Add ReAct agent concept guide, search tweaks (langchain-ai#5912)

docs/core_docs/docs/concepts.mdx

@@ -156,6 +156,18 @@ Chat Models also accept other parameters that are specific to that integration.
 
 For specifics on how to use chat models, see the [relevant how-to guides here](/docs/how_to/#chat-models).
 
+### Multimodality
+
+Some chat models are multimodal, accepting images, audio and even video as inputs.
+These are still less common, meaning model providers haven't standardized on the "best" way to define the API.
+Multimodal outputs are even less common. As such, we've kept our multimodal abstractions fairly light weight
+and plan to further solidify the multimodal APIs and interaction patterns as the field matures.
+
+In LangChain, most chat models that support multimodal inputs also accept those values in OpenAI's content blocks format.
+So far this is restricted to image inputs. For models like Gemini which support video and other bytes input, the APIs also support the native, model-specific representations.
+
+For specifics on how to use multimodal models, see the [relevant how-to guides here](/docs/how_to/#multimodal).
+
 ### LLMs
 
 <span data-heading-keywords="llm,llms"></span>
@@ -579,15 +591,28 @@ If you are still using AgentExecutor, do not fear: we still have a guide on [how
 It is recommended, however, that you start to transition to [LangGraph](https://github.com/langchain-ai/langgraphjs).
 In order to assist in this we have put together a [transition guide on how to do so](/docs/how_to/migrate_agent).
 
-### Multimodal
+#### ReAct agents
 
-Some models are multimodal, accepting images, audio and even video as inputs. These are still less common, meaning model providers haven't standardized on the "best" way to define the API.
-Multimodal **outputs** are even less common. As such, we've kept our multimodal abstractions fairly light weight and plan to further solidify the multimodal APIs and interaction patterns as the field matures.
+<span data-heading-keywords="react,react agent"></span>
 
-In LangChain, most chat models that support multimodal inputs also accept those values in OpenAI's content blocks format.
-So far this is restricted to image inputs. For models like Gemini which support video and other bytes input, the APIs also support the native, model-specific representations.
+One popular architecture for building agents is [**ReAct**](https://arxiv.org/abs/2210.03629).
+ReAct combines reasoning and acting in an iterative process - in fact the name "ReAct" stands for "Reason" and "Act".
 
-For specifics on how to use multimodal models, see the [relevant how-to guides here](/docs/how_to/#multimodal).
+The general flow looks like this:
+
+- The model will "think" about what step to take in response to an input and any previous observations.
+- The model will then choose an action from available tools (or choose to respond to the user).
+- The model will generate arguments to that tool.
+- The agent runtime (executor) will parse out the chosen tool and call it with the generated arguments.
+- The executor will return the results of the tool call back to the model as an observation.
+- This process repeats until the agent chooses to respond.
+
+There are general prompting based implementations that do not require any model-specific features, but the most
+reliable implementations use features like [tool calling](/docs/how_to/tool_calling/) to reliably format outputs
+and reduce variance.
+
+Please see the [LangGraph documentation](https://langchain-ai.github.io/langgraph/) for more information,
+or [this how-to guide](/docs/how_to/migrate_agent/) for specific information on migrating to LangGraph.
 
 ### Callbacks
 

docs/core_docs/docs/how_to/migrate_agent.ipynb

@@ -1,5 +1,19 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "8f21bf6b",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "---\n",
+    "keywords: [create_react_agent, create_react_agent()]\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "579c24a2",
@@ -12,7 +26,7 @@
     "[`AgentExecutor`](https://api.js.langchain.com/classes/langchain_agents.AgentExecutor.html)\n",
     "in particular) have multiple configuration parameters. In this notebook we will\n",
     "show how those parameters map to the LangGraph\n",
-    "[react agent executor](https://langchain-ai.github.io/langgraphjs/reference/functions/prebuilt.createReactAgent.html).\n",
+    "react agent executor using the [create_react_agent](https://langchain-ai.github.io/langgraphjs/reference/functions/prebuilt.createReactAgent.html) prebuilt helper method.\n",
     "\n",
     "For more information on how to build agentic workflows in LangGraph, check out\n",
     "the [docs here](https://langchain-ai.github.io/langgraphjs/how-tos/).\n",

docs/core_docs/docs/how_to/tool_calling.ipynb

@@ -9,7 +9,7 @@
       },
       "source": [
         "---\n",
-        "keywords: [function, function calling, tool, tool calling]\n",
+        "keywords: [function, function calling, tool, tool call, tool calling]\n",
         "---"
       ]
     },