Factory

Generates complete software projects from high-level requirements by orchestrating multi-step workflows that span architecture design, implementation, testing, and deployment configuration. Uses agentic decomposition to break down project scope into discrete tasks, executing them sequentially with inter-task dependency management and error recovery. The system maintains project context across steps, enabling coherent code generation that respects architectural decisions made in earlier phases.

Generates multiple interdependent source files simultaneously while maintaining consistency across module boundaries, import statements, and API contracts. The system models project structure as a dependency graph, ensuring that generated files respect type signatures, interface definitions, and architectural patterns established in related files. Uses constraint-based generation where later files are generated with awareness of earlier files' public APIs and contracts.

Generates authentication and authorization implementations based on security requirements specified in project specifications. The system supports multiple auth patterns (JWT, OAuth2, session-based, API keys) and generates corresponding middleware, guards, and permission checking code. Produces secure-by-default implementations with proper token handling, secret management, and role-based access control (RBAC) or attribute-based access control (ABAC).

Automatically generates logging statements, metrics collection, and monitoring instrumentation code based on application structure and monitoring requirements. The system identifies critical code paths and generates appropriate logging levels, structured logging formats, and metrics collection points. Supports integration with monitoring platforms (Datadog, New Relic, Prometheus, etc.) with proper instrumentation patterns.

Analyzes generated code for performance bottlenecks and automatically generates optimization implementations such as caching strategies, database query optimization, and async/await patterns. The system identifies hot paths and generates appropriate optimizations (memoization, connection pooling, lazy loading, etc.) with configuration options for tuning. Produces performance-aware code that balances optimization complexity with maintainability.

Automatically generates unit tests, integration tests, and end-to-end tests based on code structure and specification requirements. The system analyzes generated code to identify critical paths, edge cases, and error conditions, then generates test cases targeting those scenarios. Uses coverage metrics to guide test generation, prioritizing high-impact test cases that exercise core functionality and error handling paths.

Generates deployment manifests, infrastructure-as-code, and environment configurations tailored to specified target platforms (AWS, GCP, Kubernetes, Docker, etc.). The system models deployment requirements based on generated code characteristics (resource needs, scaling requirements, dependencies) and produces platform-specific configurations. Supports multi-environment generation (dev, staging, production) with environment-specific overrides and secrets management patterns.

Enables modification of generated code by updating specifications and regenerating affected components while preserving manual edits to unrelated code sections. The system tracks which code sections were generated from which specification elements, allowing targeted regeneration when requirements change. Uses diff-based merging to preserve developer customizations while updating generated portions.

Recommends and integrates appropriate technology stacks based on project requirements, then generates code using selected frameworks and libraries. The system evaluates requirements against framework characteristics (performance, ecosystem maturity, learning curve) and selects optimal combinations. Generated code follows framework conventions and best practices, with proper dependency declarations and configuration.

Generates type-safe API contracts, data models, and serialization code from specification documents (OpenAPI, GraphQL schemas, or natural language descriptions). The system parses specifications to extract data structures and API endpoints, then generates corresponding code with proper validation, serialization, and type checking. Supports bidirectional generation where changes to specifications drive code updates and vice versa.

Automatically generates API documentation, architecture diagrams, and developer guides from generated code and project specifications. The system analyzes code structure, function signatures, and comments to extract documentation content, then formats it for various targets (Markdown, HTML, Swagger UI, etc.). Maintains documentation in sync with code by regenerating when specifications change.

Automatically generates error handling patterns, input validation, and exception handling code based on API contracts and specification requirements. The system identifies validation requirements from data models and API specifications, then generates corresponding validation logic with appropriate error messages and recovery strategies. Supports multiple validation frameworks and error handling patterns (try-catch, Result types, error middleware, etc.).

Generates database schemas, migration scripts, and ORM model definitions from data model specifications. The system translates data models into database-specific schemas (SQL, NoSQL) with appropriate indexes, constraints, and relationships. Generates ORM code (Sequelize, TypeORM, SQLAlchemy, etc.) with query builders and relationship definitions that match the data model structure.