Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@onememory what's the current Node version for this project?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
One memory across every agent.
onememory is an open-source, self-hostable, local-first persistent memory engine for AI coding
agents and AI assistants. It gives every agent — Claude Code, OpenAI Codex, Cursor, Pi, OpenCode,
or any MCP-compatible runtime — a shared, low-token, continuously improving memory layer, so agents
arrive knowing your project, its decisions, conventions, failures, and solutions.
Status: architecture phase. No production code yet, by design. The architecture review (research, ADRs, schemas, retrieval design, phased plan) is being produced first in
docs/. See docs/plan/ for the roadmap.
Agent memory today is either a giant manually-maintained AGENTS.md or a naive vector store that
dumps thousands of tokens of stale, duplicated context into every session. onememory is built on
a different premise:
Memory is not a vector database. It is a typed knowledge system with a lifecycle.
Ten memory layers — episodic, semantic, procedural, working, project, entity, decision, failure, preference, source — instead of one undifferentiated pile of text.
An explicit lifecycle — observe → ingest → normalize → extract → classify → deduplicate → entity-resolve → score → store → retrieve → reinforce → consolidate → decay → archive.
Temporal truth — memories carry valid_from / valid_until; questions like "what Node
version does this project use?" return the current fact, not every fact ever seen.
Token efficiency as a first-class metric — retrieval packs a token budget with the most information-dense memories, not the most numerous.
Code memory without re-embedding your repo — git fingerprints mean unchanged files cost zero tokens on re-index; drift marks only affected memories stale.
Skills from repeated successes — verified failure/solution patterns become reusable skills.
Local-first — works 100% offline with no account, no telemetry, and local models (Ollama / on-device embeddings). Hosted LLM providers are optional, not required.
Postgres-compatible — same Postgres dialect and schema embedded (no Docker), via Docker Compose, or on any cloud / hosted Postgres. Multi-tenant SaaS is a deployment mode, not a fork.
Related MCP server: memento
CLI |
|
License | Apache-2.0 |
Runtime | TypeScript on Bun / Node LTS |
Storage | Postgres + pgvector (server) · PGlite embedded (local) |
Protocol | REST + MCP (stdio & Streamable HTTP) |
Path | Contents |
| Landscape research (Supermemory, Mem0, Zep, Letta, MCP memory servers) with primary-source citations |
| Architecture decision records |
| Memory model, lifecycle, event/memory schemas, database schema, retrieval design, code memory |
| Phased implementation plan |
| Issue backlog per mission (mirrored to GitHub once the repo is published) |
| Risks and unresolved architectural decisions |
Read AGENTS.md first — it is the operating manual for both human and agent contributors.
This server cannot be deployed
Persistent, inspectable memory for AI agents with lineage, correction, and a hosted MCP endpoint.
Shared memory for coding agents. Stop re-explaining your codebase every session.
Long-term memory for AI coding agents: durable project facts, recalled by every MCP client.
shared AI-context layer for teams — persistent memory your agents search and update over MCP