Home / CrewAI / Software Development
← Back to CrewAI

Software Development with CrewAI

Orchestrating autonomous agents to handle the full software development lifecycle: planning, coding, testing, documentation, and deployment.

Overview

Development crews include architects who design solutions, developers who implement features, QA agents who test thoroughly, and documentation specialists. This parallels real development teams while enabling automated coordination.

Development Team Structure

Full Development Workflow

Python - Development Crew Pipeline
from crewai import Agent, Task, Crew
from crewai_tools import FileWriterTool, FileReadTool, BrowserTool
import json

# Architect Agent
architect = Agent(
    role="Software Architect",
    goal="Design scalable, maintainable system architecture",
    backstory="Senior architect with 20 years experience",
    tools=[FileWriterTool()],
    llm="gpt-4"
)

# Developer Agent
developer = Agent(
    role="Senior Developer",
    goal="Write clean, efficient, production-ready code",
    backstory="Full-stack engineer with expertise in Python, Go, and JavaScript",
    tools=[FileWriterTool()],
    llm="gpt-4"
)

# QA Agent
qa_engineer = Agent(
    role="QA Engineer",
    goal="Ensure code quality and comprehensive test coverage",
    backstory="QA specialist with 12 years test automation experience",
    tools=[FileWriterTool()],
    llm="gpt-4"
)

# Documentation Agent
doc_specialist = Agent(
    role="Technical Documentation Specialist",
    goal="Create clear, comprehensive technical documentation",
    backstory="Technical writer with API documentation expertise",
    tools=[FileWriterTool()],
    llm="gpt-4"
)

# Task 1: Architecture Design
architecture_task = Task(
    description="""Design architecture for: {feature_request}
    Include:
    - System components and their interactions
    - Database schema (if applicable)
    - API endpoints design
    - Security considerations
    - Scalability analysis
    - Technology stack recommendations""",
    agent=architect,
    expected_output="Detailed architecture document in markdown"
)

# Task 2: Implementation
implementation_task = Task(
    description="""Implement the feature based on architecture:
    - Write production-ready code in {language}
    - Follow {style_guide} coding standards
    - Include proper error handling
    - Add logging at appropriate levels
    - Implement configuration management
    - Use environment variables for config""",
    agent=developer,
    expected_output="Complete implementation code"
)

# Task 3: Testing
testing_task = Task(
    description="""Write comprehensive tests:
    - Unit tests for core functions (90%+ coverage)
    - Integration tests for API endpoints
    - Edge case and error scenario tests
    - Performance tests if applicable
    - Security tests for authentication/authorization
    Use {test_framework}""",
    agent=qa_engineer,
    expected_output="Complete test suite with test cases"
)

# Task 4: Documentation
documentation_task = Task(
    description="""Create technical documentation:
    - API documentation with examples
    - Setup and installation guide
    - Usage examples and patterns
    - Configuration options
    - Troubleshooting guide
    - Contributing guidelines""",
    agent=doc_specialist,
    expected_output="Complete documentation in markdown"
)

# Create Development Crew
dev_crew = Crew(
    agents=[architect, developer, qa_engineer, doc_specialist],
    tasks=[
        architecture_task,
        implementation_task,
        testing_task,
        documentation_task
    ],
    verbose=True,
    process="sequential"
)

# Execute
inputs = {
    "feature_request": "Build user authentication system with OAuth 2.0 support",
    "language": "Python",
    "style_guide": "PEP 8",
    "test_framework": "pytest"
}

result = dev_crew.kickoff(inputs=inputs)

Code Review & Optimization

Python - Code Review Crew
from crewai import Agent, Task, Crew
from crewai_tools import FileReadTool

# Code Review Team
code_reviewer = Agent(
    role="Code Reviewer",
    goal="Review code for quality, security, and best practices",
    backstory="Senior engineer with 15 years peer review experience",
    tools=[FileReadTool()],
    llm="gpt-4"
)

performance_expert = Agent(
    role="Performance Optimization Expert",
    goal="Identify and suggest performance improvements",
    backstory="Performance engineer specializing in optimization",
    tools=[],
    llm="gpt-4"
)

security_specialist = Agent(
    role="Security Specialist",
    goal="Identify security vulnerabilities and risks",
    backstory="Security expert with CISSP certification",
    tools=[],
    llm="gpt-4"
)

# Code Review Tasks
review_task = Task(
    description="""Review the provided code:
    - Check for code style and readability
    - Identify potential bugs or edge cases
    - Verify error handling completeness
    - Assess code reusability
    - Check naming conventions
    - Provide actionable feedback""",
    agent=code_reviewer,
    expected_output="Detailed code review with findings"
)

performance_task = Task(
    description="""Analyze code for performance:
    - Identify algorithmic inefficiencies
    - Check for N+1 queries or similar issues
    - Suggest caching strategies if applicable
    - Recommend data structure improvements
    - Provide performance benchmarks""",
    agent=performance_expert,
    expected_output="Performance analysis and recommendations"
)

security_task = Task(
    description="""Security analysis of code:
    - Check for injection vulnerabilities
    - Verify authentication/authorization implementation
    - Check for sensitive data exposure
    - Assess cryptographic implementations
    - Check dependencies for known vulnerabilities""",
    agent=security_specialist,
    expected_output="Security findings and remediation steps"
)

review_crew = Crew(
    agents=[code_reviewer, performance_expert, security_specialist],
    tasks=[review_task, performance_task, security_task],
    verbose=True
)

Bug Investigation & Fixing

Python - Debug & Fix Workflow
from crewai import Agent, Task, Crew

# Debug Team
debugger = Agent(
    role="Debug Specialist",
    goal="Investigate and understand bugs systematically",
    backstory="Expert debugger with 18 years experience",
    tools=[FileReadTool(), BrowserTool()],
    llm="gpt-4"
)

bug_fixer = Agent(
    role="Bug Fixer",
    goal="Implement reliable fixes for identified issues",
    backstory="Developer specializing in complex bug fixes",
    tools=[FileWriterTool()],
    llm="gpt-4"
)

regression_tester = Agent(
    role="Regression Tester",
    goal="Ensure fixes don't introduce new issues",
    backstory="QA engineer specializing in regression testing",
    tools=[FileWriterTool()],
    llm="gpt-4"
)

# Bug Investigation
investigation_task = Task(
    description="""Investigate the bug:
    - Analyze error logs and stack traces
    - Reproduce the issue systematically
    - Identify root cause
    - Check related code sections
    - Document findings with examples""",
    agent=debugger,
    expected_output="Bug analysis report with root cause"
)

# Fix Implementation
fix_task = Task(
    description="""Implement the fix:
    - Apply minimal, targeted changes
    - Maintain code consistency
    - Include inline comments explaining changes
    - Update related documentation if needed
    - Consider similar issues in codebase""",
    agent=bug_fixer,
    expected_output="Fixed code with explanation"
)

# Regression Testing
regression_task = Task(
    description="""Create regression tests:
    - Write test case that reproduces original bug
    - Verify fix resolves the issue
    - Check that other tests still pass
    - Test edge cases around the fix
    - Document test coverage""",
    agent=regression_tester,
    expected_output="Regression test suite"
)

debug_crew = Crew(
    agents=[debugger, bug_fixer, regression_tester],
    tasks=[investigation_task, fix_task, regression_task],
    verbose=True
)

CI/CD Pipeline Generation

Python - Pipeline Configuration
from crewai import Agent, Task, Crew

# DevOps Agent
devops_engineer = Agent(
    role="DevOps Engineer",
    goal="Design and implement CI/CD pipelines",
    backstory="DevOps expert with Kubernetes expertise",
    tools=[FileWriterTool()],
    llm="gpt-4"
)

# Generate CI/CD Config
pipeline_task = Task(
    description="""Generate CI/CD pipeline configuration:
    - For platform: {platform}
    - Language: {language}
    - Include stages: lint, test, build, deploy
    - Implement automated testing
    - Add code coverage requirements
    - Setup notifications
    - Add deployment safeguards""",
    agent=devops_engineer,
    expected_output="Complete pipeline configuration"
)

# Infrastructure as Code
infrastructure_task = Task(
    description="""Generate infrastructure configuration:
    - Environment setup for {environment}
    - Container configuration if applicable
    - Database and service setup
    - Security group and networking
    - Monitoring and logging setup
    Use {iac_tool}""",
    agent=devops_engineer,
    expected_output="IaC configuration files"
)

devops_crew = Crew(
    agents=[devops_engineer],
    tasks=[pipeline_task, infrastructure_task],
    verbose=True
)

Best Practices