Coding guide to create an AI agency to behave using Gemini API of the Germinita Gemini Things Synchronization
In this lesson, we will examine how you can build effective developmental self-optimism using Gemini API to cut germin. This developing ment shows how to solve independent problems, evaluate the ability to make power, to learn to success, and failure, and improve their power by analysis and converting. Transportation Tourism Travels for a formal code, display methods, tracking, analysis, functional safety, and operating analysis.
import google.generativeai as genai
import json
import time
import re
from typing import Dict, List, Any
from datetime import datetime
import tracebackWe have set up the established components to create a powerful improvement in AI using Google Ai Ai API. JSON's libraries, time, and dettititition Hormedeld Pasphation, tracking work, and the processing of text, while typing plans (DICT, list, any code).
class SelfImprovingAgent:
def __init__(self, api_key: str):
"""Initialize the self-improving agent with Gemini API"""
genai.configure(api_key=api_key)
self.model = genai.GenerativeModel('gemini-1.5-flash')
self.memory = {
'successful_strategies': [],
'failed_attempts': [],
'learned_patterns': [],
'performance_metrics': [],
'code_improvements': []
}
self.capabilities = {
'problem_solving': 0.5,
'code_generation': 0.5,
'learning_efficiency': 0.5,
'error_handling': 0.5
}
self.iteration_count = 0
self.improvement_history = []
def analyze_task(self, task: str) -> Dict[str, Any]:
"""Analyze a given task and determine approach"""
analysis_prompt = f"""
Analyze this task and provide a structured approach:
Task: {task}
Please provide:
1. Task complexity (1-10)
2. Required skills
3. Potential challenges
4. Recommended approach
5. Success criteria
Format as JSON.
"""
try:
response = self.model.generate_content(analysis_prompt)
json_match = re.search(r'{.*}', response.text, re.DOTALL)
if json_match:
return json.loads(json_match.group())
else:
return {
"complexity": 5,
"skills": ["general problem solving"],
"challenges": ["undefined requirements"],
"approach": "iterative improvement",
"success_criteria": ["task completion"]
}
except Exception as e:
print(f"Task analysis error: {e}")
return {"complexity": 5, "skills": [], "challenges": [], "approach": "basic", "success_criteria": []}
def solve_problem(self, problem: str) -> Dict[str, Any]:
"""Attempt to solve a problem using current capabilities"""
self.iteration_count += 1
print(f"n=== Iteration {self.iteration_count} ===")
print(f"Problem: {problem}")
task_analysis = self.analyze_task(problem)
print(f"Task Analysis: {task_analysis}")
solution_prompt = f"""
Based on my previous learning and capabilities, solve this problem:
Problem: {problem}
My current capabilities: {self.capabilities}
Previous successful strategies: {self.memory['successful_strategies'][-3:]} # Last 3
Known patterns: {self.memory['learned_patterns'][-3:]} # Last 3
Provide a detailed solution with:
1. Step-by-step approach
2. Code implementation (if applicable)
3. Expected outcome
4. Potential improvements
"""
try:
start_time = time.time()
response = self.model.generate_content(solution_prompt)
solve_time = time.time() - start_time
solution = {
'problem': problem,
'solution': response.text,
'solve_time': solve_time,
'iteration': self.iteration_count,
'task_analysis': task_analysis
}
quality_score = self.evaluate_solution(solution)
solution['quality_score'] = quality_score
self.memory['performance_metrics'].append({
'iteration': self.iteration_count,
'quality': quality_score,
'time': solve_time,
'complexity': task_analysis.get('complexity', 5)
})
if quality_score > 0.7:
self.memory['successful_strategies'].append(solution)
print(f"✅ Solution Quality: {quality_score:.2f} (Success)")
else:
self.memory['failed_attempts'].append(solution)
print(f"❌ Solution Quality: {quality_score:.2f} (Needs Improvement)")
return solution
except Exception as e:
print(f"Problem solving error: {e}")
error_solution = {
'problem': problem,
'solution': f"Error occurred: {str(e)}",
'solve_time': 0,
'iteration': self.iteration_count,
'quality_score': 0.0,
'error': str(e)
}
self.memory['failed_attempts'].append(error_solution)
return error_solution
def evaluate_solution(self, solution: Dict[str, Any]) -> float:
"""Evaluate the quality of a solution"""
evaluation_prompt = f"""
Evaluate this solution on a scale of 0.0 to 1.0:
Problem: {solution['problem']}
Solution: {solution['solution'][:500]}... # Truncated for evaluation
Rate based on:
1. Completeness (addresses all aspects)
2. Correctness (logically sound)
3. Clarity (well explained)
4. Practicality (implementable)
5. Innovation (creative approach)
Respond with just a decimal number between 0.0 and 1.0.
"""
try:
response = self.model.generate_content(evaluation_prompt)
score_match = re.search(r'(d+.?d*)', response.text)
if score_match:
score = float(score_match.group(1))
return min(max(score, 0.0), 1.0)
return 0.5
except:
return 0.5
def learn_from_experience(self):
"""Analyze past performance and improve capabilities"""
print("n🧠 Learning from experience...")
if len(self.memory['performance_metrics']) < 2:
return
learning_prompt = f"""
Analyze my performance and suggest improvements:
Recent Performance Metrics: {self.memory['performance_metrics'][-5:]}
Successful Strategies: {len(self.memory['successful_strategies'])}
Failed Attempts: {len(self.memory['failed_attempts'])}
Current Capabilities: {self.capabilities}
Provide:
1. Performance trends analysis
2. Identified weaknesses
3. Specific improvement suggestions
4. New capability scores (0.0-1.0 for each capability)
5. New patterns learned
Format as JSON with keys: analysis, weaknesses, improvements, new_capabilities, patterns
"""
try:
response = self.model.generate_content(learning_prompt)
json_match = re.search(r'{.*}', response.text, re.DOTALL)
if json_match:
learning_results = json.loads(json_match.group())
if 'new_capabilities' in learning_results:
old_capabilities = self.capabilities.copy()
for capability, score in learning_results['new_capabilities'].items():
if capability in self.capabilities:
self.capabilities[capability] = min(max(float(score), 0.0), 1.0)
print(f"📈 Capability Updates:")
for cap, (old, new) in zip(self.capabilities.keys(),
zip(old_capabilities.values(), self.capabilities.values())):
change = new - old
print(f" {cap}: {old:.2f} → {new:.2f} ({change:+.2f})")
if 'patterns' in learning_results:
self.memory['learned_patterns'].extend(learning_results['patterns'])
self.improvement_history.append({
'iteration': self.iteration_count,
'timestamp': datetime.now().isoformat(),
'learning_results': learning_results,
'capabilities_before': old_capabilities,
'capabilities_after': self.capabilities.copy()
})
print(f"✨ Learned {len(learning_results.get('patterns', []))} new patterns")
except Exception as e:
print(f"Learning error: {e}")
def generate_improved_code(self, current_code: str, improvement_goal: str) -> str:
"""Generate improved version of code"""
improvement_prompt = f"""
Improve this code based on the goal:
Current Code:
{current_code}
Improvement Goal: {improvement_goal}
My current capabilities: {self.capabilities}
Learned patterns: {self.memory['learned_patterns'][-3:]}
Provide improved code with:
1. Enhanced functionality
2. Better error handling
3. Improved efficiency
4. Clear comments explaining improvements
"""
try:
response = self.model.generate_content(improvement_prompt)
improved_code = {
'original': current_code,
'improved': response.text,
'goal': improvement_goal,
'iteration': self.iteration_count
}
self.memory['code_improvements'].append(improved_code)
return response.text
except Exception as e:
print(f"Code improvement error: {e}")
return current_code
def self_modify(self):
"""Attempt to improve the agent's own code"""
print("n🔧 Attempting self-modification...")
current_method = """
def solve_problem(self, problem: str) -> Dict[str, Any]:
# Current implementation
pass
"""
improved_method = self.generate_improved_code(
current_method,
"Make problem solving more efficient and accurate"
)
print("Generated improved method structure")
print("Note: Actual self-modification requires careful implementation in production")
def run_improvement_cycle(self, problems: List[str], cycles: int = 3):
"""Run a complete improvement cycle"""
print(f"🚀 Starting {cycles} improvement cycles with {len(problems)} problems")
for cycle in range(cycles):
print(f"n{'='*50}")
print(f"IMPROVEMENT CYCLE {cycle + 1}/{cycles}")
print(f"{'='*50}")
cycle_results = []
for problem in problems:
result = self.solve_problem(problem)
cycle_results.append(result)
time.sleep(1)
self.learn_from_experience()
if cycle < cycles - 1:
self.self_modify()
avg_quality = sum(r.get('quality_score', 0) for r in cycle_results) / len(cycle_results)
print(f"n📊 Cycle {cycle + 1} Summary:")
print(f" Average Solution Quality: {avg_quality:.2f}")
print(f" Current Capabilities: {self.capabilities}")
print(f" Total Patterns Learned: {len(self.memory['learned_patterns'])}")
time.sleep(2)
def get_performance_report(self) -> str:
"""Generate a comprehensive performance report"""
if not self.memory['performance_metrics']:
return "No performance data available yet."
metrics = self.memory['performance_metrics']
avg_quality = sum(m['quality'] for m in metrics) / len(metrics)
avg_time = sum(m['time'] for m in metrics) / len(metrics)
report = f"""
📈 AGENT PERFORMANCE REPORT
{'='*40}
Total Iterations: {self.iteration_count}
Average Solution Quality: {avg_quality:.3f}
Average Solve Time: {avg_time:.2f}s
Successful Solutions: {len(self.memory['successful_strategies'])}
Failed Attempts: {len(self.memory['failed_attempts'])}
Success Rate: {len(self.memory['successful_strategies']) / max(1, self.iteration_count) * 100:.1f}%
Current Capabilities:
{json.dumps(self.capabilities, indent=2)}
Patterns Learned: {len(self.memory['learned_patterns'])}
Code Improvements: {len(self.memory['code_improvements'])}
"""
return reportIt describes the above, Selfimprovectagent section, such as using a solid framework for Google Api worker, self-esteem and adapt. It includes systematic memory systems, tracking energy, solving problems that strengthen continuous advancement cycles, even efforts that are controlled. This improved launches allows for an agent to improve its accuracy, efficiency, and problems with time, creating AI a flexible ai.
def main():
"""Main function to demonstrate the self-improving agent"""
API_KEY = "Use Your GEMINI KEY Here"
if API_KEY == "Use Your GEMINI KEY Here":
print("⚠️ Please set your Gemini API key in the API_KEY variable")
print("Get your API key from:
return
agent = SelfImprovingAgent(API_KEY)
test_problems = [
"Write a function to calculate the factorial of a number",
"Create a simple text-based calculator that handles basic operations",
"Design a system to find the shortest path between two points in a graph",
"Implement a basic recommendation system for movies based on user preferences",
"Create a machine learning model to predict house prices based on features"
]
print("🤖 Self-Improving Agent Demo")
print("This agent will attempt to solve problems and improve over time")
agent.run_improvement_cycle(test_problems, cycles=3)
print("n" + agent.get_performance_report())
print("n" + "="*50)
print("TESTING IMPROVED AGENT")
print("="*50)
final_problem = "Create an efficient algorithm to sort a large dataset"
final_result = agent.solve_problem(final_problem)
print(f"nFinal Problem Solution Quality: {final_result.get('quality_score', 0):.2f}")
The main function () serves as an entrance to a set of self-defraudagogent. It starts an agent and the Gemini Age key and describes applicable activities and program design activities. An agent then meets these functions, analyzing its performance to develop its problems with the development of the development. Finally, it tests advanced agent skills through a difficult new work, shows advanced progress and providing a positive performance report.
def setup_instructions():
"""Print setup instructions for Google Colab"""
instructions = """
📋 SETUP INSTRUCTIONS FOR GOOGLE COLAB:
1. Install the Gemini API client:
!pip install google-generativeai
2. Get your Gemini API key:
- Go to
- Create a new API key
- Copy the key
3. Replace 'your-gemini-api-key-here' with your actual API key
4. Run the code!
🔧 CUSTOMIZATION OPTIONS:
- Modify test_problems list to add your own challenges
- Adjust improvement cycles count
- Add new capabilities to track
- Extend the learning mechanisms
💡 IMPROVEMENT IDEAS:
- Add persistent memory (save/load agent state)
- Implement more sophisticated evaluation metrics
- Add domain-specific problem types
- Create visualization of improvement over time
"""
print(instructions)
if __name__ == "__main__":
setup_instructions()
print("n" + "="*60)
main()
Finally, we explain to sets_instraictions () work, which direct users through their Google Colab environment to use a self-improvement agent. Describing steps with steps how you can add a dependent, set and configure the Gemini API key, and highlight various customization options and improve agent performance. This methodology makes the onboard user, easy to explore and extend the agent skills of agent.
In conclusion, the implementation of this teaching provides a comprehensive framework for building AGENTS of Agents of Agents and diligently improvise their skills later. By tighting the power of the best API qualifications and combining a systematic loop of self-behavior, developers can create agents that cannot think sophisticated, and entertainment.
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