Graph Memory

A personal knowledge graph for Claude that survives across sessions, devices, and tools. Built on Neo4j with semantic embeddings, OAuth-secured for use from Claude Code, Claude Desktop, and claude.ai web — all hitting the same graph.

https://github.com/user-attachments/assets/826e5f5a-5759-4b31-83dd-6bd7e0e044b8

Asked from my phone. Pulls a decision made days ago on my laptop, citing the commit hash.

No external API keys, no LLM provider integration, no per-token costs. Entity extraction runs inside your Claude sessions (Max plan). Embedding runs locally via bge-small-en. Everything stays on your hardware unless you choose to expose it.

Why a graph

Built-in memory in Claude Code is "append facts to markdown, grep later." That gets you 80% there but breaks at scale: no relationships, no confidence, no decay, no contradiction detection, no temporal awareness. Two memories that reinforce each other look identical to two memories that contradict each other.

This project replaces flat keyword matching with weighted, relationship-aware retrieval:

• Weighted edges with configurable decay — frequently-confirmed knowledge stays strong; stale information fades naturally on per-type half-lives (preferences ~693 days, events ~99 days)
• Bi-temporal validity — separate valid_at (when fact was true), invalid_at (when superseded), ingested_at (when learned). Old facts get marked invalid rather than deleted
• Semantic + structural search — vector embeddings find conceptually similar entities; graph traversal then expands through real relationships
• Project-context affinity — when you're working in a specific project, related entities surface first
• Contradiction detection — conflicting facts are flagged, not silently coexisting
• Full provenance — every edge traces back to the conversation, transcript, or document that sourced it
• Dream process — a scheduled Claude session reviews recent transcripts and ingest documents overnight, extracts new knowledge, applies decay, and writes a changelog

Architecture

                  Claude Code      Claude Desktop      claude.ai web
                       │                  │                  │
                       └────────── OAuth 2.1 Bearer ─────────┘
                                          │
                              https://your-host.example/mcp
                                          │
                                Cloudflare Tunnel
                                          │
                                  docker-compose
                            ┌────────────┴────────────┐
                            ▼                         ▼
                    graph-memory-mcp           graph-memory-neo4j
                    (Node 22 + jose)           (Neo4j 5.20 + APOC)
                    port 3847                  bolt://neo4j:7687
                            │                         │
                            └─── bolt-internal ───────┘

Two Docker services, talking over the compose network. The MCP server is the only thing that touches Neo4j directly — it implements OAuth 2.1 itself (RS256 JWTs, public clients with PKCE-S256, RFC 7591 dynamic client registration, RFC 7009 revocation), validates bearer tokens for /mcp calls, and exposes Cloudflare Access only on /oauth/authorize for the actual user login. The Neo4j instance has no external listeners.

The dream process is just another Claude session that runs on a schedule, reads transcripts, and calls the same MCP tools any client would call — there's no separate extraction pipeline.

Schema

Entity types (canonical): Person, Project, Preference, Concept, Decision, Fact, Event, Object, Reasoning — plus a few ad-hoc types (Organization, Technology, Artifact, Infrastructure, Feature, Resource) that have emerged organically through use. The schema is permissive on labels.

Relationship types (canonical, 22): WORKS_ON, WORKS_AT, REPORTS_TO, STAKEHOLDER_IN, PREFERS, KNOWS_ABOUT, DEPENDS_ON, USES_TECH, USES, DECIDED_FOR, SUPERSEDES, CONTRADICTS, RELATED_TO, ALIAS_OF, PARTICIPATED_IN, OCCURRED_DURING, PRODUCED, TRIGGERED_BY, HOSTED_ON, PRODUCED_BY, LED_TO, INVOLVED_IN. The catch-all RELATED_TO carries a relationship_type subtype property (similar_to, part_of, enables, impacts, etc.) for cases where the typed relationships don't fit.

Every node and edge carries:

• weight (0.0–1.0) — decays over time on per-type half-lives
• confidence — separate from weight, tracks the source's certainty
• tenant_id — multi-tenant isolation (single-user by default; multi-user-ready via OAuth email claim)
• embedding (nodes) — 384-dim vector for semantic search
• valid_at / invalid_at / ingested_at (edges) — bi-temporal tracking

Concise vocabulary in GRAPH_SCHEMA.md. Full reference (weights, decay, validity windows, init Cypher) in docs/GRAPH_SCHEMA_REFERENCE.md.

Tools

The MCP server exposes 23 tools across these categories:

Slash-command wrappers (/graph, /graph-ask, /graph-search, /graph-stats, /graph-dream, /graph-briefing, /graph-find, /graph-backup, /graph-capture, /ingest, etc.) install into ~/.claude/skills/. Full reference: docs/SKILLS.md.

/graph-capture is the manual companion to the nightly dream: the dream extracts knowledge from Claude Code transcripts in ~/.claude/projects/, but cannot see claude.ai web conversations or Claude Desktop chats (those live server-side or in Electron app data). Run /graph-capture at the end of a substantive claude.ai or Desktop conversation to commit any new entities, decisions, or facts to the graph.

Prerequisites

Required:

• Node.js 22+ and npm
• Docker (Desktop on Windows/macOS, or Docker Engine on Linux) with Docker Compose v2
• Claude Code and/or Claude Desktop with a Claude plan that covers Claude Code access — Pro, Max, Team, Enterprise, or Console all work (the free Claude.ai plan does not include Claude Code). Pro is fine for light/exploratory use; Max is recommended for daily-use deployments because the nightly dream process can be transcript-heavy on a busy day, and an unattended run that exhausts Pro's 5-hour window will abort mid-extraction and skip that night.
• A few hundred MB of disk for Neo4j + embeddings model

Optional:

• MarkItDown (pip install "markitdown[pdf,docx,xlsx,pptx]") — enables ingesting binary documents (.pdf, .docx, .xlsx, .pptx, .epub, .msg, .csv, .xml, .png, .jpg). Without it, ingest is limited to .md, .txt, .json, .html, .srt, .vtt.
• yt-dlp — convenient way to grab YouTube/web video subtitle files for ingestion. yt-dlp --write-auto-sub --sub-lang en --skip-download <url> writes a .vtt you can drop into ingest/pending/. Not a runtime dependency; just a tool that produces files graph-memory can already eat.
• cloudflared + a Cloudflare account — only needed for the multi-device / claude.ai web setup described in docs/REMOTE.md. Local-only deployments don't need it.
• Python 3.10+ — required only by MarkItDown and by scripts/sync-dream-skill.py.

Install

graph-memory has exactly one "primary device" — the machine that runs the two Docker containers (Neo4j + the MCP server) and runs the nightly dream + weekly maintenance scheduled tasks. Every other device is a "secondary device" that talks to the primary over HTTPS + OAuth — secondaries don't run their own containers and don't run their own dream process. Pick the install path that matches the role of the device you're sitting at right now.

Install — Primary Device (this device runs the containers)

Use this on the machine that will host Neo4j + the MCP server. This is also where the nightly dream and weekly maintenance scheduled tasks run, so the Claude Code transcripts you want extracted should live on this device.

Linux / macOS / Windows with Git Bash or WSL:

curl -fsSL https://raw.githubusercontent.com/stevepridemore/graph-memory/v0.3.0/scripts/install-primary.sh \
  | bash -s v0.3.0
# edit ~/graph-memory/.env (NEO4J_PASSWORD, GRAPH_MEMORY_HOME, CLAUDE_PROJECTS_DIR)
cd ~/graph-memory && docker compose up -d

Windows PowerShell (no bash needed):

$v = 'v0.3.0'
iwr "https://raw.githubusercontent.com/stevepridemore/graph-memory/$v/scripts/install-primary.ps1" -UseBasicParsing -OutFile $env:TEMP\gm-install.ps1
& $env:TEMP\gm-install.ps1 -Version $v
# edit $HOME\graph-memory\.env
cd $HOME\graph-memory; docker compose up -d

Verify with /graph-stats in any Claude Code session.

Optional: see docs/REMOTE.md for the Cloudflare Tunnel + Access setup that lets secondary devices and claude.ai web reach this graph remotely.

Install — Secondary Device (this device just talks to the primary)

Use this on every additional laptop, work computer, or phone. No Docker, no Neo4j — just the slash commands and an MCP client config pointed at the primary device's Cloudflare Tunnel URL. The primary device must already have the tunnel set up per docs/REMOTE.md.

Linux / macOS / Windows with Git Bash or WSL:

curl -fsSL https://raw.githubusercontent.com/stevepridemore/graph-memory/v0.3.0/scripts/install-secondary.sh \
  | bash -s v0.3.0 your-tunnel-host.example.com

Windows PowerShell (no bash needed):

$v = 'v0.3.0'
iwr "https://raw.githubusercontent.com/stevepridemore/graph-memory/$v/scripts/install-secondary.ps1" -UseBasicParsing -OutFile $env:TEMP\gm-install.ps1
& $env:TEMP\gm-install.ps1 -Version $v -TunnelHost your-tunnel-host.example.com

First /graph-stats call triggers the OAuth browser flow once; subsequent calls use the cached bearer token.

Install — Developer (build from source)

Use this if you want to modify graph-memory itself. Requires Node 22+ and Docker.

git clone https://github.com/stevepridemore/graph-memory
cd graph-memory
cp .env.example .env  # edit as above
npm install && npm run build
docker compose -f docker-compose.yml -f docker-compose.dev.yml up -d

The docker-compose.dev.yml override switches the MCP service from the published GHCR image to a local build: . so your edits get picked up on rebuild.

Multi-device / claude.ai web access

To use the same graph from claude.ai web, your office laptop, your phone, etc., expose the MCP server through Cloudflare Tunnel + Access. The auth flow is OAuth 2.1 with Cloudflare's IdP doing the actual user login.

Step-by-step in docs/REMOTE.md. The setup is one-time:

1. Cloudflare Tunnel with cloudflared pointing at https://localhost:3847
2. A single Cloudflare Access application scoped to /oauth/authorize (everything else is public + bearer-token-protected)
3. Server generates an RSA keypair on first run, persists it, exposes via /oauth/jwks
4. Claude clients hit https://your-host.example/mcp, get a 401 with proper WWW-Authenticate: Bearer ... resource_metadata="...", walk the OAuth flow, store the bearer token, and call subsequent requests with it

This makes the graph reachable from any device or AI tool that speaks MCP + OAuth 2.1.

For Claude Code on remote machines, .mcp.json.remote.example is the matching client template — copy it to ~/.claude/.mcp.json (or a project-local .mcp.json) and replace your-host.example with your tunnel hostname:

{
  "mcpServers": {
    "graph-memory": {
      "type": "http",
      "url": "https://your-host.example/mcp"
    }
  }
}

Claude Code walks the OAuth flow on first call and caches the bearer token. claude.ai web uses its own custom-connector UI — the URL is the same.

If you use Claude Code on more than one PC and want a single dream process to ingest transcripts from all of them, see Multi-PC transcript sharing — that's a separate concern from the OAuth multi-device story above, with a one-time sync setup.

Document ingestion

Drop files into ~/graph-memory/ingest/pending/ (or call graph_ingest directly). The next dream run extracts entities and relationships into the graph. Native support for .md, .txt, .json, .html, .srt, .vtt. With MarkItDown installed (pip install "markitdown[pdf,docx,xlsx,pptx]"), also handles .pdf, .docx, .xlsx, .pptx, .epub, .msg, .csv, .xml, .png, .jpg, etc. — converted to Markdown first, then extracted. Original files archive to ingest/originals/<date>/.

Privacy

The graph stores personal information — names of colleagues, decisions, preferences, project details. Treat the database with the same care as a private journal:

• Default deployment is local-only (Docker on localhost); nothing leaves your machine
• The optional Cloudflare Tunnel exposure adds OAuth + Cloudflare Access in front
• All data lives under a directory you control (default ~/graph-memory/)
• A graph_export tool produces portable JSONL backups; ~/graph-memory/backups/ is auto-rotated
• Embedding model runs locally — no text leaves the machine for vector search
• Entity extraction runs in your Claude sessions; same trust boundary as Claude itself
• API keys, passwords, and secrets are explicitly excluded from extraction (see prompts/dream-nightly.md)

Tech stack

Status

All planned phases shipped:

• ✅ Phase 0–3: MCP server, dream process, SessionStart hook, slash commands
• ✅ Phase 4: Bootstrap complete (graph populated from transcripts and memory files)
• ✅ Phase 5: bi-temporal modeling, Reasoning entity type, semantic/vector search, community detection, build_context meta-tool
• ✅ Multi-tenant infrastructure (single-user by design, multi-user-ready)
• ✅ OAuth 2.1 + Cloudflare Tunnel for multi-device access
• ✅ Aura → local Neo4j migration with full data preservation
• ✅ OAuth 2.1 hardening: PKCE-S256 mandatory, public clients only, RFC 7009 revocation, jti tracking, refresh-token TTL 30d, redirect-URI allowlist, optional email allowlist, body-size caps (64 KB OAuth / 4 MB MCP), structured event logging
• ✅ Internal threat model fully resolved (16 of 16 findings closed)
• ✅ npm audit clean (0 vulnerabilities)
• ✅ Pre-built GHCR images + curl-pipeable installers (no clone or local build required for end users)

Current release: v0.3.0.

Currently steady-state. Active development is opportunistic; the system runs unattended via the nightly dream process.

Releases

Newest first. Each tag publishes ghcr.io/stevepridemore/graph-memory-mcp:<tag> and moves :latest.

Documentation

• docs/ARCHITECTURE.md — system design, data flows, component responsibilities
• GRAPH_SCHEMA.md — concise vocabulary (node types + edge verbs) for both agent and humans
• docs/GRAPH_SCHEMA_REFERENCE.md — full reference (decay functions, weight semantics, validity windows, example queries)
• docs/MCP_SERVER.md — every MCP tool with input/output schemas
• docs/DREAM_PROCESS.md — extraction pipeline, manifest format, changelog structure
• docs/SKILLS.md — slash command definitions
• docs/REMOTE.md — exposing graph-memory via Cloudflare Tunnel + Access
• docs/MULTI_TENANT.md — tenant isolation model
• docs/TESTING.md — running the test suite locally and in CI
• CLAUDE.md — retrieval and chat-write guidelines for Claude

License

MIT — see LICENSE.