Create Multi-Agent AI Workflows with Gemini, AutoGen, and Semantic Kernel

"Any sufficiently advanced technology is indistinguishable from magic." — Arthur C. Clarke AI has evolved far beyond simple question-answering; it now involves creating sophisticated agents that can collaborate, specialize, and tackle real-world tasks. In this tutorial, I'll guide you through how I combined Google's Gemini Flash, Microsoft's AutoGen framework, and Semantic Kernel to build a multi-agent AI assistant. This assistant can handle a range of tasks, from analyzing and summarizing texts to reviewing code and generating creative solutions. Setting Up the Environment To start, make sure you have the necessary tools and libraries installed: Gemini Flash: This is Google's powerful language model, known for its speed and efficiency. You can access it via the PaLM API. AutoGen: Developed by Microsoft, AutoGen enables the creation of autonomous agents that can manage conversations and workflows. Semantic Kernel: Also from Microsoft, this library provides a high-level interface for embedding AI capabilities into applications. Step 1: Initialize the Agents First, we initialize the AI agents using AutoGen. Each agent will have distinct roles and capabilities: ```python from autogen import AssistantAgent, UserProxyAgent Initialize the user proxy agent user_proxy = UserProxyAgent(name="UserProxy", human_input_mode="NEVER") Initialize the text analysis agent text_agent = AssistantAgent( name="TextAgent", llm_config={ "model": "gemini-flash", "temperature": 0.3, "max_tokens": 150, } ) Initialize the code review agent code_agent = AssistantAgent( name="CodeAgent", llm_config={ "model": "gemini-flash", "temperature": 0.2, "max_tokens": 200, } ) Initialize the creative agent creative_agent = AssistantAgent( name="CreativeAgent", llm_config={ "model": "gemini-flash", "temperature": 0.8, "max_tokens": 300, } ) ``` Step 2: Define the Tasks Next, define the specific tasks each agent will handle: Text Analysis Agent: Summarize long documents and extract key insights. Code Review Agent: Review and optimize code snippets. Creative Agent: Generate innovative ideas and solutions. Step 3: Implement the Workflow Now, let's implement the workflow to coordinate the agents: Text Analysis python def analyze_text(document): # Send the document to the text agent response = text_agent.send(document) # Process and return the summary return response Code Review python def review_code(code_snippet): # Send the code snippet to the code agent response = code_agent.send(code_snippet) # Process and return the review return response Creative Solutions python def generate_solutions(problem_statement): # Send the problem statement to the creative agent response = creative_agent.send(problem_statement) # Process and return the solutions return response Step 4: Integrate with Semantic Kernel To enhance the capabilities of our agents, we use Semantic Kernel to integrate additional AI functionalities. For example, we can embed sentiment analysis or context-aware generation: ```python from semantic_kernel.orchestration.sk_context import SKContext from semantic_kernel.connectors.ai.ai_service import AIService Create an instance of the Semantic Kernel service sk_service = AIService.create("your_semantic_kernel_api_key") Embed sentiment analysis in the text analysis agent def analyze_text_with_sentiment(document): context = SKContext.from_dict({"document": document}) context = sk_service.run_async("sentiment_analysis", context).result sentiment = context["sentiment"] # Send the document and sentiment to the text agent response = text_agent.send(f"Analyzing text with sentiment: {sentiment}. Document: {document}") return response ``` Step 5: Test and Iterate Finally, test your multi-agent system to ensure it performs as expected. Iterate based on feedback to refine the agents' responses and interactions. Conclusion By combining Google's Gemini Flash, Microsoft's AutoGen, and Semantic Kernel, you can build a versatile and intelligent multi-agent AI assistant. This assistant can handle complex tasks, collaborate effectively, and adapt to different scenarios, making it a valuable tool for various applications, from business analysis to software development and creative brainstorming. This tutorial demonstrates the potential of integrating these advanced technologies to create more sophisticated and practical AI systems. Feel free to experiment with different configurations and agents to tailor the system to your specific needs.