Helix Memory System

Build a durable, per-tenant agent memory platform on Helix that combines graph relationships, vector similarity, and BM25 full-text in one database. This skill covers the whole memory lifecycle: raw context ingestion, extraction, memory generation, deduplication, updating/versioning, deletion/forgetting, categorisation, profile maintenance, and hybrid retrieval.

Helix is the storage and retrieval engine. A complete memory product also needs application workers for extraction, chunking, embeddings, relationship classification, reranking, connector sync, and profile summarisation.

When To Use

Use this skill when the task is to:

• design the data model for agent memory, long-term memory, user profiles, document/chunk RAG, or a "remember what the user told me" feature
• write queries that create, deduplicate, reinforce, consolidate, version, correct, expire, forget, categorise, or retrieve memories
• decide which Helix capability (property index, graph edge, vector index, BM25 text index) a given memory operation should use
• build hybrid recall that fuses semantic + keyword + graph + profile context
• implement advanced memory components such as source documents, chunks, connectors, extracted facts, evolving profiles, relationship-aware recall, and forgetting

Do not use this skill for generic query syntax questions. For builder/method details defer to helix-query-typescript (the default DSL), helix-query-rust, or helix-query-json-dynamic. This skill assumes those and focuses on the memory architecture on top of Helix.

First Steps