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examples/lats/lats.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Language Agent Tree Search\n",
     "\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",
+    "[Language Agent Tree Search](https://andyz245.github.io/LanguageAgentTreeSearch/) (LATS), by Zhou, et. al, is a general LLM agent search algorithm 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",

examples/reflexion/reflexion.ipynb

@@ -239,7 +239,6 @@
     "                self.validator.invoke(response)\n",
     "                return response\n",
     "            except ValidationError as e:\n",
-    "                print(\"RETRYING\", attempt)\n",
     "                state = state + [HumanMessage(content=repr(e))]\n",
     "        return response"
    ]
@@ -269,24 +268,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "id": "20901d77-0f5f-4596-90a4-412c0ac5f2c2",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['answer', 'reflection', 'search_queries'])"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "parsed = parser.invoke(initial)\n",
-    "parsed[0][\"args\"].keys()"
+    "parsed"
    ]
   },
   {