In this tutorial, we explore how to integrate Microsoft AutoGen with Google’s free Gemini API using LiteLLM, enabling us to build a powerful, multi-agent conversational AI framework that runs seamlessly on Google Colab. We walk through the process of setting up the environment, configuring Gemini for compatibility with AutoGen, and building specialized teams of agents for research, business analysis, and software development tasks. By combining the strengths of structured agent roles and real-time LLM-powered collaboration, we create a versatile system that can execute complex workflows autonomously. Check out the Full Codes here.
!pip install AutoGen
!pip install pyautogen google-generativeai litellm
import os
import json
import asyncio
from typing import Dict, List, Any, Optional, Callable
from datetime import datetime
import logging
import autogen
from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
from autogen.agentchat.contrib.retrieve_assistant_agent import RetrieveAssistantAgent
from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent
import google.generativeai as genai
import litellm
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)We begin by installing the necessary libraries, AutoGen, LiteLLM, and Google Generative AI, to enable multi-agent orchestration with Gemini models. Then, we import essential modules and set up logging to monitor our framework’s execution. This prepares our environment for building intelligent agent interactions. Check out the Full Codes here.
class GeminiAutoGenFramework:
"""
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 "nn".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()
}We define a class GeminiAutoGenFramework that serves as the core engine for our multi-agent collaboration system using the free Gemini API. Within this class, we configure the model, create specialized agents for research, business, and development tasks, and enable group conversations among them. This setup allows us to simulate real-world workflows by letting AI agents research, analyze, write, and even execute code in a coordinated and modular fashion. Check out the Full Codes here.
def demo_autogen_framework():
"""Demo the AutoGen framework"""
print("
Microsoft AutoGen + Gemini Framework Demo")
print("=" * 60)
GEMINI_API_KEY = "your-gemini-api-key-here"
framework = GeminiAutoGenFramework(GEMINI_API_KEY)
print("
Framework initialized successfully!")
print(f"
Stats: {json.dumps(framework.get_framework_stats(), indent=2)}")
return framework
async def run_demo_projects(framework):
"""Run demonstration projects"""
print("n
Running Research Project...")
research_result = framework.run_research_project(
"Impact of Generative AI on Software Development in 2025"
)
print("Research Result (excerpt):")
print(research_result[:500] + "..." if len(research_result) > 500 else research_result)
print("n
Running Business Analysis...")
business_result = framework.run_business_analysis(
"A mid-sized company wants to implement AI-powered customer service. "
"They currently have 50 support staff and handle 1000 tickets daily. "
"Budget is $500K annually."
)
print("Business Analysis Result (excerpt):")
print(business_result[:500] + "..." if len(business_result) > 500 else business_result)
print("n
Running Development Project...")
dev_result = framework.run_development_project(
"Build a Python web scraper that extracts product information from e-commerce sites, "
"stores data in a database, and provides a REST API for data access."
)
print("Development Result (excerpt):")
print(dev_result[:500] + "..." if len(dev_result) > 500 else dev_result)
if __name__ == "__main__":
print("Microsoft AutoGen + Gemini Framework Ready! ")
print("n
For Google Colab, run:")
print("!pip install pyautogen google-generativeai litellm")
print("n
Get your free Gemini API key:")
print("https://makersuite.google.com/app/apikey")
print("n Quick start:")
print("""
# Initialize framework
# framework = GeminiAutoGenFramework("your-gemini-api-key")
# Run research project
result = framework.run_research_project("AI Trends 2025")
print(result)
# Run business analysis
result = framework.run_business_analysis("Market entry strategy for AI startup")
print(result)
# Run development project
result = framework.run_development_project("Build a REST API for user management")
print(result)
""")We conclude our framework by incorporating a demo function that initializes the GeminiAutoGenFramework, prints system statistics, and executes three real-world project simulations: research, business analysis, and software development. This lets us validate the capabilities of our agent teams in action and provides a plug-and-play starting point for any user working in Google Colab.
In conclusion, we have a fully functional multi-agent AI system that can conduct in-depth research, analyze business scenarios, and develop software projects with minimal human intervention. We’ve seen how to orchestrate various specialized agents and how to run projects that reflect real-world use cases. This framework showcases the potential of combining Microsoft AutoGen and Gemini and also provides a reusable blueprint for building intelligent, task-oriented agent teams in our applications.
Check out the Full Codes here. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter.
The post Building a Multi-Agent Conversational AI Framework with Microsoft AutoGen and Gemini API appeared first on MarkTechPost.
