Graph-Memory
# Graph Memory
[](https://glama.ai/mcp/servers/stevepridemore/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](https://huggingface.co/BAAI/bge-small-en-v1.5). 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- **Bi-temporal validity** — separate `valid_at` (when fact was true), `invalid_at` (when superseded), `ingested_at` (when learned).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`](GRAPH_SCHEMA.md). Full reference (weights, decay, validity windows, init Cypher) in [`docs/GRAPH_SCHEMA_REFERENCE.md`](docs/GRAPH_SCHEMA_REFERENCE.md).
## Tools
The MCP server exposes 23 tools across these categories:
| Category | Tools |
|---|---|
| Query | `graph_query`, `graph_search` (semantic), `graph_entities`, `graph_contradictions`, `graph_communities`, `graph_build_context` |
| Write | `graph_relate` (single + batch), `graph_boost`, `graph_weaken`, `graph_delete`, `graph_merge`, `graph_unmerge` |
| Maintenance | `graph_decay`, `graph_prune`, `graph_validate`, `graph_reembed`, `graph_merge_suggestions` |
| Operational | `graph_stats`, `graph_export`, `graph_audit`, `graph_ingest`, `graph_read_transcript`, `graph_cypher` (admin only) |
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`](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](https://github.com/microsoft/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](https://github.com/yt-dlp/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](https://github.com/cloudflare/cloudflared)** + a Cloudflare account — only needed for the multi-device / claude.ai web setup described in [`docs/REMOTE.md`](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:**
```bash
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):**
```powershell
$v = 'v0.3.0'
iwr "https://raw.githubusercontent.com/stevepridemore/graph-memory/$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`](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`](docs/REMOTE.md).
**Linux / macOS / Windows with Git Bash or WSL:**
```bash
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):**
```powershell
$v = 'v0.3.0'
iwr "https://raw.githubusercontent.com/stevepridemore/graph-memory/$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.
```bash
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`](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`](.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:
```json
{
"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](docs/REMOTE.md#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](https://github.com/microsoft/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
| Component | Technology |
|---|---|
| Language | TypeScript / Node.js 22 |
| Graph DB | Neo4j Community 5.20 (Docker) with APOC |
| Embedding model | `@huggingface/transformers` running bge-small-en-v1.5 (384-dim, ONNX) |
| Driver | `neo4j-driver` |
| MCP framework | `@modelcontextprotocol/sdk` |
| Auth | `jose` for JWT signing/verification (RS256) |
| Tunnel (optional) | Cloudflare Tunnel (`cloudflared`) + Cloudflare Access |
| Testing | Vitest |
## 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`](https://github.com/stevepridemore/graph-memory/releases/tag/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`.
| Version | Date | Summary |
|---|---|---|
| [`v0.3.0`](https://github.com/stevepridemore/graph-memory/releases/tag/v0.3.0) | 2026-05-10 | Curl-pipeable primary/secondary device installers + pre-built GHCR image. Multi-stage Dockerfile, auto-cert generation on first run, vendored slash commands. End users no longer need to clone or build from source. |
| [`v0.2.1`](https://github.com/stevepridemore/graph-memory/releases/tag/v0.2.1) | 2026-05-09 | STRIDE threat model fully closed (16 of 16 findings). Hardens OAuth 2.1: PKCE-S256, RFC 7009 revocation, jti tracking, refresh-token TTL, redirect-URI allowlist, email allowlist, body-size caps. |
| [`v0.2.0`](https://github.com/stevepridemore/graph-memory/releases/tag/v0.2.0) | 2026-05-09 | OAuth 2.1 security hardening pass. Public clients only, mandatory PKCE, body-size caps, structured event logging. |
| [`v0.1.1`](https://github.com/stevepridemore/graph-memory/releases/tag/v0.1.1) | 2026-05-08 | Decay correctness + test coverage. Vitest in CI, decay function bug fixes. |
| [`v0.1.0`](https://github.com/stevepridemore/graph-memory/releases/tag/v0.1.0) | 2026-05-07 | Initial public release. MCP server, dream process, slash commands, bi-temporal modeling, semantic search, OAuth multi-device. |
## Documentation
- [`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md) — system design, data flows, component responsibilities
- [`GRAPH_SCHEMA.md`](GRAPH_SCHEMA.md) — concise vocabulary (node types + edge verbs) for both agent and humans
- [`docs/GRAPH_SCHEMA_REFERENCE.md`](docs/GRAPH_SCHEMA_REFERENCE.md) — full reference (decay functions, weight semantics, validity windows, example queries)
- [`docs/MCP_SERVER.md`](docs/MCP_SERVER.md) — every MCP tool with input/output schemas
- [`docs/DREAM_PROCESS.md`](docs/DREAM_PROCESS.md) — extraction pipeline, manifest format, changelog structure
- [`docs/SKILLS.md`](docs/SKILLS.md) — slash command definitions
- [`docs/REMOTE.md`](docs/REMOTE.md) — exposing graph-memory via Cloudflare Tunnel + Access
- [`docs/MULTI_TENANT.md`](docs/MULTI_TENANT.md) — tenant isolation model
- [`docs/TESTING.md`](docs/TESTING.md) — running the test suite locally and in CI
- [`CLAUDE.md`](CLAUDE.md) — retrieval and chat-write guidelines for Claude
## License
MIT — see [`LICENSE`](LICENSE).
TDQS
Scored across 23 tools
Every tool has a clearly distinct purpose: audit, boost, build context, find communities, contradictions, run cypher, decay, delete, browse entities, export, ingest, merge, suggest merges, prune, query, read transcript, re-embed, relate, search, stats, unmerge, validate, weaken. No two tools overlap in function.
All tool names follow the consistent pattern 'graph_' + lowercase_snake_case action verb or noun (e.g., graph_audit, graph_boost, graph_build_context). No mixing of camelCase or other conventions, making prediction easy.
With 23 tools, the server covers the full lifecycle of a knowledge graph (CRUD, maintenance, quality, analysis). While on the high side, each tool is justified; it could be trimmed slightly but is not excessive.
The tool set provides comprehensive coverage: entity creation via graph_relate, reading via multiple tools, updating via boost/weaken/merge/reembed, deletion via delete/prune, plus management (export, ingest, decay, validate, contradictions, merge suggestions, unmerge, audit, build context, communities, stats, read transcript, cypher). No obvious gaps.