docs: update tutorial names/links (#2126)

docs/docs/tutorials/index.md

@@ -25,8 +25,8 @@ Learn from example implementations of graphs designed for specific scenarios and
 
 #### Multi-Agent Systems
 
-- [Collaboration](multi_agent/multi-agent-collaboration.ipynb): Enable two agents to collaborate on a task
-- [Supervision](multi_agent/agent_supervisor.ipynb): Use an LLM to orchestrate and delegate to individual agents
+- [Network](multi_agent/multi-agent-collaboration.ipynb): Enable two or more agents to collaborate on a task
+- [Supervisor](multi_agent/agent_supervisor.ipynb): Use an LLM to orchestrate and delegate to individual agents
 - [Hierarchical Teams](multi_agent/hierarchical_agent_teams.ipynb): Orchestrate nested teams of agents to solve problems
 
 #### RAG

docs/docs/tutorials/multi_agent/agent_supervisor.ipynb

@@ -10,11 +10,11 @@
    "id": "a3e3ebc4-57af-4fe4-bdd3-36aff67bf276",
    "metadata": {},
    "source": [
-    "# Agent Supervisor\n",
+    "# Multi-agent supervisor\n",
     "\n",
     "The [previous example](../multi-agent-collaboration) routed messages automatically based on the output of the initial researcher agent.\n",
     "\n",
-    "We can also choose to use an LLM to orchestrate the different agents.\n",
+    "We can also choose to use an [LLM to orchestrate](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#supervisor) the different agents.\n",
     "\n",
     "Below, we will create an agent group, with an agent supervisor to help delegate tasks.\n",
     "\n",
@@ -376,7 +376,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb

@@ -12,7 +12,7 @@
    "source": [
     "# Hierarchical Agent Teams\n",
     "\n",
-    "In our previous example ([Agent Supervisor](../agent_supervisor)), we introduced the concept of a single supervisor node to route work between different worker nodes.\n",
+    "In our previous example ([Agent Supervisor](../agent_supervisor)), we introduced the concept of a single [supervisor node](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#supervisor) to route work between different worker nodes.\n",
     "\n",
     "But what if the job for a single worker becomes too complex? What if the number of workers becomes too large?\n",
     "\n",
@@ -1117,7 +1117,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb

@@ -10,11 +10,11 @@
    "id": "39fd1948-b5c3-48c4-b10e-2ae7e8c83334",
    "metadata": {},
    "source": [
-    "# Basic Multi-agent Collaboration\n",
+    "# Multi-agent network\n",
     "\n",
     "A single agent can usually operate effectively using a handful of tools within a single domain, but even using powerful models like `gpt-4`, it can be less effective at using many tools. \n",
     "\n",
-    "One way to approach complicated tasks is through a \"divide-and-conquer\" approach: create an specialized agent for each task or domain and route tasks to the correct \"expert\".\n",
+    "One way to approach complicated tasks is through a \"divide-and-conquer\" approach: create an specialized agent for each task or domain and route tasks to the correct \"expert\". This is an example of a [multi-agent network](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#network) architecture.\n",
     "\n",
     "This notebook (inspired by the paper [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://arxiv.org/abs/2308.08155), by Wu, et. al.) shows one way to do this using LangGraph.\n",
     "\n",
@@ -535,7 +535,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,