# celiums/celiums-memory [Health: Active]

**Category:** 🧠 Knowledge & Memory  
**Repository:** https://github.com/terrizoaguimor/celiums-memory  
**GitHub Stars:** 23  
**npm Downloads (last month):** 72  
**Views:** 3  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/celiums-celiums-memory

## Description
Cognitive memory engine with 5,100+ knowledge modules, circadian rhythm awareness, and emotional state tracking (PAD model). Hybrid search (PostgreSQL + Qdrant vectors + Valkey cache), per-user memory isolation, and multi-protocol support (MCP, REST, OpenAI, LangChain, A2A). npx @celiums/memory Website

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `npx` (confidence: high):

```json
"mcpServers": {
  "celiums-memory": {
    "command": "npx",
    "args": ["-y","@celiums/memory"],
    "env": {
      "SQLITE_PATH": "",
      "DATABASE_URL": "",
      "QDRANT_URL": "",
      "VALKEY_URL": "",
      "CELIUMS_API_KEY": "",
      "PORT": "",
      "CELIUMS_LLM_API_KEY": "",
      "CELIUMS_LLM_BASE_URL": ""
    }
  }
}
```

**Requires environment variables:** `SQLITE_PATH`, `DATABASE_URL`, `QDRANT_URL`, `VALKEY_URL`, `CELIUMS_API_KEY`, `PORT`, `CELIUMS_LLM_API_KEY`, `CELIUMS_LLM_BASE_URL` — the values above are empty placeholders; fill in real credentials before running (see the repository for what each one is for).

## Documentation

## What celiums/celiums-memory does

The celiums/celiums-memory MCP server is an engine for embedding memory and related cognitive services into agents, assistants, pipelines, and other software. It has no dashboard or standalone user interface. MCP clients connect over HTTP and can use persistent memory, a first-person journal, hybrid knowledge search, ethics evaluation, and per-user time-state handling.

Memory operations include storing and recalling content, importance scoring, consolidation, lifecycle decay, and retrieval influenced by affective state. The journal is append-only and hash-chained, with support for causal chains, arcs, introspection, dialogue, and chain verification. The ethics subsystem evaluates requests through deterministic, probabilistic, philosophical, and optional precedent layers.

## How it works

Each MCP or HTTP request passes through automatic bootstrap, authorization and assurance checks, ethics checks, tool dispatch, storage, and any configured language-model provider. On a client’s first call, bootstrap adds session context so previously available information can be loaded into the response.

The celiums/celiums-memory MCP server supports two storage arrangements. SQLite provides a local, embedded mode without external services. A clustered configuration uses PostgreSQL, Qdrant for vector data, and Valkey for caching. The engine also documents local-only, zero-knowledge encrypted, and cloud-managed synchronization modes.

The `forage` capability searches skills and knowledge supplied through the `skills` table or an optional knowledge backend. The large curated module corpus is separate, so search does not depend on that corpus. Ethics Layers A and B operate without extra setup; Layer K requires the separately distributed `ethics_knowledge` release asset and an OpenSearch instance.

## Setup and configuration

Docker Compose starts the PostgreSQL, Qdrant, and Valkey stack. Set at least `POSTGRES_PASSWORD`, then start the services; the MCP and HTTP server listens on port 3210 by default. For local use, install dependencies, build the project, and start it with `SQLITE_PATH` set to a database file.

Clients connect to `/mcp` with a bearer token in the `Authorization` header. `CELIUMS_API_KEY` can provide that token; if it is omitted, the server generates one on first run. `DATABASE_URL` selects the PostgreSQL adapter, while `QDRANT_URL` and `VALKEY_URL` configure its vector and cache services. `PORT` changes the listening port.

Optional configuration includes `PERSONALITY`, BYO LLM settings through `CELIUMS_LLM_API_KEY`, `CELIUMS_LLM_BASE_URL`, and `CELIUMS_LLM_MODEL`, plus `KNOWLEDGE_API_URL` and `KNOWLEDGE_API_KEY` for an external knowledge backend.

## Tools and capabilities

The celiums/celiums-memory MCP server groups its 61 tools into several areas:

- Memory and knowledge: `remember`, `recall`, `forage`, `sense`, and `synthesize`.
- Journaling: `journal_write`, `journal_recall`, `journal_arc`, and `journal_verify_chain`.
- Ethics: `ethics_lookup`, `ethics_audit`, and `ethics_trace`.
- Research: project management, search, and export tools.
- Writing: project and scene tools plus continuity checking.
- Proactive context: `turn_context`, `turn_after`, and `compact_checkpoint`.
- Optional Atlas tools for routing and cognitive operations.

## Limitations and notes

The system is an engine rather than an application, so it does not provide a login dashboard. The curated knowledge-module corpus is not included in the repository, and Layer K remains inactive until its release asset is loaded into OpenSearch. AI-backed features require a configured language-model provider and the applicable credentials.

_Full upstream README: https://allmcps.com/mcp/celiums-celiums-memory/readme_