Mnemoverse Architecture Overview

This document formalizes the layered architecture to prevent scope creep and clarify responsibilities across the system.

Layer Map (L1–L8)

• L1 — Knowledge Layer (Noosphere Layer)

  • Global knowledge ingestion, validation, archiving, cataloging
  • Multi-method search abstraction (vector | graph | agents)
  • Analysis utilities (quality, metadata, provenance)
  • NOT project-specific; entire world knowledge (including noise, filtered)

• L2 — Project Library

  • Project-scoped subset and links to L1 (references + project-authored docs)
  • Strong emphasis on relevance, freshness, and indexing for quick access
  • Graph-forward organization for traceability within project context

• L3 — Workshop (Tooling & Lab)

  • Tool registry, tool-building pipelines, validation and improvement loops
  • Close coupling with Experience Layer for capturing tool usage outcomes

• L4 — Experience Layer (Base of Experience)

  • Task-level trails: '{task, nodes used, external refs/tools, result, validation}'
  • Enables search over “how tasks were solved” + outcomes comparison

• L5 — Mnemoverse Memory

  • Real-time context assembly focused on relevance for current step/query
  • Text mode: prompt-assembler for LLM; Multimodal: filtered streams by LOD

• L6 — Agent Runtime (Out of Scope here)

  • Agent policies/skills/planning/execution (external), connected via adapters

• L7 — Interfaces & Clients

  • IDEs, CLIs, Desktops, Assistants; leverage MCP/HTTP adapters

• L8 — Evaluation (Cross‑Cutting)

• Benchmarks, metrics, and feedback loops that observe and influence L1–L7

• Offline‑first evaluation with minimal online controls (thresholds, gates)

Boundaries & Responsibilities

• L1 owns: ingestion, normalization, quality signals, multi-method search, global catalogs.
• L2 owns: project relevance, fast access indexes, curated views, project provenance.
• L3 owns: tools lifecycle + validation harnesses.
• L4 owns: experience records and validation of outcomes.
• L5 owns: on-demand context composition with budget/LOD policies.
• L6/L7 consume L5 via adapters; not part of this repo’s core implementation.
• L8 observes L1–L7 and feeds back signals (policies remain owned by respective layers).

Data Flows (simplified)

L7 → L6 → L5 (context request) → L2 (project subset) → L1 (global lookup) ↘ L4 (prior task trails) ↘ L3 (tool hints)

Inter-Layer Communication Patterns

Request Flow Architecture

Error Handling & Resilience

Layer Fault Tolerance:

• L1 Unavailable: L2 continues with project-only context, degraded mode
• L2 Unavailable: L1 provides global fallback, quality warning issued
• L4 Unavailable: Skip experience enhancement, basic context assembly
• L5 Partial Failure: Return best-effort context within time budgets
• L8 Unavailable: Continue operations, disable quality feedback loops

Circuit Breaker Thresholds:

layer_resilience:
  L1_noosphere:
    failure_threshold: 5
    timeout_ms: 2000
    fallback: "project_only_mode"
  
  L2_project_library:
    failure_threshold: 3
    timeout_ms: 1000
    fallback: "global_search_expansion"
    
  L5_memory_assembly:
    failure_threshold: 2
    timeout_ms: 500
    fallback: "simple_concatenation"

Operational Framework

Security Model

Trust Boundaries:

• L1/L2 Boundary: Content filtering, PII redaction before indexing
• L4/L5 Boundary: Privacy mode enforcement, sensitive data blocking
• L6/L7 Boundary: Authentication, authorization, rate limiting
• L8 Cross-cutting: Audit logging, compliance validation

Security Zones:

security_zones:
  public:
    layers: [L7]
    access: "authenticated_users"
    encryption: "TLS_1.3"
    
  internal:
    layers: [L1, L2, L4, L5, L6]
    access: "service_mesh_mTLS"
    encryption: "AES_256_GCM"
    
  monitoring:
    layers: [L8]
    access: "admin_only"
    encryption: "end_to_end"

Privacy Compliance:

• GDPR Article 17: Right to erasure across all layers
• Privacy by Design: Default redaction modes, minimal data collection
• Data Minimization: Layer-specific retention policies, automatic purging

Performance Characteristics

Layer-Specific SLAs:

Layer	Operation	Target Latency (P95)	Throughput	Availability
L1	Knowledge search	< 300ms	1000 QPS	99.9%
L2	Project search	< 100ms	2000 QPS	99.95%
L4	Experience lookup	< 50ms	5000 QPS	99.9%
L5	Context assembly	< 500ms	500 QPS	99.95%
L8	Quality evaluation	< 200ms	100 QPS	99.5%

End-to-End Performance:

• Target: P95 < 800ms, P99 < 1.2s
• Degradation Handling: Graceful timeout with partial results
• Load Balancing: Layer-aware routing, predictive scaling

Monitoring & Observability

Cross-Layer Metrics:

# Layer health indicators
mnemoverse_layer_health{layer="L1", component="vector_search"}
mnemoverse_layer_health{layer="L2", component="project_index"}
mnemoverse_layer_health{layer="L5", component="context_assembly"}

# Inter-layer communication
mnemoverse_interlayer_latency{from="L5", to="L1", operation="search"}
mnemoverse_interlayer_errors{from="L5", to="L2", error_type="timeout"}

# Quality signals  
mnemoverse_context_quality{layer="L5", metric="relevance_score"}
mnemoverse_search_quality{layer="L1", metric="ndcg_at_10"}

Alerting Strategy:

• Critical: Service outages, data corruption, security breaches
• Warning: Performance degradation, quality drops, capacity issues
• Info: Deployment events, configuration changes, usage patterns

Validation & Quality Assurance

Layer Validation Framework

L1 (Noosphere) Validation:

• Data Quality: Content validation, metadata consistency, provenance tracking
• Search Quality: NDCG@K benchmarks, relevance scoring, result diversity
• Performance: Vector similarity computation time, graph traversal efficiency

L2 (Project Library) Validation:

• Relevance Accuracy: Project-specific content filtering effectiveness
• Freshness Validation: Update propagation time, stale content detection
• Index Consistency: Search result accuracy, ranking quality

L5 (Memory) Validation:

• Assembly Quality: Context coherence, contradiction detection, completeness
• Budget Compliance: Token limits, time constraints, resource utilization
• Policy Effectiveness: KV policy performance, eviction accuracy

Integration Testing Strategy

Cross-Layer Integration Tests:

integration_tests:
  L1_L2_consistency:
    description: "Verify project references align with global knowledge"
    frequency: "daily"
    success_criteria: "consistency_rate > 0.95"
    
  L2_L5_assembly:
    description: "Test project context assembly under various budgets"
    frequency: "continuous"
    success_criteria: "assembly_time < 400ms, relevance > 0.8"
    
  L4_L5_experience:
    description: "Validate experience pattern integration"
    frequency: "weekly" 
    success_criteria: "pattern_accuracy > 0.7, latency < 50ms"

  end_to_end:
    description: "Complete L7→L1→L7 user journey validation"
    frequency: "hourly"
    success_criteria: "p95_latency < 800ms, success_rate > 99%"

Quality Gates:

• Pre-deployment: All integration tests pass, performance benchmarks met
• Production: Continuous monitoring, automatic rollback triggers
• Post-deployment: Quality metric validation, user satisfaction tracking

Implementation Roadmap

Phase 1: Foundation (Months 1-3)

MVP Layers: L1, L2, L5, L8

• ✅ L1: Basic vector search, simple graph traversal
• ✅ L2: Project indexing, relevance filtering
• ✅ L5: Simple context assembly, budget enforcement
• ✅ L8: Basic metrics collection, offline evaluation

Deliverables:

• Working end-to-end query processing
• Basic quality evaluation framework
• Essential monitoring and alerting

Phase 2: Enhancement (Months 4-6)

Extended Layers: L3, L4 + Advanced Features

• 🔄 L3: Tool registry, validation pipelines
• 🔄 L4: Experience capture, pattern learning
• 🔄 Enhanced L1: Multi-method search, AI agents
• 🔄 Enhanced L5: Advanced KV policies, conflict resolution

Deliverables:

• Complete layer functionality
• Advanced quality assurance
• Production-ready operational features

Phase 3: Scale & Polish (Months 7-9)

Production Hardening + L6/L7 Adapters

• 🔄 Performance: Horizontal scaling, caching optimization
• 🔄 Security: Advanced threat protection, compliance certification
• 🔄 Adapters: VS Code extension, CLI tools, web interface
• 🔄 AI Integration: Agent runtime connectors, workflow automation

Deliverables:

• Production deployment capability
• Full adapter ecosystem
• Enterprise-grade security and compliance

Status

• Current: Implementing L1 (Noosphere Layer) MVP with vector search foundation
• L2: Initial form in Research Library; schema alignment and API standardization in progress
• L3-L5: Architecture complete, implementation scheduled for Phase 2
• L6-L7: Out of scope for core but supported via standardized adapters
• L8: Basic framework operational, advanced features in development

Next Steps

Immediate (Next 30 days):

• Finalize L1↔L2↔L5 contract specifications and API schemas
• Implement privacy/telemetry policies across data layers (L1/L2/L4)
• Deploy basic L8 evaluation framework for quality monitoring

Short-term (Next 90 days):

• Complete L1 MVP with multi-method search capabilities
• Integrate L2 project library with standardized relevance scoring
• Launch L5 context assembly with budget-aware policies

Medium-term (6 months):

• Deploy complete Phase 1 system with all MVP layers operational
• Begin Phase 2 development with L3/L4 experience capture
• Establish production monitoring and operational procedures