > {'some_text': 'Edited text'}
+      # > {'some_text': 'Edited text'}
       ```
 
       1. The graph is invoked with some initial state.
@@ -360,9 +360,9 @@ Alternatively, you can set static interrupts at run time:
         assistantID,
         {
         input: input,
-        // highlight-next-line
+        # highlight-next-line
         interruptBefore: ["node_a"], // (2)!
-        // highlight-next-line
+        # highlight-next-line
         interruptAfter: ["node_b", "node_c"] // (3)!
         }
     )
@@ -436,14 +436,14 @@ The following example shows how to add static interrupts:
     const result = await client.runs.wait(
       threadID,
       assistantID,
-      { input: input }   // (1)!
+      { input: input }   # (1)!
     );
 
     // Resume the graph
     await client.runs.wait(
       threadID,
       assistantID,
-      { input: null }   // (2)!
+      { input: null }   # (2)!
     );
     ```
 

src/langgraph-platform/add-human-in-the-loop.mdx

@@ -80,10 +80,10 @@ Below are examples of directory structures for Python and JavaScript application
 
 The `langgraph.json` file is a JSON file that specifies the dependencies, graphs, environment variables, and other settings required to deploy a LangGraph application.
 
-See the [LangGraph configuration file reference](cli#configuration-file) for details on all supported keys in the JSON file.
+See the [LangGraph configuration file reference](/langgraph-platform/cli#configuration-file) for details on all supported keys in the JSON file.
 
 <Tip>
-  The [LangGraph CLI](langgraph-cli) defaults to using the configuration file `langgraph.json` in the current directory.
+  The [LangGraph CLI](/langgraph-platform/langgraph-cli) defaults to using the configuration file `langgraph.json` in the current directory.
 </Tip>
 
 ### Examples

src/langgraph-platform/application-structure.mdx

@@ -7,28 +7,28 @@ sidebarTitle: Overview
 
 For example, imagine a general-purpose writing agent built on a common graph architecture. While the structure remains the same, different writing styles—such as blog posts and tweets—require tailored configurations to optimize performance. To support these variations, you can create multiple assistants (e.g., one for blogs and another for tweets) that share the underlying graph but differ in model selection and system prompt.
 
-![assistant versions](images/assistants.png)
+![assistant versions](/langgraph-platform/images/assistants.png)
 
-The LangGraph Cloud API provides several endpoints for creating and managing assistants and their versions. See the [API reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/server-api-ref.html#tag/assistants) for more details.
+The LangGraph Cloud API provides several endpoints for creating and managing assistants and their versions. See the [API reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/api_ref/#tag/assistants) for more details.
 
 <Info>
-  Assistants are a [LangGraph Platform](index) concept. They are not available in the open source LangGraph library.
+  Assistants are a [LangGraph Platform](/langgraph-platform/index) concept. They are not available in the open source LangGraph library.
 </Info>
 
 ## Configuration
 
 Assistants build on the LangGraph open source concept of [configuration](https://langchain-ai.github.io/langgraph/concepts/low_level/#configuration).
-While configuration is available in the open source LangGraph library, assistants are only present in [LangGraph Platform](index). This is due to the fact that assistants are tightly coupled to your deployed graph. Upon deployment, LangGraph Server will automatically create a default assistant for each graph using the graph's default configuration settings.
+While configuration is available in the open source LangGraph library, assistants are only present in [LangGraph Platform](/langgraph-platform/index). This is due to the fact that assistants are tightly coupled to your deployed graph. Upon deployment, LangGraph Server will automatically create a default assistant for each graph using the graph's default configuration settings.
 
-In practice, an assistant is just an _instance_ of a graph with a specific configuration. Therefore, multiple assistants can reference the same graph but can contain different configurations (e.g. prompts, models, tools). The LangGraph Server API provides several endpoints for creating and managing assistants. See the [API reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/server-api-ref.html) and [this how-to](configuration-cloud) for more details on how to create assistants.
+In practice, an assistant is just an _instance_ of a graph with a specific configuration. Therefore, multiple assistants can reference the same graph but can contain different configurations (e.g. prompts, models, tools). The LangGraph Server API provides several endpoints for creating and managing assistants. See the [API reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/api_ref/) and [this how-to](/langgraph-platform/configuration-cloud) for more details on how to create assistants.
 
 ## Versioning
 
 Assistants support versioning to track changes over time.
-Once you've created an assistant, subsequent edits to that assistant will create new versions. See [this how-to](configuration-cloud#create-a-new-version-for-your-assistant) for more details on how to manage assistant versions.
+Once you've created an assistant, subsequent edits to that assistant will create new versions. See [this how-to](/langgraph-platform/configuration-cloud#create-a-new-version-for-your-assistant) for more details on how to manage assistant versions.
 
 ## Execution
 
 A **run** is an invocation of an assistant. Each run may have its own input, configuration, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a [thread](https://langchain-ai.github.io/langgraph/concepts/persistence/#threads).
 
-The LangGraph Platform API provides several endpoints for creating and managing runs. See the [API reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/server-api-ref.html) for more details.
+The LangGraph Platform API provides several endpoints for creating and managing runs. See the [API reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/api_ref/) for more details.