# How to optimize your AI token usage

> Source: <https://github.com/KrishivPiduri/repo-brain/releases/tag/v1.0.0>
> Published: 2026-05-29 02:24:46+00:00

Compress an entire codebase into a single markdown context file.

Feed it to any LLM once instead of re-reading your repo every conversation.

Achieved 96% compression on a 262-file repo (154,229 → 6,487 tokens).

### What's included

**Static analysis**— Tree-sitter AST parsing for Python, JS, TS, Go, Rust;

regex fallback for Java, Ruby, C#, C/C++, Swift, Kotlin, Shell, and more**Architecture analysis**— single LLM call identifies layers, components,

entry points, and data flow**Semantic relationships**— LLM-discovered producer/consumer links,

shared data structures, parallel implementations, and polyglot bridges**Multi-provider support**— OpenAI, Claude, Deepseek, Gemini, Groq, Ollama,

Mistral, xAI, Perplexity, OpenRouter**One-liner installers**— no manual venv or config setup required

### Install

**Mac / Linux:**

curl -fsSL [https://github.com/KrishivPiduri/repo-brain/releases/latest/download/install.sh](https://github.com/KrishivPiduri/repo-brain/releases/latest/download/install.sh) | bash

**Windows (PowerShell):**

irm [https://github.com/KrishivPiduri/repo-brain/releases/latest/download/install.ps1](https://github.com/KrishivPiduri/repo-brain/releases/latest/download/install.ps1) | iex

### Assets

Upload these files to this release:

- install.sh
- install.ps1
- repo-brain.zip (zip of: main.py, llm.py, ingest.py, analyze.py,

relationships.py, generate_prompt.py, mcp_server.py,

config.example.py, requirements.txt)