Repo Prompt is a macOS native app designed to remove friction involved in iterating on code with powerful AI models. It helps AI models understand your codebase without wasting tokens on irrelevant code.
Repo Prompt is a macOS-native application designed specifically for developers and engineers who work with AI coding agents, providing a centralized orchestration platform that removes friction from iterative coding workflows. The app's core value lies in its ability to help powerful AI models understand your entire codebase without wasting tokens on irrelevant code, enabling more accurate and efficient AI-assisted development. By curating the right context, running deep analysis, and orchestrating multiple coding agents from one native interface, Repo Prompt transforms how developers leverage AI for complex engineering tasks, moving beyond simple prompt engineering to systematic agent coordination.
Traditional AI coding tools often struggle with context management, forcing developers to manually select relevant files or accept inefficient token usage that limits the AI's understanding of complex codebases. This creates significant friction when iterating on code with AI models, as developers waste time curating context instead of focusing on higher-level problem-solving. Repo Prompt directly addresses this pain point by automating context curation within defined token budgets, ensuring AI agents receive precisely the information they need to understand architectural relationships and dependencies without manual file selection. This matters because it enables developers to tackle more complex refactoring, debugging, and feature implementation tasks with confidence that their AI assistants have the complete picture.
The Context Builder represents Repo Prompt's first major feature group, functioning as an automated system that explores your codebase and selects the most relevant files within a specified token budget. This works through intelligent analysis of code relationships and dependencies, then hands curated context to powerful analysis models for deeper understanding. The system offers three detail levels: function signatures via tree-sitter that let agents see 10x more files, slices for focused examination, and full content for complete understanding. This feature is particularly useful because it eliminates the manual work of file selection while ensuring AI models receive optimal context for accurate analysis, dramatically improving the quality of AI-generated code suggestions and architectural insights.
Multi-Agent Orchestration forms the second major feature group, enabling developers to launch parallel or sequential agent sessions, steer them mid-flight, and let an orchestrator verify the work. The orchestrator decomposes complex plans into discrete tasks, assigns each sub-agent scoped file boundaries, and verifies adherence to the plan before continuing. This works through a structured workflow where a powerful reasoning model first drafts an execution plan, then the orchestrator dispatches sub-agents across different providers like Claude Code, OpenCode, and Gemini CLI to implement specific components. The system supports both sequential execution for dependent tasks and parallel execution with multi-wait functionality that unblocks when the first agent completes, maximizing efficiency for complex engineering work.
Publisher
A
admin
Launch Date2026-01-09
Platformdesktop
Pricingfreemium
Domain Authority
Ahrefs DR54/100