Mnemoverse 0.1Architecture Documentation

Build persistent memory systems that remember, learn, and coordinate like experienced developers.

Agent‑ready documentation

We design these docs to be useful for both humans and machines. Pages carry predictable metadata (frontmatter), stable IDs, and consistent section ordering. This enables automated indexing, MCP doc servers, and agent navigation.

🚀 Start here

Architecture Overview — Layer map (L1–L8), boundaries, data flows
End‑to‑End Walkthrough — Query to response flow
Contracts Registry — CEO ↔ ACS ↔ HCS interfaces
Privacy & Telemetry — Guardrails across layers
SLO/SLA per Layer — Service expectations per layer

🧭 How this doc set is organized

Top‑level directories under /architecture:

adapters/ — HTTP & MCP adapters, examples, compatibility matrix
contracts/ — canonical contracts and schemas across layers
evaluation/ — benchmarks and metrics
experience-layer/ — L4 specifications and guides
memory-layer/ — L5 policies, errors/timeouts, assembler
noosphere-layer/ — L1 architecture (vector/graph search, KG, MCP integration)
orchestration/ — ACS/CEO/HCS specs, API, implementation notes
project-library/ — L2 models, indexing and operations
workshop/ — L3 interfaces, tool registry, validation pipelines

Each directory has its own README.md acting as a local index when present.

🧩 Layers & components

L1: Noosphere — Global knowledge, vector/graph search, KG
L2: Project Library — Project-scoped content, indexing
L3: Workshop — Tool registry, validation pipelines
L4: Experience Layer — Task trails and outcomes
L5: Memory — Context assembly, policies
Orchestration — ACS/CEO/HCS coordination
Adapters — HTTP & MCP interfaces
L8: Evaluation — Cross-cutting benchmarks, metrics, and feedback

⭐ Popular references

Glossary — Terms and acronyms
Contracts Overview — All layer contracts
Evaluation — Benchmarks and metrics
Implementation Guides — Ready-to-run code examples

🔌 Machine interface (MCP)

This documentation is served by our @mcp-x/mcp-docs-server which provides these tools:

list_documents — List all available docs, optionally filtered by section
get_document — Get full content of a specific document by path
search_docs — Full-text search through all documentation

Real MCP configuration:

json

{
  "name": "@mcp-x/mcp-docs-server",
  "version": "1.0.0", 
  "description": "Universal MCP Server Demo - Template for documentation-based MCP servers",
  "tools": ["list_documents", "get_document", "search_docs"],
  "sections": ["guides", "api", "tutorials", "reference", "examples"],
  "capabilities": {
    "search": {
      "maxResults": 50,
      "fuzzyThreshold": 0.6
    }
  }
}

Configure MCP access to this documentation

Quick Start — Get started with MCP
MCP Server for Docs — Ready-to-use doc server
Server Development Guide — Build your own
Federated MCP — Multi-server coordination

📖 Principles (human + machine)

Stable IDs and links: each key doc has an id and predictable path
Consistent frontmatter: docType, version, status, audience, tags
Canonical anchors: Overview (concepts), Contracts Registry (interfaces)
Deterministic navigation: auto‑generated sidebar mirrors filesystem
Clear degradation: drafts labeled; missing pages tolerated in soft link checks

Conventions

Version: this set documents the 0.1 line; drafts are clearly marked
Ordering: pages may use order: in frontmatter or numeric filename prefixes
Acronyms: normalized (API, ACS, CEO, HCS, MCP, SLO/SLA, L1–L8, KV, ADR)
Privacy: never include raw sensitive text; secure fields must be bypassed

🏗️ System Architecture

Core Components & Technology Stack

Layer Technologies:

L1 (Noosphere): Vector databases (Pinecone/Weaviate), Graph databases (Neo4j), Python/TypeScript
L2 (Project Library): Document indexing (Elasticsearch), Git integration, Node.js
L3 (Workshop): Tool orchestration, Docker containers, Kubernetes
L4 (Experience): Time-series databases (InfluxDB), Event streaming (Kafka)
L5 (Memory): Redis caching, Memory-optimized instances, Smart eviction
L8 (Evaluation): Metrics collection (Prometheus), Jupyter notebooks, Python

Infrastructure Requirements:

Compute: 16+ vCPUs, 64+ GB RAM per layer for production
Storage: 1TB+ SSD for L1/L2, 100GB+ for other layers
Network: 10Gbps+ for inter-layer communication, CDN for static content

Data Flow Architecture

Request Lifecycle:

Query Ingestion (L7) → MCP protocol normalization
Intent Processing (L6) → Budget allocation, risk assessment
Context Assembly (L5) → Multi-layer content aggregation
Knowledge Retrieval (L1/L2/L4) → Parallel provider execution
Response Generation (L5→L6→L7) → Budget-constrained assembly
Quality Monitoring (L8) → Continuous feedback loops

🧪 Testing & Validation

Testing Strategy

Unit Testing Guidelines:

Coverage Target: 90%+ for core algorithms, 80%+ for integration layers
Frameworks: Jest (TypeScript), pytest (Python), Go test (Go services)
Focus Areas: Context assembly logic, budget enforcement, provider selection

Integration Testing:

Inter-layer Communication: Test L1↔L2↔L5 data flows end-to-end
Provider Integration: Mock external services, validate contract compliance
Performance Testing: Load testing with realistic query patterns
Error Scenarios: Network partitions, service degradation, timeout handling

End-to-End Testing:

Critical User Journeys: Debug session walkthrough, research query flow
Cross-platform: VS Code extension, CLI tools, web interface
Performance Benchmarks: P95 < 800ms end-to-end, throughput > 100 QPS

Quality Gates

Pre-deployment Validation:

bash

# Performance benchmarks
npm run test:performance -- --target-p95=800ms

# Security scanning
npm run test:security -- --mode=privacy-compliance

# Integration validation
npm run test:integration -- --layers=L1,L2,L5,L8

# Documentation coverage
npm run docs:validate -- --coverage-min=95%

Acceptance Criteria:

Quality metrics: NDCG@10 ≥ 0.60, Precision@5 ≥ 0.50
Performance: P95 ≤ 800ms, error rate < 1%
Privacy: Zero leakage in block mode, effective redaction

🔧 Development & Operations

Development Environment Setup

Prerequisites:

bash

# Required tools
node >= 18.0.0
python >= 3.9
docker >= 24.0
kubernetes >= 1.28

# Development dependencies
npm install -g @mnemoverse/cli
pip install mnemoverse-dev-tools

Local Development Stack:

yaml

# docker-compose.dev.yml
version: '3.8'
services:
  redis:
    image: redis:7-alpine
    ports: ["6379:6379"]
  
  postgres:
    image: postgres:15
    environment:
      POSTGRES_DB: mnemoverse_dev
      POSTGRES_PASSWORD: dev_password
    ports: ["5432:5432"]
  
  vector-db:
    image: pinecone/pinecone-local:latest
    ports: ["8080:8080"]

Deployment Architecture

Production Environment:

Staging: Feature validation, integration testing
Production: Blue-green deployment, gradual rollout
DR Environment: Cross-region backup, RTO < 1 hour

CI/CD Pipeline:

yaml

# .github/workflows/deploy.yml
stages:
  - test: [unit, integration, e2e]
  - security: [dependency-scan, secret-scan, privacy-validation]
  - build: [docker-build, artifact-registry]
  - deploy: [staging, production-canary, production-full]

Operational Scenarios

Scaling Procedures:

Horizontal Scaling: Add provider instances behind load balancer
Vertical Scaling: Increase memory/CPU for memory-intensive L5 operations
Data Scaling: Shard knowledge base by domain/project boundaries

Maintenance Windows:

Weekly: Dependency updates, security patches (30min maintenance window)
Monthly: Performance optimization, capacity planning (2hr maintenance window)
Quarterly: Major version upgrades, architectural improvements (4hr maintenance window)

Disaster Recovery:

Backup Strategy: Daily snapshots, cross-region replication
Recovery Procedures: Automated failover, manual validation steps
RTO Target: < 1 hour for critical services, < 4 hours for full restoration

📊 Performance & Monitoring

Key Metrics

System Performance:

Latency: P50/P95/P99 response times per layer
Throughput: Queries per second, concurrent user capacity
Availability: 99.9% uptime target, planned maintenance exclusions

Quality Metrics:

Search Quality: NDCG@K scores, user satisfaction ratings
Context Relevance: Fragment usefulness, contradiction rates
Learning Effectiveness: Experience pattern accuracy, improvement trends

Resource Utilization:

txt

# Example monitoring queries
mnemoverse_memory_usage_ratio{layer="L5"} > 0.85
mnemoverse_query_latency_p95{layer="orchestration"} > 800
mnemoverse_error_rate{service="context-assembly"} > 0.01

Alerting & Escalation

Critical Alerts (PagerDuty integration):

Service unavailability > 5 minutes
Error rate > 5% for > 2 minutes
P95 latency > 1.5s for > 5 minutes

Warning Alerts (Slack integration):

Memory usage > 85%
Quality metrics below baseline
Unusual traffic patterns

Status

Work in progress. Some deep‑dive drafts live under /architecture/orchestration and may reference upcoming pages. The Overview and Contracts Registry are the canonical sources to align on first.

🤝 Contribute

Keep frontmatter flat where possible (simple key: value) for robust tooling
Prefer stable slugs; avoid renames without adding redirects
Add order: to control sibling ordering when needed
Use short, descriptive titles; keep acronyms normalized

Agent‑ready documentation

🚀 Start here

Architecture Overview — Layer map (L1–L8), boundaries, data flows
End‑to‑End Walkthrough — Query to response flow
Contracts Registry — CEO ⇄ ACS ⇄ HCS interfaces
Privacy & Telemetry — Guardrails across layers
SLO/SLA per Layer — Service expectations per layer

🧭 How this doc set is organized

Top‑level directories under /architecture:

adapters/ — HTTP & MCP adapters, examples, compatibility matrix
contracts/ — canonical contracts and schemas across layers
evaluation/ — benchmarks and metrics
experience-layer/ — L4 specifications and guides
memory-layer/ — L5 policies, errors/timeouts, assembler
noosphere-layer/ — L1 architecture (vector/graph search, KG, MCP integration)
orchestration/ — ACS/CEO/HCS specs, API, implementation notes
project-library/ — L2 models, indexing and operations
workshop/ — L3 interfaces, tool registry, validation pipelines

Each directory has its own README.md acting as a local index when present.

🧩 Layers & components

L1: Noosphere — Global knowledge, vector/graph search, KG
L2: Project Library — Project-scoped content, indexing
L3: Workshop — Tool registry, validation pipelines
L4: Experience Layer — Task trails and outcomes
L5: Memory — Context assembly, policies
Orchestration — ACS/CEO/HCS coordination
Adapters — HTTP & MCP interfaces
L8: Evaluation — Cross-cutting benchmarks, metrics, and feedback

⭐ Popular references

Glossary — Terms and acronyms
Contracts Overview — All layer contracts
Evaluation — Benchmarks and metrics
Implementation Guides — Ready-to-run code examples

🔌 Machine interface (MCP)

This documentation is served by our @mcp-x/mcp-docs-server which provides these tools:

list_documents — List all available docs, optionally filtered by section
get_document — Get full content of a specific document by path
search_docs — Full-text search through all documentation

Real MCP configuration:

json

{
  "name": "@mcp-x/mcp-docs-server",
  "version": "1.0.0", 
  "description": "Universal MCP Server Demo - Template for documentation-based MCP servers",
  "tools": ["list_documents", "get_document", "search_docs"],
  "sections": ["guides", "api", "tutorials", "reference", "examples"],
  "capabilities": {
    "search": {
      "maxResults": 50,
      "fuzzyThreshold": 0.6
    }
  }
}

Configure MCP access to this documentation

Quick Start — Get started with MCP
MCP Server for Docs — Ready-to-use doc server
Server Development Guide — Build your own
Federated MCP — Multi-server coordination

📐 Principles (human + machine)

Stable IDs and links: each key doc has an id and predictable path
Consistent frontmatter: docType, version, status, audience, tags
Canonical anchors: Overview (concepts), Contracts Registry (interfaces)
Deterministic navigation: auto‑generated sidebar mirrors filesystem
Clear degradation: drafts labeled; missing pages tolerated in soft link checks

Conventions

Version: this set documents the 0.1 line; drafts are clearly marked
Ordering: pages may use order: in frontmatter or numeric filename prefixes
Acronyms: normalized (API, ACS, CEO, HCS, MCP, SLO/SLA, L1–L8, KV, ADR)
Privacy: never include raw sensitive text; secure fields must be bypassed

🏗️ System Architecture

Core Components & Technology Stack

Layer Technologies:

L1 (Noosphere): Vector databases (Pinecone/Weaviate), Graph databases (Neo4j), Python/TypeScript
L2 (Project Library): Document indexing (Elasticsearch), Git integration, Node.js
L3 (Workshop): Tool orchestration, Docker containers, Kubernetes
L4 (Experience): Time-series databases (InfluxDB), Event streaming (Kafka)
L5 (Memory): Redis caching, Memory-optimized instances, Smart eviction
L8 (Evaluation): Metrics collection (Prometheus), Jupyter notebooks, Python

Infrastructure Requirements:

Compute: 16+ vCPUs, 64+ GB RAM per layer for production
Storage: 1TB+ SSD for L1/L2, 100GB+ for other layers
Network: 10Gbps+ for inter-layer communication, CDN for static content

Data Flow Architecture

Request Lifecycle:

Query Ingestion (L7) → MCP protocol normalization
Intent Processing (L6) → Budget allocation, risk assessment
Context Assembly (L5) → Multi-layer content aggregation
Knowledge Retrieval (L1/L2/L4) → Parallel provider execution
Response Generation (L5→L6→L7) → Budget-constrained assembly
Quality Monitoring (L8) → Continuous feedback loops

🧪 Testing & Validation

Testing Strategy

Unit Testing Guidelines:

Coverage Target: 90%+ for core algorithms, 80%+ for integration layers
Frameworks: Jest (TypeScript), pytest (Python), Go test (Go services)
Focus Areas: Context assembly logic, budget enforcement, provider selection

Integration Testing:

Inter-layer Communication: Test L1↔L2↔L5 data flows end-to-end
Provider Integration: Mock external services, validate contract compliance
Performance Testing: Load testing with realistic query patterns
Error Scenarios: Network partitions, service degradation, timeout handling

End-to-End Testing:

Critical User Journeys: Debug session walkthrough, research query flow
Cross-platform: VS Code extension, CLI tools, web interface
Performance Benchmarks: P95 < 800ms end-to-end, throughput > 100 QPS

Quality Gates

Pre-deployment Validation:

bash

# Performance benchmarks
npm run test:performance -- --target-p95=800ms

# Security scanning
npm run test:security -- --mode=privacy-compliance

# Integration validation
npm run test:integration -- --layers=L1,L2,L5,L8

# Documentation coverage
npm run docs:validate -- --coverage-min=95%

Acceptance Criteria:

Quality metrics: NDCG@10 ≥ 0.60, Precision@5 ≥ 0.50
Performance: P95 ≤ 800ms, error rate < 1%
Privacy: Zero leakage in block mode, effective redaction

🔧 Development & Operations

Development Environment Setup

Prerequisites:

bash

# Required tools
node >= 18.0.0
python >= 3.9
docker >= 24.0
kubernetes >= 1.28

# Development dependencies
npm install -g @mnemoverse/cli
pip install mnemoverse-dev-tools

Local Development Stack:

yaml

# docker-compose.dev.yml
version: '3.8'
services:
  redis:
    image: redis:7-alpine
    ports: ["6379:6379"]
  
  postgres:
    image: postgres:15
    environment:
      POSTGRES_DB: mnemoverse_dev
      POSTGRES_PASSWORD: dev_password
    ports: ["5432:5432"]
  
  vector-db:
    image: pinecone/pinecone-local:latest
    ports: ["8080:8080"]

Deployment Architecture

Production Environment:

Staging: Feature validation, integration testing
Production: Blue-green deployment, gradual rollout
DR Environment: Cross-region backup, RTO < 1 hour

CI/CD Pipeline:

yaml

# .github/workflows/deploy.yml
stages:
  - test: [unit, integration, e2e]
  - security: [dependency-scan, secret-scan, privacy-validation]
  - build: [docker-build, artifact-registry]
  - deploy: [staging, production-canary, production-full]

Operational Scenarios

Scaling Procedures:

Horizontal Scaling: Add provider instances behind load balancer
Vertical Scaling: Increase memory/CPU for memory-intensive L5 operations
Data Scaling: Shard knowledge base by domain/project boundaries

Maintenance Windows:

Weekly: Dependency updates, security patches (30min maintenance window)
Monthly: Performance optimization, capacity planning (2hr maintenance window)
Quarterly: Major version upgrades, architectural improvements (4hr maintenance window)

Disaster Recovery:

Backup Strategy: Daily snapshots, cross-region replication
Recovery Procedures: Automated failover, manual validation steps
RTO Target: < 1 hour for critical services, < 4 hours for full restoration

📊 Performance & Monitoring

Key Metrics

System Performance:

Latency: P50/P95/P99 response times per layer
Throughput: Queries per second, concurrent user capacity
Availability: 99.9% uptime target, planned maintenance exclusions

Quality Metrics:

Search Quality: NDCG@K scores, user satisfaction ratings
Context Relevance: Fragment usefulness, contradiction rates
Learning Effectiveness: Experience pattern accuracy, improvement trends

Resource Utilization:

txt

# Example monitoring queries
mnemoverse_memory_usage_ratio{layer="L5"} > 0.85
mnemoverse_query_latency_p95{layer="orchestration"} > 800
mnemoverse_error_rate{service="context-assembly"} > 0.01

Alerting & Escalation

Critical Alerts (PagerDuty integration):

Service unavailability > 5 minutes
Error rate > 5% for > 2 minutes
P95 latency > 1.5s for > 5 minutes

Warning Alerts (Slack integration):

Memory usage > 85%
Quality metrics below baseline
Unusual traffic patterns

Status

Work in progress. Some deep‑dive drafts live under /architecture/orchestration and may reference upcoming pages. The Overview and Contracts Registry are the canonical sources to align on first.

🤝 Contribute

Keep frontmatter flat where possible (simple key: value) for robust tooling
Prefer stable slugs; avoid renames without adding redirects
Add order: to control sibling ordering when needed
Use short, descriptive titles; keep acronyms normalized

Mnemoverse 0.1Architecture Documentation

Agent‑ready documentation ​

🚀 Start here ​

🧭 How this doc set is organized ​

🧩 Layers & components ​

⭐ Popular references ​

🔌 Machine interface (MCP) ​

Configure MCP access to this documentation ​

📖 Principles (human + machine) ​

Conventions ​

🏗️ System Architecture ​

Core Components & Technology Stack ​

Data Flow Architecture ​

🧪 Testing & Validation ​

Testing Strategy ​

Quality Gates ​

🔧 Development & Operations ​

Development Environment Setup ​

Deployment Architecture ​

Operational Scenarios ​

📊 Performance & Monitoring ​

Key Metrics ​

Alerting & Escalation ​

Status ​

🤝 Contribute ​

Agent‑ready documentation ​

🚀 Start here ​

🧭 How this doc set is organized ​

🧩 Layers & components ​

⭐ Popular references ​

🔌 Machine interface (MCP) ​

Configure MCP access to this documentation ​

📐 Principles (human + machine) ​

Conventions ​

🏗️ System Architecture ​

Core Components & Technology Stack ​

Data Flow Architecture ​

🧪 Testing & Validation ​

Testing Strategy ​

Quality Gates ​

🔧 Development & Operations ​

Development Environment Setup ​

Deployment Architecture ​

Operational Scenarios ​

📊 Performance & Monitoring ​

Key Metrics ​

Alerting & Escalation ​

Status ​

🤝 Contribute ​

Agent‑ready documentation

🚀 Start here

🧭 How this doc set is organized

🧩 Layers & components

⭐ Popular references

🔌 Machine interface (MCP)

Configure MCP access to this documentation

📖 Principles (human + machine)

Conventions

🏗️ System Architecture

Core Components & Technology Stack

Data Flow Architecture

🧪 Testing & Validation

Testing Strategy

Quality Gates

🔧 Development & Operations

Development Environment Setup

Deployment Architecture

Operational Scenarios

📊 Performance & Monitoring

Key Metrics

Alerting & Escalation

Status

🤝 Contribute

Agent‑ready documentation

🚀 Start here

🧭 How this doc set is organized

🧩 Layers & components

⭐ Popular references

🔌 Machine interface (MCP)

Configure MCP access to this documentation

📐 Principles (human + machine)

Conventions

🏗️ System Architecture

Core Components & Technology Stack

Data Flow Architecture

🧪 Testing & Validation

Testing Strategy

Quality Gates

🔧 Development & Operations

Development Environment Setup

Deployment Architecture

Operational Scenarios

📊 Performance & Monitoring

Key Metrics

Alerting & Escalation

Status

🤝 Contribute