🎁 Get the FREE AI Skills Starter Guide β€” Subscribe β†’
BytesAgainBytesAgain
BytesAgain β€Ί Skills β€Ί Enhanced Memory
πŸ¦€ ClawHub

Enhanced Memory

by @jameseball

Enhanced memory search with hybrid vector+keyword scoring, temporal routing, filepath scoring, adaptive weighting, pseudo-relevance feedback, salience scoring, and knowledge graph cross-references. Replaces the default memory search with a 4-signal fusion retrieval system. Use when searching memories, indexing memory files, building cross-references, or scoring memory salience. Requires Ollama with nomic-embed-text model.

Versionv1.0.0
Downloads1,282
Installs1
#crypto-defi#legal#gaming
TERMINAL
clawhub install enhanced-memory

πŸ“– About This Skill

name: enhanced-memory description: Enhanced memory search with hybrid vector+keyword scoring, temporal routing, filepath scoring, adaptive weighting, pseudo-relevance feedback, salience scoring, and knowledge graph cross-references. Replaces the default memory search with a 4-signal fusion retrieval system. Use when searching memories, indexing memory files, building cross-references, or scoring memory salience. Requires Ollama with nomic-embed-text model.

Enhanced Memory

Drop-in enhancement for OpenClaw's memory system. Replaces flat vector search with a 4-signal hybrid retrieval pipeline that achieved 0.782 MRR (vs ~0.45 baseline vector-only).

Setup

# Install Ollama and pull the embedding model
ollama pull nomic-embed-text
Index your memory files (run from workspace root)

python3 skills/enhanced-memory/scripts/embed_memories.py
Optional: build cross-reference graph

python3 skills/enhanced-memory/scripts/crossref_memories.py build

Re-run embed_memories.py whenever memory files change significantly.

Scripts

scripts/search_memory.py β€” Primary Search

Hybrid 4-signal retrieval with automatic adaptation:

python3 skills/enhanced-memory/scripts/search_memory.py "query" [top_n]

Signals fused: 1. Vector similarity (0.4) β€” cosine similarity via nomic-embed-text embeddings 2. Keyword matching (0.25) β€” query term overlap with chunk text 3. Header matching (0.1) β€” query terms in section headers 4. Filepath scoring (0.25) β€” query terms matching file/directory names

Automatic behaviors:

  • Temporal routing β€” date references ("yesterday", "Feb 8", "last Monday") get 3x boost on matching files
  • Adaptive weighting β€” when keyword overlap is low, shifts to 85% vector weight
  • Pseudo-relevance feedback (PRF) β€” when top score < 0.45, expands query with terms from initial results and re-scores

    • scripts/enhanced_memory_search.py β€” JSON-Compatible Search

    Same pipeline with JSON output format compatible with OpenClaw's memory_search tool:

    python3 skills/enhanced-memory/scripts/enhanced_memory_search.py --json "query"

    Returns {results: [{path, startLine, endLine, score, snippet, header}], ...}.

    scripts/embed_memories.py β€” Indexing

    Chunks all .md files in memory/ plus core workspace files (MEMORY.md, AGENTS.md, etc.) by markdown headers and embeds them:

    python3 skills/enhanced-memory/scripts/embed_memories.py

    Outputs memory/vectors.json. Batches embeddings in groups of 20, truncates chunks to 2000 chars.

    scripts/memory_salience.py β€” Salience Scoring

    Surfaces stale/important memory items for heartbeat self-prompting:

    python3 skills/enhanced-memory/scripts/memory_salience.py          # Human-readable prompts
python3 skills/enhanced-memory/scripts/memory_salience.py --json   # Programmatic output
python3 skills/enhanced-memory/scripts/memory_salience.py --top 5  # More items

    Scores importance Γ— staleness considering: file type (topic > core > daily), size, access frequency, and query gap correlation.

    scripts/crossref_memories.py β€” Knowledge Graph

    Builds cross-reference links between memory chunks using embedding similarity:

    python3 skills/enhanced-memory/scripts/crossref_memories.py build          # Build index
python3 skills/enhanced-memory/scripts/crossref_memories.py show     # Show refs for file
python3 skills/enhanced-memory/scripts/crossref_memories.py graph          # Graph statistics

    Uses file-representative approach (top 5 chunks per file) to reduce O(nΒ²) to manageable comparisons. Threshold: 0.75 cosine similarity.

    Configuration

    All tunable constants are at the top of each script. Key parameters:

    | Parameter | Default | Script | Purpose | |-----------|---------|--------|---------| | VECTOR_WEIGHT | 0.4 | search_memory.py | Weight for vector similarity | | KEYWORD_WEIGHT | 0.25 | search_memory.py | Weight for keyword overlap | | FILEPATH_WEIGHT | 0.25 | search_memory.py | Weight for filepath matching | | TEMPORAL_BOOST | 3.0 | search_memory.py | Multiplier for date-matching files | | PRF_THRESHOLD | 0.45 | search_memory.py | Score below which PRF activates | | SIMILARITY_THRESHOLD | 0.75 | crossref_memories.py | Min similarity for cross-ref links | | MODEL | nomic-embed-text | all | Ollama embedding model |

    To use a different embedding model (e.g., mxbai-embed-large), change MODEL in each script and re-run embed_memories.py.

    Integration

    To replace the default memory search, point your agent's search tool at these scripts. The scripts expect:

  • memory/ directory relative to workspace root containing .md files
  • memory/vectors.json (created by embed_memories.py)
  • Ollama running locally on port 11434

    • All scripts use only Python stdlib + Ollama HTTP API. No pip dependencies.

    βš™οΈ Configuration

    All tunable constants are at the top of each script. Key parameters:

    | Parameter | Default | Script | Purpose | |-----------|---------|--------|---------| | VECTOR_WEIGHT | 0.4 | search_memory.py | Weight for vector similarity | | KEYWORD_WEIGHT | 0.25 | search_memory.py | Weight for keyword overlap | | FILEPATH_WEIGHT | 0.25 | search_memory.py | Weight for filepath matching | | TEMPORAL_BOOST | 3.0 | search_memory.py | Multiplier for date-matching files | | PRF_THRESHOLD | 0.45 | search_memory.py | Score below which PRF activates | | SIMILARITY_THRESHOLD | 0.75 | crossref_memories.py | Min similarity for cross-ref links | | MODEL | nomic-embed-text | all | Ollama embedding model |

    To use a different embedding model (e.g., mxbai-embed-large), change MODEL in each script and re-run embed_memories.py.