GPT Pilot

Coordinates a specialized agent pipeline (Spec Writer → Architect → Tech Lead → Developer → Code Monkey → Troubleshooter) that progressively refines requirements, designs architecture, decomposes tasks, and generates implementation code. Uses a centralized Orchestrator component that manages state transitions between agents, maintains project context in SQLite/PostgreSQL, and integrates human developer feedback at each stage to validate outputs before proceeding. The system implements a 95/5 split where AI handles bulk code generation while humans provide critical oversight for architectural decisions and edge cases.

Maintains an indexed representation of the entire project codebase in state management (SQLite/PostgreSQL) and implements context filtering logic that selectively includes relevant files and code snippets when generating new code. The system analyzes dependencies, imports, and semantic relationships to determine which existing code should be included in LLM prompts, reducing token usage and improving code consistency. Uses a relevance-scoring mechanism to prioritize context based on file relationships and recent modifications.

Provides multiple user interfaces for interacting with the system: a VS Code extension for integrated development, a console CLI for command-line usage, and a virtual UI for automated testing. The UI Layer handles communication between the developer and the Orchestrator, presenting generated code, requesting feedback, and displaying progress. The VS Code extension integrates directly into the editor workflow, while the console interface supports scripting and CI/CD integration. All UIs communicate with the same backend Orchestrator, ensuring consistent behavior.

Implements a Prompt Engineering System that maintains specialized prompt templates for each agent type (Spec Writer, Architect, Tech Lead, Developer, Code Monkey, Troubleshooter, Bug Hunter, Frontend Agent). Prompts are parameterized with project context, previous decisions, and feedback history. The system uses dynamic prompt construction to include relevant code snippets, architectural decisions, and developer feedback, ensuring each agent has the necessary context without exceeding token limits. Prompt templates are versioned and can be updated to improve agent behavior.

Provides Docker containerization for running generated code in isolated environments, preventing system contamination and enabling safe testing of untrusted generated code. The Docker Environment layer handles container creation, dependency installation, code execution, and output capture. Supports both local Docker and cloud-based container services. Generated code can be executed in containers with specific resource limits (CPU, memory) and network isolation, enabling safe testing before deployment.

Generates deployment configurations and infrastructure-as-code (Docker Compose, Kubernetes manifests, cloud provider templates) based on the project architecture and technology stack. The system can generate deployment scripts, environment configurations, and cloud provider-specific setup (AWS, GCP, Azure). Supports both containerized and serverless deployments. Generated deployment code includes monitoring, logging, and scaling configurations appropriate to the technology stack.

Implements specialized planning agents (Architect Agent for technology stack decisions, Tech Lead Agent for task decomposition, Developer Agent for detailed implementation planning) that progressively break down high-level requirements into concrete, implementable tasks. Each agent uses domain-specific prompts and reasoning patterns to handle its responsibility. The Tech Lead Agent specifically decomposes projects into manageable subtasks with dependency ordering, while the Architect Agent evaluates technology choices and creates system design documents. This multi-stage planning reduces hallucination and improves code quality by separating concerns.

Provides a unified LLM client interface that abstracts across multiple providers (OpenAI, Anthropic, Groq) and supports dynamic model selection based on task requirements. The LLM Client Architecture layer handles provider-specific API differences, token counting, and cost optimization. Agents can specify preferred models or let the system select based on context window requirements, cost constraints, or latency needs. Supports both synchronous and asynchronous LLM calls with configurable retry logic and fallback providers.

Implements a feedback loop where generated code is presented to the developer, who can provide corrections, request changes, or approve implementation. The system captures this feedback as structured input (approval, rejection with reasons, specific change requests) and feeds it back into the agent pipeline. The Troubleshooter and Bug Hunter agents specifically handle code issues and generate fixes based on developer-reported problems. State management tracks feedback history and uses it to inform subsequent generation attempts, enabling iterative refinement without full regeneration.

Provides pre-configured project templates for common application types (Vite React, backend APIs, etc.) that include technology-specific scaffolding, dependency configurations, and architectural patterns. Templates are stored as blueprint files that the system uses to initialize new projects with appropriate directory structures, configuration files, and starter code. The Architect Agent can select templates based on technology stack decisions, accelerating project setup. Templates include build configurations, testing frameworks, and deployment scripts specific to each technology stack.

Maintains complete project state across agent invocations using a relational database (SQLite for local development, PostgreSQL for production). Stores project metadata, generated code, agent conversation history, developer feedback, task status, and architectural decisions. The State Management System provides a unified interface for agents to query and update state, enabling agents to access project context without re-reading files. Supports both synchronous and asynchronous database operations with connection pooling for concurrent agent access.

Implements a specialized Frontend Development Agent that handles UI-specific code generation for frameworks like React (with Vite). This agent understands component hierarchies, state management patterns, styling approaches, and frontend-specific testing. It generates not just component code but also routing configurations, state management setup, and integration with backend APIs. The agent uses frontend-specific prompts and context filtering to understand existing UI patterns and maintain design consistency.

Implements specialized Quality Assurance Agents (Bug Hunter, Troubleshooter) that analyze generated code for potential issues, test coverage gaps, and architectural problems. The Bug Hunter Agent reviews code for common bug patterns, security vulnerabilities, and performance issues. The Troubleshooter Agent helps diagnose and fix issues reported by developers. These agents use code analysis patterns and domain knowledge to identify problems without requiring full test execution, reducing feedback latency.

Provides a configuration system (JSON-based, environment variable support) that allows customization of LLM behavior, model selection, temperature settings, token limits, and provider preferences without code changes. Configuration is loaded at startup and can be overridden per-project. Supports environment variable expansion for sensitive credentials (API keys). The Configuration System layer abstracts provider-specific settings and enables different configurations for different project types or development stages.