memory-extension

name	Memory Extension
description	Use this skill to store the chat history to MongoDB

Gemini CLI: MongoDB Agentic Memory Extension

This extension provides a persistent memory layer for the Gemini CLI using MongoDB Atlas. It enables the AI to "remember" previous interactions, user preferences, and project-specific context across different terminal sessions.

Workflow

• Session Persistence: Maintains a sliding window of the last 20 interactions for immediate context.
• Semantic Long-Term Memory: Uses MongoDB Atlas Vector Search to recall relevant facts from past weeks/months.
• Preference Management: Store environment-specific configurations (e.g., "always use Yarn over NPM").
• Automatic Context Injection: Automatically fetches relevant "memory documents" before Gemini generates a response.

🛠️ Skills Included

1. store_memory

Saves a specific fact or interaction to the long-term database.

• Input: fact (string), category (string: 'preference', 'project', 'snippet')
• Usage: "Gemini, remember that this project uses MongoDB version 7.0."

2. recall_context

Searches the database for information relevant to the current prompt.

• Input: query (string)
• Logic: Performs a $vectorSearch against the memory collection.

3. clear_session

Wipes the short-term memory for the current session ID.

📋 Prerequisites

• MongoDB Atlas Cluster (Free Tier works great)
• Gemini API Key
• Node.js / Python environment for the CLI extension

⚙️ Configuration

Add your MongoDB connection string to your environment variables:

The MONGODB_URI value will be read from the mongo.config.json file.

EXPORT MONGODB_URI="mongodb+srv://<user>:<password>@cluster.mongodb.net/gemini_memory"
EXPORT VECTOR_INDEX_NAME="vector_index"