novamem documentation
One memory across every AI agent you use. Hybrid keyword + vector + graph + recency + entity retrieval (5-signal engine, graph/entity defaulted to 0 in production calibration), per-user isolation with shareable sub-brains, MCP and HTTP transports, built-in dashboard. Integrates with 30+ AI agent hosts. Self-hostable on a laptop or as a multi-tenant brain for a whole company. pgvector as an alternative to Qdrant for the cold vector tier.
This is the long-form documentation. For the marketing landing page see novamem.github.io/novamem. For source see github.com/azrtydxb/novamem.
Where to start
- New to novamem? → Getting started
- Standing up a server? → Docker Compose or Kubernetes
- Connecting your AI host? → novamem-init CLI
- Understanding the model? → Mental model
What's here
| Section | Goes deep on |
|---|---|
| Install | Docker Compose env reference, Kubernetes manifest walkthrough, manual Postgres + Qdrant setup |
| Connect agents | The npx @azrtydxb/novamem-init CLI in detail; per-host (Claude Code, Desktop, ChatGPT, Cursor, Cline, Continue, Kilo, others); custom HTTP integration |
| Dashboard | Sign-in & roles, every page tour, projects + sharing, tenant + user admin, API tokens |
| Architecture | System shape, tiered storage, hybrid search internals, worthiness gate + dedup, decay maths + dream cycle, multi-tenancy |
| API reference | Auth flows, the data plane, admin & users, MCP tools, OpenAPI spec |
| Operations | Security model, hardening checklist, audit log, backup/restore, upgrades |
| Contribute | Local dev setup, project layout, testing, release flow, filing bugs |
Three minutes overview
novamem is a single Fastify server that holds memory entries for AI agents and exposes two equivalent transports — JSON HTTP and the Model Context Protocol. Entries flow through five layers:
- Warm tier — Postgres full-text search, low-latency hot path
- Cold tier — Qdrant or pgvector vector embeddings, semantic recall over older entries
- Relations — co-occurrence edges in Postgres (
memory_relations), traversed by/v1/neighbors - Recency — exponential decay rank prior applied on search results
- Entity bridge — exact identifier bridging via extracted identifiers from the query (currently at weight 0 in production)
Search fuses five signals — Postgres full-text keyword match, Qdrant/pgvector vector similarity, graph adjacency, recency rank prior, and entity bridge — with an optional cross-encoder rerank; adjacency between memories is served separately by /v1/neighbors over the memory_relations table.
The same code runs on a laptop (Docker Compose, single user) or as a multi-tenant deployment on Kubernetes, supporting 30+ AI agent hosts via the novamem-init CLI.