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CEMS Implementation Status

Last Updated: 2026-01-19

This document tracks the implementation status of CEMS (Continuous Evolving Memory System) against the original plan in mem0-tech-spec.md.

Executive Summary

Phase Status Notes
Phase 1: Core Memory Wrapper ✅ Complete CEMSMemory, MetadataStore, namespace isolation
Phase 2: Scheduled Maintenance ✅ Complete LLM-based summarization and merging
Phase 3: MCP Server ✅ Complete Simplified: 5 tools + 3 resources
Phase 4: Claude Code Integration ✅ Complete Skills, config templates, install script
Extended: Deployment ✅ Complete Docker, PostgreSQL schema, docs
Extended: Indexer ✅ Complete Repository indexer with patterns
Extended: Pinned Memories ✅ Complete Pinned memories excluded from decay
Extended: OpenRouter ✅ Complete Enterprise LLM provider support
V2: Priority/Decay Search ✅ Complete Search now uses priority and time decay
V2: Hybrid Search ✅ Internal Available via Python API, not exposed as MCP tool
V2: Smart Search ✅ Internal Logic incorporated into unified pipeline
V2: Unified Retrieval Pipeline ✅ Complete 5-stage inference pipeline in retrieve_for_inference()
V2: Enhanced Prompts ✅ Complete Few-shot examples for LLM operations
V3: Simplified API ✅ Complete Reduced from 15 to 5 essential MCP tools
Tests ✅ Complete 82 tests passing

Detailed Status by Component

1. Core Memory Wrapper (Phase 1) ✅

Item Status File Notes
CEMSMemory class ✅ src/cems/memory.py Wraps Mem0 with namespace isolation
Namespace isolation ✅ src/cems/memory.py:66-73 Uses personal:{user_id} / shared:{team_id}
MetadataStore (SQLite) ✅ src/cems/models.py:91-500 Extended metadata tracking
Access count tracking ✅ src/cems/models.py:215-229 record_access() method
Priority boosting ✅ src/cems/models.py:278-292 increase_priority() method
Archive/soft delete ✅ src/cems/models.py:266-276 archive_memory() method
Configuration ✅ src/cems/config.py Pydantic settings with env vars

2. Scheduled Maintenance (Phase 2) ✅ COMPLETE

Item Status File Notes
APScheduler setup ✅ src/cems/scheduler.py Background scheduler with cron triggers
Nightly job scheduling ✅ src/cems/scheduler.py:43-49 3 AM default
Weekly job scheduling ✅ src/cems/scheduler.py:52-60 Sunday 4 AM default
Monthly job scheduling ✅ src/cems/scheduler.py:63-72 1st of month 5 AM default
Consolidation logic ✅ src/cems/maintenance/consolidation.py LLM merges duplicates into single memory
Summarization logic ✅ src/cems/maintenance/summarization.py LLM generates category summaries
Reindex logic ✅ src/cems/maintenance/reindex.py Triggers Mem0's embedding pipeline
Maintenance logging ✅ src/cems/models.py:303-337 maintenance_log table
LLM utilities ✅ src/cems/llm.py OpenAI/Anthropic support with fallbacks

Maintenance Job Details

Consolidation (consolidation.py):

  • ✅ Finds duplicates by vector similarity (>0.92)
  • ✅ Uses LLM to merge duplicate content into comprehensive memory
  • ✅ Promotes hot memories based on access count

Summarization (summarization.py):

  • ✅ Groups old memories (30+ days) by category
  • ✅ Uses LLM to generate coherent summaries per category
  • ✅ Prunes stale memories (90+ days not accessed)

Reindex (reindex.py):

  • ✅ Updates all memories to trigger Mem0's embedding regeneration
  • ✅ Archives dead memories (180+ days not accessed)
  • Note: Uses Mem0's update mechanism since it doesn't expose direct embedding APIs

3. MCP Server (Phase 3) ✅ COMPLETE (Simplified in V3)

MCP Tools (5 essential tools)

The server was simplified from 15 tools down to 5 essential tools. Internal methods remain available via Python API and CLI.

Tool Status Description
memory_add ✅ Store memories (personal or shared namespace)
memory_search ✅ Unified search with 5-stage retrieval pipeline
memory_forget ✅ Delete or archive a memory
memory_update ✅ Update memory content
memory_maintenance ✅ Run maintenance jobs (consolidation, summarization, reindex)

Removed Tools (available via CLI/Python API):

  • memory_get, memory_list, memory_history → Use CLI: cems list, cems search
  • memory_get_summary, memory_list_categories → Use CLI: cems categories
  • memory_graph_related, memory_graph_by_entity, memory_graph_stats → Internal to search pipeline
  • memory_hybrid_search, memory_smart_search → Incorporated into unified memory_search

MCP Resources (3 essential resources)

Resource Status Description
memory://status ✅ System status and configuration
memory://personal/summary ✅ Personal memory overview
memory://shared/summary ✅ Shared team memory overview

Removed Resources: memory://categories, memory://recent, memory://summaries (admin/internal use)

4. Claude Code Integration (Phase 4) ✅

Item Status Location Notes
MCP config template ✅ examples/mcp_config.json
Cursor config template ✅ examples/cursor_mcp.json
/remember skill ✅ src/cems/data/claude/skills/cems/remember.md
/share skill ✅ src/cems/data/claude/skills/cems/share.md
/recall skill ✅ src/cems/data/claude/skills/cems/recall.md
/forget skill ✅ src/cems/data/claude/skills/cems/forget.md
/context skill ✅ src/cems/data/claude/skills/cems/context.md
Install (remote) ✅ remote-install.sh curl | bash one-liner
Install (dev) ✅ install.sh For cloned repos
Setup command ✅ src/cems/commands/setup.py cems setup

5. Extended Features (Added After Initial Plan)

Docker Deployment ✅

Item Status Location
docker-compose.yml ✅ docker-compose.yml
Dockerfile ✅ Dockerfile
PostgreSQL schema ✅ deploy/init.sql
.env.example ✅ deploy/.env.example
Deployment guide ✅ docs/DEPLOYMENT.md
User onboarding ✅ docs/USER_ONBOARDING.md

Repository Indexer ✅

Item Status Location Notes
RepositoryIndexer class ✅ src/cems/indexer/indexer.py
Pattern definitions ✅ src/cems/indexer/patterns.py 10 default patterns
Content extractors ✅ src/cems/indexer/extractors.py Markdown, RSpec, ADR, config
CLI commands ✅ src/cems/cli.py:314-429 cems index repo, cems index git

Pinned Memories ✅

Item Status Location Notes
MemoryMetadata.pinned ✅ src/cems/models.py:64-67
pin_memory() ✅ src/cems/models.py:365-381
unpin_memory() ✅ src/cems/models.py:383-397
get_pinned_memories() ✅ src/cems/models.py:399-421
Stale query excludes pinned ✅ src/cems/models.py:231-247 AND pinned = 0
CLI commands ✅ src/cems/cli.py:432-510 cems pin, cems unpin, cems pinned

OpenRouter Provider ✅

Item Status Location Notes
OpenRouter client support ✅ src/cems/llm.py:51-65 Uses OpenAI SDK with custom base_url
Model name resolution ✅ src/cems/llm.py:70-91 Maps standard names to OpenRouter format
Config integration ✅ src/cems/config.py:36-57 CEMS_LLM_PROVIDER=openrouter
Attribution headers ✅ src/cems/llm.py:61-64 HTTP-Referer, X-Title
Documentation ✅ README.md, docs/DEPLOYMENT.md Enterprise setup guide
Tests ✅ tests/test_llm.py 11 OpenRouter-specific tests

Architecture Decisions

3-Layer Memory Hierarchy

The plan specified a 3-layer hierarchy. Here's how it's implemented:

Layer Plan Status Implementation
Layer 1: Resources class Resource - raw data N/A Mem0 handles raw storage internally
Layer 2: Items class MemoryItem - facts ✅ Via Mem0 Mem0 handles fact extraction
Layer 3: Categories class CategorySummary ✅ category_summaries table with LLM summaries

V2/V3 Search Optimizations ✅ COMPLETE

Feature Status Implementation Notes
Priority/Decay in Search ✅ memory.py:224-240 Search scores incorporate priority boost and time decay
Hybrid Search ✅ Internal memory.py:595-696 Available via Python API, used internally by pipeline
Smart Search (Tiered) ✅ Internal memory.py:698-805 Logic incorporated into unified pipeline
Unified Retrieval Pipeline ✅ memory.py:811-930 V3: 5-stage pipeline for memory_search
Enhanced LLM Prompts ✅ llm.py:166-260 Few-shot examples for summarization and merging

V3: Unified 5-Stage Inference Retrieval Pipeline

The memory_search tool now uses a single retrieve_for_inference() method that implements:

Stage 1: Query Synthesis → LLM expands query for better retrieval
Stage 2: Candidate Retrieval → Vector search (Mem0) + Graph traversal (Kuzu)
Stage 3: Relevance Filtering → Filter by threshold (default 0.5)
Stage 4: Temporal Ranking → Time decay + priority scoring
Stage 5: Token-Budgeted Assembly → Select results within token budget

Configuration:

  • CEMS_ENABLE_QUERY_SYNTHESIS=true - Enable/disable Stage 1
  • CEMS_RELEVANCE_THRESHOLD=0.5 - Filter threshold for Stage 3
  • CEMS_DEFAULT_MAX_TOKENS=2000 - Token budget for Stage 5

Search Ranking Formula

The search applies the following adjustments to Mem0's base similarity score:

# Priority boost (1.0 default, up to 2.0 for hot memories)
score *= metadata.priority

# Time decay: 10% penalty per month since last access
days_since_access = (now - metadata.last_accessed).days
time_decay = 1.0 / (1.0 + (days_since_access / 30) * 0.1)
score *= time_decay

# Pinned memory boost
if metadata.pinned:
    score *= 1.1

Internal Methods (Available via Python API/CLI)

These methods are no longer exposed as MCP tools but remain functional:

  • hybrid_search() - Combines vector + graph retrieval with configurable weights
  • smart_search() - Tiered retrieval that checks summaries first

Optional Features (Not Implemented - By Design)

Feature Status Rationale
memory_set_context Not needed Context set via environment variables
LLM-based entity extraction Deferred Regex is fast; LLM would slow down add()

These can be added later if needed but aren't required for core functionality.

Files Reference

src/cems/
├── __init__.py           # Package init
├── config.py             # ✅ Configuration (Pydantic) + retrieval settings
├── models.py             # ✅ Data models + MetadataStore
├── memory.py             # ✅ CEMSMemory wrapper + retrieve_for_inference()
├── retrieval.py          # ✅ V3: 5-stage retrieval pipeline helpers
├── server.py             # ✅ MCP server (FastMCP) - 5 tools, 3 resources
├── tools.py              # ✅ MCP tool schemas
├── scheduler.py          # ✅ APScheduler setup
├── cli.py                # ✅ CLI commands
├── llm.py                # ✅ LLM utilities (OpenRouter) + enhanced prompts
├── graph.py              # ✅ Kuzu knowledge graph store
├── maintenance/
│   ├── __init__.py
│   ├── consolidation.py  # ✅ LLM duplicate merging
│   ├── summarization.py  # ✅ LLM category summarization
│   └── reindex.py        # ✅ Embedding refresh via Mem0
└── indexer/
    ├── __init__.py
    ├── indexer.py        # ✅ Repository indexer
    ├── patterns.py       # ✅ Index patterns
    └── extractors.py     # ✅ Content extractors

tests/
├── __init__.py
├── test_config.py        # ✅ 10 tests
├── test_models.py        # ✅ 11 tests
├── test_models_extended.py # ✅ 9 tests
├── test_llm.py           # ✅ 21 tests (including OpenRouter)
├── test_maintenance.py   # ✅ 11 tests
├── test_retrieval.py     # ✅ Retrieval pipeline tests
└── test_server.py        # ✅ 5-tool simplified API tests

Total: 82+ tests passing

Verification Checklist

# 1. Start the server
cems-server start

# 2. Test memory operations
cems add "Test memory" --scope personal
cems search "test"
cems list

# 3. Test maintenance
cems maintenance run consolidation
cems maintenance run summarization  # Note: Uses placeholder logic
cems maintenance run reindex        # Note: Uses workaround

# 4. Test indexer
cems index patterns  # List available patterns
cems index repo /path/to/repo

# 5. Test pinning
cems pin <memory_id> --reason "Important" --category guideline
cems pinned