Memory Types

SmartMemory's memory types fall into two cohorts:

• Knowledge Layer — what's true: semantic, episodic, procedural, pending, zettel. Captured through add() / ingest() and the standard pipeline.
• Expertise Layer — what to do, and what not to do: decision, constraint, learned, opinion, reasoning, observation. Each has its own Manager + Queries lifecycle (supersession, reinforcement, contradiction handling) and an ergonomic capture wrapper. See Expertise vs Knowledge for the full distinction and recall channel.

The five knowledge-layer types are detailed below. The six expertise-layer types are catalogued at the end of this page and have their own per-type concept docs.

Looking for the API? The Memory Type API Matrix is a per-type cheatsheet of how to create, query, and use every memory type across the SDK, MCP tools, and REST routes.

Semantic Memory

Stores facts, concepts, and general knowledge that is context-independent.

Characteristics:

• Timeless information
• Factual knowledge
• Concepts and definitions
• General principles

Examples:

• "Python is a programming language"
• "The capital of France is Paris"
• "Machine learning requires training data"

Episodic Memory

Stores personal experiences and events with temporal and contextual information.

Characteristics:

• Time-bound experiences
• Personal context
• Situational details
• Autobiographical events

Examples:

• "I learned Python in 2020 during the pandemic"
• "Yesterday's team meeting discussed the new API"
• "The debugging session on Friday solved the memory leak"

Procedural Memory

Stores skills, procedures, and how-to knowledge for performing tasks.

Characteristics:

• Step-by-step processes
• Skills and techniques
• Operational knowledge
• Action sequences

Examples:

• "How to deploy a Docker container"
• "Steps for debugging memory issues"
• "Process for code review workflow"

Pending Memory

Temporary storage for information currently being processed or actively used.

Characteristics:

• Short-term storage
• Active processing
• Current context
• Session-specific

Examples:

• Current conversation context
• Active problem-solving steps
• Immediate tasks and goals

Zettelkasten Memory

Atomic, interconnected knowledge management inspired by the traditional Zettelkasten method. Stores individual ideas as notes with automatic cross-linking and knowledge graph formation.

Characteristics:

• Atomic notes (one idea per note)
• Automatic entity extraction
• Bidirectional linking
• Emergent knowledge structure
• Visual graph navigation

Examples:

• "Attention mechanisms allow models to focus on relevant input parts"
• "Neural networks learn through backpropagation" (auto-linked to optimization notes)
• Research insights with cross-references to related concepts

Unique Features:

• Interactive graph visualization
• Background enrichment and auto-linking
• Integration with external knowledge bases
• Support for [[note-id]] cross-reference syntax

Learn More: See Zettelkasten Memory for comprehensive documentation.

Expertise Layer Types

The expertise layer. Read the overview →

Beyond the five knowledge-layer types, SmartMemory supports six expertise memory types that store what to do — and what not to do. Each has its own Manager + Queries lifecycle (supersession, reinforcement, contradiction handling) and an ergonomic capture wrapper:

• decision — A choice made, with structured rejected_alternatives, rationale, constraints. Confidence tracking, provenance chains, supersession. Capture: mem.add_decision(...). See Decision Memory.
• constraint — A hard rule discovered (or imposed). Capture: mem.add_constraint(...).
• learned — A lesson learned the hard way. Capture: mem.add_learning(...).
• opinion — A held belief with confidence; strengthens or weakens via reinforcement. Capture: mem.add_opinion(...). See Opinions & Observations.
• reasoning — Premises → conclusion chain-of-thought trace, with validation and supersession. Capture: mem.add_reasoning(...). See Reasoning Traces.
• observation — An attested fact about the world or self, synthesized from scattered evidence. Capture: mem.add_observation(...). See Opinions & Observations.

Recall partitioned by expertise type via mem.search(query, expertise=True) — returns a typed dict keyed by the six types.

Other extended types

• Plan: Task decompositions as DAGs with progress tracking, dependency resolution, and completion graduation. See Plan Memory.
• Code: Source code entities (classes, functions, routes) from code indexing.

SmartMemory automatically classifies memories based on content analysis, but you can also specify the type explicitly:

memory.add("Paris is the capital of France", memory_type="semantic")
memory.add("I visited paris last summer", memory_type="episodic")
memory.add("How to book a flight to paris", memory_type="procedural")
memory.add("Attention mechanisms allow models to focus on relevant input parts", memory_type="zettel")
memory.add("We chose PostgreSQL for the new project", memory_type="decision")

Structured Memory Types

Structured types bypass the NLP pipeline entirely — ingested via ingest_structured() where the data shape IS the content. Each type has a dedicated handler that determines its ingestion strategy.

Ingestion strategies:

• FULL — embedding + entity extraction (searchable via semantic search)
• INDEXED — graph node + typed indexes, no embedding (queryable by typed fields)
• APPEND — minimal graph node, recency-only (high-volume telemetry)

# Structured ingestion — schema determines handler and strategy
memory.ingest_structured({"content": "Use JWT for auth", "domain": "security"}, schema="decision")
memory.ingest_structured({"tool_name": "grep", "result": "3 matches"}, schema="tool_call")

Bitemporal Capabilities

SmartMemory implements full bitemporal support, tracking two distinct time dimensions for each memory item:

Valid Time vs Transaction Time

Valid Time (valid_time)

• When the fact was true in the real world
• Represents the actual time period of validity
• Can be in the past, present, or future
• Example: "John worked at Company X from 2020-2023"

Transaction Time (transaction_time)

• When the fact was recorded in the system
• Always in the past (when we learned about it)
• Immutable once recorded
• Used for audit trails and data provenance

Temporal Enrichment

SmartMemory automatically enriches memories with temporal metadata:

# Example temporal enrichment result
temporal_data = {
    "entities": {
        "John Smith": {
            "valid_start": "2020-01-15T00:00:00Z",
            "valid_end": "2023-12-31T23:59:59Z",
            "transaction_time": "2024-01-15T10:30:00Z"
        }
    }
}

Use Cases for Bitemporal Data

• Historical Analysis: Query what we knew at any point in time
• Fact Correction: Update information without losing historical context
• Audit Trails: Track when facts were recorded vs when they were valid
• Time-Travel Queries: "What did we know about X on date Y?"
• Data Provenance: Understand the evolution of knowledge over time

Comparison with Competitors

• Mem0: No bitemporal support (basic timestamps only)
• Zep: Single temporal dimension (no valid/transaction time separation)
• SmartMemory: Full bitemporal model with automated temporal enrichment

Cross-Type Relationships

SmartMemory automatically discovers relationships between different memory types:

• Semantic ↔ Episodic: Facts connected to personal experiences
• Procedural ↔ Episodic: Skills learned through specific events
• Pending ↔ All Types: Temporary connections to long-term memories

This creates a rich, interconnected memory network that mirrors human cognitive patterns.

Memory Evolution

Memory types can evolve and transform:

• Pending → Episodic: Recent interactions become experiences
• Episodic → Semantic: Repeated patterns become general knowledge
• Episodic → Zettelkasten: Important events become structured notes
• Semantic ↔ Zettelkasten: Facts and notes cross-reference each other