Pack Your Codebase into an LLM with Repomix: A Developer’s Guide

A Clean, Developer-Focused Guide

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Overview

Repomix is a developer tool that compiles your entire codebase into a single, structured document optimized for large language models (LLMs) like ChatGPT, Claude, and Grok. It allows for intelligent reasoning across your whole repository—structure, content, and even history—by minimizing token usage and maximizing semantic clarity.

Whether you're feeding your code to an LLM for refactoring, auditing, or documentation, Repomix streamlines the process into one command.

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What Does Repomix Do?

Repomix scans your repo and generates a file like repomix-output.md, repomix-output.xml, or repomix-output.txt—containing the entire logical structure of your project in a linear format, ready for AI ingestion.

Core Benefits

• LLM-Friendly: Optimized for Grok, GPT-4, Claude and other models with minimal token waste.

• Secure Defaults: Excludes sensitive files (.env, node_modules/, etc.).

• Highly Configurable: Use .repomixignore and repomix.config.json for fine-tuning.

• RAG-Ready: Prepares your code for vector database pipelines like Pinecone or Chroma.

• Cross-Language: Works across any language or framework: JS, Python, Go, Rust, etc.

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Why Not Feed Files One by One?

❌ Traditional Script-by-Script Feeding

Feeding files individually (e.g., main.js, utils.py, routes/) into an LLM often results in:

• Context overflow: LLMs can process only a limited number of tokens (8k–128k).

• Truncation: Older parts of the conversation are dropped.

• Loss of architectural understanding: Harder for the LLM to reason about cross-file logic.

✅ Repomix + RAG (Retrieval-Augmented Generation)

Repomix prepares your repo for chunking. Combined with RAG, you get:

• Full-codebase embeddings in a vector store (e.g., Pinecone, Weaviate, Chroma)

• Query-time semantic search to retrieve only relevant sections

• Zero truncation and dramatically improved response accuracy

🧠 RAG + Repomix = High precision + Full context + Optimal token usage

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Installation

Repomix requires Node.js v16+.

Windows 🪟

winget install OpenJS.NodeJS
npm install -g repomix

macOS 🍎

brew install node
npm install -g repomix

Linux 🐧

sudo apt install nodejs npm
npm install -g repomix

Verify Installation

repomix -v

If needed:

npm install -g repomix --force

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Quick Start

1. Navigate to your project directory:

cd /your/project/path

2. Run:

repomix

3. Output:
   By default, this generates repomix-output.xml in the current directory.

4. Upload to your LLM or AI system (ChatGPT, Claude, Grok etc.)

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Optional Configuration

.repomixignore

Ignore specific paths or files:

node_modules/
.env
*.log
*.lock
build/

repomix.config.json

Use --init to generate:

repomix --init

Example config:

{
  "output": {
    "style": "markdown",
    "filePath": "repomix-output.md"
  },
  "ignore": {
    "customPatterns": ["*.log", "node_modules/", "dist/"]
  }
}

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Example LLM Prompt

Once uploaded, start with:

This file represents my full codebase. Please analyze it and suggest improvements in structure, modularity, and performance.

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Integrating with RAG

Repomix output can be chunked and embedded into vector stores using frameworks like LangChain or LlamaIndex.

Example (LangChain + Chroma)

from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings

loader = TextLoader("repomix-output.md")
documents = loader.load()

splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(documents)

db = Chroma.from_documents(chunks, embedding=OpenAIEmbeddings())
retriever = db.as_retriever()

results = retriever.get_relevant_documents("Where is the login middleware implemented?")

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Supported Output Formats

Control this in your config:

{
  "output": {
    "style": "text", 
    "filePath": "repomix-output.txt"
  }
}

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Summary

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Resources

• GitHub: https://github.com/pashpashpash/repomix

• LangChain Docs: https://docs.langchain.com

• Vector DBs: Pinecone, Chroma

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Final Thought

Repomix brings clarity and completeness to AI-assisted development. Whether you're auditing legacy code, onboarding new teammates, or embedding context into a semantic search system, it saves time and increases LLM accuracy—all from a single file.