Agent Learn

Continuous learning system for capturing patterns, preferences, and insights from agent interactions

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

Diagram

Quick Start

Simple Shorthand (Recommended)

The easiest way to enable Agent Learn:

python

from praisonaiagents import Agent

agent = Agent(
    name="Learning Agent",
    instructions="You are a helpful assistant that learns from interactions",
    learn=True  # Enables AGENTIC mode (auto-learning)
)

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

With Specific Capabilities

python

from praisonaiagents import Agent, LearnConfig

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

All 7 Stores (Full Learning)

python

from praisonaiagents import Agent, LearnConfig

# Enable all 7 learning stores for comprehensive learning
agent = Agent(
    name="Full Learning Agent",
    instructions="You are a comprehensive learner",
    learn=LearnConfig(
        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

Diagram

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

xml

## 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

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

python

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

bash

praisonai memory learn status --user-id default

Show Learned Entries

bash

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

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

Add Learning Entry

bash

# 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

bash

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

Clear Learnings

bash

# 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

python

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

python

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

python

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,
        )
    )
)

Active Learning Tools

For agents that need explicit control over what they learn and recall, use the store_learning and search_learning tool functions — the Learn system counterparts to store_memory / search_memory.

python

from praisonaiagents import Agent
from praisonaiagents.tools import store_learning, search_learning

agent = Agent(
    instructions="You learn user preferences.",
    memory=True,
    learn=True,
    tools=[store_learning, search_learning],
)

agent.start("I prefer bullet-point answers")
# Agent calls store_learning("prefers bullet-point answers", category="persona")

agent.start("How do I like my answers?")
# Agent calls search_learning("answer format") → recalls "bullet-point answers"

store_learning

Store a learning entry in the agent’s learn system.

Parameter	Type	Required	Default	Description
`content`	`str`	Yes	—	The learning to store
`category`	`str`	No	`"persona"`	Category: `persona`, `insights`, `patterns`, `decisions`, `feedback`, `improvements`

search_learning

Search previously stored learnings.

Parameter	Type	Required	Default	Description
`query`	`str`	Yes	—	Search query
`limit`	`int`	No	`5`	Max results to return

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

Agent Learn

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

Active Learning Tools

store_learning

search_learning

Best Practices

Related