To create an improved Agen-agent AI flows by traveling with AUVERGING AUTOGEN AND SEMATIC Kernel
In this lesson, we visit the autogen-based integration and Semantic Kernel with a Gemin Flash model. We begin by setting our Geminiwrapper classes and SemantickernelgemipripplipricIPlipplip- geminipripplippliper with the Agent Organization Agent. From there, we prepare professional agents, from the Code reviewers to the old critics, showing how we can tolerate the adogen kernel tasks, summarizing, review code, and resolving. By combining a strong Autogen's agent's framework with the Semantic Kernel operated, we develop a developed AI assistant that is consistent with various formal, effective information.
!pip install pyautogen semantic-kernel google-generativeai python-dotenv
import os
import asyncio
from typing import Dict, Any, List
import autogen
import google.generativeai as genai
from semantic_kernel import Kernel
from semantic_kernel.functions import KernelArguments
from semantic_kernel.functions.kernel_function_decorator import kernel_functionWe start by installing basic depth: Pyauutogen, Semantic-Kernel, Google-Generativeia, Python-Dotenv, to ensure all the appropriate libraries of the agent and the Semantic function. Then we import the important Python (OS, Asyns, Setups) and AgeGen Forten formattent for agent in agent APIs and Semantic classes to describe our AI services.
GEMINI_API_KEY = "Use Your API Key Here"
genai.configure(api_key=GEMINI_API_KEY)
config_list = [
{
"model": "gemini-1.5-flash",
"api_key": GEMINI_API_KEY,
"api_type": "google",
"api_base": "
}
]
We describe our Gemini_p_yy area and we immediately stop the Genai client so all Gemini calls are authorized. Then we build a Config_list containing a model flash model, API name, the baseline type, and the basic URL, which will submit it to our agents.
class GeminiWrapper:
"""Wrapper for Gemini API to work with AutoGen"""
def __init__(self, model_name="gemini-1.5-flash"):
self.model = genai.GenerativeModel(model_name)
def generate_response(self, prompt: str, temperature: float = 0.7) -> str:
"""Generate response using Gemini"""
try:
response = self.model.generate_content(
prompt,
generation_config=genai.types.GenerationConfig(
temperature=temperature,
max_output_tokens=2048,
)
)
return response.text
except Exception as e:
return f"Gemini API Error: {str(e)}"We include all Gemini Flash classroom interactions in GeminiliWrapper class, where we start generatModel with our selected model and to produce an easy-to-line_respoon. In this way, we pass quickly and heat into Gemino's Execre's API_Content API (204-tokens) and return a mature text or formatted text.
class SemanticKernelGeminiPlugin:
"""Semantic Kernel plugin using Gemini Flash for advanced AI operations"""
def __init__(self):
self.kernel = Kernel()
self.gemini = GeminiWrapper()
@kernel_function(name="analyze_text", description="Analyze text for sentiment and key insights")
def analyze_text(self, text: str) -> str:
"""Analyze text using Gemini Flash"""
prompt = f"""
Analyze the following text comprehensively:
Text: {text}
Provide analysis in this format:
- Sentiment: [positive/negative/neutral with confidence]
- Key Themes: [main topics and concepts]
- Insights: [important observations and patterns]
- Recommendations: [actionable next steps]
- Tone: [formal/informal/technical/emotional]
"""
return self.gemini.generate_response(prompt, temperature=0.3)
@kernel_function(name="generate_summary", description="Generate comprehensive summary")
def generate_summary(self, content: str) -> str:
"""Generate summary using Gemini's advanced capabilities"""
prompt = f"""
Create a comprehensive summary of the following content:
Content: {content}
Provide:
1. Executive Summary (2-3 sentences)
2. Key Points (bullet format)
3. Important Details
4. Conclusion/Implications
"""
return self.gemini.generate_response(prompt, temperature=0.4)
@kernel_function(name="code_analysis", description="Analyze code for quality and suggestions")
def code_analysis(self, code: str) -> str:
"""Analyze code using Gemini's code understanding"""
prompt = f"""
Analyze this code comprehensively:
```
{code}
```
Provide analysis covering:
- Code Quality: [readability, structure, best practices]
- Performance: [efficiency, optimization opportunities]
- Security: [potential vulnerabilities, security best practices]
- Maintainability: [documentation, modularity, extensibility]
- Suggestions: [specific improvements with examples]
"""
return self.gemini.generate_response(prompt, temperature=0.2)
@kernel_function(name="creative_solution", description="Generate creative solutions to problems")
def creative_solution(self, problem: str) -> str:
"""Generate creative solutions using Gemini's creative capabilities"""
prompt = f"""
Problem: {problem}
Generate creative solutions:
1. Conventional Approaches (2-3 standard solutions)
2. Innovative Ideas (3-4 creative alternatives)
3. Hybrid Solutions (combining different approaches)
4. Implementation Strategy (practical steps)
5. Potential Challenges and Mitigation
"""
return self.gemini.generate_response(prompt, temperature=0.8)We include Semantic Logic in Semantickernelgemipliniplinipliniplinipliniplikin, where we start both the Kernel and our GeminilerRapper to activate AI. Using the @Delle decoration, we declare methods such as comic analysis.com. This plugin allows to register outside of seams and ask an advanced AI advancement within our Kernel area.
class AdvancedGeminiAgent:
"""Advanced AI Agent using Gemini Flash with AutoGen and Semantic Kernel"""
def __init__(self):
self.sk_plugin = SemanticKernelGeminiPlugin()
self.gemini = GeminiWrapper()
self.setup_agents()
def setup_agents(self):
"""Initialize AutoGen agents with Gemini Flash"""
gemini_config = {
"config_list": [{"model": "gemini-1.5-flash", "api_key": GEMINI_API_KEY}],
"temperature": 0.7,
}
self.assistant = autogen.ConversableAgent(
name="GeminiAssistant",
llm_config=gemini_config,
system_message="""You are an advanced AI assistant powered by Gemini Flash with Semantic Kernel capabilities.
You excel at analysis, problem-solving, and creative thinking. Always provide comprehensive, actionable insights.
Use structured responses and consider multiple perspectives.""",
human_input_mode="NEVER",
)
self.code_reviewer = autogen.ConversableAgent(
name="GeminiCodeReviewer",
llm_config={**gemini_config, "temperature": 0.3},
system_message="""You are a senior code reviewer powered by Gemini Flash.
Analyze code for best practices, security, performance, and maintainability.
Provide specific, actionable feedback with examples.""",
human_input_mode="NEVER",
)
self.creative_analyst = autogen.ConversableAgent(
name="GeminiCreativeAnalyst",
llm_config={**gemini_config, "temperature": 0.8},
system_message="""You are a creative problem solver and innovation expert powered by Gemini Flash.
Generate innovative solutions, and provide fresh perspectives.
Balance creativity with practicality.""",
human_input_mode="NEVER",
)
self.data_specialist = autogen.ConversableAgent(
name="GeminiDataSpecialist",
llm_config={**gemini_config, "temperature": 0.4},
system_message="""You are a data analysis expert powered by Gemini Flash.
Provide evidence-based recommendations and statistical perspectives.""",
human_input_mode="NEVER",
)
self.user_proxy = autogen.ConversableAgent(
name="UserProxy",
human_input_mode="NEVER",
max_consecutive_auto_reply=2,
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
llm_config=False,
)
def analyze_with_semantic_kernel(self, content: str, analysis_type: str) -> str:
"""Bridge function between AutoGen and Semantic Kernel with Gemini"""
try:
if analysis_type == "text":
return self.sk_plugin.analyze_text(content)
elif analysis_type == "code":
return self.sk_plugin.code_analysis(content)
elif analysis_type == "summary":
return self.sk_plugin.generate_summary(content)
elif analysis_type == "creative":
return self.sk_plugin.creative_solution(content)
else:
return "Invalid analysis type. Use 'text', 'code', 'summary', or 'creative'."
except Exception as e:
return f"Semantic Kernel Analysis Error: {str(e)}"
def multi_agent_collaboration(self, task: str) -> Dict[str, str]:
"""Orchestrate multi-agent collaboration using Gemini"""
results = {}
agents = {
"assistant": (self.assistant, "comprehensive analysis"),
"code_reviewer": (self.code_reviewer, "code review perspective"),
"creative_analyst": (self.creative_analyst, "creative solutions"),
"data_specialist": (self.data_specialist, "data-driven insights")
}
for agent_name, (agent, perspective) in agents.items():
try:
prompt = f"Task: {task}nnProvide your {perspective} on this task."
response = agent.generate_reply([{"role": "user", "content": prompt}])
results[agent_name] = response if isinstance(response, str) else str(response)
except Exception as e:
results[agent_name] = f"Agent {agent_name} error: {str(e)}"
return results
def run_comprehensive_analysis(self, query: str) -> Dict[str, Any]:
"""Run comprehensive analysis using all Gemini-powered capabilities"""
results = {}
analyses = ["text", "summary", "creative"]
for analysis_type in analyses:
try:
results[f"sk_{analysis_type}"] = self.analyze_with_semantic_kernel(query, analysis_type)
except Exception as e:
results[f"sk_{analysis_type}"] = f"Error: {str(e)}"
try:
results["multi_agent"] = self.multi_agent_collaboration(query)
except Exception as e:
results["multi_agent"] = f"Multi-agent error: {str(e)}"
try:
results["direct_gemini"] = self.gemini.generate_response(
f"Provide a comprehensive analysis of: {query}", temperature=0.6
)
except Exception as e:
results["direct_gemini"] = f"Direct Gemini error: {str(e)}"
return resultsWe include our END-ENTDERT ENTEMEMEMENT in the best class, where we start our Semantic Kernel plugin, and prepare for Suite of Autogen Agents (Administrator, Data Expert, and User Professional). In simple ways to tie the Semantic-Kernel, multiple agents, and specific gemini telephones, we enable seamless, complete pipe for analyzing the pipe.
def main():
"""Main execution function for Google Colab with Gemini Flash"""
print("🚀 Initializing Advanced Gemini Flash AI Agent...")
print("⚡ Using Gemini 1.5 Flash for high-speed, cost-effective AI processing")
try:
agent = AdvancedGeminiAgent()
print("✅ Agent initialized successfully!")
except Exception as e:
print(f"❌ Initialization error: {str(e)}")
print("💡 Make sure to set your Gemini API key!")
return
demo_queries = [
"How can AI transform education in developing countries?",
"def fibonacci(n): return n if n <= 1 else fibonacci(n-1) + fibonacci(n-2)",
"What are the most promising renewable energy technologies for 2025?"
]
print("n🔍 Running Gemini Flash Powered Analysis...")
for i, query in enumerate(demo_queries, 1):
print(f"n{'='*60}")
print(f"🎯 Demo {i}: {query}")
print('='*60)
try:
results = agent.run_comprehensive_analysis(query)
for key, value in results.items():
if key == "multi_agent" and isinstance(value, dict):
print(f"n🤖 {key.upper().replace('_', ' ')}:")
for agent_name, response in value.items():
print(f" 👤 {agent_name}: {str(response)[:200]}...")
else:
print(f"n📊 {key.upper().replace('_', ' ')}:")
print(f" {str(value)[:300]}...")
except Exception as e:
print(f"❌ Error in demo {i}: {str(e)}")
print(f"n{'='*60}")
print("🎉 Gemini Flash AI Agent Demo Completed!")
print("💡 To use with your API key, replace 'your-gemini-api-key-here'")
print("🔗 Get your free Gemini API key at:
if __name__ == "__main__":
main()Finally, we run the main work that starts adventinaginagent, printing status messages, and sticks with a set of discussion set. As we use each question, we collect results from the results from the Emhatic-kernel commentary, multiple work interactions, and specific Gemini response, ensures the appearance of a clear Ai Agent Ai.
In conclusion, we showed that Autogen and Semantic Kernel works how to produce each other to produce a variable system, with Aul-Alent AI awe-given to the power of Gemini Flash. We highlighted how Autogen made an Inchement for various expert agents, while Semantic Kernel offers a clean, pure explanatory system and illumination AI developed activities. By combining these tools in a Colob booklet, enabling you to try to try quick and the enhancement of easy performance without self-sacrifice without self-sacrifice or control.
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