Cursor Memories

Product Introduction

1. Cursor Memories is a CLI tool designed to help developers systematically store and retrieve technical insights, patterns, and solutions using Supabase for storage and OpenAI embeddings for semantic search. It integrates directly with Cursor IDE's AI agents to automate knowledge management during development workflows.
2. The core value lies in its ability to transform fragmented developer knowledge into a searchable, context-aware memory system, reducing redundant problem-solving and accelerating decision-making through AI-powered semantic matching.

Main Features

1. The tool stores development memories with structured metadata, including repository names, categories (e.g., Database, Security), and technology stacks, ensuring organized retrieval through Supabase vector indexing.
2. Semantic search leverages OpenAI's text-embedding-3-small model to process natural language queries, enabling developers to find relevant memories using conversational terms like "performance optimization database" instead of exact keywords.
3. Repository-aware organization allows memories to be filtered by specific projects, while predefined categories and tech stack tagging ensure granular filtering for architecture patterns, debugging techniques, or domain-specific solutions.

Problems Solved

1. It addresses the inefficiency of losing or re-resolving technical solutions by providing a centralized, queryable knowledge base that persists across projects and teams.
2. The tool targets developers and engineering teams working with complex systems or multiple repositories who need to maintain institutional knowledge and avoid context-switching delays.
3. Typical use cases include recalling database optimization patterns for a specific microservice, troubleshooting recurring errors in CI/CD pipelines, or sharing validated security practices across distributed teams.

Unique Advantages

1. Unlike generic note-taking tools, it combines repository context, technical taxonomy, and vector search to deliver precision-tailored results for software development scenarios.
2. The integration with Cursor IDE enables automatic memory capture during AI agent interactions, ensuring real-time knowledge logging without manual input from developers.
3. Competitive differentiation stems from its optimized Supabase schema (IVFFlat indexing for cosine similarity) and hybrid filtering that combines semantic search with exact matches for categories/tech stacks.

Frequently Asked Questions (FAQ)

1. How does Cursor Memories handle multiple projects? The tool uses a single Supabase project by default but supports local installations or forks for isolated memory databases, allowing separate configurations per directory via npm link.
2. Is OpenAI API mandatory for basic functionality? While semantic search requires an OpenAI key, users can still add and filter memories manually using category/tech stack/repository filters without embeddings.
3. How secure are memories stored in Supabase? Data protection is enforced through Supabase's Row-Level Security and service role keys, with optional encryption at the application layer for sensitive technical details.
4. Can memories be exported or migrated? Memories reside in a standard PostgreSQL table, enabling direct export via pg_dump or migration through Supabase's native import/export tools.
5. What happens if the vector index grows too large? The IVFFlat index with 100 lists balances speed and accuracy for typical development teams, though enterprise-scale deployments may require adjusting lists parameter during index creation.