Learn vs Train

Understanding the difference between passive learning and active training

PraisonAI provides two distinct systems for improving agent behavior: Agent Learn (passive) and Agent Train (active). Understanding when to use each is key to building effective agents.

Agent Learn (Passive)

Automatically captures patterns during normal interactions:

Agent Train (Active)

Explicit iterative improvement with feedback:

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Quick Comparison

Aspect	Agent Learn	Agent Train
Type	Passive / Automatic	Active / Explicit
When	During normal interactions	Dedicated training sessions
Feedback	Implicit (auto-detected patterns)	Explicit (human/LLM scores)
Purpose	Remember preferences & patterns	Improve specific behaviors
Storage	7 specialized stores	Scenarios & reports
Iterations	Continuous	Fixed (1-N iterations)
Code Location	praisonaiagents (core SDK)	praisonai (wrapper)

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When to Use Each

Use Agent Learn When:

• Remembering user preferences - Dark mode, language, communication style
• Capturing domain knowledge - Project-specific terms, codebase patterns
• Building context over time - Session history, conversation threads
• Automatic adaptation - No intervention needed
• Long-term memory - Persistent across sessions

Use Agent Training When:

• Improving specific responses - Better greetings, more accurate answers
• Quality assurance - Iterative refinement with scoring
• Human feedback loops - Expert-in-the-loop improvement
• Benchmarking behavior - Measurable improvement metrics
• One-time improvement sessions - Focused training runs

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Data Flow Comparison

Agent Learn Flow

Key Points:

• Happens automatically during agent.start()
• No explicit feedback required
• Stores in 7 specialized stores
• Retrieved automatically for future interactions

Agent Training Flow

Key Points:

• Explicit training session
• Human or LLM provides scores
• Iterative improvement loop
• Generates measurable reports

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Storage Comparison

Agent Learn Stores

Store	Purpose	Auto-Captured
PersonaStore	User preferences, profile	✅ Yes
InsightStore	Observations, learnings	✅ Yes
ThreadStore	Session/conversation context	✅ Yes
PatternStore	Reusable knowledge patterns	Optional
DecisionStore	Decision logging	Optional
FeedbackStore	Outcome signals	Optional
ImprovementStore	Self-improvement proposals	Optional

Agent Training Storage

Data	Purpose	Format
TrainingScenario	Input + expected output	JSON
TrainingIteration	Per-iteration results	JSON
TrainingReport	Summary with scores	JSON

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Code Examples

Agent Learn

from praisonaiagents import Agent

# Enable learning with simple shorthand
agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant",
    learn=True  # Enables AGENTIC mode (auto-learning)
)

# Learnings captured automatically
agent.start("I prefer concise answers")
agent.start("Always use Python examples")

# Later - agent remembers preferences
agent.start("How do I sort a list?")
# Agent gives concise Python answer

Agent Training

from praisonai.train.agents import AgentTrainer, TrainingScenario, apply_training
from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant"
)

# Create explicit training session
trainer = AgentTrainer(
    agent=agent,
    iterations=5,
    human_mode=True  # Get human feedback
)

trainer.add_scenario(TrainingScenario(
    id="greeting",
    input_text="Hello!",
    expected_output="Friendly, welcoming response"
))

# Run training with feedback
report = trainer.run()
print(f"Score improved: {report.improvement:+.1f}")

# Apply the best iteration at runtime
apply_training(agent, session_id=report.session_id)

# Now agent uses learned improvements
response = agent.start("Hello!")

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Using Both Together

Agent Learn and Agent Training are complementary. Use them together for best results:

from praisonaiagents import Agent
from praisonai.train.agents import AgentTrainer, TrainingScenario

# Create agent with learning enabled
agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant",
    learn=True  # Passive learning always on
)

# Periodically run training sessions
trainer = AgentTrainer(agent=agent, iterations=3, human_mode=True)
trainer.add_scenario(TrainingScenario(
    id="quality_check",
    input_text="Explain machine learning",
    expected_output="Clear, accurate explanation"
))

# Training improves specific behavior
report = trainer.run()

# Agent continues learning passively
# AND benefits from training improvements

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Summary

Feature	Agent Learn	Agent Training
Enable	learn=True	AgentTrainer(agent)
Trigger	Automatic	Manual
Feedback	None needed	Score + suggestions
Best for	Preferences, context	Quality improvement
Persistence	Learn stores	Training reports
CLI	praisonai memory learn	praisonai train agents

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Related

Agent Learn

Passive continuous learning

Agent Train

Active iterative training

Memory

Agent memory systems

Train CLI

CLI reference