MDEMG - Multi-Dimensional Emergent Memory Graph

A persistent memory system for AI coding agents built on Neo4j with native vector indexes. Implements semantic retrieval with hidden layer concept abstraction and Hebbian learning.

Key insight: The critical metric isn't average retrieval score—it's state survival under context compaction. Baseline agents forget architectural decisions after compactions. MDEMG maintains decision persistence indefinitely.

Quick Start

Prerequisites

Docker — required for Neo4j database (Docker Desktop on macOS/Windows, Docker Engine on Linux)
Embedding provider (choose one):
- OpenAI API key (recommended, requires account)
- Ollama (local, free, no API key needed)

Step 1: Install

# macOS (Homebrew)
brew tap reh3376/mdemg
brew install mdemg

# Linux (curl installer)
curl -fsSL https://raw.githubusercontent.com/reh3376/mdemg_linux/main/install.sh | bash

# Verify installation
mdemg version

Build from source (alternative)

git clone https://github.com/reh3376/mdemg.git && cd mdemg
go build -o bin/mdemg ./cmd/mdemg
./bin/mdemg version

Step 2: Initialize a Project

cd /path/to/your/project
mdemg init

The interactive wizard will:

Set a space ID (defaults to directory name)
Detect Neo4j on localhost:7687
Ask for your embedding provider (ollama/openai/disabled)
If OpenAI: prompt for your API key (stored in .env, never in config)
Create .mdemg/config.yaml, .mdemgignore, and .env
Optionally install a git post-commit hook for auto-ingestion
Optionally configure MCP for your IDE (Cursor, VS Code, Claude Code)

Step 3: Start Services

mdemg db start              # Launches a project-scoped Neo4j container
                            # Auto-selects port if 7687 is in use
mdemg start --auto-migrate  # Starts server daemon + applies schema migrations
mdemg status                # Verify everything is running

Each project gets its own isolated Neo4j container and data volume. If you have other Neo4j instances running, ports are resolved automatically.

Step 4: Ingest Your Codebase

mdemg ingest --path .       # Index your codebase into the memory graph
mdemg hooks install         # Auto-ingest on every git commit (optional)

That's it. Your AI agent now has persistent memory.

One-Command Setup (Sidecar)

For projects using Claude Code, the sidecar quickstart handles everything — config, services, MCP integration, and hooks — in a single command:

cd /path/to/your/project
mdemg sidecar quickstart    # init + install + up + attach-agent + generate-hooks

This creates .mdemg/sidecar.yaml, starts Neo4j + MDEMG server, writes .claude/mcp.json, generates session-start.sh and prompt-context.sh hooks, registers them in settings.local.json, and enables enableAllProjectMcpServers.

See the Quickstart Guide for a detailed walkthrough, or run mdemg demo to try it with sample data.

Upgrading? Run mdemg upgrade to self-update to the latest release.

Reproduce the Benchmark

Everything needed to independently verify our results is included.

Prerequisites

Docker (Neo4j)
Go 1.24+
Python 3.10+ (grading + utilities)

Embeddings (choose one):

OpenAI API key, or
Ollama (local embeddings)

Agent under test (choose one):

Claude Code (recommended baseline runner), or
Any tool-using LLM agent that can call the MDEMG API

Reproduction Steps

# 1. Clone and setup
git clone https://github.com/reh3376/mdemg.git && cd mdemg
cp .env.example .env  # Add your embedding provider credentials

# 2. Build and start services
go build -o bin/mdemg ./cmd/mdemg
./bin/mdemg db start              # Launch Neo4j container
./bin/mdemg start --auto-migrate  # Start server daemon with migrations

# 3. Ingest test codebase (or use your own)
./bin/mdemg ingest --space-id=benchmark --path=/path/to/target-repo

# 4. Run consolidation
curl -X POST http://localhost:9999/v1/memory/consolidate \
  -H "Content-Type: application/json" -d '{"space_id": "benchmark"}'

# 5. Run benchmark
# Questions: docs/benchmarks/whk-wms/test_questions_120_agent.json
# Grader: docs/benchmarks/grader_v4.py

Report output: grades_*.json contains per-question scores with evidence breakdown.

Verify Integrity

# Question bank hash (SHA-256)
shasum -a 256 docs/benchmarks/whk-wms/test_questions_120_agent.json
# Expected: 24aa17a215e4e58b8b44c7faef9f14228edb0e6d3f8f657d867b1bfa850f7e9e

Benchmark Receipts

Full reproducibility details for skeptics:

Item	Value
MDEMG Commit	`779d753`
Question Set	`test_questions_120_agent.json` (120 questions)
Question Hash	`sha256:24aa17a2...`
Answer Key	`test_questions_120.json`
Grader	`grade_answers.py`
Scoring Weights	Evidence: 0.70 / Concept: 0.15 / Semantic: 0.15
Target Codebase	whk-wms (507K LOC TypeScript)
Include Patterns	`*/.ts`, `*/.tsx`, `*/.json`
Exclude Patterns	`node_modules/`, `dist/`, `.git/`, `docs-website/`
Agent Model	Claude Haiku (via Claude Code)
Runs	2 per condition (run 3 excluded for consistency)
Embedding Provider	OpenAI text-embedding-3-small

Baseline definition: Same agent runner and tool permissions, no MDEMG retrieval, relying on long-context + auto-compaction only (memory off).

Key Results (2026-01-30, whk-wms 507K LOC)

Metric	Baseline	MDEMG + Edge Attention	Delta
Mean Score	0.854	0.898	+5.2%
Standard Deviation	0.088	0.059	-51% variance
High Score Rate (≥0.7)	97.9%	100%	+2.1pp
Strong Evidence Rate	97.9%	100%	+2.1pp

Category Performance (Edge Attention):

Category	Mean Score
Disambiguation	0.958
Service Relationships	0.916
Architecture Structure	0.889
Data Flow Integration	0.882

Key Metric: State Survival

The Q&A battery measures single-turn retrieval accuracy. The critical differentiator is state survival under compaction:

Metric	Baseline	MDEMG	Source
Decision Persistence @5 compactions	0%	95%	Compaction torture test

When context windows fill and auto-compaction kicks in, baseline agents lose architectural decisions. MDEMG persists them in the graph.

The baseline forgets under pressure. MDEMG remembers.

Overview

MDEMG provides long-term memory for AI agents, enabling them to:

Store observations: Persist code patterns, decisions, and architectural knowledge
Semantic recall: Retrieve relevant memories via vector similarity search
Concept abstraction: Automatically form higher-level concepts from related memories (hidden layers)
Associative learning: Build connections between memories through Hebbian reinforcement
LLM re-ranking: Apply GPT-powered relevance scoring for improved retrieval quality

Key Features

Multi-layer graph architecture: Base observations (L0) → Hidden concepts (L1) → Abstract concepts (L2+)
Hybrid search: Combines vector similarity with graph traversal
Conversation Memory System (CMS): Persistent memory across agent sessions with surprise-weighted learning
Symbol extraction (UPTS): Unified Parser Test Schema supporting 27 languages with file:line evidence and SHA256 fixture verification
Plugin system: Extensible via ingestion, reasoning, and APE (Autonomous Pattern Extraction) modules
Evidence-based retrieval: Returns symbol-level citations (file:line references) with results
Capability gap detection: Identifies missing knowledge areas for targeted improvement
Codebase ingestion API: Background job processing for large codebase ingestion with consolidation
Git commit hooks: Automatic incremental ingestion on every commit via post-commit hook
Freshness tracking: TapRoot-level staleness detection with configurable thresholds
Scheduled sync: Periodic background sync to keep memory graphs up-to-date
Webhook integration: Linear webhook endpoint with HMAC signature verification and debouncing
File watcher: Standalone mdemg-watch binary for automatic re-ingestion on file changes
Orphan detection: Timestamp-based detection of nodes missing from re-ingestion with archive/delete/list actions
Graph orphan cleanup: Cross-space zero-edge node scan with consolidate/archive/delete fix actions and protected space enforcement
Edge consistency: Automatic staleness tracking and edge weight refresh during consolidation
Backup & restore: Automated full database dumps and partial space exports with retention policies and scheduler
Neo4j state monitoring: Single endpoint for consolidated database health, per-space statistics (nodes, edges, layers, health score, staleness), and backup overview
Meta-cognition enforcement: Server-side anomaly detection (empty-resume, empty-recall), hook circuit breakers with CRITICAL warnings, multi-dimensional watchdog monitoring, Hebbian signal learning for adaptive enforcement
Jiminy Inner Voice: Proactive guidance service injected into every prompt — surfaces constraints, prior corrections, contradictions, and frontier exploration opportunities from the knowledge graph via 4-source parallel fan-out with 6s timeout
Space Transfer & DevSpace: Export/import space graphs as .mdemg files or via gRPC; optional DevSpace hub for agent registration, publish/pull exports, and inter-agent messaging (see cmd/space-transfer/README.md and docs/specs/development-space-collaboration.md)

Architecture

┌─────────────────────────────────────────────────────────────┐
│                      AI Coding Agent                        │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐  │
│  │   IDE/CLI    │◄──►│  MCP Server  │◄──►│ MDEMG Client │  │
│  │  (Cursor)    │    │   (tools)    │    │    (HTTP)    │  │
│  └──────────────┘    └──────────────┘    └──────┬───────┘  │
└─────────────────────────────────────────────────┼──────────┘
                                                  │
                    ┌─────────────────────────────▼─────────┐
                    │     MDEMG Service (dynamic port)      │
                    │  ┌─────────┐  ┌───────────────────┐  │
                    │  │Embedding│  │    Neo4j Graph    │  │
                    │  │ Provider│  │ (Vector + Graph)  │  │
                    │  └─────────┘  └───────────────────┘  │
                    └───────────────────────────────────────┘

Development Setup

Prerequisites

Go 1.24+
Docker (for Neo4j)
Embedding provider: OpenAI API key or Ollama (local, free)

Build from Source

git clone https://github.com/reh3376/mdemg.git && cd mdemg
go build -o bin/mdemg ./cmd/mdemg
./bin/mdemg --help            # See all commands

Configure and Start

cd /path/to/your/project
mdemg init                    # Interactive wizard creates .mdemg/config.yaml + .env
mdemg db start                # Project-scoped Neo4j container (auto-selects free port)
mdemg start --auto-migrate    # Server daemon with schema migrations

Ingest a Codebase

mdemg ingest --space-id=my-project --path=/path/to/repo
mdemg ingest --space-id=my-project --path=/path/to/repo --incremental  # Changed files only
mdemg hooks install  # Auto-ingest on git commit

API Endpoints

Core Memory Operations

Endpoint	Method	Description
`/v1/memory/retrieve`	POST	Semantic search with optional LLM re-ranking
`/v1/memory/consult`	POST	SME-style Q&A with evidence citations
`/v1/memory/ingest`	POST	Store a single observation
`/v1/memory/ingest/batch`	POST	Store multiple observations
`/v1/memory/consolidate`	POST	Trigger hidden layer creation
`/v1/memory/stats`	GET	Per-space memory statistics
`/v1/memory/ingest-codebase`	POST	Background codebase ingestion job
`/v1/memory/symbols`	GET	Query extracted code symbols
`/v1/memory/ingest/files`	POST	Ingest files with background job processing
`/v1/memory/spaces/{id}/freshness`	GET	Space freshness and staleness status
`/v1/webhooks/linear`	POST	Linear webhook receiver with HMAC verification

Web Scraper

Endpoint	Method	Description
`/v1/scraper/jobs`	POST	Create a new scrape job
`/v1/scraper/jobs`	GET	List all scrape jobs
`/v1/scraper/jobs/{id}`	GET	Get job status and scraped content
`/v1/scraper/jobs/{id}`	DELETE	Cancel a running job
`/v1/scraper/jobs/{id}/review`	POST	Approve/reject/edit scraped content
`/v1/scraper/spaces`	GET	List available target spaces

Backup & Restore (Phase 70)

Endpoint	Method	Description
`/v1/backup/trigger`	POST	Trigger backup (full database dump or partial space export)
`/v1/backup/status/{id}`	GET	Backup job status and progress
`/v1/backup/list`	GET	List all backups (optional `?type=` filter)
`/v1/backup/manifest/{id}`	GET	Get full backup manifest (checksum, sizes, spaces)
`/v1/backup/{id}`	DELETE	Delete a backup artifact
`/v1/backup/restore`	POST	Trigger restore from full backup
`/v1/backup/restore/status/{id}`	GET	Restore job status

Hash Verification (UNTS)

Endpoint	Method	Description
`/v1/hash-verification/register`	POST	Register a file for hash tracking
`/v1/hash-verification/files`	GET	List tracked files (optional `?framework=`/`?status=` filters)
`/v1/hash-verification/files/{path}`	GET	Get single file status and history
`/v1/hash-verification/verify`	POST	Verify single file hash against expected
`/v1/hash-verification/verify-all`	POST	Verify all tracked files
`/v1/hash-verification/update`	POST	Update expected hash for a file
`/v1/hash-verification/revert`	POST	Revert to a previous hash from history
`/v1/hash-verification/scan`	POST	Scan manifest + specs to register files

Requires UNTS_ENABLED=true and UNTS_BASE_PATH (default: .). Returns 503 when disabled.

Admin — Space Lifecycle

Endpoint	Method	Description
`/v1/admin/spaces`	GET	List all spaces with metadata and prunable status
`/v1/admin/spaces/{id}`	PATCH	Update space metadata (prunable flag)
`/v1/admin/spaces/prune`	POST	Batch prune prunable/orphan spaces (dry-run supported)

A background auto-prune scheduler runs every 24 hours by default (SPACE_PRUNE_INTERVAL_HOURS, 0 to disable).

Conversation Memory System (CMS)

Endpoint	Method	Description
`/v1/conversation/resume`	POST	Restore session context with themes and concepts
`/v1/conversation/observe`	POST	Record observations (decisions, corrections, learnings)
`/v1/conversation/correct`	POST	Record user corrections for learning
`/v1/conversation/recall`	POST	Query conversation history
`/v1/conversation/consolidate`	POST	Create themes from observations
`/v1/conversation/session/anomalies`	GET	Aggregated session anomalies and health
`/v1/conversation/templates`	GET/POST	List or create observation templates
`/v1/conversation/templates/{id}`	GET/PUT/DELETE	CRUD for specific template
`/v1/conversation/snapshot`	POST	Create session snapshot
`/v1/conversation/snapshot/{id}`	GET/DELETE	Retrieve or delete snapshot
`/v1/conversation/snapshot/{id}/restore`	POST	Restore session from snapshot
`/v1/conversation/relevance`	POST	Compute observation relevance scores
`/v1/conversation/truncate`	POST	Truncate old observations
`/v1/conversation/org-review`	GET	Get observations pending org review
`/v1/conversation/org-review/{id}/approve`	POST	Approve observation for org sharing
`/v1/conversation/org-review/{id}/reject`	POST	Reject observation from org sharing

Learning Control

Endpoint	Method	Description
`/v1/learning/stats`	GET	Hebbian learning edge statistics
`/v1/learning/freeze`	POST	Freeze learning for stable scoring
`/v1/learning/unfreeze`	POST	Resume learning edge creation
`/v1/learning/prune`	POST	Remove decayed edges

Symbol Extraction (UPTS)

For the comprehensive guide to UxTS methodology (architecture, spec writing, CI integration, governance, and all 11 frameworks), see docs/guides/UXTS_DEVELOPER_GUIDE.md.

MDEMG extracts code symbols during ingestion using the Unified Parser Test Schema (UPTS):

Supported Languages (27 UPTS-validated, 100% pass rate):

Systems: Go, Rust, C, C++, CUDA
JVM: Java, Kotlin
.NET: C#
Scripting: Python, TypeScript/JavaScript, Lua, Shell
API Schemas: Protocol Buffers, GraphQL, OpenAPI
Configuration: YAML, TOML, JSON, INI
Infrastructure: Terraform/HCL, Dockerfile, Makefile
Database: SQL, Cypher (Neo4j)
Documentation: Markdown, XML, Scraper Markdown (web-scraped content with section chunking)

Extracted Symbol Types:

Functions, methods, classes, interfaces, types
Constants, variables, enums
Imports, exports, module declarations

Symbols include file:line references for evidence-based retrieval.

Conversation Memory System (CMS)

CMS provides persistent memory for AI agents across sessions:

# Resume session context (call at session start)
curl -X POST http://localhost:9999/v1/conversation/resume \
  -H "Content-Type: application/json" \
  -d '{"space_id": "my-agent", "session_id": "session-1", "max_observations": 10}'

# Record an observation (decision, learning, correction)
curl -X POST http://localhost:9999/v1/conversation/observe \
  -H "Content-Type: application/json" \
  -d '{
    "space_id": "my-agent",
    "session_id": "session-1",
    "content": "User prefers TypeScript for new files",
    "obs_type": "preference"
  }'

Observation Types: decision, correction, learning, preference, error, progress

Observations are surprise-weighted (novel information persists longer) and form themes via consolidation.

MCP Integration

MDEMG provides an MCP server for IDE integration.

Automatic Setup (Claude Code)

mdemg sidecar attach-agent claude-code   # Writes .claude/mcp.json + enables MCP in settings
# Or as part of full setup:
mdemg sidecar quickstart                 # init + install + up + attach + hooks

Manual Setup (Cursor IDE)

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "mdemg": {
      "command": "/path/to/mdemg/bin/mdemg-mcp",
      "args": [],
      "env": {
        "MDEMG_ENDPOINT": "http://localhost:9999"
      }
    }
  }
}

Project Structure

mdemg/
├── cmd/                  # CLI tools (server, ingest, MCP, etc.)
├── internal/             # Core logic
│   ├── api/              # HTTP handlers
│   ├── retrieval/        # Search algorithms
│   ├── hidden/           # Concept abstraction
│   ├── learning/         # Hebbian edges
│   ├── conversation/     # Conversation Memory System (CMS)
│   ├── backup/           # Backup & restore (full, partial, scheduler, retention)
│   ├── symbols/          # Code symbol extraction
│   └── plugins/          # Plugin system
├── docs/                 # Documentation
│   └── benchmarks/       # Benchmark questions, graders, results
├── migrations/           # Neo4j schema (Cypher)
├── tests/                # Integration tests
└── docker-compose.yml    # Neo4j container

Observability Stack

MDEMG includes a complete observability stack for monitoring and debugging.

Quick Start

cd deploy/docker
docker compose -f docker-compose.observability.yml up -d

# Access dashboards
open http://localhost:3000  # Grafana (admin/admin)
open http://localhost:9090  # Prometheus

Components

Component	Port	Description
Prometheus	9090	Metrics collection and alerting
Grafana	3000	Dashboard visualization
Blackbox Exporter	9115	HTTP/TCP health probes

MDEMG Overview Dashboard

Pre-configured dashboard with 10 panels:

Request Rate, P95 Latency, Error Rate, Circuit Breakers
Request Latency Distribution (p50/p95/p99)
Requests by Status, Cache Hit Ratios
Retrieval Latency, Rate Limit Rejections, Embedding Latency

Metrics Endpoint

curl http://localhost:9999/v1/prometheus

Exposes all MDEMG metrics in Prometheus format.

Development Roadmap

Recently Completed

Phase	Name	Status
104	Active MCP Guardrails (constraint validation pipeline, MCP tool)	✅ Complete
103/103b	Dynamic Emergence + UETS Model Evaluation (LLM concept naming, 8-model benchmark)	✅ Complete
102	Intent Translation (LLM query rewriting before vector embedding)	✅ Complete
101	SME Synthesis Engine (LLM-driven synthesis for consult endpoint)	✅ Complete
97	Process Lifecycle + Secret Management (daemon mode, keychain)	✅ Complete
96	IDE + Repo Integration (git hooks, MCP config, serve --mcp)	✅ Complete
95	Database + Embedding + Migrations (Go-native runner, Docker mgmt)	✅ Complete
94	Config Simplification + Project Init (YAML config, mdemg init)	✅ Complete
93	Unified CLI Foundation (12 binaries → single mdemg binary)	✅ Complete
92	Gap Analysis — Deployable Package (15-gap analysis, Phase 93-100 roadmap)	✅ Complete
—	Space Pruning Framework (admin API, auto-prune scheduler, UATS specs)	✅ Complete
D	Validation (2nd codebase benchmark, 28K scale test, architecture docs)	✅ Complete
91	RSIC Observability & Operations (Prometheus metrics, Grafana dashboard, alert rules)	✅ Complete
90	RSIC Conformance & CI Gating (integration tests, CI split, tag filtering)	✅ Complete
89	RSIC Persistence & Multi-Space Correctness (write-behind, RSICState nodes)	✅ Complete
88	RSIC Safety & Policy Enforcement (dry-run, rollback, blast-radius)	✅ Complete
87	RSIC Orchestration Activation (trigger sources, cooldown, dedupe)	✅ Complete
80	CMS ANN Meta-Cognition & Self-Improvement Enforcement	✅ Complete
76	Neo4j State Monitor & Space Overview	✅ Complete
75	Cross-File Relationship Extraction & Graph Topology Hardening	✅ Complete
70	Neo4j Backup & Restore (Full & Partial) with Scheduler	✅ Complete
38	UNTS Hash Verification REST API (8 endpoints, 8 UATS specs)	✅ Complete
60b	Recursive Self-Improvement Cycle (RSIC)	✅ Complete
60	CMS Advanced Functionality II (Templates, Snapshots, Relevance, Truncation, Org-Review)	✅ Complete
51	Web Scraper Ingestion Module	✅ Complete
49	LLM Plugin SDK (Scaffolding, Validation, Gap Detection)	✅ Complete

See AGENT_HANDOFF.md for detailed phase specifications.

Documentation

ELI5 (Explain Like I'm 5) - Simplified explanation of what MDEMG does and how it works
Quickstart Guide - 10-minute setup walkthrough
FAQ - Common questions and troubleshooting
CLI Reference - All CLI commands
API Reference - Full API endpoint documentation
Architecture - System design and components
Graph Schema - Neo4j labels and relationships
Retrieval & Scoring - Scoring algorithm details
Benchmarking Guide - Running and validating benchmarks
Backup & Restore Guide - Backup configuration and retention
Agent Handoff - Complete development context and phase registry

Contributing

See CONTRIBUTING.md for development setup and guidelines.

Security

See SECURITY.md for the vulnerability reporting policy.

License

MIT License - see LICENSE for details.

Name		Name	Last commit message	Last commit date
Latest commit History 799 Commits
.claude/hooks		.claude/hooks
.github		.github
.vscode		.vscode
api		api
cmd		cmd
deploy		deploy
docs		docs
internal		internal
migrations		migrations
packaging		packaging
plugins		plugins
scripts		scripts
tests		tests
.claudeignore		.claudeignore
.env.example		.env.example
.gitignore		.gitignore
.gitleaks.toml		.gitleaks.toml
.gitmodules		.gitmodules
.golangci.yml		.golangci.yml
.goreleaser.yaml		.goreleaser.yaml
.markdownlint.json		.markdownlint.json
.trivyignore		.trivyignore
AGENT_HANDOFF.md		AGENT_HANDOFF.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
CMS.md		CMS.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
ELI5.md		ELI5.md
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
SECURITY.md		SECURITY.md
VISION.md		VISION.md
article-02.md		article-02.md
docker-compose.yml		docker-compose.yml
go.mod		go.mod
go.sum		go.sum

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MDEMG - Multi-Dimensional Emergent Memory Graph

Quick Start

Prerequisites

Step 1: Install

Step 2: Initialize a Project

Step 3: Start Services

Step 4: Ingest Your Codebase

One-Command Setup (Sidecar)

Reproduce the Benchmark

Prerequisites

Reproduction Steps

Verify Integrity

Benchmark Receipts

Key Results (2026-01-30, whk-wms 507K LOC)

Key Metric: State Survival

Overview

Key Features

Architecture

Development Setup

Prerequisites

Build from Source

Configure and Start

Ingest a Codebase

API Endpoints

Core Memory Operations

Web Scraper

Backup & Restore (Phase 70)

Hash Verification (UNTS)

Admin — Space Lifecycle

Conversation Memory System (CMS)

Learning Control

Symbol Extraction (UPTS)

Conversation Memory System (CMS)

MCP Integration

Automatic Setup (Claude Code)

Manual Setup (Cursor IDE)

Project Structure

Observability Stack

Quick Start

Components

MDEMG Overview Dashboard

Metrics Endpoint

Development Roadmap

Recently Completed

Documentation

Contributing

Security

License

About

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License

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