Building a Multi-Agent Conversational AI Framework with Microsoft AutoGen and Gemini API
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Complete AutoGen framework using free Gemini API
Supports multi-agent conversations, code execution, and retrieval
“””
def __init__(self, gemini_api_key: str):
“””Initialize with Gemini API key”””
self.gemini_api_key = gemini_api_key
self.setup_gemini_config()
self.agents: Dict[str, autogen.Agent] = {}
self.group_chats: Dict[str, GroupChat] = {}
def setup_gemini_config(self):
“””Configure Gemini for AutoGen”””
os.environ[“GOOGLE_API_KEY”] = self.gemini_api_key
self.llm_config = {
“config_list”: [
{
“model”: “gemini/gemini-1.5-flash”,
“api_key”: self.gemini_api_key,
“api_type”: “google”,
“temperature”: 0.7,
“max_tokens”: 4096,
}
],
“timeout”: 120,
“cache_seed”: 42,
}
self.llm_config_pro = {
“config_list”: [
{
“model”: “gemini/gemini-1.5-pro”,
“api_key”: self.gemini_api_key,
“api_type”: “google”,
“temperature”: 0.5,
“max_tokens”: 8192,
}
],
“timeout”: 180,
“cache_seed”: 42,
}
def create_assistant_agent(self, name: str, system_message: str,
use_pro_model: bool = False) -> AssistantAgent:
“””Create a specialized assistant agent”””
config = self.llm_config_pro if use_pro_model else self.llm_config
agent = AssistantAgent(
name=name,
system_message=system_message,
llm_config=config,
human_input_mode=”NEVER”,
max_consecutive_auto_reply=10,
code_execution_config=False,
)
self.agents[name] = agent
return agent
def create_user_proxy(self, name: str = “UserProxy”,
enable_code_execution: bool = True) -> UserProxyAgent:
“””Create user proxy agent with optional code execution”””
code_config = {
“work_dir”: “autogen_workspace”,
“use_docker”: False,
“timeout”: 60,
“last_n_messages”: 3,
} if enable_code_execution else False
agent = UserProxyAgent(
name=name,
human_input_mode=”TERMINATE”,
max_consecutive_auto_reply=0,
is_termination_msg=lambda x: x.get(“content”, “”).rstrip().endswith(“TERMINATE”),
code_execution_config=code_config,
system_message=”””A human admin. Interact with the agents to solve tasks.
Reply TERMINATE when the task is solved.”””
)
self.agents[name] = agent
return agent
def create_research_team(self) -> Dict[str, autogen.Agent]:
“””Create a research-focused agent team”””
researcher = self.create_assistant_agent(
name=”Researcher”,
system_message=”””You are a Senior Research Analyst. Your role is to:
1. Gather and analyze information on given topics
2. Identify key trends, patterns, and insights
3. Provide comprehensive research summaries
4. Cite sources and maintain objectivity
Always structure your research with clear sections and bullet points.
Be thorough but concise.”””
)
analyst = self.create_assistant_agent(
name=”DataAnalyst”,
system_message=”””You are a Data Analysis Expert. Your role is to:
1. Analyze quantitative data and statistics
2. Create data visualizations and charts
3. Identify patterns and correlations
4. Provide statistical insights and interpretations
Use Python code when needed for calculations and visualizations.
Always explain your analytical approach.”””
)
writer = self.create_assistant_agent(
name=”Writer”,
system_message=”””You are a Technical Writer and Content Strategist. Your role is to:
1. Transform research and analysis into clear, engaging content
2. Create well-structured reports and articles
3. Ensure content is accessible to the target audience
4. Maintain professional tone and accuracy
Structure content with clear headings, bullet points, and conclusions.”””
)
executor = self.create_user_proxy(“CodeExecutor”, enable_code_execution=True)
return {
“researcher”: researcher,
“analyst”: analyst,
“writer”: writer,
“executor”: executor
}
def create_business_team(self) -> Dict[str, autogen.Agent]:
“””Create business analysis team”””
strategist = self.create_assistant_agent(
name=”BusinessStrategist”,
system_message=”””You are a Senior Business Strategy Consultant. Your role is to:
1. Analyze business problems and opportunities
2. Develop strategic recommendations and action plans
3. Assess market dynamics and competitive landscape
4. Provide implementation roadmaps
Think systematically and consider multiple perspectives.
Always provide actionable recommendations.”””,
use_pro_model=True
)
financial_analyst = self.create_assistant_agent(
name=”FinancialAnalyst”,
system_message=”””You are a Financial Analysis Expert. Your role is to:
1. Perform financial modeling and analysis
2. Assess financial risks and opportunities
3. Calculate ROI, NPV, and other financial metrics
4. Provide budget and investment recommendations
Use quantitative analysis and provide clear financial insights.”””
)
market_researcher = self.create_assistant_agent(
name=”MarketResearcher”,
system_message=”””You are a Market Research Specialist. Your role is to:
1. Analyze market trends and consumer behavior
2. Research competitive landscape and positioning
3. Identify target markets and customer segments
4. Provide market sizing and opportunity assessment
Focus on actionable market insights and recommendations.”””
)
return {
“strategist”: strategist,
“financial_analyst”: financial_analyst,
“market_researcher”: market_researcher,
“executor”: self.create_user_proxy(“BusinessExecutor”)
}
def create_development_team(self) -> Dict[str, autogen.Agent]:
“””Create software development team”””
developer = self.create_assistant_agent(
name=”SeniorDeveloper”,
system_message=”””You are a Senior Software Developer. Your role is to:
1. Write high-quality, efficient code
2. Design software architecture and solutions
3. Debug and optimize existing code
4. Follow best practices and coding standards
Always explain your code and design decisions.
Focus on clean, maintainable solutions.”””
)
devops = self.create_assistant_agent(
name=”DevOpsEngineer”,
system_message=”””You are a DevOps Engineer. Your role is to:
1. Design deployment and infrastructure solutions
2. Automate build, test, and deployment processes
3. Monitor system performance and reliability
4. Implement security and scalability best practices
Focus on automation, reliability, and scalability.”””
)
qa_engineer = self.create_assistant_agent(
name=”QAEngineer”,
system_message=”””You are a Quality Assurance Engineer. Your role is to:
1. Design comprehensive test strategies and cases
2. Identify potential bugs and edge cases
3. Ensure code quality and performance standards
4. Validate requirements and user acceptance criteria
Be thorough and think about edge cases and failure scenarios.”””
)
return {
“developer”: developer,
“devops”: devops,
“qa_engineer”: qa_engineer,
“executor”: self.create_user_proxy(“DevExecutor”, enable_code_execution=True)
}
def create_group_chat(self, agents: List[autogen.Agent], chat_name: str,
max_round: int = 10) -> GroupChat:
“””Create group chat with specified agents”””
group_chat = GroupChat(
agents=agents,
messages=[],
max_round=max_round,
speaker_selection_method=”round_robin”,
allow_repeat_speaker=False,
)
self.group_chats[chat_name] = group_chat
return group_chat
def run_research_project(self, topic: str, max_rounds: int = 8) -> str:
“””Run a comprehensive research project”””
team = self.create_research_team()
agents_list = [team[“researcher”], team[“analyst”], team[“writer”], team[“executor”]]
group_chat = self.create_group_chat(agents_list, “research_chat”, max_rounds)
manager = GroupChatManager(
groupchat=group_chat,
llm_config=self.llm_config
)
initial_message = f”””
Research Project: {topic}
Please collaborate to produce a comprehensive research report following this workflow:
1. Researcher: Gather information and key insights about {topic}
2. DataAnalyst: Analyze any quantitative aspects and create visualizations if needed
3. Writer: Create a well-structured final report based on the research and analysis
4. CodeExecutor: Execute any code needed for analysis or visualization
The final deliverable should be a professional research report with:
– Executive summary
– Key findings and insights
– Data analysis (if applicable)
– Conclusions and recommendations
Begin the research process now.
“””
chat_result = team[“executor”].initiate_chat(
manager,
message=initial_message,
max_consecutive_auto_reply=0
)
return self._extract_final_result(chat_result)
def run_business_analysis(self, business_problem: str, max_rounds: int = 8) -> str:
“””Run business analysis project”””
team = self.create_business_team()
agents_list = [team[“strategist”], team[“financial_analyst”],
team[“market_researcher”], team[“executor”]]
group_chat = self.create_group_chat(agents_list, “business_chat”, max_rounds)
manager = GroupChatManager(
groupchat=group_chat,
llm_config=self.llm_config_pro
)
initial_message = f”””
Business Analysis Project: {business_problem}
Please collaborate to provide comprehensive business analysis following this approach:
1. BusinessStrategist: Analyze the business problem and develop strategic framework
2. FinancialAnalyst: Assess financial implications and create financial models
3. MarketResearcher: Research market context and competitive landscape
4. BusinessExecutor: Coordinate and compile final recommendations
Final deliverable should include:
– Problem analysis and root causes
– Strategic recommendations
– Financial impact assessment
– Market opportunity analysis
– Implementation roadmap
Begin the analysis now.
“””
chat_result = team[“executor”].initiate_chat(
manager,
message=initial_message,
max_consecutive_auto_reply=0
)
return self._extract_final_result(chat_result)
def run_development_project(self, project_description: str, max_rounds: int = 10) -> str:
“””Run software development project”””
team = self.create_development_team()
agents_list = [team[“developer”], team[“devops”], team[“qa_engineer”], team[“executor”]]
group_chat = self.create_group_chat(agents_list, “dev_chat”, max_rounds)
manager = GroupChatManager(
groupchat=group_chat,
llm_config=self.llm_config
)
initial_message = f”””
Development Project: {project_description}
Please collaborate to deliver a complete software solution:
1. SeniorDeveloper: Design architecture and write core code
2. DevOpsEngineer: Plan deployment and infrastructure
3. QAEngineer: Design tests and quality assurance approach
4. DevExecutor: Execute code and coordinate implementation
Deliverables should include:
– System architecture and design
– Working code implementation
– Deployment configuration
– Test cases and QA plan
– Documentation
Start development now.
“””
chat_result = team[“executor”].initiate_chat(
manager,
message=initial_message,
max_consecutive_auto_reply=0
)
return self._extract_final_result(chat_result)
def _extract_final_result(self, chat_result) -> str:
“””Extract and format final result from chat”””
if hasattr(chat_result, ‘chat_history’):
messages = chat_result.chat_history
else:
messages = chat_result
final_messages = []
for msg in messages[-5:]:
if isinstance(msg, dict) and ‘content’ in msg:
final_messages.append(f”{msg.get(‘name’, ‘Agent’)}: {msg[‘content’]}”)
return “\n\n”.join(final_messages)
def get_framework_stats(self) -> Dict[str, Any]:
“””Get framework statistics”””
return {
“agents”: list(self.agents.keys()),
“group_chats”: list(self.group_chats.keys()),
“llm_config”: {
“model”: self.llm_config[“config_list”][0][“model”],
“temperature”: self.llm_config[“config_list”][0][“temperature”]
},
“timestamp”: datetime.now().isoformat()
}
