How to optimize your AI token usage

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Release repo-brain v1.0.0 · KrishivPiduri/repo-brain · GitHub

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KrishivPiduri

repo-brain

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repo-brain v1.0.0

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KrishivPiduri

released this

28 May 15:11

v1.0.0

b8552d4

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 | bash

Windows (PowerShell):

irm 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)

Assets

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