Memory vs Learning

Understand the difference between Memory (storage) and Learning (adaptive knowledge extraction)

Memory and Learning are different but complementary systems. Memory stores raw data, while Learning extracts actionable knowledge from it.

Key Differences

Dimension	Memory	Learning
Analogy	Notebook / filing cabinet	Adaptive brain tissue
Question	”What happened before?"	"What should I know going forward?”
When	Every interaction (read/write)	Post-interaction analysis
How	Direct storage (key-value, vector, relational)	LLM-powered extraction + structured stores
Lifetime	Session (STM) or persistent (LTM)	Always persistent, grows over time
Output	Raw conversation history, entity records	User profiles, behavioral patterns, decision logs
Cost	Low (no LLM calls for basic ops)	Higher (requires LLM extraction step)

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When to Use Each

Use Memory When

• You need conversation context across sessions
• You want to store/retrieve specific facts
• You need entity tracking (people, places)
• You want lightweight, zero-LLM-cost storage

Use Learning When

• Agents should adapt to user preferences over time
• You want pattern recognition across interactions
• You need decision logging and rationale tracking
• You want self-improvement proposals

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API Comparison

Memory and Learning are peer-level systems — both are top-level Agent parameters:

Memory Only

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant",
    memory=True   # STM + LTM + Entity + Episodic
)

Learning Only

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You learn from every interaction",
    learn=True  # Enables AGENTIC mode (auto-learning)
)

Both Together

from praisonaiagents import Agent, LearnConfig

agent = Agent(
    name="Full Assistant",
    instructions="You remember and learn",
    memory=True,                    # Memory system
    learn=LearnConfig(              # Learning layer
        persona=True,               # User preferences
        insights=True,              # Observations
        patterns=True,              # Reusable knowledge
        decisions=True,             # Decision logging
    )
)

Custom Config

from praisonaiagents import Agent, LearnConfig

agent = Agent(
    name="Production Agent",
    instructions="You learn and adapt",
    learn=LearnConfig(
        persona=True,
        insights=True,
        patterns=True,
        mode="agentic",          # Auto-extract learnings
        scope="private",         # User-isolated data
    )
)

learn=True auto-creates a minimal memory backend if memory= is not set. Learning works independently — you don’t need to configure memory to use it.

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What Each System Stores

Memory Types

Type	What It Stores	Persistence
Short-term	Recent conversation context	Rolling buffer, auto-expires
Long-term	Important facts with importance scores	Permanent
Entity	Named entities (people, places) with attributes	Permanent
Episodic	Date-based interaction history	Configurable retention

Learning Stores

Store	What It Extracts	Default
Persona	User preferences, communication style	✅ On
Insights	Observations and learnings	✅ On
Thread	Session context	✅ On
Patterns	Reusable knowledge patterns	❌ Off
Decisions	Decision rationale and trade-offs	❌ Off
Feedback	Outcome signals and corrections	❌ Off
Improvements	Self-improvement proposals	❌ Off

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How They Work Together

Memory provides the raw recall. Learning provides the adaptive intelligence on top.

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Architecture

Both systems are first-class peers on the Agent:

Learning Modes

Mode	Behavior
AGENTIC	Automatically extracts learnings after each interaction (default with learn=True)
PROPOSE	Agent proposes learnings, human approves
DISABLED	Learning is off

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Related

Memory

Complete memory guide — types, backends, API

Agent Learn

Learning stores, configuration, CLI

Context vs Memory

Context window vs persistent memory

Knowledge vs Memory vs Context

Full comparison of all data systems