Add testing

The callback manager was not passed to the Python tool, making it hard
to observe some intermediate steps.

NOTE: The mypy hints seem to suggest that the tool does not accept None
as an input—but I didn't check the code—so I use get_callback_manager()
to get the real default callback manager in case None is passed to the
Pandas tool.

Co-authored-by: Sami Liedes <sami.liedes@rocket-science.ch>

docs/modules/agents/toolkits/examples/pandas.ipynb

@@ -14,11 +14,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "0cdd9bf5",
-   "metadata": {},
+   "execution_count": null,
+   "id": "65a8ebbe-cd83-401a-8ca7-cb6541121d0c",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
+    "!pip install \"pandas\" > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0cdd9bf5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks import tracing_enabled\n",
     "from langchain.agents import create_pandas_dataframe_agent"
    ]
   },
@@ -26,7 +41,9 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "051ebe84",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.llms import OpenAI\n",
@@ -37,17 +54,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "id": "4185ff46",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=True)"
+    "agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "id": "a9207a2e",
    "metadata": {},
    "outputs": [
@@ -55,32 +74,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: I need to count the number of rows\n",
-      "Action: python_repl_ast\n",
-      "Action Input: len(df)\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m891\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: There are 891 rows in the dataframe.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "Error in on_llm_start callback: Parent run with UUID 1b224cee-9715-4f0b-ba33-527468c2cebb not found.\n",
+      "Error in on_llm_end callback: No LLMRun found to be traced\n",
+      "Error in on_llm_start callback: Parent run with UUID 51658270-136c-4763-9c8a-49db12e2efc9 not found.\n",
+      "Error in on_llm_end callback: No LLMRun found to be traced\n",
+      "There are 891 rows.\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'There are 891 rows in the dataframe.'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent.run(\"how many rows are there?\")"
+    "with tracing_enabled():\n",
+    "    print(agent.run(\"how many rows are there?\"))"
    ]
   },
   {
@@ -96,11 +100,91 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3m\n",
+      "You are working with a pandas dataframe in Python. The name of the dataframe is `df`.\n",
+      "You should use the tools below to answer the question posed of you:\n",
+      "\n",
+      "python_repl_ast: A Python shell. Use this to execute python commands. Input should be a valid python command. When using this tool, sometimes output is abbreviated - make sure it does not look abbreviated before using it in your answer.\n",
+      "\n",
+      "Use the following format:\n",
+      "\n",
+      "Question: the input question you must answer\n",
+      "Thought: you should always think about what to do\n",
+      "Action: the action to take, should be one of [python_repl_ast]\n",
+      "Action Input: the input to the action\n",
+      "Observation: the result of the action\n",
+      "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
+      "Thought: I now know the final answer\n",
+      "Final Answer: the final answer to the original input question\n",
+      "\n",
+      "\n",
+      "This is the result of `print(df.head())`:\n",
+      "|    |   PassengerId |   Survived |   Pclass | Name                                                | Sex    |   Age |   SibSp |   Parch | Ticket           |    Fare | Cabin   | Embarked   |\n",
+      "|---:|--------------:|-----------:|---------:|:----------------------------------------------------|:-------|------:|--------:|--------:|:-----------------|--------:|:--------|:-----------|\n",
+      "|  0 |             1 |          0 |        3 | Braund, Mr. Owen Harris                             | male   |    22 |       1 |       0 | A/5 21171        |  7.25   | nan     | S          |\n",
+      "|  1 |             2 |          1 |        1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female |    38 |       1 |       0 | PC 17599         | 71.2833 | C85     | C          |\n",
+      "|  2 |             3 |          1 |        3 | Heikkinen, Miss. Laina                              | female |    26 |       0 |       0 | STON/O2. 3101282 |  7.925  | nan     | S          |\n",
+      "|  3 |             4 |          1 |        1 | Futrelle, Mrs. Jacques Heath (Lily May Peel)        | female |    35 |       1 |       0 | 113803           | 53.1    | C123    | S          |\n",
+      "|  4 |             5 |          0 |        3 | Allen, Mr. William Henry                            | male   |    35 |       0 |       0 | 373450           |  8.05   | nan     | S          |\n",
+      "\n",
+      "Begin!\n",
+      "Question: how many people have more than 3 sibligngs\n",
+      "\u001b[0m\n",
+      "Error in on_llm_start callback: Parent run with UUID 6b86a7f0-bdf6-43e8-b94d-937c743b60f5 not found.\n",
+      "Error in on_llm_end callback: No LLMRun found to be traced\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3mThought: I need to count the number of people with more than 3 siblings\n",
       "Action: python_repl_ast\n",
       "Action Input: df[df['SibSp'] > 3].shape[0]\u001b[0m\n",
       "Observation: \u001b[36;1m\u001b[1;3m30\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Thought:\n",
+      "\n",
+      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3m\n",
+      "You are working with a pandas dataframe in Python. The name of the dataframe is `df`.\n",
+      "You should use the tools below to answer the question posed of you:\n",
+      "\n",
+      "python_repl_ast: A Python shell. Use this to execute python commands. Input should be a valid python command. When using this tool, sometimes output is abbreviated - make sure it does not look abbreviated before using it in your answer.\n",
+      "\n",
+      "Use the following format:\n",
+      "\n",
+      "Question: the input question you must answer\n",
+      "Thought: you should always think about what to do\n",
+      "Action: the action to take, should be one of [python_repl_ast]\n",
+      "Action Input: the input to the action\n",
+      "Observation: the result of the action\n",
+      "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
+      "Thought: I now know the final answer\n",
+      "Final Answer: the final answer to the original input question\n",
+      "\n",
+      "\n",
+      "This is the result of `print(df.head())`:\n",
+      "|    |   PassengerId |   Survived |   Pclass | Name                                                | Sex    |   Age |   SibSp |   Parch | Ticket           |    Fare | Cabin   | Embarked   |\n",
+      "|---:|--------------:|-----------:|---------:|:----------------------------------------------------|:-------|------:|--------:|--------:|:-----------------|--------:|:--------|:-----------|\n",
+      "|  0 |             1 |          0 |        3 | Braund, Mr. Owen Harris                             | male   |    22 |       1 |       0 | A/5 21171        |  7.25   | nan     | S          |\n",
+      "|  1 |             2 |          1 |        1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female |    38 |       1 |       0 | PC 17599         | 71.2833 | C85     | C          |\n",
+      "|  2 |             3 |          1 |        3 | Heikkinen, Miss. Laina                              | female |    26 |       0 |       0 | STON/O2. 3101282 |  7.925  | nan     | S          |\n",
+      "|  3 |             4 |          1 |        1 | Futrelle, Mrs. Jacques Heath (Lily May Peel)        | female |    35 |       1 |       0 | 113803           | 53.1    | C123    | S          |\n",
+      "|  4 |             5 |          0 |        3 | Allen, Mr. William Henry                            | male   |    35 |       0 |       0 | 373450           |  8.05   | nan     | S          |\n",
+      "\n",
+      "Begin!\n",
+      "Question: how many people have more than 3 sibligngs\n",
+      "Thought: I need to count the number of people with more than 3 siblings\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df[df['SibSp'] > 3].shape[0]\n",
+      "Observation: 30\n",
+      "Thought:\u001b[0m\n",
+      "Error in on_llm_start callback: Parent run with UUID 73a825e3-1270-4735-b40b-208a0915e8be not found.\n",
+      "Error in on_llm_end callback: No LLMRun found to be traced\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I now know the final answer\n",
       "Final Answer: 30 people have more than 3 siblings.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -134,23 +218,253 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3m\n",
+      "You are working with a pandas dataframe in Python. The name of the dataframe is `df`.\n",
+      "You should use the tools below to answer the question posed of you:\n",
+      "\n",
+      "python_repl_ast: A Python shell. Use this to execute python commands. Input should be a valid python command. When using this tool, sometimes output is abbreviated - make sure it does not look abbreviated before using it in your answer.\n",
+      "\n",
+      "Use the following format:\n",
+      "\n",
+      "Question: the input question you must answer\n",
+      "Thought: you should always think about what to do\n",
+      "Action: the action to take, should be one of [python_repl_ast]\n",
+      "Action Input: the input to the action\n",
+      "Observation: the result of the action\n",
+      "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
+      "Thought: I now know the final answer\n",
+      "Final Answer: the final answer to the original input question\n",
+      "\n",
+      "\n",
+      "This is the result of `print(df.head())`:\n",
+      "|    |   PassengerId |   Survived |   Pclass | Name                                                | Sex    |   Age |   SibSp |   Parch | Ticket           |    Fare | Cabin   | Embarked   |\n",
+      "|---:|--------------:|-----------:|---------:|:----------------------------------------------------|:-------|------:|--------:|--------:|:-----------------|--------:|:--------|:-----------|\n",
+      "|  0 |             1 |          0 |        3 | Braund, Mr. Owen Harris                             | male   |    22 |       1 |       0 | A/5 21171        |  7.25   | nan     | S          |\n",
+      "|  1 |             2 |          1 |        1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female |    38 |       1 |       0 | PC 17599         | 71.2833 | C85     | C          |\n",
+      "|  2 |             3 |          1 |        3 | Heikkinen, Miss. Laina                              | female |    26 |       0 |       0 | STON/O2. 3101282 |  7.925  | nan     | S          |\n",
+      "|  3 |             4 |          1 |        1 | Futrelle, Mrs. Jacques Heath (Lily May Peel)        | female |    35 |       1 |       0 | 113803           | 53.1    | C123    | S          |\n",
+      "|  4 |             5 |          0 |        3 | Allen, Mr. William Henry                            | male   |    35 |       0 |       0 | 373450           |  8.05   | nan     | S          |\n",
+      "\n",
+      "Begin!\n",
+      "Question: whats the square root of the average age?\n",
+      "\u001b[0m\n",
+      "Error in on_llm_start callback: Parent run with UUID 8d771bc6-208a-4580-b43d-ec92ca04e39f not found.\n",
+      "Error in on_llm_end callback: No LLMRun found to be traced\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3mThought: I need to calculate the average age first\n",
       "Action: python_repl_ast\n",
       "Action Input: df['Age'].mean()\u001b[0m\n",
       "Observation: \u001b[36;1m\u001b[1;3m29.69911764705882\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
+      "Thought:\n",
+      "\n",
+      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3m\n",
+      "You are working with a pandas dataframe in Python. The name of the dataframe is `df`.\n",
+      "You should use the tools below to answer the question posed of you:\n",
+      "\n",
+      "python_repl_ast: A Python shell. Use this to execute python commands. Input should be a valid python command. When using this tool, sometimes output is abbreviated - make sure it does not look abbreviated before using it in your answer.\n",
+      "\n",
+      "Use the following format:\n",
+      "\n",
+      "Question: the input question you must answer\n",
+      "Thought: you should always think about what to do\n",
+      "Action: the action to take, should be one of [python_repl_ast]\n",
+      "Action Input: the input to the action\n",
+      "Observation: the result of the action\n",
+      "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
+      "Thought: I now know the final answer\n",
+      "Final Answer: the final answer to the original input question\n",
+      "\n",
+      "\n",
+      "This is the result of `print(df.head())`:\n",
+      "|    |   PassengerId |   Survived |   Pclass | Name                                                | Sex    |   Age |   SibSp |   Parch | Ticket           |    Fare | Cabin   | Embarked   |\n",
+      "|---:|--------------:|-----------:|---------:|:----------------------------------------------------|:-------|------:|--------:|--------:|:-----------------|--------:|:--------|:-----------|\n",
+      "|  0 |             1 |          0 |        3 | Braund, Mr. Owen Harris                             | male   |    22 |       1 |       0 | A/5 21171        |  7.25   | nan     | S          |\n",
+      "|  1 |             2 |          1 |        1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female |    38 |       1 |       0 | PC 17599         | 71.2833 | C85     | C          |\n",
+      "|  2 |             3 |          1 |        3 | Heikkinen, Miss. Laina                              | female |    26 |       0 |       0 | STON/O2. 3101282 |  7.925  | nan     | S          |\n",
+      "|  3 |             4 |          1 |        1 | Futrelle, Mrs. Jacques Heath (Lily May Peel)        | female |    35 |       1 |       0 | 113803           | 53.1    | C123    | S          |\n",
+      "|  4 |             5 |          0 |        3 | Allen, Mr. William Henry                            | male   |    35 |       0 |       0 | 373450           |  8.05   | nan     | S          |\n",
+      "\n",
+      "Begin!\n",
+      "Question: whats the square root of the average age?\n",
+      "Thought: I need to calculate the average age first\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df['Age'].mean()\n",
+      "Observation: 29.69911764705882\n",
+      "Thought:\u001b[0m\n",
+      "Error in on_llm_start callback: Parent run with UUID b30b9eac-eee3-406c-b7c3-a3db3b549948 not found.\n",
+      "Error in on_llm_end callback: No LLMRun found to be traced\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I now need to calculate the square root of the average age\n",
       "Action: python_repl_ast\n",
       "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
       "Observation: \u001b[36;1m\u001b[1;3mname 'math' is not defined\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to import the math library\n",
+      "Thought:\n",
+      "\n",
+      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3m\n",
+      "You are working with a pandas dataframe in Python. The name of the dataframe is `df`.\n",
+      "You should use the tools below to answer the question posed of you:\n",
+      "\n",
+      "python_repl_ast: A Python shell. Use this to execute python commands. Input should be a valid python command. When using this tool, sometimes output is abbreviated - make sure it does not look abbreviated before using it in your answer.\n",
+      "\n",
+      "Use the following format:\n",
+      "\n",
+      "Question: the input question you must answer\n",
+      "Thought: you should always think about what to do\n",
+      "Action: the action to take, should be one of [python_repl_ast]\n",
+      "Action Input: the input to the action\n",
+      "Observation: the result of the action\n",
+      "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
+      "Thought: I now know the final answer\n",
+      "Final Answer: the final answer to the original input question\n",
+      "\n",
+      "\n",
+      "This is the result of `print(df.head())`:\n",
+      "|    |   PassengerId |   Survived |   Pclass | Name                                                | Sex    |   Age |   SibSp |   Parch | Ticket           |    Fare | Cabin   | Embarked   |\n",
+      "|---:|--------------:|-----------:|---------:|:----------------------------------------------------|:-------|------:|--------:|--------:|:-----------------|--------:|:--------|:-----------|\n",
+      "|  0 |             1 |          0 |        3 | Braund, Mr. Owen Harris                             | male   |    22 |       1 |       0 | A/5 21171        |  7.25   | nan     | S          |\n",
+      "|  1 |             2 |          1 |        1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female |    38 |       1 |       0 | PC 17599         | 71.2833 | C85     | C          |\n",
+      "|  2 |             3 |          1 |        3 | Heikkinen, Miss. Laina                              | female |    26 |       0 |       0 | STON/O2. 3101282 |  7.925  | nan     | S          |\n",
+      "|  3 |             4 |          1 |        1 | Futrelle, Mrs. Jacques Heath (Lily May Peel)        | female |    35 |       1 |       0 | 113803           | 53.1    | C123    | S          |\n",
+      "|  4 |             5 |          0 |        3 | Allen, Mr. William Henry                            | male   |    35 |       0 |       0 | 373450           |  8.05   | nan     | S          |\n",
+      "\n",
+      "Begin!\n",
+      "Question: whats the square root of the average age?\n",
+      "Thought: I need to calculate the average age first\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df['Age'].mean()\n",
+      "Observation: 29.69911764705882\n",
+      "Thought: I now need to calculate the square root of the average age\n",
+      "Action: python_repl_ast\n",
+      "Action Input: math.sqrt(df['Age'].mean())\n",
+      "Observation: name 'math' is not defined\n",
+      "Thought:\u001b[0m\n",
+      "Error in on_llm_start callback: Parent run with UUID 5c463668-dfa6-45e5-856d-2da5c91516d8 not found.\n",
+      "Error in on_llm_end callback: No LLMRun found to be traced\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to import the math library\n",
       "Action: python_repl_ast\n",
       "Action Input: import math\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mNone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
+      "Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
+      "Thought:\n",
+      "\n",
+      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3m\n",
+      "You are working with a pandas dataframe in Python. The name of the dataframe is `df`.\n",
+      "You should use the tools below to answer the question posed of you:\n",
+      "\n",
+      "python_repl_ast: A Python shell. Use this to execute python commands. Input should be a valid python command. When using this tool, sometimes output is abbreviated - make sure it does not look abbreviated before using it in your answer.\n",
+      "\n",
+      "Use the following format:\n",
+      "\n",
+      "Question: the input question you must answer\n",
+      "Thought: you should always think about what to do\n",
+      "Action: the action to take, should be one of [python_repl_ast]\n",
+      "Action Input: the input to the action\n",
+      "Observation: the result of the action\n",
+      "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
+      "Thought: I now know the final answer\n",
+      "Final Answer: the final answer to the original input question\n",
+      "\n",
+      "\n",
+      "This is the result of `print(df.head())`:\n",
+      "|    |   PassengerId |   Survived |   Pclass | Name                                                | Sex    |   Age |   SibSp |   Parch | Ticket           |    Fare | Cabin   | Embarked   |\n",
+      "|---:|--------------:|-----------:|---------:|:----------------------------------------------------|:-------|------:|--------:|--------:|:-----------------|--------:|:--------|:-----------|\n",
+      "|  0 |             1 |          0 |        3 | Braund, Mr. Owen Harris                             | male   |    22 |       1 |       0 | A/5 21171        |  7.25   | nan     | S          |\n",
+      "|  1 |             2 |          1 |        1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female |    38 |       1 |       0 | PC 17599         | 71.2833 | C85     | C          |\n",
+      "|  2 |             3 |          1 |        3 | Heikkinen, Miss. Laina                              | female |    26 |       0 |       0 | STON/O2. 3101282 |  7.925  | nan     | S          |\n",
+      "|  3 |             4 |          1 |        1 | Futrelle, Mrs. Jacques Heath (Lily May Peel)        | female |    35 |       1 |       0 | 113803           | 53.1    | C123    | S          |\n",
+      "|  4 |             5 |          0 |        3 | Allen, Mr. William Henry                            | male   |    35 |       0 |       0 | 373450           |  8.05   | nan     | S          |\n",
+      "\n",
+      "Begin!\n",
+      "Question: whats the square root of the average age?\n",
+      "Thought: I need to calculate the average age first\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df['Age'].mean()\n",
+      "Observation: 29.69911764705882\n",
+      "Thought: I now need to calculate the square root of the average age\n",
+      "Action: python_repl_ast\n",
+      "Action Input: math.sqrt(df['Age'].mean())\n",
+      "Observation: name 'math' is not defined\n",
+      "Thought: I need to import the math library\n",
+      "Action: python_repl_ast\n",
+      "Action Input: import math\n",
+      "Observation: \n",
+      "Thought:\u001b[0m\n",
+      "Error in on_llm_start callback: Parent run with UUID 3e09fe75-cb84-4b8d-83a1-7037d8e12aab not found.\n",
+      "Error in on_llm_end callback: No LLMRun found to be traced\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I now need to calculate the square root of the average age\n",
       "Action: python_repl_ast\n",
       "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
       "Observation: \u001b[36;1m\u001b[1;3m5.449689683556195\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Thought:\n",
+      "\n",
+      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3m\n",
+      "You are working with a pandas dataframe in Python. The name of the dataframe is `df`.\n",
+      "You should use the tools below to answer the question posed of you:\n",
+      "\n",
+      "python_repl_ast: A Python shell. Use this to execute python commands. Input should be a valid python command. When using this tool, sometimes output is abbreviated - make sure it does not look abbreviated before using it in your answer.\n",
+      "\n",
+      "Use the following format:\n",
+      "\n",
+      "Question: the input question you must answer\n",
+      "Thought: you should always think about what to do\n",
+      "Action: the action to take, should be one of [python_repl_ast]\n",
+      "Action Input: the input to the action\n",
+      "Observation: the result of the action\n",
+      "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
+      "Thought: I now know the final answer\n",
+      "Final Answer: the final answer to the original input question\n",
+      "\n",
+      "\n",
+      "This is the result of `print(df.head())`:\n",
+      "|    |   PassengerId |   Survived |   Pclass | Name                                                | Sex    |   Age |   SibSp |   Parch | Ticket           |    Fare | Cabin   | Embarked   |\n",
+      "|---:|--------------:|-----------:|---------:|:----------------------------------------------------|:-------|------:|--------:|--------:|:-----------------|--------:|:--------|:-----------|\n",
+      "|  0 |             1 |          0 |        3 | Braund, Mr. Owen Harris                             | male   |    22 |       1 |       0 | A/5 21171        |  7.25   | nan     | S          |\n",
+      "|  1 |             2 |          1 |        1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female |    38 |       1 |       0 | PC 17599         | 71.2833 | C85     | C          |\n",
+      "|  2 |             3 |          1 |        3 | Heikkinen, Miss. Laina                              | female |    26 |       0 |       0 | STON/O2. 3101282 |  7.925  | nan     | S          |\n",
+      "|  3 |             4 |          1 |        1 | Futrelle, Mrs. Jacques Heath (Lily May Peel)        | female |    35 |       1 |       0 | 113803           | 53.1    | C123    | S          |\n",
+      "|  4 |             5 |          0 |        3 | Allen, Mr. William Henry                            | male   |    35 |       0 |       0 | 373450           |  8.05   | nan     | S          |\n",
+      "\n",
+      "Begin!\n",
+      "Question: whats the square root of the average age?\n",
+      "Thought: I need to calculate the average age first\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df['Age'].mean()\n",
+      "Observation: 29.69911764705882\n",
+      "Thought: I now need to calculate the square root of the average age\n",
+      "Action: python_repl_ast\n",
+      "Action Input: math.sqrt(df['Age'].mean())\n",
+      "Observation: name 'math' is not defined\n",
+      "Thought: I need to import the math library\n",
+      "Action: python_repl_ast\n",
+      "Action Input: import math\n",
+      "Observation: \n",
+      "Thought: I now need to calculate the square root of the average age\n",
+      "Action: python_repl_ast\n",
+      "Action Input: math.sqrt(df['Age'].mean())\n",
+      "Observation: 5.449689683556195\n",
+      "Thought:\u001b[0m\n",
+      "Error in on_llm_start callback: Parent run with UUID 32e38f5f-a71a-4040-89c5-cca7752ba72d not found.\n",
+      "Error in on_llm_end callback: No LLMRun found to be traced\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I now know the final answer\n",
       "Final Answer: 5.449689683556195\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -196,7 +510,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,

langchain/agents/agent_toolkits/pandas/base.py

@@ -1,19 +1,41 @@
 """Agent for working with pandas objects."""
+import warnings
 from typing import Any, List, Optional
 
 from langchain.agents.agent import AgentExecutor
 from langchain.agents.agent_toolkits.pandas.prompt import PREFIX, SUFFIX
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.manager import CallbackManager, Callbacks
 from langchain.chains.llm import LLMChain
 from langchain.llms.base import BaseLLM
 from langchain.tools.python.tool import PythonAstREPLTool
 
 
+def _get_callbacks(
+    callbacks: Callbacks,
+    callbacks_manager: Optional[BaseCallbackManager],
+    verbose: bool,
+) -> CallbackManager:
+    if callbacks_manager is not None and callbacks is not None:
+        raise ValueError(
+            "Arguments 'callbacks' and "
+            "'callbacks_manager' are mutually exclusive. "
+            " Please only provide 'callbacks'"
+        )
+    if callbacks_manager is not None:
+        warnings.warn(
+            "callback_manager is deprecated. Please use callbacks instead.",
+            DeprecationWarning,
+        )
+        callbacks = callbacks_manager
+    return CallbackManager.configure(inheritable_callbacks=callbacks, verbose=verbose)
+
+
 def create_pandas_dataframe_agent(
     llm: BaseLLM,
     df: Any,
-    callback_manager: Optional[BaseCallbackManager] = None,
+    callbacks: Callbacks = None,
     prefix: str = PREFIX,
     suffix: str = SUFFIX,
     input_variables: Optional[List[str]] = None,
@@ -22,16 +44,44 @@ def create_pandas_dataframe_agent(
     max_iterations: Optional[int] = 15,
     max_execution_time: Optional[float] = None,
     early_stopping_method: str = "force",
+    callback_manager: Optional[BaseCallbackManager] = None,
     **kwargs: Any,
 ) -> AgentExecutor:
-    """Construct a pandas agent from an LLM and dataframe."""
+    """Construct a pandas agent from an LLM and dataframe.
+
+    Args:
+        llm: The LLM to use
+        df: The dataframe to provide to the REPL
+        callbacks: The callbacks to use for tracing
+        prefix: The prefix to use for the prompt
+        suffix: The suffix to use for the prompt
+        input_variables: The input variables
+        verbose: Whether to print output
+        return_intermediate_steps: Whether to return intermediate steps
+        max_iterations: The maximum number of iterations before exiting
+        max_execution_time: The maximum execution time before exiting
+        early_stopping_method: The early stopping method
+        callback_manager: (DEPRECATED) Use 'callbacks' instead
+    """
     import pandas as pd
 
     if not isinstance(df, pd.DataFrame):
         raise ValueError(f"Expected pandas object, got {type(df)}")
     if input_variables is None:
         input_variables = ["df", "input", "agent_scratchpad"]
-    tools = [PythonAstREPLTool(locals={"df": df})]
+
+    callback_manager_ = _get_callbacks(
+        callbacks=callbacks,
+        callbacks_manager=callback_manager,
+        verbose=verbose,
+    )
+
+    tools = [
+        PythonAstREPLTool(
+            locals={"df": df},
+            callbacks=callback_manager_,
+        )
+    ]
     prompt = ZeroShotAgent.create_prompt(
         tools, prefix=prefix, suffix=suffix, input_variables=input_variables
     )
@@ -39,22 +89,22 @@ def create_pandas_dataframe_agent(
     llm_chain = LLMChain(
         llm=llm,
         prompt=partial_prompt,
-        callback_manager=callback_manager,
+        callbacks=callback_manager_,
     )
     tool_names = [tool.name for tool in tools]
     agent = ZeroShotAgent(
         llm_chain=llm_chain,
         allowed_tools=tool_names,
-        callback_manager=callback_manager,
+        callbacks=callback_manager_,
         **kwargs,
     )
     return AgentExecutor.from_agent_and_tools(
         agent=agent,
         tools=tools,
+        callbacks=callback_manager_,
         verbose=verbose,
         return_intermediate_steps=return_intermediate_steps,
         max_iterations=max_iterations,
         max_execution_time=max_execution_time,
         early_stopping_method=early_stopping_method,
-        callback_manager=callback_manager,
     )

langchain/agents/load_tools.py

@@ -6,6 +6,7 @@ from mypy_extensions import Arg, KwArg
 
 from langchain.agents.tools import Tool
 from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.manager import Callbacks
 from langchain.chains.api import news_docs, open_meteo_docs, podcast_docs, tmdb_docs
 from langchain.chains.api.base import APIChain
 from langchain.chains.llm_math.base import LLMMathChain
@@ -256,6 +257,7 @@ def load_tools(
     tool_names: List[str],
     llm: Optional[BaseLLM] = None,
     callback_manager: Optional[BaseCallbackManager] = None,
+    callbacks: Optional[Callbacks] = None,
     **kwargs: Any,
 ) -> List[BaseTool]:
     """Load tools based on their name.

langchain/tools/interaction/tool.py

@@ -5,7 +5,7 @@ from typing import Optional
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
-    RunManager,
+    CallbackManagerForToolRun,
 )
 from langchain.tools.base import BaseTool
 
@@ -20,7 +20,9 @@ class StdInInquireTool(BaseTool):
         " question (to disambiguate) or a request for more context."
     )
 
-    def _run(self, prompt: str, run_manager: Optional[RunManager] = None) -> str:
+    def _run(
+        self, prompt: str, run_manager: Optional[CallbackManagerForToolRun] = None
+    ) -> str:
         """Prompt the user for more input."""
         return input(f"\n{prompt}")
 

tests/unit_tests/agents/agent_toolkits/pandas/test_pandas.py

@@ -0,0 +1,50 @@
+import pandas as pd
+import pytest
+
+from langchain.agents.agent import AgentExecutor
+from langchain.agents.agent_toolkits.pandas.base import (
+    _get_callbacks,
+    create_pandas_dataframe_agent,
+)
+from langchain.callbacks.manager import CallbackManager
+from tests.unit_tests.llms.fake_llm import FakeLLM
+
+
+# Test cases for _get_callbacks function
+def test_get_callbacks_default() -> None:
+    callbacks = _get_callbacks(None, None, False)
+    assert isinstance(callbacks, CallbackManager)
+
+
+def test_get_callbacks_with_callback_manager() -> None:
+    cb = CallbackManager.configure()
+    with pytest.raises(ValueError):
+        _get_callbacks(cb, cb, False)
+
+
+def test_get_callbacks_deprecation_warning() -> None:
+    cb = CallbackManager.configure()
+    with pytest.warns(DeprecationWarning):
+        _get_callbacks(None, cb, False)
+
+
+# Test cases for create_pandas_dataframe_agent function
+def test_create_agent_with_non_dataframe() -> None:
+    with pytest.raises(ValueError, match="Expected pandas object, got"):
+        create_pandas_dataframe_agent(FakeLLM(), "Not a dataframe")
+
+
+def test_create_agent_with_dataframe() -> None:
+    agent = create_pandas_dataframe_agent(FakeLLM(), pd.DataFrame())
+    assert isinstance(agent, AgentExecutor)
+
+
+def test_create_agent_with_callback_manager() -> None:
+    cb = CallbackManager.configure()
+    with pytest.raises(ValueError):
+        create_pandas_dataframe_agent(
+            FakeLLM(),
+            pd.DataFrame(),
+            callback_manager=cb,
+            callbacks=cb,
+        )