docs: fix links

README.md

@@ -1,4 +1,4 @@
 # 🦜🕸️LangGraph
 
 ![Version](https://img.shields.io/pypi/v/langgraph)
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
@@ -224,7 +224,7 @@
 
 * [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples.
 * [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
-* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
+* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
 * [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
 * [Cloud (beta)](https://langchain-ai.github.io/langgraph/cloud/): With one click, deploy LangGraph applications to LangGraph Cloud.
 

docs/docs/how-tos/branching.ipynb

@@ -174,7 +174,7 @@
    "metadata": {},
    "source": [
     "<details class=\"note\"> <summary>Exception handling?</summary>\n",
-    " <p>LangGraph executes nodes within <a href=\"https://langchain-ai.github.io/langgraph/concepts/#core-design\">\"supersteps\"</a>, meaning that while parallel branches are executed in parallel, the entire superstep is <b>transactional</b>. If any of these branches raises an exception, <b>none</b> of the updates are applied to the state (the entire superstep errors).<br><br>\n",
+    " <p>LangGraph executes nodes within <a href=\"https://langchain-ai.github.io/langgraph/concepts/low_level/#graphs\">\"supersteps\"</a>, meaning that while parallel branches are executed in parallel, the entire superstep is <b>transactional</b>. If any of these branches raises an exception, <b>none</b> of the updates are applied to the state (the entire superstep errors).<br><br>\n",
     " If you have error-prone (perhaps want to handle flakey API calls), LangGraph provides two ways to address this:<br>\n",
     " <ol>\n",
     "  <li>You can write regular python code within your node to catch and handle exceptions.</li>\n",
@@ -642,7 +642,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/streaming-content.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# How to stream arbitrary nested content\n",
     "\n",
-    "The most common use case for streaming from inside a node is to stream LLM tokens, but you may have other long-running streaming functions you wish to render for the user. While individual nodes in LangGraph cannot return generators (since they are executed to completion for each [superstep](https://langchain-ai.github.io/langgraph/concepts/#core-design)), we can still stream arbitrary custom functions from within a node using a similar tact and calling `astream_events` on the graph.\n",
+    "The most common use case for streaming from inside a node is to stream LLM tokens, but you may have other long-running streaming functions you wish to render for the user. While individual nodes in LangGraph cannot return generators (since they are executed to completion for each [superstep](https://langchain-ai.github.io/langgraph/concepts/low_level)), we can still stream arbitrary custom functions from within a node using a similar tact and calling `astream_events` on the graph.\n",
     "\n",
     "We do so using a [RunnableGenerator](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableGenerator.html#langchain-core-runnables-base-runnablegenerator) (which your function will automatically behave as if wrapped as a [RunnableLambda](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableLambda.html#langchain_core.runnables.base.RunnableLambda)).\n",
     "\n",

libs/langgraph/README.md

@@ -224,7 +224,7 @@ final_state["messages"][-1].content
 
 * [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples.
 * [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
-* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
+* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
 * [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
 * [Cloud (beta)](https://langchain-ai.github.io/langgraph/cloud/): With one click, deploy LangGraph applications to LangGraph Cloud.