Introduction

Bring your code, context, and AI coding tools together.

RepoPrompt gives you a well-thought-out, unified workspace for exploring a repository and working with your go-to AI coding tools. Use its first-class tooling to explore an extensive repo, trace unfamiliar code, work through an architectural decision, review a series of changes, or coordinate work across coding agents you already use.

The macOS application is free and open source. Connect your own coding-runtime account, API provider, or local model service; AI service costs depend on the connection you use.

tl;dr: Jump In Right Now

Don't want to read right now? That's OK...we'll be here when you get back.

Take a five-minute jump into the pool with Getting Started, or take your time and explore the workflow with Going Deeper.

Give your coding work the right tools and the right context

Before any coding agent can make production-ready changes, it needs to understand the code already there: the relevant files, their interfaces, the changes in progress, and the constraints that matter. We all know that finding and sharing that context can take longer than writing the plan or prompt.

RepoPrompt helps you build a focused view of the task and assemble that context accurately and narrowly so you get the best results from your favorite models from your preferred providers. It gathers full files when implementation details matter, slices when only a few lines are relevant, and delivers Code Maps when structure and signatures are enough. Add Git changes and clear instructions to show the model what you want it to examine.

Then select a battle-tested, pre-built workflow or manually inspect and adjust the package before asking for a plan, review, or implementation. You still judge the answer and verify the resulting changes.

See how context preparation works →

Choose how you want to work

Run a coding session in RepoPrompt

Choose a connected coding runtime, then describe the task, choose a workflow, and follow the work in conversations and the Oracles that guide the results. You can inspect tool activity and changes, answer questions, and steer the agent as it works, all without switching between terminal sessions.

Get to know the session interface →

Keep using your preferred coding tools

Connect an MCP-compatible client to RepoPrompt's repository and context tools. Your external coding agent can search files, inspect Code Maps, assemble context, and consult an Oracle while you review the work in RepoPrompt.

Connect an external coding tool →

Prepare a prompt for another model

Select code, add instructions and Git changes, then copy a self-contained package. Use it for a design discussion or an independent review in ChatGPT or another service, and bring the useful findings back to your coding session.

Prepare and copy your context →

Start with the task you need to finish

Choose a built-in workflow from the welcome cards or the Workflow selector, then describe what you want to accomplish. Let RepoPrompt go from there.

What you want to do	Where to start
Coordinate a larger piece of work	Orchestrate breaks down the task, delegates work, and checks the results.
Check changes for bugs and regressions	Review examines the change with its surrounding code.
Understand a bug or unfamiliar behavior	Investigate gathers evidence and explores the relevant code.
Plan a substantial change before editing	Deep Plan develops an implementation plan grounded in the repository.
Build a feature or fix	Plan & Build guides research, planning, and implementation.
Improve code structure	Refactor focuses on preserving intended behavior while improving the implementation.
Get an independent perspective	ChatGPT Export prepares context for a conversation outside RepoPrompt.

Choose a workflow for your task →

Make RepoPrompt Your Partner

1. Describe the task. The Context Builder gathers relevant code, and Oracle drafts an execution plan. A narrow context window and a double-checked plan is key for the best results.
2. Divide the work. The Orchestrator assigns tasks with clear file boundaries. No hidden "Thinking..." output here.
3. Implement. Agents work sequentially or in parallel from a shared plan, with the Orchestrator steering progress. The division of tasks and clarity that the Orchestrator offers is part of our "secret sauce."
4. Verify. The Orchestrator checks the results against the plan and continues until complete. All together, this is what yields production-quality code.

Coordinate larger changes without losing sight of them

RepoPrompt overcomes the AI-assisted nightmare of big edits. For a larger change, you can use separate sessions for investigation, implementation, and review. Overseer Mode lets a coordinating conversation follow linked sessions and direct the next steps.

Use worktrees when parallel tasks need separate checkouts; separate conversations can still edit the same checkout. Review the work, merge it into the intended target, and verify the combined result.

Coordinate sessions with Overseer → · Create and use a worktree → · Review and merge the result →

Keep control as the work develops

• Make the request clearer. Use Interviewer to resolve open questions or Re-Prompter to refine a draft before sending.
• Show the problem. Attach a screenshot or mockup. Eligible Codex sessions can also use Computer Use for tasks that need the visible interface.
• Choose how the model works. Set the model and reasoning effort yourself, or configure Agent Decisions for eligible new tasks.
• Review the decisions that matter. Inspect approval requests and edit reviews, then return to session history when you need the context behind a result.

Use the connection that fits your work

Connect a supported coding runtime for implementation, a direct API for model features, or a local model server. Configure Oracle consultations separately when you want another model to assess a plan or review the code.

The Providers & Models reference explains what each integration needs and what it supports. Available models depend on your connected accounts or APIs you bring to the table. You can start with a focused task and adjust the settings as you go; you do not need to choose every model and experiment with thinking levels and settings before trying.

Compare providers and model availability →

Try it on one real task

Start with a question you can evaluate: understand an unfamiliar function, investigate a reproducible bug, or review a small change. A narrow first task makes it easier to see what RepoPrompt adds to your existing workflow.

1. Open your project and connect the provider you want to use.
2. Describe the outcome. Include the behavior you care about, known constraints, and how you will check the result.
3. Choose the workflow and location. Use a separate worktree when you want an isolated checkout.
4. Inspect and guide the work. Review the selected context, answer questions, and check the resulting code and test results.

Follow the getting-started walkthrough →

Talk to These Docs

Use Copy page to bring an article into an AI conversation. For the complete documentation, use the LLM index or full documentation export. Include your app version and the task you need help with.