Memory Search (OpenClaw Docs)
Memory Search (OpenClaw Docs)
Source: https://docs.openclaw.ai/concepts/memory-search
Dual retrieval
Two parallel search paths, results merged:
| Path | Strengths |
|---|---|
| Vector (embedding) | Semantic similarity; handles paraphrase and synonyms |
| BM25 keyword | Exact matches: IDs, error strings, config keys |
Embedding providers
Eight providers supported. Most auto-detect from credentials; some require explicit config. Includes OpenAI, Gemini, GitHub Copilot, Voyage, Mistral, Ollama, local GGUF, and Gemini Embedding 2 (multimodal).
Search quality features
Temporal decay
Reduces ranking weight of older notes over time. Default: a note from last month scores at ~50% of its original weight. Keeps recent context prominent.
MMR (Maximal Marginal Relevance)
Diversifies results — top results cover different topics rather than repeating similar content. Useful for large memory collections.
Advanced capabilities
| Feature | Description |
|---|---|
| Multimodal indexing | Images and audio via Gemini Embedding 2 |
| Session memory search | Optional transcript indexing for conversation recall |
| Degraded mode | Falls back to lexical ranking when no embedding provider is available |
Diagnostics
openclaw memory status # check indexing health
openclaw memory index --force # rebuild indexes