## 1. Module Scaffold & Data Schemas

- [x] 1.1 Create `memory-engine/` directory tree with all subdirectories and `__init__.py` files
- [x] 1.2 Create `config.py` with `MemoryConfig` pydantic model (embedding, chunking, search, tier settings)
- [x] 1.3 Create `core/schemas.py` with `MemoryChunk`, `SearchResult`, `Fact`, `RunningSummary`, `ExtractedMemory` data classes
- [x] 1.4 Create `utils/token_counter.py` with tiktoken + char-fallback token counting
- [x] 1.5 Create `utils/namespace.py` with `NamespaceTemplate` for runtime namespace resolution
- [x] 1.6 Create `utils/chunker.py` with `TextChunker` (line-based, overlapping, configurable max_tokens)

## 2. Core Store: SQLite + FTS5 + Vector

- [x] 2.1 Create `core/store.py` with `MemoryStore` — SQLite init with WAL mode, FTS5 tables, integrity checks
- [x] 2.2 Implement `create_chunks_table()` with embedding BLOB storage, indexes, meta table
- [x] 2.3 Implement `create_fts5_tables()` with standard unicode61 tokenizer + trigram tokenizer for CJK
- [x] 2.4 Implement FTS5 triggers (AFTER INSERT/UPDATE/DELETE) for auto-sync
- [x] 2.5 Implement `save_chunk()` / `save_chunks_batch()` with SQLite UPSERT (INSERT ... ON CONFLICT DO UPDATE)
- [x] 2.6 Implement `delete_by_path()`, `get_file_hash()`, `update_file_metadata()`
- [x] 2.7 Implement FTS5 self-healing: `_fts5_state_inconsistent()`, `_fts5_shadow_corrupt()`, `reset_fts5()`
- [x] 2.8 Implement embedding encode/decode (float32 BLOB via numpy, struct fallback, legacy JSON fallback)
- [x] 2.9 Implement `get_stats()` and `close()` methods

## 3. Hybrid Search

- [x] 3.1 Implement `search_vector()` — numpy matrix cosine similarity with argpartition top-K (pure-Python fallback)
- [x] 3.2 Implement FTS5 keyword search with BM25 scoring: `_search_fts5()`, `_search_fts5_trigram()`
- [x] 3.3 Implement `_search_like()` — CJK (1+ chars) + ASCII word (3+ chars) with dynamic scoring
- [x] 3.4 Implement `search_keyword()` — three-tier strategy (FTS5 → trigram FTS5 → LIKE)
- [x] 3.5 Implement BM25 rank to score conversion (`0.3 + 0.69 * abs(r)/(1+abs(r))`)
- [x] 3.6 Create `core/hybrid_search.py` with weighted merge (vector_weight, keyword_weight) + temporal decay
- [x] 3.7 Implement `_compute_temporal_decay(path, half_life=30)` — exponential decay for dated files

## 4. LLM Memory Extraction

- [x] 4.1 Create `extraction/prompts.py` with memory update system prompt (structured JSON output)
- [x] 4.2 Create `extraction/manager.py` with `MemoryUpdater` — LLM fact extraction from conversation
- [x] 4.3 Implement `_prepare_update_prompt()` — loads current memory, formats conversation, builds prompt
- [x] 4.4 Implement `_parse_memory_update_response()` — JSON extraction from LLM response (handles fences/thinking)
- [x] 4.5 Implement `_apply_updates()` — update user/history sections, add/remove facts, enforce max_facts
- [x] 4.6 Implement `create_fact()`, `update_fact()`, `delete_memory_fact()` CRUD operations
- [x] 4.7 Implement content deduplication (casefold comparison) and confidence threshold filtering
- [x] 4.8 Implement upload-mention scrubbing from memory data

## 5. Tiered Consolidation

- [x] 5.1 Create `tiers/daily.py` with `DailyTier` — lazy file creation, append-only writes with timestamped headers
- [x] 5.2 Create `tiers/context.py` with `ContextTier` — short-term context window management with RunningSummary
- [x] 5.3 Create `tiers/core.py` with `CoreTier` — wraps MemoryStore, manages MEMORY.md file
- [x] 5.4 Create `tiers/__init__.py` with `flush_messages()` — context summarization + daily file append
- [x] 5.5 Implement incremental summarization (initial summary, extend existing, RunningSummary tracking)
- [x] 5.6 Create `background/deep_dream.py` with `DeepDream` — LLM-based MEMORY.md consolidation
- [x] 5.7 Implement Deep Dream dedup (content-hash check), dream diary writing, empty-output guard

## 6. Background Processing Queue

- [x] 6.1 Create `background/queue.py` with `MemoryUpdateQueue` — thread-safe, debounced, keyed by (thread, user, agent)
- [x] 6.2 Implement `add()` with debounce timer reset, `add_nowait()` for immediate processing
- [x] 6.3 Implement timer-triggered processing with rate limiting between updates
- [x] 6.4 Implement signal detection: `detect_correction()`, `detect_reinforcement()` with pattern matching
- [x] 6.5 Create `background/__init__.py` with `flush_messages()` — dedup + background thread LLM summarization
- [x] 6.6 Support `context_summary_callback` for in-context injection of summaries

## 7. Agent Tools & Public API

- [x] 7.1 Create `tools/manage.py` with `manage_memory()` — create/update/delete facts with namespace isolation
- [x] 7.2 Create `tools/search.py` with `search_memory()` — hybrid search with query/filter/limit/offset
- [x] 7.3 Implement `__init__.py` with `MemoryEngine` unified class: `manage()`, `search()`, `flush()`, `dream()`, `format_for_injection()`
- [x] 7.4 Implement `format_for_injection()` — token-budgeted memory string for system prompts
- [x] 7.5 Thread-safe singleton pattern for `MemoryUpdateQueue` and `MemoryStore`

## 8. Embedding Provider Interface

- [x] 8.1 Create `embedding/base.py` with `EmbeddingProvider` ABC — `embed_query()`, `embed_batch()`
- [x] 8.2 Create `embedding/openai.py` with `OpenAIEmbeddingProvider` implementation
- [x] 8.3 Implement `EmbeddingCache` — per-session cache keyed by (provider, model, text_hash)
- [x] 8.4 Create `embedding/__init__.py` with `create_embedding_provider()` factory

## 9. Integration Tests

- [x] 9.1 Test short-term context summarization with token budget enforcement
- [x] 9.2 Test long-term fact extraction with LLM mock
- [x] 9.3 Test hybrid search: vector-only, keyword-only, and combined
- [x] 9.4 Test tiered consolidation: flush → daily file → Deep Dream → MEMORY.md rewrite
- [x] 9.5 Test background queue: debounce, dedup, async execution
- [x] 9.6 Test namespace isolation: scoped searches across tenants
- [x] 9.7 Test graceful degradation: no embeddings → keyword-only, no numpy → Python fallback
- [x] 9.8 Test memory tools: create/update/delete/search round-trip