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Enhanced Memory

Enhanced memory search with hybrid vector+keyword scoring, temporal routing, filepath scoring, adapt

Rating
4.1 (30 reviews)
Downloads
702 downloads
Version
1.0.0

Overview

Enhanced memory search with hybrid vector+keyword scoring, temporal routing, filepath scoring, adaptive weighting.

Complete Documentation

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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

bash
# 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 — Primary Search

Hybrid 4-signal retrieval with automatic adaptation:

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

Signals fused:

  • Vector similarity (0.4) — cosine similarity via nomic-embed-text embeddings
  • Keyword matching (0.25) — query term overlap with chunk text
  • Header matching (0.1) — query terms in section headers
  • Filepath scoring (0.25) — query terms matching file/directory names
Automatic behaviors:
  • Temporal routing — date references ("yesterday", "Feb 8", "last Monday") get 3x boost on matching files
  • Adaptive weighting — when keyword overlap is low, shifts to 85% vector weight
  • Pseudo-relevance feedback (PRF) — when top score < 0.45, expands query with terms from initial results and re-scores

scripts/enhanced_memory_search.py — JSON-Compatible Search

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

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

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

scripts/embed_memories.py — Indexing

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

bash
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 — Salience Scoring

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

bash
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 × staleness considering: file type (topic > core > daily), size, access frequency, and query gap correlation.

scripts/crossref_memories.py — Knowledge Graph

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

bash
python3 skills/enhanced-memory/scripts/crossref_memories.py build          # Build index
python3 skills/enhanced-memory/scripts/crossref_memories.py show <file>    # 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(n²) to manageable comparisons. Threshold: 0.75 cosine similarity.

Configuration

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

ParameterDefaultScriptPurpose
VECTOR_WEIGHT0.4search_memory.pyWeight for vector similarity
KEYWORD_WEIGHT0.25search_memory.pyWeight for keyword overlap
FILEPATH_WEIGHT0.25search_memory.pyWeight for filepath matching
TEMPORAL_BOOST3.0search_memory.pyMultiplier for date-matching files
PRF_THRESHOLD0.45search_memory.pyScore below which PRF activates
SIMILARITY_THRESHOLD0.75crossref_memories.pyMin similarity for cross-ref links
MODELnomic-embed-textallOllama 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.

Installation

Terminal bash 

openclaw install enhanced-memory
Copied!

💻Code Examples

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

python3-skillsenhanced-memoryscriptscrossrefmemoriespy-build.txt
Re-run `embed_memories.py` whenever memory files change significantly.

## Scripts

### `scripts/search_memory.py` — Primary Search

Hybrid 4-signal retrieval with automatic adaptation:

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

python3-skillsenhanced-memoryscriptssearchmemorypy-query-topn.txt
**Signals fused:**
1. **Vector similarity** (0.4) — cosine similarity via nomic-embed-text embeddings
2. **Keyword matching** (0.25) — query term overlap with chunk text
3. **Header matching** (0.1) — query terms in section headers
4. **Filepath scoring** (0.25) — query terms matching file/directory names

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

### `scripts/enhanced_memory_search.py` — 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"

python3-skillsenhanced-memoryscriptsenhancedmemorysearchpy---json-query.txt
Returns `{results: [{path, startLine, endLine, score, snippet, header}], ...}`.

### `scripts/embed_memories.py` — 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

python3-skillsenhanced-memoryscriptsembedmemoriespy.txt
Outputs `memory/vectors.json`. Batches embeddings in groups of 20, truncates chunks to 2000 chars.

### `scripts/memory_salience.py` — Salience Scoring

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

python3 skills/enhanced-memory/scripts/memory_salience.py --top 5 # More items

python3-skillsenhanced-memoryscriptsmemorysaliencepy---top-5--more-items.txt
Scores `importance × staleness` considering: file type (topic > core > daily), size, access frequency, and query gap correlation.

### `scripts/crossref_memories.py` — Knowledge Graph

Builds cross-reference links between memory chunks using embedding similarity:
example.sh
# 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
example.sh
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
example.sh
python3 skills/enhanced-memory/scripts/crossref_memories.py build          # Build index
python3 skills/enhanced-memory/scripts/crossref_memories.py show <file>    # Show refs for file
python3 skills/enhanced-memory/scripts/crossref_memories.py graph          # Graph statistics

⚙️Configuration Options

Option Type Default Description
VECTOR_WEIGHTstring0.4search_memory.py
KEYWORD_WEIGHTstring0.25search_memory.py
FILEPATH_WEIGHTstring0.25search_memory.py
TEMPORAL_BOOSTstring3.0search_memory.py
PRF_THRESHOLDstring0.45search_memory.py
SIMILARITY_THRESHOLDstring0.75crossref_memories.py
MODELstringnomic-embed-textall

Tags

#search_and-research

Quick Info

Category Web Scrapers
Model Claude 3.5
Complexity One-Click
Author jameseball
Last Updated 3/10/2026
🚀
Optimized for
Claude 3.5
🧠

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openclaw install enhanced-memory

Resources

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