Transforming Developer Workflows with Intelligent Automation
AI agents are transforming every aspect of software development, from code generation and testing to deployment and maintenance, creating more efficient and intelligent workflows.
from langchain.agents import Agent, Tool
from langchain.llms import OpenAI
from langchain.memory import ConversationBufferMemory
import subprocess
import os
import requests
from typing import List, Dict, Any
class DevelopmentWorkflowAgent:
"""Comprehensive AI agent for software development workflows"""
def __init__(self, github_token: str, openai_api_key: str):
self.github_token = github_token
self.llm = OpenAI(api_key=openai_api_key, temperature=0.1)
self.memory = ConversationBufferMemory()
self.setup_tools()
self.setup_agents()
def setup_tools(self):
"""Setup development tools for the agent"""
self.tools = [
Tool(
name="code_generator",
description="Generate code based on requirements and specifications",
func=self.generate_code
),
Tool(
name="code_reviewer",
description="Review code for quality, security, and best practices",
func=self.review_code
),
Tool(
name="test_generator",
description="Generate comprehensive test cases for given code",
func=self.generate_tests
),
Tool(
name="bug_detector",
description="Analyze code for potential bugs and issues",
func=self.detect_bugs
),
Tool(
name="performance_analyzer",
description="Analyze code performance and suggest optimizations",
func=self.analyze_performance
),
Tool(
name="documentation_generator",
description="Generate documentation from code and comments",
func=self.generate_documentation
),
Tool(
name="deployment_manager",
description="Manage deployment processes and configurations",
func=self.manage_deployment
),
Tool(
name="github_integrator",
description="Integrate with GitHub for repository management",
func=self.github_integration
)
]
def setup_agents(self):
"""Setup specialized development agents"""
# Code Generation Agent
self.code_agent = Agent(
llm=self.llm,
tools=[self.tools[0], self.tools[5]], # code_generator, documentation_generator
memory=self.memory,
system_message="""You are a senior software engineer specializing in code generation.
You write clean, efficient, well-documented code following best practices.
Always include proper error handling and consider edge cases."""
)
# Quality Assurance Agent
self.qa_agent = Agent(
llm=self.llm,
tools=[self.tools[1], self.tools[2], self.tools[3]], # reviewer, test_generator, bug_detector
memory=self.memory,
system_message="""You are a QA engineer focused on code quality and testing.
You ensure code meets quality standards, is properly tested, and is free of bugs.
You provide detailed feedback and actionable recommendations."""
)
# DevOps Agent
self.devops_agent = Agent(
llm=self.llm,
tools=[self.tools[4], self.tools[6], self.tools[7]], # performance_analyzer, deployment_manager, github_integrator
memory=self.memory,
system_message="""You are a DevOps engineer specializing in deployment and operations.
You optimize performance, manage deployments, and ensure system reliability.
You focus on automation, monitoring, and continuous improvement."""
)
def generate_code(self, requirements: str) -> str:
"""Generate code based on requirements"""
prompt = f"""
Generate high-quality code based on these requirements:
{requirements}
Requirements:
- Follow best practices and design patterns
- Include proper error handling
- Add comprehensive comments
- Consider edge cases and validation
- Make code modular and reusable
Provide the complete implementation with explanations.
"""
response = self.llm(prompt)
return response
def review_code(self, code: str) -> str:
"""Review code for quality and best practices"""
prompt = f"""
Review this code for quality, security, and best practices:
{code}
Analyze:
- Code quality and readability
- Security vulnerabilities
- Performance issues
- Best practices adherence
- Potential bugs or edge cases
- Suggestions for improvement
Provide detailed feedback with specific recommendations.
"""
response = self.llm(prompt)
return response
def generate_tests(self, code: str) -> str:
"""Generate comprehensive test cases"""
prompt = f"""
Generate comprehensive test cases for this code:
{code}
Include:
- Unit tests for all functions/methods
- Edge case testing
- Error condition testing
- Integration tests where applicable
- Performance tests if relevant
- Mock objects for dependencies
Use appropriate testing framework and follow testing best practices.
"""
response = self.llm(prompt)
return response
def detect_bugs(self, code: str) -> str:
"""Analyze code for potential bugs"""
prompt = f"""
Analyze this code for potential bugs and issues:
{code}
Look for:
- Logic errors
- Memory leaks
- Race conditions
- Null pointer exceptions
- Buffer overflows
- Input validation issues
- Error handling problems
Provide specific locations and fix suggestions.
"""
response = self.llm(prompt)
return response
def analyze_performance(self, code: str) -> str:
"""Analyze code performance and suggest optimizations"""
prompt = f"""
Analyze this code for performance issues and optimizations:
{code}
Evaluate:
- Time complexity
- Space complexity
- Algorithm efficiency
- Database query optimization
- Caching opportunities
- Bottlenecks and hotspots
Provide specific optimization recommendations.
"""
response = self.llm(prompt)
return response
def generate_documentation(self, code: str) -> str:
"""Generate documentation from code"""
prompt = f"""
Generate comprehensive documentation for this code:
{code}
Include:
- API documentation
- Function/method descriptions
- Parameter explanations
- Return value descriptions
- Usage examples
- Installation instructions
- Configuration details
Format as markdown with clear structure.
"""
response = self.llm(prompt)
return response
def manage_deployment(self, deployment_config: str) -> str:
"""Manage deployment processes"""
prompt = f"""
Create deployment strategy and configuration:
{deployment_config}
Include:
- Deployment pipeline steps
- Environment configurations
- Health checks and monitoring
- Rollback procedures
- Security considerations
- Performance monitoring
Provide complete deployment automation scripts.
"""
response = self.llm(prompt)
return response
def github_integration(self, action: str) -> str:
"""Integrate with GitHub for repository management"""
# Implement GitHub API integration
headers = {
'Authorization': f'token {self.github_token}',
'Accept': 'application/vnd.github.v3+json'
}
if action.startswith('create_pr'):
# Create pull request logic
return "Pull request created successfully"
elif action.startswith('review_pr'):
# Review pull request logic
return "Pull request reviewed"
elif action.startswith('merge_pr'):
# Merge pull request logic
return "Pull request merged"
return f"GitHub action completed: {action}"
def complete_development_workflow(self, requirements: str) -> Dict[str, Any]:
"""Execute complete development workflow"""
workflow_results = {}
# Step 1: Generate code
print("๐ Generating code...")
code = self.code_agent.run(f"Generate code for: {requirements}")
workflow_results['generated_code'] = code
# Step 2: Review code quality
print("๐ Reviewing code quality...")
review = self.qa_agent.run(f"Review this code: {code}")
workflow_results['code_review'] = review
# Step 3: Generate tests
print("๐งช Generating tests...")
tests = self.qa_agent.run(f"Generate tests for: {code}")
workflow_results['generated_tests'] = tests
# Step 4: Analyze performance
print("โก Analyzing performance...")
performance = self.devops_agent.run(f"Analyze performance of: {code}")
workflow_results['performance_analysis'] = performance
# Step 5: Generate documentation
print("๐ Generating documentation...")
docs = self.code_agent.run(f"Generate documentation for: {code}")
workflow_results['documentation'] = docs
# Step 6: Prepare deployment
print("๐ Preparing deployment...")
deployment = self.devops_agent.run(f"Create deployment strategy for: {code}")
workflow_results['deployment_strategy'] = deployment
return workflow_results
def continuous_integration_workflow(self, repo_url: str, branch: str) -> Dict[str, Any]:
"""Execute CI/CD workflow"""
ci_results = {}
# Clone repository
print("๐ฅ Cloning repository...")
subprocess.run(['git', 'clone', repo_url, 'temp_repo'])
# Switch to branch
os.chdir('temp_repo')
subprocess.run(['git', 'checkout', branch])
# Read code files
code_files = []
for root, dirs, files in os.walk('.'):
for file in files:
if file.endswith(('.py', '.js', '.java', '.cpp', '.c')):
with open(os.path.join(root, file), 'r') as f:
code_files.append({
'file': file,
'content': f.read()
})
# Analyze each file
for file_info in code_files:
print(f"๐ Analyzing {file_info['file']}...")
# Code review
review = self.review_code(file_info['content'])
# Bug detection
bugs = self.detect_bugs(file_info['content'])
# Performance analysis
performance = self.analyze_performance(file_info['content'])
ci_results[file_info['file']] = {
'review': review,
'bugs': bugs,
'performance': performance
}
# Cleanup
os.chdir('..')
subprocess.run(['rm', '-rf', 'temp_repo'])
return ci_results
# Usage example
def main():
# Initialize development agent
dev_agent = DevelopmentWorkflowAgent(
github_token="your_github_token",
openai_api_key="your_openai_key"
)
# Complete development workflow
requirements = """
Create a REST API for a task management system with the following features:
- User authentication and authorization
- CRUD operations for tasks
- Task assignment and collaboration
- Real-time notifications
- Data persistence with PostgreSQL
- Comprehensive error handling
- API documentation
"""
results = dev_agent.complete_development_workflow(requirements)
print("โ
Development workflow completed!")
print(f"Generated code: {len(results['generated_code'])} characters")
print(f"Code review: {len(results['code_review'])} characters")
print(f"Tests generated: {len(results['generated_tests'])} characters")
# CI/CD workflow example
ci_results = dev_agent.continuous_integration_workflow(
"https://github.com/user/repo.git",
"feature-branch"
)
print("โ
CI/CD workflow completed!")
print(f"Analyzed {len(ci_results)} files")
if __name__ == "__main__":
main()