Memory Scoring

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Scoring Signals

• Relevance: How well the memory matches the search query. Exact content phrases outrank loose overlap, while tag/source matches provide a capped supporting signal instead of being treated as visible content. Optional semantic similarity remains provider-backed.
• Importance: A manual or model-assigned priority score (0.0 to 1.0).
• Recency: How recently the memory was updated. Scores decay over time based on a configurable half-life.
• Frequency: How often the memory has been accessed by the agent.
• Freshness: How long ago the memory was originally created.

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Explainable Scores

results = client.search("What is Mneno?")

for result in results:
    score = result.score
    print(f"Total Score: {score.total}")
    print(f"Signals: Rel={score.relevance}, Imp={score.importance}, Rec={score.recency}")
    print(f"Reasons: {score.reasons}")

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Example Reasons

• Matched query term: mneno
• High importance memory
• Recently updated
• Frequently accessed memory

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Customizing Weights

from mneno import MemoryClient, MemoryPolicy

# Create a policy that prioritizes recency over importance
policy = MemoryPolicy(
    recency_weight=0.5,
    importance_weight=0.1,
    relevance_weight=0.3,
    access_weight=0.1
)

client = MemoryClient(policy=policy)

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Policy Parameters