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Intelligence Layer

The Intelligence Layer uses LLMs and advanced algorithms to keep your memory store healthy, your retrieval sharp, and your agents onboarded faster.

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Extraction

Turns raw text (conversations, logs, documents) into structured memory operations using LLMs. The extractor classifies each piece of information as an ADD, UPDATE, DELETE, or NOOP operation.

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Auto Entity & Relationship Extraction

Automatically extracts entities (services, people, tools, infrastructure) and relationships (depends_on, uses, manages, monitors) from decision traces and memory entries using LLMs. Extracted data is stored in dedicated tables with confidence scores, temporal validity, and trace provenance. Enable with AMFS_AUTO_EXTRACT=true.

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Memory Critic

Automated quality analyzer that scans the memory store and detects five issue classes:

• Toxic — repeated negative correlations
• Stale — entries not referenced in a long time
• Contradictory — conflicting entries for the same key
• Uncalibrated — confidence scores misaligned with outcome history
• Orphaned — entries with no links to any outcome or other entries

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Memory Distiller

Compacts large stores into smaller, higher-quality sets via pruning, consolidation, and bootstrap set generation for agent onboarding.

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Memory Safety Validator

Pre-write guardrails: contradiction detection, temporal consistency, confidence thresholds, and causal chain integrity.

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Multi-Strategy Retrieval

Combines semantic, BM25, temporal, and confidence signals via Reciprocal Rank Fusion (RRF). When a learned ranking model is trained, it automatically receives 30% weight in the fusion pipeline.

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Pro MCP Tools