Upgrade to autogen v0.4
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2 changed files with 41 additions and 63 deletions
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@ -1,6 +1,10 @@
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import asyncio
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import streamlit as st
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import autogen
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from autogen.agentchat import GroupChat, GroupChatManager
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from autogen_agentchat.agents import AssistantAgent
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from autogen_agentchat.teams import RoundRobinGroupChat
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from autogen_agentchat.conditions import TextMentionTermination
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from autogen_agentchat.ui import Console
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from autogen_ext.models.openai import OpenAIChatCompletionClient
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# Initialize session state
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if 'output' not in st.session_state:
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@ -108,29 +112,13 @@ if st.button("Generate Game Concept"):
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"""
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# Configure OpenAI model client with the API key
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llm_config = {
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"timeout": 600,
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"cache_seed": 44, # change the seed for different trials
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"config_list": [
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{
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"model": "gpt-4o-mini",
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"api_key": api_key,
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}
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],
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"temperature": 0,
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}
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# Define a task-provider agent
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task_agent = autogen.AssistantAgent(
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name="task_agent",
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llm_config=llm_config,
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system_message="You are a task provider. Your only job is to provide the task details to the other agents.",
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)
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model_client = OpenAIChatCompletionClient(model="gpt-4o-mini", api_key=api_key, temperature=0.0)
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# Define agents with detailed system prompts
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story_agent = autogen.AssistantAgent(
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name="story_agent",
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llm_config=llm_config,
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story_agent = AssistantAgent(
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"story_agent",
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model_client=model_client,
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system_message="""
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You are an experienced game story designer specializing in narrative design and world-building. Your task is to:
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1. Create a compelling narrative that aligns with the specified game type and target audience.
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@ -143,9 +131,9 @@ if st.button("Generate Game Concept"):
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"""
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)
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gameplay_agent = autogen.AssistantAgent(
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name="gameplay_agent",
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llm_config=llm_config,
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gameplay_agent = AssistantAgent(
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"gameplay_agent",
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model_client=model_client,
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system_message="""
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You are a senior game mechanics designer with expertise in player engagement and systems design. Your task is to:
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1. Design core gameplay loops that match the specified game type and mechanics.
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@ -159,9 +147,9 @@ if st.button("Generate Game Concept"):
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"""
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)
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visuals_agent = autogen.AssistantAgent(
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name="visuals_agent",
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llm_config=llm_config,
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visuals_agent = AssistantAgent(
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"visuals_agent",
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model_client=model_client,
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system_message="""
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You are a creative art director with expertise in game visual and audio design. Your task is to:
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1. Define the visual style guide matching the specified art style.
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@ -175,9 +163,9 @@ if st.button("Generate Game Concept"):
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"""
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)
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tech_agent = autogen.AssistantAgent(
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tech_agent = AssistantAgent(
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name="tech_agent",
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llm_config=llm_config,
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model_client=model_client,
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system_message="""
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You are a technical director with extensive game development experience. Your task is to:
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1. Recommend appropriate game engine and development tools.
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@ -194,17 +182,10 @@ if st.button("Generate Game Concept"):
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# Function to run agents sequentially
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def run_agents_sequentially(task):
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# Task agent provides the task to each agent one by one
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task_agent.initiate_chat(story_agent, message=task, max_turns=1)
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story_response = story_agent.last_message()["content"]
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task_agent.initiate_chat(gameplay_agent, message=task, max_turns=1)
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gameplay_response = gameplay_agent.last_message()["content"]
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task_agent.initiate_chat(visuals_agent, message=task, max_turns=1)
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visuals_response = visuals_agent.last_message()["content"]
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task_agent.initiate_chat(tech_agent, message=task, max_turns=1)
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tech_response = tech_agent.last_message()["content"]
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story_response = asyncio.run(story_agent.run(task=task)).messages[-1].content
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gameplay_response = asyncio.run(gameplay_agent.run(task=task)).messages[-1].content
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visuals_response = asyncio.run(visuals_agent.run(task=task)).messages[-1].content
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tech_response = asyncio.run(tech_agent.run(task=task)).messages[-1].content
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return {
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"story": story_response,
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@ -235,27 +216,25 @@ if st.button("Generate Game Concept"):
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with st.expander("Technical Recommendations"):
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st.markdown(st.session_state.output['tech'])
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groupchat = GroupChat(
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agents=[task_agent, story_agent, gameplay_agent, visuals_agent, tech_agent],
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messages=[],
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speaker_selection_method="round_robin", # Ensures agents speak in order
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allow_repeat_speaker=False, # Prevents agents from speaking more than once
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max_round=5, # Each agent speaks exactly once
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)
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termination = TextMentionTermination("TERMINATE")
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# Create the group chat manager
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manager = GroupChatManager(
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groupchat=groupchat,
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llm_config=llm_config,
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is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
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groupchat = RoundRobinGroupChat(
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[story_agent, gameplay_agent, visuals_agent, tech_agent],
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termination_condition=termination,
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max_turns=4, # Each agent gets 1 turn
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)
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# Function to run the agent collaboration
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def run_agents(task):
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task_agent.initiate_chat(manager, message=task)
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return {
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"story": story_agent.last_message()["content"],
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"gameplay": gameplay_agent.last_message()["content"],
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"visuals": visuals_agent.last_message()["content"],
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"tech": tech_agent.last_message()["content"],
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}
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result = asyncio.run(Console(groupchat.run_stream(task=task)))
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responses = {}
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for message in result.messages:
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if message.source == "story_agent":
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responses["story"] = message.content
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elif message.source == "gameplay_agent":
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responses["gameplay"] = message.content
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elif message.source == "visuals_agent":
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responses["visuals"] = message.content
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elif message.source == "tech_agent":
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responses["tech"] = message.content
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return responses
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@ -1,4 +1,3 @@
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pyautogen>=0.7.0
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autogen==0.6.1
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streamlit==1.41.1
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openai
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autogen-agentchat>=0.4.2,<0.5
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autogen-ext[openai]>=0.4.2,<0.5
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