MegaCoder
Modern AI coding assistants work on one file at a time and lose context across complex changes. MegaCoder takes a different approach: it orchestrates multiple AI coding agents in parallel, each working on a specific part of the task in isolated git worktrees, then merges results into a coherent whole. Think of it as a senior tech lead that can decompose a feature into subtasks, assign them to specialized agents, manage dependencies between their outputs, and deliver a working implementation — all from a CLI interface. Built for developers who want to use AI not as an autocomplete, but as a force multiplier for ambitious engineering work.
Describe the Task
Tell MegaCoder what you want built in natural language. It analyzes your codebase and creates an execution plan.
Review the Plan
See how the task is decomposed into subtasks, which files each agent will touch, and how dependencies are managed.
Parallel Execution
Agents spin up in isolated git worktrees and work simultaneously. Monitor progress in real time from the CLI.
Merge & Ship
Review the combined changes, approve the merge, and commit. What would take hours of manual work ships in minutes.
Parallel Agent Orchestration
Decompose complex tasks and run multiple AI coding agents simultaneously, each in its own isolated git worktree. Merge results automatically when all agents complete.
Task Decomposition
Break down features, refactors, and migrations into independent subtasks that can be parallelized. The orchestrator manages dependencies between agent outputs.
Git Worktree Isolation
Each agent works in an isolated git worktree — no conflicts, no race conditions, no state corruption. Changes are merged cleanly when ready.
Multi-Language Support
Orchestrate changes across TypeScript, Rust, Python, Go, and any language your project uses. Agents understand your full codebase context.
CLI-First Workflow
Designed for the terminal. Describe what you want built, review the decomposition plan, watch agents execute in parallel, and approve the merged result.
Context-Aware Agents
Each agent receives relevant context about the codebase, related files, and the overall task. They make informed decisions, not isolated guesses.
Large Feature Implementation
Build complex features that span multiple files and modules by running specialized agents in parallel on each component.
Codebase-Wide Refactors
Rename interfaces, update API contracts, and migrate patterns across hundreds of files with coordinated parallel agent execution.
Multi-Service Changes
Implement changes that span frontend, backend, and infrastructure code simultaneously — each agent handles its domain.
Senior developers and engineering teams working on complex codebases who want to use AI agents as a force multiplier for multi-file, cross-cutting engineering work.
A Kuberstar Product