Memory Daemon — Project Status¶

Project Overview¶

Memory Daemon is a modular, local-first long-term memory engine designed for Large Language Models.

Unlike traditional vector stores, Memory Daemon combines structured memories, semantic retrieval, graph relationships, ranking heuristics, feedback learning, and cognitive orchestration into a unified architecture. The project is designed around transparency, deterministic behavior, and user ownership of data.

Current Development Stage¶

Version: V4 — Reasoning Infrastructure Release

Status: Pre-release, functionally complete, undergoing final review

Version History¶

Version	Status	Focus
V1	✅ Archived	Core memory + basic retrieval
V2	✅ Archived	Ranking pipeline + feedback
V3	✅ Archived	Type routing + BM25 + inverted index
V4	🚧 Current	Reasoning infrastructure + goals + blackboard
V5	🔬 Research	Hierarchical memory + compression

Project Health¶

Area	Status	Notes
Core Memory Engine	✅ Complete	MemoryDB, Pruner, RelevanceManager
Database Layer	✅ Complete	SQLite with WAL, type tables
Embedding Pipeline	✅ Complete	SentenceTransformer + FAISS
Vector Search (FAISS)	✅ Complete	CPU-optimized, sharding support
Graph Relationships	✅ Complete	Numpy graph, EntityStore, EdgeStore
Ranking Pipeline	✅ Complete	10 signals, MMR, adaptive weights
Feedback Loop	✅ Complete	Clicks, dwell time, skips
Blackboard	✅ Complete	Thread-safe, event-driven
Task Scheduler	✅ Complete	Parallel execution
Benchmark Suite	✅ Complete	Accuracy, speed, regression
Synthetic World Generator	✅ Complete	Test data generation
Documentation	🟡 In Progress	V4 docs in final review
CLI Interface	✅ Complete	Full command set
TUI Interface	✅ Complete	Interactive chat mode
GUI Interface	✅ Complete	Web-based interface
API (FastAPI)	✅ Complete	Full REST endpoints
Packaging	🟡 In Progress	PyPI preparation
Public Release	🔵 Planned	V4 release candidate

Core Systems Status¶

Memory Engine¶

[x] Structured Memory
[x] Semantic Memory
[x] Episodic Memory
[x] Goal Tracking
[x] Memory Controller
[x] Memory Pruner
[x] Relevance Manager
[x] Feedback Loop

Retrieval¶

[x] Query Processing
[x] Embedding Cache
[x] FAISS Search
[x] BM25 Search
[x] Inverted Index
[x] Phrase Search
[x] Database Retrieval
[x] Graph Retrieval
[x] Attribute Search
[x] Shard Manager

Ranking¶

[x] Score Normalization
[x] Attribute Boosting
[x] MMR Diversification
[x] Importance Scoring
[x] Final Score Aggregation
[x] Adaptive Weighter
[x] BM25 Ranking
[x] TF/IDF Scoring

Knowledge Graph¶

[x] Entity Resolution
[x] Relationship Builder
[x] Edge Storage
[x] Graph Search
[x] Numpy Graph

Reasoning (V4)¶

[x] Goals
[x] Blackboard
[x] Task Scheduler
[ ] Reasoning Nodes (In Progress)
[ ] Computation Graph (In Progress)
[ ] Planner (Planned)
[ ] Execution Queue (Planned)

Benchmarking¶

[x] Synthetic World Generator
[x] Batch Loader
[x] Benchmark Runner
[x] Benchmark Analyzer
[x] Regression Suite
[x] Flight Recorder

Release Checklist¶

Documentation¶

[x] Architecture Overview
[x] Data Flow
[x] Design Principles
[x] Project Manifesto
[ ] Master README
[ ] Installation Guide
[ ] Quick Start Guide
[ ] API Documentation
[ ] Contributor Guide
[ ] Release Notes

Interfaces¶

[x] CLI (full command set)
[x] TUI (interactive chat)
[x] GUI (web interface)
[x] API (FastAPI routes)

Release¶

[x] Code freeze
[ ] Dependency audit
[ ] Packaging
[ ] Version tag
[ ] GitHub Release
[ ] Initial public documentation

Immediate Priorities¶

✅ Complete code review (V4)
✅ Update documentation
🔄 Performance pass (recover 150ms latency)
🔄 Final benchmark suite
🔜 Packaging for PyPI
🔜 V4 public release

Long-Term Roadmap¶

V4 — Reasoning Infrastructure (Current)¶

✅ Goals and planning
✅ Blackboard architecture
✅ Parallel task execution
🔄 Reasoning nodes
🔄 Computation graphs

V5 — Cognitive Architecture (Research)¶

Hierarchical memory representation
Minimal reconstruction cost
Compression as a side effect
Active reasoning over memory

V6 — Agent Operating System (Vision)¶

Complete cognitive architecture
Self-improving over time
Fully local, fully private
Multi-agent coordination

Key Metrics¶

Metric	Current	Target
Query Latency	~150ms	~150ms
Memory Footprint	~4GB RAM	~4GB RAM
CPU	CPU-only	CPU-only
Storage	SQLite + FAISS	SQLite + FAISS
Supported Models	Mistral, Llama, GPT	LLM-agnostic
Languages	Python 3.12+	Python 3.12+