Repopack is a new open-source tool Iβve been testing out. Itβs designed to help developers work more efficiently with LLM coding assistants. It tackles a common headache for developers: efficiently sharing entire codebases with large language models.
Most LLM models have strict token limits, making it tough to share large codebases or complex project structures. This often leads to a tedious process of manually copying and pasting relevant code snippets, which is not only time-consuming but also prone to errors.
Another issue is the inconsistent formatting across different parts of a project, which can sometimes confuse LLM models. Thereβs also the ever-present worry of accidentally sharing sensitive information when selecting code to share.
Most importantly, when we only share isolated parts of a project with an LLM assistant, we rob it of the broader context. This can lead to suggestions that donβt quite fit with the overall architecture or project goals.
What is Repopack?
At its core, Repopack is a command-line tool that packages your entire code repository into a single file. This file is formatted in a way thatβs easy for LLM models like GPT-4, Claude, or Gemini to process.
Now if I want to share my project with LLM models, I can simply send them the repopack-output.txt
file. This way, the LLM model can see the entire project structure and context, making it easier to provide relevant suggestions.
Think of Repopack like a tar designed specifically for feeding codebases to LLMs. Itβs tailored for the unique requirements of working with large language models in a coding context.
Sharing the repopack-output.txt
with large language models
Once youβve generated your repopack-output.txt
file, you can easily share it with various AI coding assistants. Hereβs how it looks when used with two popular platforms:
Claude
ChatGPT
As you can see, both Claude and ChatGPT can easily process the repopack-output.txt
file, allowing them to understand your entire codebase context when providing assistance.
Basic Usage
Getting started with Repopack is straightforward:
Run it in your project directory:
To pack specific files or directories using glob patterns:
Finally, find the repopack-output.txt
file in your current directory.
Key Features
- Simplicity: Itβs a one-command operation to package your repo.
- AI-Optimized: The output is formatted for easy consumption by AI models.
- Token Awareness: It provides token counts, helping you stay within AI model limits.
- Customizable: You can configure what to include or exclude.
- Security-Minded: It respects
.gitignore
files and uses Secretlint to avoid exposing sensitive info.
A Word of Caution
While Repopack is useful, itβs important to use it thoughtfully:
- Security: Always double-check that sensitive information isnβt included in the output.
- AI Limitations: Remember that AI tools, while powerful, arenβt infallible. Use their suggestions as a starting point, not gospel.
- Context Matters: Sometimes, less is more. Consider if the AI really needs your entire codebase or just specific parts.
Use it wisely, always be mindful of security concerns, and donβt forget that your expertise and judgment are still the most valuable assets in any development project.