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

✨ 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.")