A Step-by-Step Coding Tutorial to Implement GBrain: The Self-Wiring Memory Layer Built by Y Combinator’s Garry Tan for AI Agents

Introduction

In this tutorial, you'll learn how to implement GBrain, a self-wiring memory layer developed by Y Combinator's Garry Tan. GBrain allows AI agents to remember past interactions, decisions, and knowledge, which is crucial for building more intelligent and context-aware AI systems. Unlike traditional memory systems that rely on LLM calls, GBrain uses a markdown-first knowledge graph that wires itself through regex inference, making it lightweight and fast.

This tutorial will guide you through setting up GBrain v0.38.2.0, creating a brain repository, running hybrid search, and connecting it to Claude Code via MCP. By the end of this tutorial, you'll have a working GBrain instance that can be used to enhance your AI agent's memory capabilities.

Prerequisites

Before you begin, ensure you have the following installed on your system:

• Python 3.8 or higher (Check with python --version)
• Git (Check with git --version)
• Node.js and npm (Check with node --version and npm --version)
• Basic terminal knowledge (Navigating directories, running commands)

Step-by-Step Instructions

1. Install GBrain

First, we'll install GBrain using pip. Open your terminal and run the following command:

pip install gbrain

This command installs the GBrain package, which includes all necessary components to build and manage your brain repository.

2. Create a Brain Repository

Next, we'll create a directory for our brain repository and initialize it with GBrain:

mkdir my-brain
 cd my-brain
 gbrain init

The gbrain init command creates a new brain repository with the required directory structure and configuration files. This sets up a foundation for storing your knowledge graph.

3. Add Knowledge to Your Brain

Now, let's add some sample knowledge to our brain. Create a markdown file in the brain directory:

echo "# Meeting Notes

## 2026-05-22
- Discussed GBrain implementation
- Agreed on using regex inference for memory wiring
- Next steps: integrate with Claude Code" > knowledge/meeting-notes.md

This command creates a markdown file with sample meeting notes. GBrain uses markdown files to store knowledge, making it easy to integrate with existing documentation tools.

4. Build the Knowledge Graph

After adding knowledge, we need to build the knowledge graph:

gbrain build

This command processes the markdown files and creates a knowledge graph that GBrain can use for retrieval. The knowledge graph is built using regex inference, which identifies relationships between different pieces of information without requiring LLM calls.

5. Run Hybrid Search

Now, let's test the search functionality of GBrain:

gbrain search "memory wiring"

This command searches the knowledge graph for the term "memory wiring" and returns relevant results. Hybrid search combines both keyword-based and semantic search techniques to provide more accurate results.

6. Connect to Claude Code via MCP

To connect GBrain to Claude Code, we'll use the MCP (Model Communication Protocol). First, install the MCP client:

npm install -g @modelcontextprotocol/client

Then, configure the connection by creating a mcp.config.json file:

{
  "brain": "/path/to/my-brain",
  "port": 8080
}

This configuration tells MCP where to find your brain repository and which port to use for communication.

7. Start the GBrain Server

Finally, start the GBrain server:

gbrain serve

This command starts a local server that allows other tools like Claude Code to access your brain's knowledge graph.

Summary

In this tutorial, you've learned how to implement GBrain, a self-wiring memory layer for AI agents. You've installed GBrain, created a brain repository, added knowledge in markdown format, built a knowledge graph, performed hybrid search, and connected it to Claude Code via MCP. GBrain's approach to memory management using regex inference makes it lightweight and efficient, perfect for AI agents that need to remember past interactions without relying on expensive LLM calls.

With this setup, you can now enhance your AI agents with persistent memory, making them more intelligent and capable of learning from past experiences.