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:

Quick Comparison

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

Data Flow Comparison

Storage Comparison

Agent Learn Stores

Agent Training Storage

Code Examples

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

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!")

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

Summary

Related