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BytesAgain β€Ί Skills β€Ί MidOS Memory Cascade
πŸ¦€ ClawHub

MidOS Memory Cascade

by @msruruguay

Auto-escalating multi-tier memory search that cascades from in-memory cache through SQLite, grep, and LanceDB vector search to find the best answer with mini...

Versionv1.0.0
Downloads901
#clawhub
TERMINAL
clawhub install midos-memory-cascade

πŸ“– About This Skill

name: midos-memory-cascade description: Auto-escalating multi-tier memory search that cascades from in-memory cache through SQLite, grep, and LanceDB vector search to find the best answer with minimal latency. metadata: {}

MidOS Memory Cascade

A self-tuning, auto-escalating search engine that tries each memory tier from fastest to slowest, stopping as soon as it finds a high-confidence answer.

What It Does

Instead of the agent deciding which storage layer to query, the cascade tries each tier automatically:

| Tier | Storage | Latency | Strategy | |------|---------|---------|----------| | T0 | In-memory session cache | <1ms | Exact + fuzzy key match | | T1 | JSON state files | <5ms | Filename + key match | | T2 | SQLite (pipeline_synergy.db) | <5ms | Structured SQL LIKE | | T3 | SQLite FTS5 | <1ms | Full-text keyword on 22K rows | | T4 | Grep over 46K chunks | ~3s | Brute-force ripgrep fallback | | T5 | LanceDB keyword (BM25) | slow | 670K vector rows, no embeddings | | T5b | LanceDB semantic | 3–30s | Embedding similarity, last resort |

Question routing: Queries starting with how/what/why/etc. skip keyword tiers and route directly to semantic search.

Self-learning: The cascade records which tier resolves each query. After enough history, evolve() learns shortcuts (skip directly to the winning tier) and marks consistently-empty tiers for skip.

Usage

Python API

from tools.memory.memory_cascade import recall, store
Search across all tiers

result = recall("adaptive alpha reranking")

β†’ {"answer": {...}, "tier": "T5:lancedb", "latency_ms": 340, "confidence": 0.87}
Write to the right storage automatically

store("pattern", content="...", tags=["ml", "reranking"])

CLI

# Search
python memory_cascade.py recall "query here"
View tier resolution stats

python memory_cascade.py stats
Run self-evolution (learn shortcuts + tier skips)

python memory_cascade.py evolve

recall() Options

recall(
    query: str,
    min_confidence: float = 0.5,  # stop escalating at this threshold
    max_tier: int = 6             # 0=T0 only, 6=all tiers
)

Returns: 

{
  "answer": { "source": "...", "text": "..." },
  "confidence": 0.87,
  "latency_ms": 340.2,
  "tiers_tried": 3,
  "resolved_at": "T5:lancedb",
  "shortcut": null,
  "question_routed": false,
  "escalation": [...]
}

Requirements

  • Python 3.10+ (stdlib only for core cascade logic)
  • Optional: hive_commons for LanceDB tiers (T5/T5b)
  • Optional: tools.memory.memory_router for store() routing

    • The cascade degrades gracefully β€” if LanceDB is unavailable, it stops at grep (T4). All stdlib tiers (T0–T4) work with zero dependencies.

    Architecture Notes

  • Thread-safe: Session cache uses threading.Lock; stats writes use separate locks
  • Cross-process safe: JSONL writes use OS-level file locking (msvcrt on Windows, fcntl on Unix)
  • Confidence scoring: Term overlap Γ— score Γ— content richness β†’ normalized 0–1
  • Stats persistence: knowledge/SYSTEM/cascade_stats.json accumulates hit rates per tier

    • Built with MidOS. 1 of 200+ skills. Full ecosystem at midos.dev/pro

    πŸ’‘ Examples

    Python API

    from tools.memory.memory_cascade import recall, store
    Search across all tiers
    
result = recall("adaptive alpha reranking")

    β†’ {"answer": {...}, "tier": "T5:lancedb", "latency_ms": 340, "confidence": 0.87}
    Write to the right storage automatically
    
store("pattern", content="...", tags=["ml", "reranking"])

    CLI

    # Search
python memory_cascade.py recall "query here"
    View tier resolution stats
    
python memory_cascade.py stats
    Run self-evolution (learn shortcuts + tier skips)
    
python memory_cascade.py evolve

    recall() Options

    recall(
    query: str,
    min_confidence: float = 0.5,  # stop escalating at this threshold
    max_tier: int = 6             # 0=T0 only, 6=all tiers
)

    Returns: 

    {
  "answer": { "source": "...", "text": "..." },
  "confidence": 0.87,
  "latency_ms": 340.2,
  "tiers_tried": 3,
  "resolved_at": "T5:lancedb",
  "shortcut": null,
  "question_routed": false,
  "escalation": [...]
}