Feature deep dive

Semantic Search Memory for AI Agents

Retrieve memory by meaning, not only keywords, so agents find the right context when wording changes.

Keyword search misses context when users phrase the same idea differently. Semantic retrieval closes this gap by ranking memories based on intent and meaning.

ClawVault combines local memory structure with semantic search commands for practical agent workflows.

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Why teams lose context

  • •Exact keyword queries miss relevant past decisions.
  • •Memory recall quality drops with inconsistent phrasing.
  • •Teams over-inject context to compensate for weak retrieval.
  • •Low-signal results reduce trust in memory systems.

How ClawVault helps

  • •Use semantic ranking for ambiguous and natural-language queries.
  • •Pair semantic retrieval with explicit memory categories.
  • •Keep concise retrieval outputs for token-efficient injection.
  • •Review query logs to improve memory quality over time.

01When semantic retrieval matters most

Semantic retrieval is critical when your prompts vary across sessions, users, and operators. It helps preserve intent even when wording shifts.

02Retrieval pattern for production agents

A practical pattern is to run semantic retrieval first, then narrow with exact search if precision is needed.

Hybrid recall loop
$clawvault vsearch "what did we decide about auth token rotation"
$clawvault search "token rotation"
$clawvault vsearch "security decision for refresh tokens"

03Quality controls for semantic memory

Retrieval quality should be measured, not assumed. Track hit relevance and correction rates to improve memory authoring standards.

  • •Review top retrieved memories for recurring query classes.
  • •Add structured titles and categories to improve ranking quality.
  • •Archive low-value or duplicate memory entries periodically.

When should I use ClawVault?

  • •Use semantic memory when teams phrase the same problem in different ways.
  • •Use semantic memory when keyword search alone misses critical decisions.
  • •Use semantic memory when token budget forces concise context injection.

Frequently asked questions

01What is semantic memory retrieval in AI agents?
Semantic retrieval finds stored memories by meaning and intent, not only exact string matches.
02Why is vector-based memory useful for CLI agents?
It improves recall quality for natural-language queries while keeping memory retrieval available through simple command workflows.
03Can I combine keyword and semantic search?
Yes. Hybrid retrieval often gives the best balance of broad recall and precise filtering.
04How do I improve semantic retrieval quality?
Use structured memory entries, monitor retrieval outcomes, and remove stale or duplicate records regularly.

Related guides

OpenClaw Memory Fix Guide

Apply semantic retrieval directly to OpenClaw context loss.

Markdown Memory

Pair semantic retrieval with transparent storage.

AI Agent Memory Architecture

System-level design patterns for scalable memory.

How OpenClaw Memory Works

Understand retrieval pathways and memory failure modes.