Memograph

Name: Memograph
Author: Indhar01

by Indhar01version 0.4.0i★ –iRuns on your computer

Registry nameio.github.Indhar01/memograph

Install

Overview

Description from the maker (original English)

Graph-based memory system for LLMs with knowledge graphs and semantic search.

PopNx MCP’s three pillars

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

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

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: stdio
Category: Search & AI memory
You need to fill in: MEMOGRAPH_VAULT, ANTHROPIC_API_KEY, OPENAI_API_KEY

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: MEMOGRAPH_VAULT, MEMOGRAPH_READONLY, MEMOGRAPH_PROVIDER, MEMOGRAPH_MODEL, ANTHROPIC_API_KEY, OPENAI_API_KEY, OLLAMA_HOST

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "memograph": {
      "command": "uvx",
      "args": [
        "memograph",
        "memograph-mcp"
      ],
      "env": {
        "MEMOGRAPH_VAULT": "your-value",
        "MEMOGRAPH_READONLY": "your-value",
        "MEMOGRAPH_PROVIDER": "your-value",
        "MEMOGRAPH_MODEL": "your-value",
        "ANTHROPIC_API_KEY": "your-value",
        "OPENAI_API_KEY": "your-value",
        "OLLAMA_HOST": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add memograph --env MEMOGRAPH_VAULT=your-value --env MEMOGRAPH_READONLY=your-value --env MEMOGRAPH_PROVIDER=your-value --env MEMOGRAPH_MODEL=your-value --env ANTHROPIC_API_KEY=your-value --env OPENAI_API_KEY=your-value --env OLLAMA_HOST=your-value -- uvx memograph memograph-mcp

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": {
    "memograph": {
      "command": "uvx",
      "args": [
        "memograph",
        "memograph-mcp"
      ],
      "env": {
        "MEMOGRAPH_VAULT": "your-value",
        "MEMOGRAPH_READONLY": "your-value",
        "MEMOGRAPH_PROVIDER": "your-value",
        "MEMOGRAPH_MODEL": "your-value",
        "ANTHROPIC_API_KEY": "your-value",
        "OPENAI_API_KEY": "your-value",
        "OLLAMA_HOST": "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": {
    "memograph": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "memograph",
        "memograph-mcp"
      ],
      "env": {
        "MEMOGRAPH_VAULT": "your-value",
        "MEMOGRAPH_READONLY": "your-value",
        "MEMOGRAPH_PROVIDER": "your-value",
        "MEMOGRAPH_MODEL": "your-value",
        "ANTHROPIC_API_KEY": "your-value",
        "OPENAI_API_KEY": "your-value",
        "OLLAMA_HOST": "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

MEMOGRAPH_VAULT required env
Path to your MemoGraph vault directory (created if missing).
MEMOGRAPH_READONLY env
Set to 'true' to block destructive MCP tools (delete_memory, update_memory, bulk_create, batch_delete, batch_update). Recommended for shared or untrusted clients.
MEMOGRAPH_PROVIDER env
Optional LLM provider for query_with_context tool. Only needed if you want AI-generated answers.
MEMOGRAPH_MODEL env
Optional LLM model name (e.g. 'claude-sonnet-4-6'). Used together with MEMOGRAPH_PROVIDER.
ANTHROPIC_API_KEY secret env
API key for Anthropic Claude. Required when MEMOGRAPH_PROVIDER=claude or when using the [anthropic] extra.
OPENAI_API_KEY secret env
API key for OpenAI. Required when using the [openai] extra (embeddings or LLM).
OLLAMA_HOST env
Base URL for a local Ollama server (default http://localhost:11434). Only needed when MEMOGRAPH_PROVIDER=ollama or the [ollama] extra is installed.

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 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account Indhar01, 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 7250 servers in this category
Published to the registry more recently than 18% of the other 7250 servers in this category
Basic checks equal to or better than 100% of the other 7250 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 Memograph 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?

You need to fill in: MEMOGRAPH_VAULT, ANTHROPIC_API_KEY, OPENAI_API_KEY

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 (0.4.0) 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 Python (uv) 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 today

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 last month

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.

MemoGraph 🧠

MemoGraph turns a folder of markdown notes into a queryable, AI-ready knowledge graph. It solves the LLM memory problem — your AI assistants forget last Tuesday's decision, can't find a related note across two projects, and re-derive the same insight again and again — by giving them a persistent, navigable, attribution-friendly memory layer that lives in plain markdown files you control.

You write notes the way you already do. MemoGraph indexes them, builds a graph from [[wikilinks]], ranks them by salience, and serves them back to your LLM (or your team) on demand.

⚡ Try it in 60 seconds

pip install memograph

memograph quickstart

That's it. The quickstart command drops a small, interconnected sample vault on your disk (15 notes about Python development, with real wikilinks between them), ingests it, and runs three live demo queries so you can see the graph + hybrid retrieval working before you decide whether to commit. Try this query in particular:

memograph --vault ~/memograph-quickstart search "FastAPI dependency injection"

The vault contains a note titled FastAPI dependencies (about Depends(...)) — the words "dependency" and "injection" never appear in any note's title. MemoGraph still finds it, because hybrid retrieval understands "dependency injection" semantically and the wikilink graph stitches related notes together. That's the product, demonstrated in one query.

Re-run memograph quickstart --force any time to reset to a fresh demo. When you're ready, point MemoGraph at your real notes: memograph --vault ~/your-notes ingest.

What you get

As a solo user / knowledge worker

A vault of human-readable markdown files — nothing proprietary, no lock-in. Your notes outlive any tool.
Hybrid retrieval that combines keyword search, semantic similarity, and graph traversal so you find the right note even when you don't remember the exact words.
AI-assisted tagging, link suggestions, and gap detection that grow your knowledge base instead of letting it rot.
A CLI and a web UI for browsing, editing, and visualizing the graph.

As an AI agent / IDE user

A first-class Model Context Protocol (MCP) server with 30+ tools, working out of the box with Claude Desktop, Claude Code, Cursor, Cline, Windsurf, Continue, Zed, VS Code, Goose, Gemini CLI, OpenAI Codex CLI, and others.
Autonomous "auto-save" hooks that capture decisions and context from your AI conversations into the vault automatically.
Per-conversation memory recall — your assistant can pull "what did we decide last week about X" without you copy-pasting context every time.

As an enterprise / SaaS operator

Multi-tenant deployment with filesystem-level isolation per tenant, end-to-end isolation tests, and a warm-LRU kernel cache.
OIDC + API-key authentication with JWKS support (Auth0, Clerk, WorkOS, Keycloak, Azure AD, Okta), restrictive CORS, request-size caps, and rate limiting.
GDPR-compliant scheduled deletion: tombstone-with-grace-period flow, automatic final backups, daily reaper, and an audit log of every deletion.
Observability built in: OpenTelemetry traces + Prometheus /metrics, structured JSON logging with request IDs, and a separate /healthz / /readyz for orchestration.
Operations runbooks shipped with the code: install, SSO setup, RBAC, backup-restore, and GDPR procedures.

How consumers benefit

You want to…	MemoGraph gives you…
Stop your AI assistant from forgetting context across conversations	Persistent vault + MCP server, plus optional auto-save hooks
Find a note across thousands when you only half-remember it	Hybrid retrieval (keyword + semantic + graph) with salience ranking
Connect related ideas without manual cross-linking	AI link suggestions, backlink graph, BFS traversal
Discover what's missing in your knowledge base	Gap detector + topic clustering + learning-path suggestions
Self-host a memory backend for a team or product	Web UI, FastAPI HTTP API, OpenAPI v1 contract, Docker compose
Ship MemoGraph to multiple paying customers	Multi-tenant kernel registry, OIDC, quotas (roadmap), GDPR runbook
Survive an SOC 2 audit conversation	Audit log with user + tenant binding, observability, security workflow, compliance roadmap doc

✨ Capabilities at a glance

Core memory engine

Graph-based memory — bidirectional [[wikilinks]] build a navigable knowledge graph automatically.
Hybrid retrieval — keyword + semantic embeddings + graph traversal, combined and re-ranked.
Memory types inspired by cognitive science: episodic, semantic, procedural, fact.
Salience scoring (0–1) that decays over time and boosts on access.
Smart indexing — mtime-cached, only re-parses changed files.
Context compression — token-budget-aware windowing for LLM prompts.
Markdown-native vault — every memory is a .md file with YAML frontmatter; no proprietary format.

AI features

Smart Auto-Organization Engine — extract topics, people, action items, decisions, questions, sentiment, risks, ideas, and timeline events from memories.
AutoTagger — suggest tags via semantic analysis, structure detection, and pattern learning.
LinkSuggester — propose [[wikilinks]] to related notes; bidirectional opportunities included.
GapDetector — surface missing topics, weak coverage, isolated notes, and unmade links.
Knowledge analysis — vault stats, topic clustering, learning paths, connection analysis.

Interfaces

Python API — MemoryKernel with sync, async, and batch variants.
CLI — 24+ commands for ingest, search, batch ops, import, export, backup, and AI features.
MCP server — 30+ tools, stdio transport, drop-in for any MCP-compatible client.
Web UI — React + D3 graph visualization, search, and editing (FastAPI backend + Vite frontend).
HTTP API — versioned /api/v1/, OpenAPI snapshot in CI, ready for service-to-service integration.

Enterprise & SaaS readiness

Multi-tenancy with filesystem-isolated tenants, an LRU registry of warm kernels, per-tenant audit logs, and end-to-end isolation tests gating release.
Authentication via OIDC (JWKS) or hashed API keys; per-route auth scope; identity bound into the audit log.
Web hardening — restrictive CORS, slowapi rate limiting, request-size caps, structured JSON logging with request IDs, info-leak-free 500 handler.
Storage hardening — path-traversal-safe vault writes, vault size soft/hard caps, schema-versioned cache files.
Scheduled deletion for GDPR Art. 17: tombstone with configurable grace period, automatic final backup, daily reaper script, cancel-before-grace endpoint.
Observability — OpenTelemetry FastAPI/asyncio auto-instrumentation, Prometheus /metrics, OTLP export.
Reliability — concurrency audit, stress tests for concurrent writes, versioned backup format with integrity checks.
Distribution — pinned-and-locked dependencies, Docker compose for self-host, security workflow (bandit + pip-audit).

See docs/INSTALL_ENTERPRISE.md, docs/SSO_SETUP.md, docs/GDPR_RUNBOOK.md, docs/BACKUP_RESTORE_RUNBOOK.md, docs/OBSERVABILITY_GUIDE.md, and docs/RBAC_GUIDE.md for the operator-facing details.

🚀 Quick Start

Hosting it yourself? docs/HOSTING_GUIDE.md

covers four genuinely-free paths — Oracle Free Tier, Cloudflare

Tunnel + your hardware (recommended for most), GCP always-free

stitch, and GitHub-repo-as-vault. Workspace identity via OIDC and

Drive-as-portability-backup are documented in

docs/GOOGLE_WORKSPACE_SETUP.md.

Installation

pip install memograph

Install with optional dependencies:

# For OpenAI support

pip install memograph[openai]

For Anthropic Claude support

pip install memograph[anthropic]

For Ollama support

pip install memograph[ollama]

For embedding support

pip install memograph[embeddings]

Install everything

pip install memograph[all]

Python Usage

from memograph import MemoryKernel, MemoryType

Initialize the kernel attached to your vault path

kernel = MemoryKernel("~/my-vault")

Ingest all notes in the vault

stats = kernel.ingest() print(f"Indexed {stats['indexed']} memories.")

Programmatically add a new memory

kernel.remember( title="Meeting Note", content="Decided to use BFS graph traversal for retrieval.", memory_type=MemoryType.EPISODIC, tags=["design", "retrieval"] )

Retrieve context for an LLM query

context = kernel.context_window( query="how does retrieval work?", tags=["retrieval"], depth=2, top_k=8 )

print(context)

🔌 MCP Server (Model Context Protocol)

MemoGraph includes a full-featured MCP server for seamless integration with AI assistants like Cline and Claude Desktop.

📖 New to MemoGraph MCP? See the [MCP User Guide](docs/MCP_USER_GUIDE.md) for practical usage instructions and examples!

🚨 Having connection issues? See [Setup & Troubleshooting Guide](docs/MCP_SETUP_TROUBLESHOOTING.md) - Common fixes for "cannot connect" errors!

19 Available Tools

Category	Tools	Description
Search	`search_vault`, `query_with_context`	Semantic search and context retrieval
Create	`create_memory`, `import_document`	Add memories and import documents
Read	`list_memories`, `get_memory`, `get_vault_info`	Browse and retrieve memories
Update	`update_memory`	Modify existing memories
Delete	`delete_memory`	Remove memories by ID
Analytics	`get_vault_stats`	Vault statistics and insights
Discovery	`list_available_tools`	List all available tools
Autonomous	`auto_hook_query`, `auto_hook_response`, `configure_autonomous_mode`, `get_autonomous_config`	Autonomous memory management
Graph	`relate_memories`, `search_by_graph`, `find_path`	Graph-native linking and traversal
Bulk	`bulk_create`	Create multiple memories in one call

Supported Clients

MemoGraph's MCP server is a stdio server — it runs alongside any MCP-compatible agentic CLI or editor. The full setup cookbook (config-file paths, format quirks, verification steps) lives in [docs/MCP_CLIENTS.md](docs/MCP_CLIENTS.md):

Client	Format	Quick reference

| Claude Code (CLI) | mcpServers | [claude_code_config.json](memograph/mcp/

Only the beginning is shown; read the rest in the repo

Memograph

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

MemoGraph 🧠

⚡ Try it in 60 seconds

What you get

As a solo user / knowledge worker

As an AI agent / IDE user

As an enterprise / SaaS operator

How consumers benefit

✨ Capabilities at a glance

Core memory engine

AI features

Interfaces

Enterprise & SaaS readiness

🚀 Quick Start

Installation

For Anthropic Claude support

For Ollama support

For embedding support

Install everything

Python Usage

Initialize the kernel attached to your vault path

Ingest all notes in the vault

Programmatically add a new memory

Retrieve context for an LLM query

🔌 MCP Server (Model Context Protocol)

19 Available Tools

Supported Clients