context-memory

Manage Qdrant-backed memory operations with schemas, namespaces, and retrieval orders.

4|Updated Oct 31, 2024

Search Tags:#vector database #knowledge base #ai agent #memory #data management #qdrant

Ready to run

npx skills add https://github.com/akshay-na/dotfiles --skill context-memory-akshay-na

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: context-memory
Source: https://github.com/akshay-na/dotfiles/tree/main/cursor/.cursor/skills/context-memory
Command: npx skills add https://github.com/akshay-na/dotfiles --skill context-memory-akshay-na

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a standardized way for all agents to interact with Qdrant-backed memory, ensuring consistent data storage, retrieval, and schema management.

Core Features & Use Cases

Centralized Memory: Manages all memory operations exclusively through Qdrant, eliminating file-based storage.
Structured Data: Defines clear schemas for memory entries, including namespaces, categories, and statuses.
Diagram Handling: Supports storing and retrieving architectural diagrams as first-class memory entities.
Use Case: When an agent needs to recall a past decision or store a new one, it uses this Skill to interact with the Qdrant database, ensuring the information is correctly formatted and stored for future retrieval.

Quick Start

Use the context-memory skill to store a new decision about API authentication with the summary 'Implement OAuth2 for user authentication' and tags ['api', 'auth', 'security'].

Frequently Asked Questions about context-memory

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I store AI agent conclusions in a Qdrant vector database?▼

Yes, you can store architectural diagrams in Qdrant by using this Skill, which treats diagrams as first-class memory entities and formats them for structured storage and retrieval within the project_memory collection.

What is the best way to manage centralized AI memory without file-based storage?▼

The best way to manage centralized AI memory without file-based storage is using this Skill, which exclusively uses Qdrant to enforce consistent data storage, retrieval, and schema management across all AI agents.

Does this Qdrant memory management approach support multiple isolated namespaces?▼

Yes, this Qdrant memory management approach supports multiple namespaces by enforcing structured storage across distinct collections including org_memory, project_memory, session_memory, and cache_memory.

How do I retrieve past decisions from an AI knowledge base using Qdrant?▼

To retrieve past decisions from an AI knowledge base using Qdrant, agents use this Skill to interact with the Qdrant MCP server, defining retrieval orders and interaction protocols to fetch structured memory entries.

When do I need a memory-broker for AI agent data management?▼

You need a memory-broker for AI agent data management when enforcing structured storage and consistent interaction protocols with the Qdrant MCP server, ensuring all agents format schemas and retrieval orders correctly.