Agent Learn

Agent Learn enables agents to capture and recall patterns, preferences, and insights from interactions, improving future responses through persistent learning stores.

Quick Start

Simple Shorthand (Recommended)

The easiest way to enable Agent Learn:

from praisonaiagents import Agent

agent = Agent(
    name="Learning Agent",
    instructions="You are a helpful assistant that learns from interactions",
    memory="learn"  # Simple shorthand for enabling learn
)

agent.start("What's my preferred coding style?")

memory="learn" is equivalent to memory=MemoryConfig(learn=True) but much simpler.

Explicit Configuration

For more control over the memory backend:

from praisonaiagents import Agent, MemoryConfig

agent = Agent(
    name="Learning Agent",
    instructions="You are a helpful assistant that learns from interactions",
    memory=MemoryConfig(learn=True)
)

agent.start("What's my preferred coding style?")

With Specific Capabilities

from praisonaiagents import Agent, MemoryConfig, LearnConfig

agent = Agent(
    name="Learning Agent",
    instructions="You learn and adapt to user preferences",
    memory=MemoryConfig(
        learn=LearnConfig(
            persona=True,      # User preferences
            insights=True,     # Observations
            patterns=True,     # Reusable knowledge
            decisions=True,    # Decision logging
        )
    )
)

All 7 Stores (Full Learning)

from praisonaiagents import Agent

# Enable all 7 learning stores for comprehensive learning
agent = Agent(
    name="Full Learning Agent",
    instructions="You are a comprehensive learner",
    memory={
        "backend": "file",
        "learn": {
            "persona": True,       # User preferences & profile
            "insights": True,      # Observations & learnings
            "thread": True,        # Session context
            "patterns": True,      # Reusable knowledge
            "decisions": True,     # Decision rationale
            "feedback": True,      # Outcome signals
            "improvements": True,  # Self-improvement proposals
        }
    }
)

How It Works

Auto-Injection: When memory="learn" is enabled, learned context is automatically injected into the agent’s system prompt before each response. No manual wiring needed!

Phase	Description
Retrieve	`get_learn_context()` fetches learnings from all enabled stores
Auto-Inject	Context automatically added to system prompt as “Learned Context” section
Generate	Agent uses learned context to personalize response
Persist	Learnings stored in JSON files for cross-session persistence

What Gets Injected

When memory="learn" is enabled, the agent’s system prompt automatically includes a “Learned Context” section with:

## Learned Context

### User Persona
- Prefers dark mode and vim keybindings
- Senior developer working on Python projects

### Insights  
- User values concise, technical responses
- Prefers code examples over lengthy explanations

### Patterns
- Common workflow: test → implement → refactor
- Prefers pytest over unittest

### Decisions
- Chose PostgreSQL over MySQL for type safety
- Selected FastAPI for async performance

### Feedback
- Positive: step-by-step debugging approach
- Improvement: add more context in error messages

### Improvements
- Consider caching frequent queries
- Add retry logic for API calls

Each section only appears if the corresponding store is enabled AND contains data. Thread context is excluded from injection as it’s session-specific.

Learning Stores

Agent Learn organizes knowledge into specialized stores:

Store	Purpose	Default
`persona`	User preferences, communication style, profile	`True`
`insights`	Observations and learnings from interactions	`True`
`thread`	Session and conversation context	`True`
`patterns`	Reusable knowledge patterns	`False`
`decisions`	Decision logging and rationale	`False`
`feedback`	Outcome signals and corrections	`False`
`improvements`	Self-improvement proposals	`False`

Configuration Options

from praisonaiagents import LearnConfig

config = LearnConfig(
    # Learning capabilities
    persona=True,           # User preferences and profile
    insights=True,          # Observations and learnings
    thread=True,            # Session/conversation context
    patterns=False,         # Reusable knowledge patterns
    decisions=False,        # Decision logging
    feedback=False,         # Outcome signals
    improvements=False,     # Self-improvement proposals
    
    # Scope configuration
    scope="private",        # "private" or "shared"
    
    # Storage
    store_path=None,        # Custom storage path
    
    # Maintenance
    auto_consolidate=True,  # Auto-consolidate learnings
    retention_days=None,    # Days to retain (None = forever)
)

Option	Type	Default	Description
`persona`	`bool`	`True`	Capture user preferences and profile
`insights`	`bool`	`True`	Store observations and learnings
`thread`	`bool`	`True`	Maintain session context
`patterns`	`bool`	`False`	Store reusable knowledge patterns
`decisions`	`bool`	`False`	Log decisions with rationale
`feedback`	`bool`	`False`	Capture outcome signals
`improvements`	`bool`	`False`	Track self-improvement proposals
`scope`	`str`	`"private"`	Learning visibility: `"private"` or `"shared"`
`store_path`	`str`	`None`	Custom storage directory
`auto_consolidate`	`bool`	`True`	Automatically consolidate learnings
`retention_days`	`int`	`None`	Days to retain entries (None = forever)

CLI Commands

Manage learning data via the command line:

Show Status

praisonai memory learn status --user-id default

Show Learned Entries

# Show all stores
praisonai memory learn show all --limit 10

# Show specific store
praisonai memory learn show persona --user-id default

Add Learning Entry

# Add to insights store
praisonai memory learn add "User prefers concise responses" --store insights

# Add to persona store
praisonai memory learn add "Prefers Python over JavaScript" --store persona

Search Learnings

praisonai memory learn search "coding style" --limit 5

Clear Learnings

# Clear all stores
praisonai memory learn clear all --force

# Clear specific store
praisonai memory learn clear persona --user-id default

Common Patterns

Personal Assistant with Memory

from praisonaiagents import Agent, MemoryConfig, LearnConfig

agent = Agent(
    name="Personal Assistant",
    instructions="You are a personal assistant that remembers user preferences",
    memory=MemoryConfig(
        backend="sqlite",
        user_id="user123",
        learn=LearnConfig(
            persona=True,
            insights=True,
            patterns=True,
        )
    )
)

# First interaction
agent.start("I prefer dark mode and vim keybindings")

# Later interaction - agent remembers preferences
agent.start("Set up my development environment")

Team Knowledge Base

from praisonaiagents import Agent, MemoryConfig, LearnConfig

agent = Agent(
    name="Team Knowledge Agent",
    instructions="You help the team by learning from shared experiences",
    memory=MemoryConfig(
        learn=LearnConfig(
            scope="shared",      # Share learnings across team
            patterns=True,       # Capture reusable patterns
            decisions=True,      # Log architectural decisions
            improvements=True,   # Track improvement proposals
        )
    )
)

Feedback-Driven Learning

from praisonaiagents import Agent, MemoryConfig, LearnConfig

agent = Agent(
    name="Adaptive Agent",
    instructions="You improve based on feedback",
    memory=MemoryConfig(
        learn=LearnConfig(
            feedback=True,       # Capture outcome signals
            improvements=True,   # Track self-improvement
            auto_consolidate=True,
        )
    )
)

Best Practices

Use private scope for personal data

Keep scope="private" (default) when storing user-specific preferences or sensitive information. Use scope="shared" only for team knowledge that should benefit all agents.

Enable stores incrementally

Start with the default stores (persona, insights, thread) and enable additional stores (patterns, decisions, feedback, improvements) as your use case requires them.

Set retention for transient data

Use retention_days for stores that capture temporal patterns. Thread context often benefits from 7-30 day retention to avoid clutter.

Consolidate periodically

Keep auto_consolidate=True to automatically merge and summarize learnings over time, preventing store bloat.

Agent Train

Active iterative training

Learn vs Train

Compare passive learning vs active training

Memory

Understanding agent memory systems

Knowledge

RAG and knowledge retrieval

Getting Started

Core Concepts

Guides

Features

Models

Databases

Observability

Memory

Knowledge

RAG

Persistence

Tools

Other Features

Developers

Configuration

Best Practices

Getting Started (No Code)

Quick Start

How It Works

What Gets Injected

Learning Stores

Configuration Options

CLI Commands

Show Status

Show Learned Entries

Add Learning Entry

Search Learnings

Clear Learnings

Common Patterns

Personal Assistant with Memory

Team Knowledge Base

Feedback-Driven Learning

Best Practices

Agent Train

Learn vs Train

Memory

Knowledge

Getting Started

Core Concepts

Guides

Features

Models

Databases

Observability

Memory

Knowledge

RAG

Persistence

Tools

Other Features

Developers

Configuration

Best Practices

Getting Started (No Code)

​Quick Start

​How It Works

​What Gets Injected

​Learning Stores

​Configuration Options

​CLI Commands

​Show Status

​Show Learned Entries

​Add Learning Entry

​Search Learnings

​Clear Learnings

​Common Patterns

​Personal Assistant with Memory

​Team Knowledge Base

​Feedback-Driven Learning

​Best Practices

​Related

Agent Train

Learn vs Train

Memory

Knowledge

Quick Start

How It Works

What Gets Injected

Learning Stores

Configuration Options

CLI Commands

Show Status

Show Learned Entries

Add Learning Entry

Search Learnings

Clear Learnings

Common Patterns

Personal Assistant with Memory

Team Knowledge Base

Feedback-Driven Learning

Best Practices

Related