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An AI-assisted, long-term memory system for IDEs, powered by — an embedded, columnar, MIT-licensed graph database. GraphMemory-IDE is an MCP (Model Context Protocol) server that provides semantic vector search, graph-based knowledge storage, and real-time analytics. It integrates with VSCode, Cursor, and Windsurf through dedicated IDE plugins.
AI-assisted development MCP providing long-term, on-device "AI memory" for IDEs. Powered by Kuzu GraphDB and exposed via MCP server
Graph database: the backend runs on LadybugDB 0.21.2, the actively developed successor to KuzuDB (whose open-source development halted after its acquisition by Apple). The full migration record — verified API differences, phase-by-phase execution, and the operational migration guide — lives in
MIGRATION_PLAN_LADYBUG.mdanddocs/migration/KUZU_TO_LADYBUG.md.
array_cosine_similarity Top-K queries over node embedding columnsgit clone https://github.com/elementalcollision/GraphMemory-IDE.git
cd GraphMemory-IDE/docker
docker compose up -d
Services will be available at:
# 1. Install dependencies (Python 3.11+)
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
# 2. Initialise the graph database schema (REQUIRED before first start —
# the app opens the database but does not create the schema itself)
LADYBUG_DB_PATH=./data/ladybug.lbdb python server/init_db.py
# 3. Start the FastAPI server
uvicorn server.main:app --host 0.0.0.0 --port 8080 --reload
# 4. (optional) Start the Streamlit dashboard (separate terminal)
cd dashboard && streamlit run streamlit_app.py
Note on env prefixes: database-related settings are read by pydantic with the
DB_prefix — e.g. the settingLADYBUG_DB_PATHis set via theDB_LADYBUG_DB_PATHenvironment variable. The plainLADYBUG_DB_PATHform is honoured byserver/init_db.py(andKUZU_DB_PATHremains as a deprecated fallback there, for one release cycle).
GraphMemory-IDE uses a hybrid architecture where I/O-bound code (FastAPI, database clients, WebSocket) runs in CPython, while compute-heavy graph algorithms can optionally run as native-compiled Codon modules.
IDE Plugins (VSCode/Cursor/Windsurf)
|
v
FastAPI Server (CPython)
├── JWT Auth (EdDSA) + Security Middleware (TrustedHost / rate limit)
├── MCP Protocol Handler
├── Telemetry Ingestion + Query (/telemetry/*)
├── Top-K Vector Search (/tools/topk)
├── Analytics Engine (/api/v1/analytics/*)
└── Dashboard Routes + SSE (/dashboard/*)
|
┌────┴─────────┐
v v
LadybugDB PostgreSQL + Redis
(embedded (relational store,
graph DB, sessions, cache)
sync engine)
|
v
Codon Bridge (optional)
├── Graph Kernels (.so/.dylib)
└── Python/NetworkX fallback
Graph database layer (server/graph_database.py): an embedded LadybugDB instance opened in-process — no separate DB server. It provides a thread-safe connection pool (Condition-variable based), LadybugDB's native AsyncConnection for async handlers (GraphDatabaseManager.get_async_connection()), schema management (User / Project / Memory node tables and OWNS / COLLABORATES / CONTAINS / RELATES_TO / ACCESSED_BY relationship tables), a query engine with parameterised Cypher execution and Prometheus instrumentation, and health checks (CALL show_tables() schema-integrity validation). A read_only mode opens the database for read-only workloads (e.g. analytics replicas).
See Architecture Overview for detailed system design.
The codon/ directory contains graph algorithm implementations compiled to native machine code via Codon. The bridge layer (codon/bridge/) automatically routes computations based on graph size and library availability:
Compile Codon modules with:
./scripts/build_codon.sh
Full reference: API Guide. Interactive docs at http://localhost:8080/docs (disabled in production).
GraphMemory-IDE/
├── server/ # FastAPI backend
│ ├── main.py # Application entry point
│ ├── core/ # Configuration and settings
│ ├── auth/ # SSO, MFA, onboarding
│ ├── auth_jwt.py # JWT authentication
│ ├── analytics/ # Analytics engine
│ ├── collaboration/ # Real-time collaboration
│ ├── dashboard/ # Dashboard API routes
│ ├── monitoring/ # Health checks, metrics, alerting
│ ├── security/ # Security middleware
│ ├── streaming/ # WebSocket/SSE streaming
│ ├── backup/ # LadybugDB/PostgreSQL/Redis backup orchestration
│ ├── graph_database.py # LadybugDB integration (pool, schema, query engine, health)
│ └── init_db.py # Graph schema initialisation (run before first start)
├── dashboard/ # Streamlit UI
├── frontend/ # TypeScript/React frontend
├── codon/ # Codon acceleration layer
│ ├── bridge/ # Python-Codon interop with fallback
│ ├── graph_kernels/ # Native graph algorithms (.codon)
│ └── data_processing/ # Native data operations (.codon)
├── docker/ # Docker Compose configs
│ ├── docker-compose.yml # Development environment
│ ├── docker-compose.test.yml # Test services (PostgreSQL + Redis, ephemeral)
│ └── production/ # Production multi-service setup
├── tests/ # Test suites
│ ├── integration/ # Integration tests
│ ├── production/ # Production validation
│ ├── load_testing/ # Locust load tests
│ └── smoke/ # Smoke tests
├── docs/ # Documentation (incl. docs/migration/)
├── ide-plugins/ # VSCode, Cursor, Windsurf extensions
├── monitoring/ # Prometheus & Grafana configs
├── scripts/ # Build, deployment, and test-DB scripts
└── kubernetes/ # Kubernetes manifests
# Run all tests
PYTHONPATH=. pytest tests/ -v
# Run with coverage
PYTHONPATH=. pytest tests/ --cov=server --cov-report=html
# Run specific test categories
PYTHONPATH=. pytest tests/ -m unit
PYTHONPATH=. pytest tests/ -m integration
PYTHONPATH=. pytest tests/ -m "not slow"
Registered test markers: asyncio, unit, integration, slow, real_time, real_services, performance, mock_data, load, external, database, authentication (--strict-markers is enforced).
Database integration tests expect PostgreSQL and Redis backing services. The ephemeral test profile brings them up (Docker / OrbStack), matching the fixture defaults:
docker compose -f docker/docker-compose.test.yml up -d
python scripts/init_test_db.py # create the app schema in the test DB
PYTHONPATH=. pytest tests/integration/test_database_integration.py -v
docker compose -f docker/docker-compose.test.yml down -v
Without the services, PG-dependent fixtures skip cleanly; LadybugDB-backed tests run in-process against a temporary database and need no external services.
Coverage target: 85% minimum.
For production, use the multi-service Docker Compose configuration:
cd docker/production
cp env.production.template .env # Configure environment variables
docker compose -f docker-compose.prod.yml up -d
This starts 7+ services: Nginx (reverse proxy), FastAPI, Streamlit, Analytics, PostgreSQL, Redis, Prometheus, and Grafana — with isolated network subnets and resource limits. A LadybugDB api-server sidecar (ghcr.io/ladybugdb/api-server) is available for external/admin graph access; pin a version tag in production.
See the Docker Deployment Guide for complete production setup instructions.
This codebase was migrated from Kuzu 0.10.0 to LadybugDB 0.21.2 (2026-10). Key facts for anyone extending the integration:
import ladybug as lb; Connection(db) + init_connection(); snake_case result iteration (has_next() / get_next(), rows are lists)array_cosine_similarity (Kuzu's list_cosine_similarity does not exist).lbdb file; the parent directory must exist (unlike Kuzu, LadybugDB does not create it)lbug (brew install ladybug); EXPORT DATABASE / IMPORT DATABASE supported for data migrationFull verified difference catalogue: MIGRATION_PLAN_LADYBUG.md §3. Operational runbook for deployed instances: docs/migration/KUZU_TO_LADYBUG.md.
See Contributing Guidelines for development workflow, commit conventions, and code review process.
This project is licensed under the MIT License. See LICENSE for details.
AI-assisted development MCP providing long-term, on-device "AI memory" for IDEs. Powered by Kuzu GraphDB and exposed via MCP server
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FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
GraphMemory IDE ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-10-28 AI-assisted development MCP providing long-term, on-device "AI memory" for IDEs. Powered by Kuzu GraphDB and exposed via MCP server
