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Memory Engine MCP

by SimoneB79version 1.5.3i★ –iRuns on your computer
Registry nameio.github.SimoneB79/memory-engine-mcp

Install

Overview

Description from the maker (original English)

Local-first graph memory for AI assistants with SQLite, semantic search, and MCP tools.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: SSE
  • Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: MEMORY_DB_PATH, MARKDOWN_SOURCE, MEMORY_PORT

You need

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "memory-engine": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "MEMORY_DB_PATH",
        "-e",
        "MARKDOWN_SOURCE",
        "-e",
        "MEMORY_PORT",
        "ghcr.io/simoneb79/memory-engine-mcp:1.5.3"
      ],
      "env": {
        "MEMORY_DB_PATH": "your-value",
        "MARKDOWN_SOURCE": "your-value",
        "MEMORY_PORT": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add memory-engine --env MEMORY_DB_PATH=your-value --env MARKDOWN_SOURCE=your-value --env MEMORY_PORT=your-value -- docker run -i --rm -e MEMORY_DB_PATH -e MARKDOWN_SOURCE -e MEMORY_PORT ghcr.io/simoneb79/memory-engine-mcp:1.5.3

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "memory-engine": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "MEMORY_DB_PATH",
        "-e",
        "MARKDOWN_SOURCE",
        "-e",
        "MEMORY_PORT",
        "ghcr.io/simoneb79/memory-engine-mcp:1.5.3"
      ],
      "env": {
        "MEMORY_DB_PATH": "your-value",
        "MARKDOWN_SOURCE": "your-value",
        "MEMORY_PORT": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "memory-engine": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "MEMORY_DB_PATH",
        "-e",
        "MARKDOWN_SOURCE",
        "-e",
        "MEMORY_PORT",
        "ghcr.io/simoneb79/memory-engine-mcp:1.5.3"
      ],
      "env": {
        "MEMORY_DB_PATH": "your-value",
        "MARKDOWN_SOURCE": "your-value",
        "MEMORY_PORT": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • MEMORY_DB_PATH env
    SQLite database path inside the container.
  • MARKDOWN_SOURCE env
    Optional directory for one-way markdown import.
  • MEMORY_PORT env
    SSE server port. Default: 8085.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account SimoneB79, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 7226 servers in this category
  • Published to the registry more recently than 23% of the other 7226 servers in this category
  • Basic checks equal to or better than 100% of the other 7226 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Memory Engine MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.5.3) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 3 days ago
Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago
Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 44 days ago
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

🧠 Memory Engine MCP

Local-first, graph-aware long-term memory for AI assistants.

SQLite + semantic search + knowledge graph + MCP tools for agents that need continuity.

Works with Claude Desktop · Claude Code · Cursor · Cline · Windsurf · OpenClaw · any MCP client


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.

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 — canonical OpenClaw SQLite ingestion (schema 17), reset-aware digests, and JSONL legacy fallback.
  • 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 — 144 tests covering CRUD, ranking, migrations, auth, backup, concurrency, and transcript ingestion.
  • 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 — OpenClaw SQLite + JSONL fallback

MCP Tools

Memory
ToolPurpose
rememberCreate or update an atom
recallSmart hybrid recall with graph expansion
working_setBuild a task-oriented context pack
semantic_searchPure semantic search
get_atomRead one atom with bonds
list_atomsBrowse atoms by domain/type/status
merge_atomsMerge duplicate atoms
export_atomExport one atom as markdown
Knowledge graph
ToolPurpose
link / unlinkCreate or remove typed bonds
search_graphTraverse the graph from one atom
suggest_bondsSuggest bonds for one atom
suggest_bonds_allSuggest or create bonds in bulk
Learning and maintenance
ToolPurpose
curator_runConservative curation pass
cognitive_statusGraph and memory health metrics
learning_runDetect contradictions, weak atoms, merge candidates, gaps
ask_pending / answer_humanHuman-in-the-loop clarification
decay_runRun decay cycle
cleanup_sessionsRemove expired session atoms
cleanup_duplicatesRemove duplicate session atoms
reindex_embeddingsRebuild embeddings
Error memory and preferences
ToolPurpose
error_checkCheck past failures before doing a task
error_logRecord a mistake and the correction
error_listBrowse unresolved/resolved errors
preference_searchSearch structured preferences
Import and introspection
ToolPurpose
import_markdownImport markdown notes into atoms
memory_summary3-level summary: global → domain → detail
statsDatabase statistics
versionServer version
recall_sessionSearch one OpenClaw session
session_summarySummarize one OpenClaw session
memory_contradictSupersede an old atom with a newer contradictory one
list_contradictionsList explicit contradiction/supersession records
classify_memory_tierInfer the 3-tier class (episodic/semantic/procedural)
memory_impactImpact analysis: what depends on this atom
Backup, restore & export
ToolPurpose
backup_databaseCreate, list, verify, or clean up SQLite snapshots
restore_databaseRestore from a backup (with automatic safety backup)
export_allExport all memory data as portable JSON
import_dataImport from JSON (merge or replace mode)

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

Web UI: interactive graph, atom details, contradiction browser, stats dashboard

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:2.1.0 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:

VariableDefaultPurpose
MEMORY_DB_PATH/data/memory.dbSQLite database path
MARKDOWN_SOURCE/workspace/memoryMarkdown directory for import
MEMORY_HOST127.0.0.1Server bind address (secure default)
MEMORY_PORT8085SSE port
MEMORY_API_TOKEN_(none)_Optional API token for auth (see Security)
OPENCLAW_AGENT_DB_(none)_Preferred per-agent OpenClaw SQLite DB (schema 17)
OPENCLAW_SESSIONS_DIR/sessionsLegacy JSONL fallback when no agent DB is configured
SESSION_DIGEST_DIR/data/session_digestsOptional session digest output

For the SQLite mount, WAL/SHM handling, filtering, and security boundary, see OpenClaw transcript ingestion.

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
  • Web UI API endpoints require ?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.


Made with 🧠 by SimoneB79

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