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myBrAIn is an MCP (Model Context Protocol) server designed to provide persistent and contextual memory to language model
Deterministic, Idempotent, Context-Aware Second Brain for Google Antigravity.
myBrAIn is an MCP (Model Context Protocol) server designed to provide persistent and contextual memory to language models (like Google Antigravity). It acts as a "second brain" for your development environment, allowing the AI to remember project rules, architectural decisions, and technical insights across different chat sessions.
The system is divided into three main components:
db.py) and analytical intelligence (analyzer.py) for extracting insights from code.server.py) that allows Antigravity to interact with the brain.admin.py) based on Streamlit to visualize, manage, and debug stored memories with advanced features like Knowledge Graphs, Memory Export, and the Silent Observer dashboard.observer.py) that monitors codebase drift in real-time.The easiest way to run myBrAIn is using Docker Compose.
# Clone & Prepare
git clone https://github.com/lilium360/myBrAIn.git
cd myBrAIn
cp .env.example .env
# Spin up
docker compose up -d
The Admin UI will be available at: http://localhost:8501
git clone https://github.com/lilium360/myBrAIn.git
cd myBrAIn
python -m venv venv && .\venv\Scripts\activatepython3 -m venv venv && source venv/bin/activatepip install -r requirements.txtTo graphically manage the database and visualize insights:
docker compose up. Open http://localhost:8501.streamlit run admin.py (or python -m streamlit run admin.py).Add this to your mcpServers configuration JSON:
{
"mcpServers": {
"mybrain": {
"command": "docker",
"args": ["exec", "-i", "mybrain-admin", "python", "server.py"],
"env": {
"MYBRAIN_DATA_DIR": "/data/mybrain"
}
}
}
}
{
"mcpServers": {
"mybrain": {
"command": "python",
"args": ["/ABSOLUTE/PATH/TO/mybrain/server.py"]
}
}
}
[!IMPORTANT] Change the path in
argsto the ABSOLUTE path on your machine.
myBrAIn is specifically designed to handle "Project Onboarding" — a process where the AI analyzes your current codebase and stores its architectural DNA.
Once you have the MCP server configured, simply use the following prompt:
"I want to perform a full onboarding of this project into your long-term memory. Sequentially perform: Structural Link, Stack Analysis, Architectural Analysis, Style Analysis, and Exclusion Analysis."
To ensure the AI uses myBrAIn correctly and consistently, copy and paste the following instructions into your project's custom instructions or .cursorrules file:
# myBrAIn Integration Protocol (MANDATORY)
You are connected to **myBrAIn**, an MCP server that acts as your Long-Term Memory and "Single Source of Truth" for this project.
You MUST NEVER rely solely on the current chat context or your general knowledge when project-specific rules are saved.
Strictly follow this operational cycle for EVERY interaction:
## 1. RECALL PHASE (Before Reasoning)
BEFORE generating any code or technical response, you MUST consult the memory:
- **Action:** Run `recall_context` using relevant keywords (e.g., "auth logic", "styling conventions", "api patterns").
- **Goal:** Retrieve the tech stack, architectural rules, and standard components already defined.
- **Constraint:** If you find an existing rule or component (e.g., "Always use `CustomButton`"), you MUST use it. Creating duplicates or unauthorized variations is FORBIDDEN.
## 2. CONSISTENCY PHASE (During Reasoning)
While generating the response:
- Strictly adhere to retrieved Constraints.
- If the user requests something that violates a saved rule (e.g., "Use jQuery" in a React project saved as "No jQuery"), warn the user of the conflict before proceeding.
- Use the code style (naming, comments, structure) retrieved from memory to maintain uniformity.
## 3. MEMORIZATION PHASE (During/After Action)
Your task is to keep the memory alive and updated. DO NOT wait for the user to ask.
Use `store_insight` proactively in the following cases:
- **New Decisions:** If a new architectural rule is established (e.g., "From now on, use only Tailwind").
- **New Patterns:** If you write a reusable generic component, save it as a "Standard Component".
- **Corrections:** If the user corrects your error, save the correction (e.g., "Do not use library X because it's buggy").
- **Onboarding:** If you detect a new empty project, propose or run `initialize_workbase`.
## 4. CONFLICT MANAGEMENT
- **myBrAIn Priority:** If your general knowledge suggests "X" but myBrAIn says "Y", the correct answer is "Y".
- **Updates:** If an old memory is obsolete, use `store_insight` to overwrite it (the system handles semantic collision automatically via `replace_id`).
---
**Correct Workflow Example:**
User: "Create a login page."
AI (Internal Thought): "Check myBrAIn for 'login', 'auth', 'ui components'."
AI Action: `recall_context("login auth ui style")`
AI (Found): "The project uses NextAuth and Shadcn components."
AI Response: Generates code using EXACTLY NextAuth and Shadcn, without inventing custom CSS unless necessary.
AI Action (Post): Since a new useful auth hook was created, run `store_insight` to save it.
initialize_workbase: Link a directory to the brain.store_insight: Manually save a rule or context.recall_context: Retrieve relevant memories for the current task.critique_code: Validate code against stored architectural rules.audit_codebase: Scan the entire codebase for architectural drift and contradictions.You can customize the system behavior via environment variables (in Docker) or by modifying core/config.py.
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