Memwright FAQs

Question 1

What is Memwright and how does it help AI agents?

Accepted Answer

Memwright is an embedded, inspectable memory system for AI agents that prevents them from forgetting information. It uses SQLite for core storage, pgvector for semantic search, and Neo4j for graph traversal to enable efficient, multi-layer information retrieval.

Question 2

How does Memwright achieve multi-layer retrieval and token efficiency?

Accepted Answer

Memwright combines tag matching, Neo4j graph traversal, and pgvector semantic search, fusing results with Reciprocal Rank Fusion. This multi-layered approach provides highly relevant context, enabling token-efficient recall (300-500 tokens vs. 15,000+ for full history replay).

Question 3

Is Memwright fully local and private?

Accepted Answer

Yes, Memwright is designed for complete local operation using Docker. This ensures maximum privacy and eliminates any cloud dependencies, allowing you to run your AI agents' memory systems entirely on your own infrastructure.

Question 4

Can Memwright detect and manage contradictions in memory?

Accepted Answer

Absolutely. Memwright features automatic contradiction detection and supersession of outdated facts. This intelligent system ensures that your AI agents always retrieve the most current and accurate information, maintaining memory integrity over time.

Question 5

What platforms and frameworks does Memwright integrate with?

Accepted Answer

Memwright works seamlessly as a Claude Code MCP server, a Cursor MCP server, and is available as a Python library. This flexibility allows for easy integration into various AI agent development workflows and environments.

Memwright

Memwright

주요 기능

사용 사례

주요 기능

사용 사례