autogen/notebook/agentchat_graph_modelling_language_using_select_speaker.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_graph_modelling_language_using_select_speaker.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Auto Generated Agent Chat: Graph Modeling Language with using select_speaker\n",
    "\n",
    "AutoGen offers conversable agents powered by LLM, tool, or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
    "Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
    "\n",
    "This notebook is about using graphs to define the transition paths amongst speakers.\n",
    "\n",
    "Benefits\n",
    "- This contribution fills the gap between the current modes of GroupChat Class (auto, manual, round_robin) and an expressive directed graph. See Motivation for more detailed discussion.\n",
    "\n",
    "\n",
    "## Requirements\n",
    "\n",
    "AutoGen requires `Python>=3.8`. To run this notebook example, please install:\n",
    "```bash\n",
    "pip install pyautogen\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
    "# %pip install \"pyautogen>=0.2.3\"\n",
    "%pip install networkX~=3.2.1\n",
    "%pip install matplotlib~=3.8.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random  # noqa E402\n",
    "\n",
    "import matplotlib.pyplot as plt  # noqa E402\n",
    "import networkx as nx  # noqa E402\n",
    "\n",
    "import autogen  # noqa E402\n",
    "from autogen.agentchat.assistant_agent import AssistantAgent  # noqa E402\n",
    "from autogen.agentchat.groupchat import GroupChat  # noqa E402"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2.3\n"
     ]
    }
   ],
   "source": [
    "print(autogen.__version__)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motivation\n",
    "\n",
    "\n",
    "The current GroupChat class allows transition to any agent (without or without the decision of LLM), some use case might demand for more control over transition. A graph is a possible way to control the transition paths, where each node represents an agent and each directed edge represent possible transition path. Let's illustrate the current transition paths for a GroupChat with five agents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an empty directed graph\n",
    "graph = nx.DiGraph()\n",
    "\n",
    "# Add 5 nodes to the graph using a for loop\n",
    "for node_id in range(5):\n",
    "    graph.add_node(node_id, label=str(node_id))\n",
    "\n",
    "# Add edges between all nodes using a nested for loop\n",
    "for source_node in range(5):\n",
    "    for target_node in range(5):\n",
    "        if source_node != target_node:  # To avoid self-loops\n",
    "            graph.add_edge(source_node, target_node)\n",
    "\n",
    "nx.draw(graph, with_labels=True, font_weight=\"bold\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Possibly interesting transition paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hub and Spoke\n",
    "# Create an empty directed graph\n",
    "graph = nx.DiGraph()\n",
    "\n",
    "# Add 5 nodes to the graph using a for loop\n",
    "for node_id in range(5):\n",
    "    graph.add_node(node_id, label=str(node_id))\n",
    "\n",
    "# Add edges between all nodes using a nested for loop\n",
    "for source_node in range(5):\n",
    "    target_node = 0\n",
    "    if source_node != target_node:  # To avoid self-loops\n",
    "        graph.add_edge(source_node, target_node)\n",
    "        graph.add_edge(target_node, source_node)\n",
    "\n",
    "\n",
    "nx.draw(graph, with_labels=True, font_weight=\"bold\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sequential Team Operations\n",
    "# Create an empty directed graph\n",
    "graph = nx.DiGraph()\n",
    "\n",
    "# Outer loop for prefixes 'A', 'B', 'C'\n",
    "for prefix in [\"A\", \"B\", \"C\"]:\n",
    "    # Add 5 nodes with each prefix to the graph using a for loop\n",
    "    for i in range(5):\n",
    "        node_id = f\"{prefix}{i}\"\n",
    "        graph.add_node(node_id, label=node_id)\n",
    "\n",
    "    # Add edges between nodes with the same prefix using a nested for loop\n",
    "    for source_node in range(5):\n",
    "        source_id = f\"{prefix}{source_node}\"\n",
    "        for target_node in range(5):\n",
    "            target_id = f\"{prefix}{target_node}\"\n",
    "            if source_node != target_node:  # To avoid self-loops\n",
    "                graph.add_edge(source_id, target_id)\n",
    "\n",
    "graph.add_edge(\"A0\", \"B0\")\n",
    "graph.add_edge(\"B0\", \"C0\")\n",
    "\n",
    "# Draw the graph\n",
    "nx.draw(graph, with_labels=True, font_weight=\"bold\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Think aloud and debate\n",
    "graph = nx.DiGraph()\n",
    "\n",
    "for source_node in range(2):\n",
    "    graph.add_node(source_node, label=source_node)\n",
    "\n",
    "# Add edges between nodes with the same prefix using a nested for loop\n",
    "for source_node in range(2):\n",
    "    for target_node in range(2):\n",
    "        graph.add_edge(source_node, target_node)\n",
    "\n",
    "nx.draw(graph, with_labels=True, font_weight=\"bold\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set your API Endpoint\n",
    "\n",
    "The [`config_list_from_json`](https://microsoft.github.io/autogen/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The default config list in notebook.\n",
    "config_list_gpt4 = autogen.config_list_from_json(\n",
    "    \"OAI_CONFIG_LIST\",\n",
    "    filter_dict={\n",
    "        \"model\": [\"gpt-4\", \"gpt-4-0314\", \"gpt4\", \"gpt-4-32k\", \"gpt-4-32k-0314\", \"gpt-4-32k-v0314\"],\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It first looks for environment variable \"OAI_CONFIG_LIST\" which needs to be a valid json string. If that variable is not found, it then looks for a json file named \"OAI_CONFIG_LIST\". It filters the configs by models (you can filter by other keys as well). Only the gpt-4 models are kept in the list based on the filter condition.\n",
    "\n",
    "The config list looks like the following:\n",
    "```python\n",
    "config_list = [\n",
    "    {\n",
    "        'model': 'gpt-4',\n",
    "        'api_key': '<your OpenAI API key here>',\n",
    "    },\n",
    "    {\n",
    "        'model': 'gpt-4',\n",
    "        'api_key': '<your Azure OpenAI API key here>',\n",
    "        'base_url': '<your Azure OpenAI API base here>',\n",
    "        'api_type': 'azure',\n",
    "        'api_version': '2023-06-01-preview',\n",
    "    },\n",
    "    {\n",
    "        'model': 'gpt-4-32k',\n",
    "        'api_key': '<your Azure OpenAI API key here>',\n",
    "        'base_url': '<your Azure OpenAI API base here>',\n",
    "        'api_type': 'azure',\n",
    "        'api_version': '2023-06-01-preview',\n",
    "    },\n",
    "]\n",
    "```\n",
    "\n",
    "If you open this notebook in colab, you can upload your files by clicking the file icon on the left panel and then choosing \"upload file\" icon.\n",
    "\n",
    "You can set the value of config_list in other ways you prefer, e.g., loading from a YAML file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are printing out debug messages so that the reader can understand the conversation flow and select_speaker method better.\n",
    "\n",
    "Overrides the `select_speaker` method with custom logic including:\n",
    "  - Handling of `NEXT:` and `TERMINATE` tags in the last message.\n",
    "  - Selection of the first-round speaker based on the `first_round_speaker` attribute in the graph nodes.\n",
    "  - Selection of subsequent speakers based on the successors in the graph of the previous speaker.\n",
    "  - Random selection of the next speaker from the eligible candidates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomGroupChat(GroupChat):\n",
    "    def __init__(self, agents, messages, max_round=10, graph=None):\n",
    "        super().__init__(agents, messages, max_round)\n",
    "        self.previous_speaker = None  # Keep track of the previous speaker\n",
    "        self.graph = graph  # The graph depicting who are the next speakers available\n",
    "\n",
    "    def select_speaker(self, last_speaker, selector):\n",
    "        self.previous_speaker = last_speaker\n",
    "\n",
    "        # Check if last message suggests a next speaker or termination\n",
    "        last_message = self.messages[-1] if self.messages else None\n",
    "        suggested_next = None\n",
    "\n",
    "        if last_message:\n",
    "            if \"NEXT:\" in last_message[\"content\"]:\n",
    "                suggested_next = last_message[\"content\"].split(\"NEXT: \")[-1].strip()\n",
    "                # Strip full stop and comma\n",
    "                suggested_next = suggested_next.replace(\".\", \"\").replace(\",\", \"\")\n",
    "                print(f\"Suggested next speaker from the last message: {suggested_next}\")\n",
    "\n",
    "            elif \"TERMINATE\" in last_message[\"content\"]:\n",
    "                try:\n",
    "                    return self.agent_by_name(\"User_proxy\")\n",
    "                except ValueError:\n",
    "                    print(f\"agent_by_name failed suggested_next: {suggested_next}\")\n",
    "\n",
    "        # Debugging print for the current previous speaker\n",
    "        if self.previous_speaker is not None:\n",
    "            print(\"Current previous speaker:\", self.previous_speaker.name)\n",
    "\n",
    "        # Selecting first round speaker\n",
    "        if self.previous_speaker is None and self.graph is not None:\n",
    "            eligible_speakers = [\n",
    "                agent for agent in agents if self.graph.nodes[agent.name].get(\"first_round_speaker\", False)\n",
    "            ]\n",
    "            print(\"First round eligible speakers:\", [speaker.name for speaker in eligible_speakers])\n",
    "\n",
    "        # Selecting successors of the previous speaker\n",
    "        elif self.previous_speaker is not None and self.graph is not None:\n",
    "            eligible_speaker_names = [target for target in self.graph.successors(self.previous_speaker.name)]\n",
    "            eligible_speakers = [agent for agent in agents if agent.name in eligible_speaker_names]\n",
    "            print(\"Eligible speakers based on previous speaker:\", eligible_speaker_names)\n",
    "\n",
    "        else:\n",
    "            eligible_speakers = agents\n",
    "\n",
    "        # Debugging print for the next potential speakers\n",
    "        print(\n",
    "            f\"Eligible speakers based on graph and previous speaker {self.previous_speaker.name if self.previous_speaker else 'None'}: {[speaker.name for speaker in eligible_speakers]}\"\n",
    "        )\n",
    "\n",
    "        # Three attempts at getting the next_speaker\n",
    "        # 1. Using suggested_next if suggested_next is in the eligible_speakers.name\n",
    "        # 2. Using LLM to pick from eligible_speakers, given that there is some context in self.message\n",
    "        # 3. Random (catch-all)\n",
    "        next_speaker = None\n",
    "\n",
    "        if eligible_speakers:\n",
    "            print(\"Selecting from eligible speakers:\", [speaker.name for speaker in eligible_speakers])\n",
    "            # 1. Using suggested_next if suggested_next is in the eligible_speakers.name\n",
    "            if suggested_next in [speaker.name for speaker in eligible_speakers]:\n",
    "                print(\"suggested_next is in eligible_speakers\")\n",
    "                next_speaker = self.agent_by_name(suggested_next)\n",
    "\n",
    "            else:\n",
    "                msgs_len = len(self.messages)\n",
    "                print(f\"msgs_len is now {msgs_len}\")\n",
    "                if len(self.messages) > 1:\n",
    "                    # 2. Using LLM to pick from eligible_speakers, given that there is some context in self.message\n",
    "                    print(\n",
    "                        f\"Using LLM to pick from eligible_speakers: {[speaker.name for speaker in eligible_speakers]}\"\n",
    "                    )\n",
    "                    selector.update_system_message(self.select_speaker_msg(eligible_speakers))\n",
    "                    _, name = selector.generate_oai_reply(\n",
    "                        self.messages\n",
    "                        + [\n",
    "                            {\n",
    "                                \"role\": \"system\",\n",
    "                                \"content\": f\"Read the above conversation. Then select the next role from {[agent.name for agent in eligible_speakers]} to play. Only return the role.\",\n",
    "                            }\n",
    "                        ]\n",
    "                    )\n",
    "\n",
    "                    # If exactly one agent is mentioned, use it. Otherwise, leave the OAI response unmodified\n",
    "                    mentions = self._mentioned_agents(name, eligible_speakers)\n",
    "                    if len(mentions) == 1:\n",
    "                        name = next(iter(mentions))\n",
    "                        next_speaker = self.agent_by_name(name)\n",
    "\n",
    "                if next_speaker is None:\n",
    "                    # 3. Random (catch-all)\n",
    "                    next_speaker = random.choice(eligible_speakers)\n",
    "\n",
    "            print(f\"Selected next speaker: {next_speaker.name}\")\n",
    "\n",
    "            return next_speaker\n",
    "        else:\n",
    "            # Cannot return next_speaker with no eligible speakers\n",
    "            raise ValueError(\"No eligible speakers found based on the graph constraints.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demonstration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Team Operations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# llm config\n",
    "llm_config = {\"config_list\": config_list_gpt4, \"cache_seed\": 100}\n",
    "\n",
    "# Create an empty directed graph\n",
    "graph = nx.DiGraph()\n",
    "\n",
    "agents = []\n",
    "\n",
    "# Outer loop for prefixes 'A', 'B', 'C'\n",
    "for prefix in [\"A\", \"B\", \"C\"]:\n",
    "    # Add 3 nodes with each prefix to the graph using a for loop\n",
    "    for i in range(3):\n",
    "        node_id = f\"{prefix}{i}\"\n",
    "        secret_value = random.randint(1, 5)  # Generate a random secret value\n",
    "        graph.add_node(node_id, label=node_id, secret_value=secret_value)\n",
    "\n",
    "        # Create an AssistantAgent for each node (assuming AssistantAgent is a defined class)\n",
    "        agents.append(\n",
    "            AssistantAgent(\n",
    "                name=node_id,\n",
    "                system_message=f\"\"\"Your name is {node_id}.\n",
    "                                          Do not respond as the speaker named in the NEXT tag if your name is not in the NEXT tag. Instead, suggest a relevant team leader to handle the mis-tag, with the NEXT: tag.\n",
    "\n",
    "                                          You have {secret_value} chocolates.\n",
    "\n",
    "                                          The list of players are [A0, A1, A2, B0, B1, B2, C0, C1, C2].\n",
    "\n",
    "                                            Your first character of your name is your team, and your second character denotes that you are a team leader if it is 0.\n",
    "                                            CONSTRAINTS: Team members can only talk within the team, whilst team leader can talk to team leaders of other teams but not team members of other teams.\n",
    "\n",
    "                                            You can use NEXT: to suggest the next speaker. You have to respect the CONSTRAINTS, and can only suggest one player from the list of players, i.e., do not suggest A3 because A3 is not from the list of players.\n",
    "                                            Team leaders must make sure that they know the sum of the individual chocolate count of all three players in their own team, i.e., A0 is responsible for team A only.\n",
    "\n",
    "                                          Keep track of the player's tally using a JSON format so that others can check the total tally. Use\n",
    "                                          A0:?, A1:?, A2:?,\n",
    "                                          B0:?, B1:?, B2:?,\n",
    "                                          C0:?, C1:?, C2:?\n",
    "\n",
    "                                          If you are the team leader, you should aggregate your team's total chocolate count to cooperate.\n",
    "                                          Once the team leader know their team's tally, they can suggest another team leader for them to find their team tally, because we need all three team tallys to succeed.\n",
    "                                          Use NEXT: to suggest the next speaker, e.g., NEXT: A0.\n",
    "\n",
    "                                          Once we have the total tally from all nine players, sum up all three teams' tally, then terminate the discussion using TERMINATE.\n",
    "\n",
    "                                          \"\"\",\n",
    "                llm_config=llm_config,\n",
    "            )\n",
    "        )\n",
    "\n",
    "    # Add edges between nodes with the same prefix using a nested for loop\n",
    "    for source_node in range(3):\n",
    "        source_id = f\"{prefix}{source_node}\"\n",
    "        for target_node in range(3):\n",
    "            target_id = f\"{prefix}{target_node}\"\n",
    "            if source_node != target_node:  # To avoid self-loops\n",
    "                graph.add_edge(source_id, target_id)\n",
    "\n",
    "# Adding edges between teams\n",
    "graph.add_edge(\"A0\", \"B0\")\n",
    "graph.add_edge(\"A0\", \"C0\")\n",
    "graph.add_edge(\"B0\", \"A0\")\n",
    "graph.add_edge(\"B0\", \"C0\")\n",
    "graph.add_edge(\"C0\", \"A0\")\n",
    "graph.add_edge(\"C0\", \"B0\")\n",
    "\n",
    "\n",
    "# Updating node A0\n",
    "graph.nodes[\"A0\"][\"first_round_speaker\"] = True\n",
    "\n",
    "\n",
    "def get_node_color(node):\n",
    "    if graph.nodes[node].get(\"first_round_speaker\", False):\n",
    "        return \"red\"\n",
    "    else:\n",
    "        return \"green\"\n",
    "\n",
    "\n",
    "# Draw the graph with secret values annotated\n",
    "plt.figure(figsize=(12, 10))\n",
    "pos = nx.spring_layout(graph)  # positions for all nodes\n",
    "\n",
    "# Draw nodes with their colors\n",
    "nx.draw(graph, pos, with_labels=True, font_weight=\"bold\", node_color=[get_node_color(node) for node in graph])\n",
    "\n",
    "# Annotate secret values\n",
    "for node, (x, y) in pos.items():\n",
    "    secret_value = graph.nodes[node][\"secret_value\"]\n",
    "    plt.text(x, y + 0.1, s=f\"Secret: {secret_value}\", horizontalalignment=\"center\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Termination message detection\n",
    "\n",
    "\n",
    "def is_termination_msg(content) -> bool:\n",
    "    have_content = content.get(\"content\", None) is not None\n",
    "    if have_content and \"TERMINATE\" in content[\"content\"]:\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "\n",
    "# Terminates the conversation when TERMINATE is detected.\n",
    "user_proxy = autogen.UserProxyAgent(\n",
    "    name=\"User_proxy\",\n",
    "    system_message=\"Terminator admin.\",\n",
    "    code_execution_config=False,\n",
    "    is_termination_msg=is_termination_msg,\n",
    "    human_input_mode=\"NEVER\",\n",
    ")\n",
    "\n",
    "agents.append(user_proxy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mA0\u001b[0m (to chat_manager):\n",
      "\n",
      "\n",
      "                        There are 9 players in this game, split equally into Teams A, B, C. Therefore each team has 3 players, including the team leader. \n",
      "                        The task is to find out the sum of chocolate count from all nine players. I will now start with my team. \n",
      "                        NEXT: A1\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Suggested next speaker from the last message: A1\n",
      "Current previous speaker: A0\n",
      "Eligible speakers based on previous speaker: ['A1', 'A2', 'B0', 'C0']\n",
      "Eligible speakers based on graph and previous speaker A0: ['A1', 'A2', 'B0', 'C0']\n",
      "Selecting from eligible speakers: ['A1', 'A2', 'B0', 'C0']\n",
      "suggested_next is in eligible_speakers\n",
      "Selected next speaker: A1\n",
      "\u001b[33mA1\u001b[0m (to chat_manager):\n",
      "\n",
      "As A1 I have 1 chocolate right now. Our team leader A0, please note my count.\n",
      "\n",
      "NEXT: A2\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Suggested next speaker from the last message: A2\n",
      "Current previous speaker: A1\n",
      "Eligible speakers based on previous speaker: ['A0', 'A2']\n",
      "Eligible speakers based on graph and previous speaker A1: ['A0', 'A2']\n",
      "Selecting from eligible speakers: ['A0', 'A2']\n",
      "suggested_next is in eligible_speakers\n",
      "Selected next speaker: A2\n",
      "\u001b[33mA2\u001b[0m (to chat_manager):\n",
      "\n",
      "As part of Team A, I have 2 chocolates at the moment.\n",
      "\n",
      "Now that each member of Team A has reported their tally, our team leader A0 should be able to calculate and report our team's total sum to the other team leaders. \n",
      "\n",
      "NEXT: A0.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Suggested next speaker from the last message: A0\n",
      "Current previous speaker: A2\n",
      "Eligible speakers based on previous speaker: ['A0', 'A1']\n",
      "Eligible speakers based on graph and previous speaker A2: ['A0', 'A1']\n",
      "Selecting from eligible speakers: ['A0', 'A1']\n",
      "suggested_next is in eligible_speakers\n",
      "Selected next speaker: A0\n",
      "\u001b[33mA0\u001b[0m (to chat_manager):\n",
      "\n",
      "I, A0, have 4 chocolates, A1 reported having 1 chocolate, and A2 reported having 2 chocolates. So the total chocolate count for Team A is 4 + 1 + 2 = 7 chocolates. \n",
      "\n",
      "I'm saving this in our JSON format as: \n",
      "A0:4, A1:1, A2:2,\n",
      "B0:?, B1:?, B2:?,\n",
      "C0:?, C1:?, C2:?\n",
      "\n",
      "Let's move on to Team B for their counts.\n",
      "NEXT: B0.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Suggested next speaker from the last message: B0\n",
      "Current previous speaker: A0\n",
      "Eligible speakers based on previous speaker: ['A1', 'A2', 'B0', 'C0']\n",
      "Eligible speakers based on graph and previous speaker A0: ['A1', 'A2', 'B0', 'C0']\n",
      "Selecting from eligible speakers: ['A1', 'A2', 'B0', 'C0']\n",
      "suggested_next is in eligible_speakers\n",
      "Selected next speaker: B0\n",
      "\u001b[33mB0\u001b[0m (to chat_manager):\n",
      "\n",
      "As B0, the team leader of Team B, I already have my count which is 5 chocolates. Now, I will ask the other members of my team to report their counts. \n",
      "\n",
      "NEXT: B1\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Suggested next speaker from the last message: B1\n",
      "Current previous speaker: B0\n",
      "Eligible speakers based on previous speaker: ['B1', 'B2', 'A0', 'C0']\n",
      "Eligible speakers based on graph and previous speaker B0: ['A0', 'B1', 'B2', 'C0']\n",
      "Selecting from eligible speakers: ['A0', 'B1', 'B2', 'C0']\n",
      "suggested_next is in eligible_speakers\n",
      "Selected next speaker: B1\n",
      "\u001b[33mB1\u001b[0m (to chat_manager):\n",
      "\n",
      "As B1, I have 4 chocolates currently. It's now time for our team member B2 to report their count. \n",
      "\n",
      "NEXT: B2.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Suggested next speaker from the last message: B2\n",
      "Current previous speaker: B1\n",
      "Eligible speakers based on previous speaker: ['B0', 'B2']\n",
      "Eligible speakers based on graph and previous speaker B1: ['B0', 'B2']\n",
      "Selecting from eligible speakers: ['B0', 'B2']\n",
      "suggested_next is in eligible_speakers\n",
      "Selected next speaker: B2\n",
      "\u001b[33mB2\u001b[0m (to chat_manager):\n",
      "\n",
      "As B2, I have 1 chocolate right now. Our team leader B0, please note my count.\n",
      "\n",
      "NEXT: B0.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Suggested next speaker from the last message: B0\n",
      "Current previous speaker: B2\n",
      "Eligible speakers based on previous speaker: ['B0', 'B1']\n",
      "Eligible speakers based on graph and previous speaker B2: ['B0', 'B1']\n",
      "Selecting from eligible speakers: ['B0', 'B1']\n",
      "suggested_next is in eligible_speakers\n",
      "Selected next speaker: B0\n",
      "\u001b[33mB0\u001b[0m (to chat_manager):\n",
      "\n",
      "As B0, I acknowledge receipt of both B1 and B2's counts. I have 5 chocolates, B1 has 4 and B2 has 1. Adding these counts together, Team B has a total of 5 + 4 + 1 = 10 chocolates.\n",
      "\n",
      "Updating the JSON tally:\n",
      "A0:4, A1:1, A2:2,\n",
      "B0:5, B1:4, B2:1,\n",
      "C0:?, C1:?, C2:?\n",
      "\n",
      "Now it's time for Team C to share their counts. \n",
      "\n",
      "NEXT: C0.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Suggested next speaker from the last message: C0\n",
      "Current previous speaker: B0\n",
      "Eligible speakers based on previous speaker: ['B1', 'B2', 'A0', 'C0']\n",
      "Eligible speakers based on graph and previous speaker B0: ['A0', 'B1', 'B2', 'C0']\n",
      "Selecting from eligible speakers: ['A0', 'B1', 'B2', 'C0']\n",
      "suggested_next is in eligible_speakers\n",
      "Selected next speaker: C0\n",
      "\u001b[33mC0\u001b[0m (to chat_manager):\n",
      "\n",
      "As C0, I currently have 2 chocolates. I will need the counts from C1 and C2 to complete our team's tally. \n",
      "\n",
      "NEXT: C1.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Suggested next speaker from the last message: C1\n",
      "Current previous speaker: C0\n",
      "Eligible speakers based on previous speaker: ['C1', 'C2', 'A0', 'B0']\n",
      "Eligible speakers based on graph and previous speaker C0: ['A0', 'B0', 'C1', 'C2']\n",
      "Selecting from eligible speakers: ['A0', 'B0', 'C1', 'C2']\n",
      "suggested_next is in eligible_speakers\n",
      "Selected next speaker: C1\n",
      "\u001b[33mC1\u001b[0m (to chat_manager):\n",
      "\n",
      "As C1, I have 2 chocolates. C2, please share your count so that our team leader, C0, can calculate our team's total.\n",
      "\n",
      "NEXT: C2.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Suggested next speaker from the last message: C2\n",
      "Current previous speaker: C1\n",
      "Eligible speakers based on previous speaker: ['C0', 'C2']\n",
      "Eligible speakers based on graph and previous speaker C1: ['C0', 'C2']\n",
      "Selecting from eligible speakers: ['C0', 'C2']\n",
      "suggested_next is in eligible_speakers\n",
      "Selected next speaker: C2\n",
      "\u001b[33mC2\u001b[0m (to chat_manager):\n",
      "\n",
      "As C2, I have 5 chocolates. Now our team leader, C0, can calculate our team's total sum.\n",
      "\n",
      "NEXT: C0.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Suggested next speaker from the last message: C0\n",
      "Current previous speaker: C2\n",
      "Eligible speakers based on previous speaker: ['C0', 'C1']\n",
      "Eligible speakers based on graph and previous speaker C2: ['C0', 'C1']\n",
      "Selecting from eligible speakers: ['C0', 'C1']\n",
      "suggested_next is in eligible_speakers\n",
      "Selected next speaker: C0\n",
      "\u001b[33mC0\u001b[0m (to chat_manager):\n",
      "\n",
      "As C0, I have 2 chocolates, C1 reported having 2 chocolates, and C2 reported having 5 chocolates. So, the total chocolate count for Team C is 2 + 2 + 5 = 9 chocolates.\n",
      "\n",
      "Updating the JSON tally:\n",
      "A0:4, A1:1, A2:2,\n",
      "B0:5, B1:4, B2:1,\n",
      "C0:2, C1:2, C2:5\n",
      "\n",
      "Let's sum up all the team totals. \n",
      "\n",
      "TERMINATE.\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "group_chat = CustomGroupChat(agents=agents, messages=[], max_round=20, graph=graph)  # Include all agents\n",
    "\n",
    "\n",
    "# Create the manager\n",
    "manager = autogen.GroupChatManager(groupchat=group_chat, llm_config=llm_config)\n",
    "\n",
    "\n",
    "# Initiates the chat with Alice\n",
    "agents[0].initiate_chat(\n",
    "    manager,\n",
    "    message=\"\"\"\n",
    "                        There are 9 players in this game, split equally into Teams A, B, C. Therefore each team has 3 players, including the team leader.\n",
    "                        The task is to find out the sum of chocolate count from all nine players. I will now start with my team.\n",
    "                        NEXT: A1\"\"\",\n",
    ")"
   ]
  }
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