MCP Memory Graph

MCP Memory Graph

A memory server for Claude Code and any other MCP client. It gives your AI assistant a permanent, searchable memory that lives in one SQLite file on your machine. Store a decision today, ask about it next month, and the answer comes back. Everything runs locally: the embedding model, the search index, the knowledge graph. No cloud account, no API key, no per-token cost.

License: source-available and free for noncommercial use (PolyForm Noncommercial 1.0.0): personal projects, hobby, study, research, charity, education, and government. Commercial use requires a paid license (COMMERCIAL.md).

Who it's for: developers who want Claude (or Cursor, Codex, any MCP client) to remember decisions across sessions. Solo builders and hobbyists use it free. Teams share a knowledge base over git. And anyone who wants to replace a cloud memory service (mem0, Zep, Letta, Supermemory) with something that runs entirely on their own machine.

What it looks like

Run npx mcp-memory-graph serve and you get a local web dashboard for browsing and searching your memory outside Claude.

Search works by meaning, not keywords. The query below ("how do we handle payments") finds the Stripe, GDPR, and Postgres notes even though none of them contains that phrase — each result carries a confidence score and a match-type badge:

Browse and sort the whole store in one table — scope, type, tags, quality score, and how often each memory has been read:

How it compares

mem0, Zep, Letta, and Supermemory are the usual names for AI memory, and several of them have open-source cores. This one is built around a different default: nothing leaves your machine and there's no infrastructure to run.

The trade-off is honest: a single-process SQLite server tops out in the low hundreds of thousands of vectors (see Limitations), and a hosted service will scale past that without you thinking about it. If you're a solo developer or a small team who wants memory that's private, free, and zero-ops, that ceiling is rarely the thing you hit first.

Why this exists

AI assistants forget everything between sessions. Your decisions, your patterns, the bug you fixed last Tuesday: all gone when the conversation ends. This server fixes that.

• Knowledge stored today is searchable tomorrow, next week, next year.
• Search works by meaning, not just keywords. "contract notice period" finds "90-day renewal clause".
• It improves itself. It tracks what gets used, scores quality, extracts learnings from your sessions, and cleans itself up on a schedule.
• It stays private. Local embeddings, no cloud APIs, no telemetry. The one exception is the optional Stop hook, which sends your session transcript to your own locally installed Claude Code (claude -p) for learning extraction. You can turn that off with review_on_stop: false.
• It works for any kind of knowledge. Engineers store architecture decisions, lawyers store contract patterns, accountants store audit procedures.

Quick start (about 5 minutes)

You need Node.js 20 or newer and Claude Code installed.

1. Get the server. From npm (easiest):

npm install -g mcp-memory-graph

Or from source:

git clone https://github.com/YonasValentin/mcp-memory-graph.git

cd mcp-memory-graph npm install npm run build

2. Register the server with Claude Code *(optional — init in step 3 does this for you at user scope):*

# npm install:

claude mcp add memory-server -- npx -y mcp-memory-graph

from source:

claude mcp add memory-server node /path/to/mcp-memory-graph/dist/index.js

3. Install the hooks (recommended):

npx mcp-memory-graph init

This is the one command that wires everything up: it registers the MCP server (user scope), installs the auto-capture/recall hooks and the usage skill, writes config, and schedules a nightly cleanup. Answer the prompts, or pass --yes to accept the defaults. (Skip the auto-registration with --no-register if you manage claude mcp yourself.)

4. Try it. Open a Claude Code session and say:

Remember this: we use Postgres for the main app database. Decided 2026-06-01,

because we need JSONB and full-text search in one place.

Then, in a later session:

What database did we decide to use, and why?

Claude searches its memory and answers with the stored decision. That's the whole loop.

5. Verify the install. Ask Claude:

What memory tools do you have available?

It should list all 51 tools (45 memory_*, 3 vault_*, 3 core_memory_*).

The first time a memory tool runs, the embedding model (about 30 MB) downloads from HuggingFace and is cached at ~/.cache/huggingface/. Every start after that is instant.

To undo everything: npx mcp-memory-graph uninstall.

Upgrading

npm install -g mcp-memory-graph@latest   # or just let `npx -y mcp-memory-graph` pull it

npx mcp-memory-graph init # re-run to refresh on-disk hooks + the nightly schedule

Upgrading the package updates the code that runs each session (hooks, tools, the server), so server-side fixes apply the next time a tool runs — nothing else needed for those.

But files that init wrote earlier are not rewritten by a package upgrade: the Claude Code hook registrations in settings.json and the macOS launchd plist at ~/Library/LaunchAgents/com.mcp-memory.consolidate.plist. If you installed before 2.6.3, that plist used a bare node that launchd (whose minimal PATH excludes nvm) could not run — so the nightly consolidation silently never fired. Re-run `npx mcp-memory-graph init` once after upgrading to regenerate it with an absolute node path and an output log. Verify it then runs:

launchctl start com.mcp-memory.consolidate

cat ~/.mcp-memory/consolidation.log # should show a "Consolidation complete" report

To clear conflict noise that accumulated while the job wasn't running: npx mcp-memory-graph consolidate.

How it works, in plain terms

When you store a memory, the server turns the text into a vector (a list of 384 numbers that captures its meaning) using a small model that runs inside Node.js. It also indexes the text for keyword search. Both live in one SQLite file, by default at ~/.mcp-memory/memory.db.

When you search, the server runs both kinds of search at once, merges the rankings, and returns the best matches with a confidence label. A second model can then re-sort the top results for better precision (this is the reranker, on by default for MCP clients, and it costs about 200 ms).

On top of that sits a knowledge graph: memories link to entities and to each other, so the server can answer questions that need more than one hop, like "what does the payment service depend on?". A nightly "dream cycle" deduplicates, re-scores, prunes, and reports gaps.

The benchmarks, and how to read them

Every number below was produced locally: real embedding model, real production handlers, no network. You can rerun all of them on your own machine.

A quick primer if benchmarks are new to you. A *gold set* is a list of questions where the right answer is known in advance. *Precision@1* asks: was the top result the right one? *Recall@5* asks: was the right answer anywhere in the top 5? *MRR* (mean reciprocal rank) rewards putting the right answer near the top. The *reranker* is a second model that re-sorts the top 50 results; it is slower but noticeably more accurate.

Local gold set

Reproduce with npm run bench. Full methodology, the gold set itself, and every miss are printed and documented in docs/BENCHMARKS.md.

Scale

With the real embedder and a file-backed SQLite database, retrieval p95 is 9.1 ms at 10,000 vectors and 30 ms at 50,000. The rerank pass adds a roughly constant 200 ms on top. Most memory products publish self-reported, cloud-hosted numbers; these are measured locally and reproducible from a committed corpus and runner.

Public benchmarks

Four public memory benchmarks, run untuned (stock MiniLM embedder, production handlers, zero benchmark-specific tweaks), matching or beating MemPalace on all four:

Run them yourself: npm run bench:longmemeval, bench:locomo, bench:convomem, bench:membench. The honest notes (where the reranker helps and where it hurts, the dedup floor on MemBench, gold-set size caveats) are in docs/BENCHMARKS.md.

Features

Core

• 51 MCP tools: CRUD and retrieval, a confidence-tagged knowledge graph, a self-correcting write gate, signed provenance and verification, an event bus with SSRF-guarded webhooks, change propagation and advisor surfaces, resumable session state, expertise profiles, memory tiers, Obsidian vault round-tripping, and GDPR-grade forget and history. Full list below.
• Hybrid search: vector similarity (meaning) plus keyword matching (exact terms), merged with Reciprocal Rank Fusion. rerank: true adds the cross-encoder pass. use_graph: true blends in HippoRAG Personalized PageRank multi-hop scores. as_of: searches the graph as it stood at a past moment.
• Local embeddings: Transformers.js running all-MiniLM-L6-v2 (384 dimensions) inside Node.js. No Python, no cloud API, no GPU.