In this tutorial, we will explore how to create a sophisticated Self-Improving AI Agent using Google’s cutting-edge Gemini API. This self-improving agent demonstrates autonomous problem-solving, dynamically evaluates performance, learns from successes and failures, and iteratively enhances its capabilities through reflective analysis and self-modification. The tutorial walks through structured code implementation, detailing mechanisms for memory management, capability tracking, iterative task analysis, solution generation, and performance evaluation, all integrated within a powerful self-learning feedback loop.
import google.generativeai as genai
import json
import time
import re
from typing import Dict, List, Any
from datetime import datetime
import tracebackWe set up the foundational components to build an AI-powered self-improving agent utilizing Google’s Generative AI API. Libraries such as json, time, re, and datetime facilitate structured data management, performance tracking, and text processing, while type hints (Dict, List, Any) help ensure robust and maintainable 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']) 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 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 reportWe define the above class, SelfImprovingAgent, as implementing a robust framework leveraging Google’s Gemini API for autonomous task-solving, self-assessment, and adaptive learning. It incorporates structured memory systems, capability tracking, iterative problem-solving with continuous improvement cycles, and even attempts controlled self-modification. This advanced implementation allows the agent to progressively enhance its accuracy, efficiency, and problem-solving sophistication over time, creating a dynamic AI that can autonomously evolve and adapt.
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: https://makersuite.google.com/app/apikey")
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 the entry point for demonstrating the SelfImprovingAgent class. It initializes the agent with the user’s Gemini API key and defines practical programming and system design tasks. The agent then iteratively tackles these tasks, analyzing its performance to refine its problem-solving abilities over multiple improvement cycles. Finally, it tests the agent’s enhanced capabilities with a new complex task, showcasing measurable progress and providing a detailed 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 https://makersuite.google.com/app/apikey
- 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 define the setup_instructions() function, which guides users through preparing their Google Colab environment to run the self-improving agent. It explains step-by-step how to install dependencies, set up and configure the Gemini API key, and highlight various options for customizing and enhancing the agent’s functionality. This approach simplifies user onboarding, facilitating easy experimentation and extending the agent’s capabilities further.
In conclusion, the implementation demonstrated in this tutorial offers a comprehensive framework for creating AI agents that perform tasks and actively enhance their capabilities over time. By harnessing the Gemini API’s advanced generative power and integrating a structured self-improvement loop, developers can build agents capable of sophisticated reasoning, iterative learning, and self-modification.
Check out the Notebook on GitHub. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter.
Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
