diff --git a/examples/lats/img/lats.png b/examples/lats/img/lats.png
new file mode 100644
index 000000000..79e96a304
Binary files /dev/null and b/examples/lats/img/lats.png differ
diff --git a/examples/lats/lats.ipynb b/examples/lats/lats.ipynb
index 00519b368..54f42b86d 100644
--- a/examples/lats/lats.ipynb
+++ b/examples/lats/lats.ipynb
@@ -9,11 +9,14 @@
     "\n",
     "[Language Agent Tree Search](https://andyz245.github.io/LanguageAgentTreeSearch/) (LATS), by Zhou, et. al, is a general LLM agent frameowrk that combines reflection/evaluation and search (specifically monte-carlo trees search) to get achieve better overall task performance compared to similar techniques like ReACT, Reflexion, or Tree of Thoughts.\n",
     "\n",
+    "![LATS diagram](./img/lats.png)\n",
+    "\n",
     "It has four main steps:\n",
-    "1. Expand and simulate: select the \"best\" 5 potential actions to take and execute them in parallel.\n",
-    "2. Reflect + Evaluate: observe the outcomes of these actions and score the decisions based on reflection (and possibly external feedback)\n",
-    "3. Backpropagate: update the scores of the root trajectories based on the outcomes.\n",
-    "4. Select: pick the best next actions based on the aggreate rewards from step (2). Either respond (if a solution is found or the max search depth is reached) or continue searching."
+    "\n",
+    "1. Select: pick the best next actions based on the aggreate rewards from step (2). Either respond (if a solution is found or the max search depth is reached) or continue searching.\n",
+    "2. Expand and simulate: select the \"best\" 5 potential actions to take and execute them in parallel.\n",
+    "3. Reflect + Evaluate: observe the outcomes of these actions and score the decisions based on reflection (and possibly external feedback)\n",
+    "4. Backpropagate: update the scores of the root trajectories based on the outcomes."
    ]
   },
   {
@@ -285,6 +288,7 @@
    "source": [
     "from langchain_community.tools.tavily_search import TavilySearchResults\n",
     "from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper\n",
+    "\n",
     "from langgraph.prebuilt.tool_executor import ToolExecutor, ToolInvocation\n",
     "\n",
     "search = TavilySearchAPIWrapper()\n",