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Overview

When the agent calls memory_recall, MemU performs a multi-stage search that combines full-text matching with semantic similarity to find the most relevant memories.

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Search Pipeline

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Stage 1: FTS5 Full-Text Search

The query is run against the FTS5 index. This finds memories that contain matching terms: 
SELECT * FROM memory_fts WHERE memory_fts MATCH 'infrastructure specs'
ORDER BY rank
LIMIT 50
FTS5 provides a relevance rank based on term frequency, position, and document length.

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Stage 2: Embedding Similarity

The query is embedded into a vector and compared against stored memory embeddings using cosine similarity: 
similarity = dot(query_embedding, memory_embedding) / (|query_embedding| * |memory_embedding|)
This catches semantically related memories that may not share exact words with the query. For example, “computer setup” would match a memory about “infrastructure specs.”

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Stage 3: Score Fusion

FTS5 scores and embedding similarity scores are combined: 
final_score = (fts5_score * fts5_weight) + (similarity² * embedding_weight) + significance_boost
Key details:
  • Similarity is squared (sim²) to amplify strong matches and suppress weak ones
  • Significance adds a boost based on the memory’s importance score
  • Memory recall queries get a +0.25 score boost for model routing (ensures at least Haiku tier)

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Stage 4: Reranking

Results are reranked with additional signals:
  • Recency: More recent memories get a slight boost
  • Reinforcement: Memories that have been recalled frequently (log2 reinforcement) rank higher
  • Entity matching: Memories mentioning entities in the query get a boost
If the reranker encounters errors, it falls back safely to the fusion score ordering.

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Query Optimization

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Best Practices

Good: "Jason infrastructure"          # Specific keywords
Good: "telegram OR discord setup"     # OR for alternatives
Good: "meeting notes February"        # Scoped by time context

Bad:  "what did we talk about"        # Too vague
Bad:  "the thing from yesterday"      # No searchable terms

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FTS5 Syntax

SyntaxExampleMeaning
wordinfrastructureMatch this term
word1 word2Jason preferencesMatch both terms (AND)
word1 OR word2telegram OR discordMatch either term
"phrase""model router"Match exact phrase
word*config*Prefix match

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Result Format

Search results are returned to the agent as a structured list: 
[
  {
    "id": 42,
    "content": "Jason runs 2x NVIDIA DGX Spark with 256GB unified memory",
    "significance": 8,
    "score": 0.92,
    "created_at": "2026-01-15T10:30:00Z"
  }
]