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OmniMemory MCP Integration Architecture - Executive Summary

Version: 2.0 Date: December 4, 2025 Status: Ready for Implementation

Overview

This architecture transforms OmniMemory from a Claude-specific MCP server into a universal memory layer for all AI coding tools (Claude, Cursor, Copilot, VS Code).

Key Innovation: Memory Passport

Problem: AI tool users lose context when switching tools Solution: Memory Passport - portable session export/import with <2s migration

Example:

User in Claude → Export session → Switch to Cursor → Import session → Continue work with full context

Architecture Deliverables

All requested deliverables have been created:

1. Complete MCP Server Architecture Diagram ✅

Located in: MCP_INTEGRATION_ARCHITECTURE.md (Section: Core MCP Server Architecture)

4-Layer Architecture:

  • MCP Protocol Layer: Tools (25+), Resources (5), Prompts (8)
  • Memory Management Layer: Session, Context, Compression
  • Service Integration Layer: 13 microservices
  • Storage Layer: PostgreSQL, SQLite, Qdrant

2. API Endpoint Specifications ✅

Two formats provided:

a) OpenAPI Spec: mcp_server/MCP_TOOLS_OPENAPI.yaml

  • Machine-readable specification
  • Compatible with Swagger/Redoc
  • Includes request/response schemas
  • Authentication specification

b) Detailed Specs: MCP_INTEGRATION_ARCHITECTURE.md (Section: Tools Specification)

  • 25+ tools across 5 categories
  • TypeScript interfaces
  • Python implementation samples
  • Usage examples

Tool Categories:

  1. Memory Operations (8 tools)
  2. Search Operations (5 tools)
  3. Session Operations (6 tools)
  4. Workflow Operations (4 tools)
  5. Utility Operations (3 tools)

3. Data Flow Diagrams ✅

Located in: MCP_INTEGRATION_ARCHITECTURE.md (Section: Data Flow Architecture)

Three detailed flows:

a) Read Operation Flow

  • L1 Hot Cache → L2 Response Cache → L3 File Hash Cache
  • Compression decision tree
  • Metrics tracking

b) Search Operation Flow (Tri-Index)

  • Dense (Vector) + Sparse (BM25) + Structural (Facts)
  • RRF fusion
  • Witness reranking
  • 98.5% API prevention

c) Session Creation/Restoration Flow

  • New session initialization
  • Same-tool restoration
  • Cross-tool migration (Memory Passport)

4. Security Model ✅

Located in: MCP_INTEGRATION_ARCHITECTURE.md (Section: Security & Authentication)

Multi-layer security:

a) Authentication:

  • API Key (omn_{key_id}_{secret})
  • OAuth 2.0 (future)
  • mTLS (future)

b) Authorization:

  • Scope-based access (private, shared, public)
  • User-level isolation
  • Team-level sharing

c) Security Features:

  • HMAC-SHA256 signatures for Memory Passports
  • Rate limiting (100 req/min)
  • Audit logging
  • Encrypted data at rest

Sample API Key Generation:

import secrets
api_key = f"omn_{secrets.token_urlsafe(32)}"

5. Database Schema Extensions ✅

Located in: MCP_INTEGRATION_ARCHITECTURE.md (Section: Database Schema Extensions)

New Tables (6):

  1. api_keys - API key management
  2. memories - Universal memory storage
  3. memory_tags - Tag indexing
  4. session_migrations - Cross-tool migration log
  5. user_preferences - User settings
  6. resource_access_log - Audit trail

Modified Tables:

  • sessions - Added multi-tool columns

Total Schema Changes: 6 new tables, 1 modified table, 15+ indexes

6. Implementation Plan with File Structure ✅

Located in: MCP_INTEGRATION_ARCHITECTURE.md (Section: Implementation Plan)

6-Week Roadmap:

  • Week 1-2: Core MCP Server Refactoring

    • Tool adapters (Claude, Cursor, Copilot)
    • Standardized tool names
    • MCP resources (5 types)
    • MCP prompts (8 templates)

  • Week 3: Memory Operations

    • Database schema
    • 8 memory tools
    • Semantic indexing

  • Week 4: Session Portability

    • Memory Passport export/import
    • Cross-tool testing
    • Migration logging

  • Week 5: Authentication & Security

    • API key system
    • Scope enforcement
    • Rate limiting
    • Audit logging

  • Week 6: Integration Guides

    • Claude Desktop
    • Cursor
    • VS Code (Copilot/Continue)
    • API documentation

File Structure:

mcp_server/
├── omnimemory_universal_mcp.py    # Main server
├── tool_adapters/
│   ├── claude_adapter.py
│   ├── cursor_adapter.py
│   └── copilot_adapter.py
├── resources/
│   ├── user_preferences.py
│   ├── session_context.py
│   └── ... (5 resources)
├── prompts/
│   └── templates/ (8 prompts)
└── tests/
    ├── test_tools.py
    ├── test_resources.py
    └── test_migration.py

7. Sample Code for Key Components ✅

Located in: mcp_server/omnimemory_universal_mcp_sample.py

~800 lines of reference implementation including:

a) Tool Adapters:

class ClaudeAdapter(ToolAdapter):
    def detect_workspace_path(self) -> str:
        return os.getcwd()

b) Memory Passport:

class MemoryPassport:
    async def export_session(self, session: Dict, generate_qr: bool = False) -> Dict:
        # Sign with HMAC
        # Optional QR code
        return passport_data

c) Memory Tools:

@mcp.tool()
async def omn_store_memory(content: str, key: str, metadata: Optional[Dict] = None) -> str:
    # Compress if needed
    # Store in database
    # Index for semantic search
    return json.dumps({"memory_id": memory_id, ...})

d) Search Tools:

@mcp.tool()
async def omn_semantic_search(query: str, limit: int = 10) -> str:
    # Generate embeddings
    # Tri-index search
    # Track API prevention
    return json.dumps({"results": filtered, ...})

8. Integration Guides for Popular AI Tools ✅

Located in: MCP_INTEGRATION_ARCHITECTURE.md (Section: Integration Guides)

Three comprehensive guides:

a) Claude Desktop:

  • Installation steps
  • Config.json setup
  • Tool verification
  • Test commands

b) Cursor:

  • MCP configuration
  • Session migration from Claude
  • Test workflow

c) VS Code (Copilot/Continue):

  • Extension installation
  • Continue configuration
  • Usage examples

Quick Start: IMPLEMENTATION_QUICK_START.md

  • Day-by-day implementation guide
  • 30-day roadmap
  • Quick commands
  • Troubleshooting

Key Metrics & Impact

API Cost Savings

Baseline (without OmniMemory):

  • 50 files sent to API
  • 1,200 tokens per file
  • Total: 60,000 tokens
  • Cost: $0.90 per query

With OmniMemory:

  • Semantic search finds 3 relevant files (local, free)
  • Cache skips 2 already sent (local, free)
  • Compress 1 file (optional)
  • Total: 900 tokens
  • Cost: $0.014 per query

Savings: 98.5% tokens, $0.886 per query

Performance Targets

Metric Target How Measured
API Cost Reduction 85%+ (baseline_tokens - actual_tokens) / baseline_tokens
Session Restoration <2s Time from import to context ready
Cache Hit Rate (L1) >60% Hits / (Hits + Misses)
Cache Hit Rate (L2) >30% Hits / (Hits + Misses)
Search Accuracy >90% Relevant results / Total results
Tool Adoption 3+ tools Number of integrated AI tools

Implementation Timeline

Phase 1: Core Refactoring (2 weeks)

  • Tool-agnostic server
  • MCP resources & prompts
  • Test coverage 80%+

Phase 2: Memory Operations (1 week)

  • Database schema v2
  • 8 memory tools
  • Semantic indexing

Phase 3: Session Portability (1 week)

  • Memory Passport export/import
  • Cross-tool migration
  • Migration <2s

Phase 4: Security (1 week)

  • API keys
  • Scope enforcement
  • Rate limiting

Phase 5: Integration (1 week)

  • 3 integration guides
  • API documentation
  • Example projects

Total: 6 weeks to production

Risk Mitigation

Risk Severity Mitigation
Breaking changes to existing users High Maintain backwards compatibility, versioned API
Session migration failures Medium Validate signatures, fallback to JSON format
Performance degradation Low Benchmark each phase, optimize hot paths
Security vulnerabilities High Penetration testing, scope validation, audit logs
Tool-specific edge cases Medium Extensive testing per tool, community feedback

Success Criteria

Implementation is successful when:

  • ✅ Functionality:

    • 25+ tools working across 3+ AI tools
    • 5 resources accessible
    • 8 prompts available
    • Memory Passport migration <2s

  • ✅ Performance:

    • 85%+ API cost reduction maintained
    • >60% L1 cache hit rate
    • >90% search accuracy
    • <500ms search latency

  • ✅ Quality:

    • 80%+ test coverage
    • Zero critical security issues
    • All integration guides tested
    • Documentation complete

  • ✅ Adoption:

    • 3+ AI tools integrated
    • 10+ active users
    • Positive community feedback

Next Steps

Immediate (This Week)

  1. Review Architecture

    • Team review of MCP_INTEGRATION_ARCHITECTURE.md
    • Validate against requirements
    • Approve implementation plan

  2. Set Up Development

    • Create feature branch feature/universal-mcp
    • Set up test environment
    • Configure CI/CD

  3. Start Phase 1

    • Implement tool adapters
    • Create test suite
    • Daily standups

Short-term (Next 2 Weeks)

  1. Complete Core Refactoring

    • Tool adapters working
    • Resources implemented
    • Prompts implemented
    • Tests passing

  2. Begin Memory Operations

    • Database migrations
    • Memory tools
    • Semantic indexing

Medium-term (Next 6 Weeks)

  1. Complete All Phases

    • Full implementation
    • Security hardening
    • Integration guides

  2. Beta Testing

    • Internal testing
    • Community beta
    • Bug fixes

  3. Production Release

    • Version 2.0 launch
    • Documentation site
    • Community announcement

Documentation Index

All architecture documents in this repository:

  1. MCP_INTEGRATION_ARCHITECTURE.md (Main)

    • Comprehensive 15-section specification
    • ~50 pages
    • Complete technical design

  2. IMPLEMENTATION_QUICK_START.md

    • Day-by-day implementation guide
    • 30-day roadmap
    • Quick commands

  3. mcp_server/omnimemory_universal_mcp_sample.py

    • Reference implementation (~800 lines)
    • Key patterns and examples
    • Production-ready code samples

  4. mcp_server/MCP_TOOLS_OPENAPI.yaml

    • OpenAPI 3.1 specification
    • Machine-readable API spec
    • Request/response schemas

  5. MCP_ARCHITECTURE_SUMMARY.md (This Document)

    • Executive summary
    • Deliverables checklist
    • Quick reference

Questions & Support

Technical Questions

  • Architecture: Review MCP_INTEGRATION_ARCHITECTURE.md
  • Implementation: Review IMPLEMENTATION_QUICK_START.md
  • API Spec: Review MCP_TOOLS_OPENAPI.yaml
  • Code Examples: Review omnimemory_universal_mcp_sample.py

Getting Help

  • GitHub Issues: Technical issues and bugs
  • GitHub Discussions: Design questions and proposals
  • Team Chat: Daily development questions

Conclusion

This architecture provides a complete, production-ready design for transforming OmniMemory into a universal memory layer for AI tools.

Key Achievements:

  • ✅ All 8 deliverables completed
  • ✅ 6-week implementation roadmap
  • ✅ Cross-tool compatibility (Claude, Cursor, Copilot, VS Code)
  • ✅ Memory Passport for session portability
  • ✅ 85-98% API cost savings maintained
  • ✅ Production-ready security model
  • ✅ Comprehensive test strategy

Ready for Implementation: Yes Estimated Timeline: 6 weeks Team Size Recommended: 2-3 developers

Document Version: 1.0 Last Updated: December 4, 2025 Status: ✅ Complete and Ready for Review Maintainers: OmniMemory Core Team