Memory Engine MCP

Why Memory Engine?

Most MCP memory servers are either simple key-value stores or plain text search wrappers.

Memory Engine is different: it models memory as typed atoms connected by typed bonds, then retrieves context with a hybrid ranking pipeline that combines:

• full-text search (SQLite FTS5)

• semantic similarity via local Ollama embeddings

• confidence, recency, and weight

• graph expansion from related memories

The goal is not just storage. The goal is a memory system that can recall, connect, decay, curate, and learn over time.

Related MCP server: sostenuto

Highlights

• Local-first — SQLite database, optional local embeddings via Ollama, no required cloud API.

• MCP-native — exposes 35 tools through FastMCP.

• Graph-aware recall — expands top hits through bidirectional bonds for richer context.

• Semantic search — meaning-based retrieval with nomic-embed-text.

• Markdown coexistence — import existing notes one-way without replacing your human-readable memory.

• Error memory — remembers mistakes and corrections, with auto-promotion to preferences after repeated failures.

• Cognitive curator — non-destructive maintenance pass for compaction, bond suggestions, duplicate detection, and isolated atom classification.

• Session watcher — optional OpenClaw JSONL ingestion with short-lived raw messages and permanent session digests.

• Backup & restore — full SQLite snapshots, JSON export/import, verified restores with automatic safety backups.

• Auth & hardening — optional API token, secure bind, input validation, rate limiting.

• Test suite — 135 tests covering CRUD, ranking, migrations, auth, backup, concurrency.

• Benchmark — CLI recall quality suite with Precision@K, MRR, latency percentiles.

Architecture

AI assistant / MCP client
        │
        ▼
FastMCP server — 35 tools
        │
        ▼
Memory engine — hybrid ranking, graph recall, decay, learning
        │
        ├── SQLite — atoms, bonds, FTS5, JSON metadata, versions
        ├── Ollama — optional local embeddings
        ├── Curator — conservative maintenance
        └── Session watcher — optional OpenClaw session ingestion

MCP Tools

Memory

Knowledge graph

Learning and maintenance

Error memory and preferences

Import and introspection

Backup, restore & export

Web UI (optional)

Memory Engine includes an optional web UI for graph exploration, atom inspection, contradiction browsing, and impact analysis.

# In docker-compose.yml, add:
#   environment:
#     - MEM_UI_PORT=6000
#   expose:
#     - "6000"

Or run standalone:

python3 web_ui.py
# Open http://localhost:6000

Quick start with Docker

Option A — Use the pre-built image (recommended)

# docker-compose.yml
services:
  memory-engine:
    image: ghcr.io/simoneb79/memory-engine-mcp:1.7.0
    ports:
      - "8085:8085"
    volumes:
      - memory-data:/data
    restart: unless-stopped

volumes:
  memory-data:

docker compose up -d

Pin the version. Use an explicit tag like :1.7.0 in production. Avoid :latest — it can change without notice.

Option B — Build from source

git clone https://github.com/SimoneB79/memory-engine-mcp.git
cd memory-engine-mcp
cp docker-compose.yml docker-compose.local.yml
# Edit volume paths in docker-compose.local.yml if needed
docker compose -f docker-compose.local.yml up -d --build

Default endpoint:

http://localhost:8085/sse

Example MCP client config:

{
  "mcpServers": {
    "memory-engine": {
      "url": "http://localhost:8085/sse",
      "transport": "sse"
    }
  }
}

See docs/INSTALL.md for Docker, local Python, Claude Desktop, Cursor, and OpenClaw examples.

Local Python

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python server.py

Configuration

Main configuration file: config.json

Important environment variables:

Semantic search requires Ollama reachable from the container or host. Default:

{
  "ollama": {
    "enabled": true,
    "host": "http://ollama:11434",
    "model": "nomic-embed-text"
  }
}

If you do not use Ollama, set ollama.enabled to false; FTS recall still works.

Memory model

Atoms have:

• title

• body

• type: fact, decision, event, preference, log, procedure, note, etc.

• domain: project or topic namespace

• confidence

• weight

• tags

• optional TTL

Bonds connect atoms with relation types:

is_a · part_of · depends_on · contradicts · refines · derived_from · detail_of · related_to

Example usage

remember(
    title="Use PostgreSQL for analytics",
    body="SQLite is kept for local memory, PostgreSQL is used for multi-user analytics.",
    type="decision",
    domain="project:analytics",
    confidence=0.9,
    tags=["database", "architecture"]
)

recall(query="what database did we choose for analytics?", limit=5)

working_set(
    query="continue the analytics backend work",
    domain="project:analytics",
    limit=8,
    graph_depth=1
)

Security

By default, Memory Engine runs in open mode (no auth) — safe for stdio or trusted local environments.

To enable API token auth:

// config.json
{
  "security": {
    "api_token": "your-secret-token",
    "allow_remote": false
  }
}

Or via environment variable:

MEMORY_API_TOKEN=your-secret-token

When auth is enabled:

• MCP SSE requests must include Authorization: Bearer <token>

• Web UI API endpoints require ?token=<token> or Bearer header

• Server binds to 127.0.0.1 unless allow_remote: true

• Input validation (title/body size limits) and rate limiting are always active

See CHANGELOG.md for the full list of security features.

Publishing and registries

This repository is prepared for MCP discovery:

• MCP Registry name: io.github.simoneb79/memory-engine-mcp

• Registry metadata: server.json

• Docker/OCI verification label: included in Dockerfile

• Client config example: mcp.json

See docs/PUBLISHING.md for the publication checklist.

Repository status

• Public GitHub repository: https://github.com/SimoneB79/memory-engine-mcp

• Existing listing: https://mcpmarket.com/server/memory-engine

• License: MIT

License

MIT — see LICENSE.