Learn (Continuous Learning)

Let your agent capture and reuse insights, patterns, and user preferences across conversations

Make your agent smarter over time — it learns from every conversation and applies those insights automatically.

from praisonaiagents import Agent

agent = Agent(
    name="LearningAssistant",
    instructions="You are a helpful personal assistant.",
    learn=True  # Agent captures and reuses learnings automatically
)

result = agent.start("I prefer concise bullet points over long paragraphs")
print(result)

import { Agent } from 'praisonai';

const agent = new Agent({
  name: 'LearningAssistant',
  instructions: 'You are a helpful personal assistant.',
  learn: true, // Agent captures and reuses learnings automatically
});

const result = await agent.start('I prefer concise bullet points over long paragraphs');
console.log(result);

The user shares preferences in chat; the agent stores learnings and applies them on the next turn.

Quick Start

1. Simple Usage

   Enable learning with a boolean — the agent captures persona and insights by default:

   from praisonaiagents import Agent
   
   agent = Agent(
       name="PersonalAssistant",
       instructions="You are my personal assistant.",
       learn=True
   )
   
   agent.start("Remember: I always want responses in markdown format")

2. With LearnConfig

   Choose what to learn and how to store it:

   from praisonaiagents import Agent, LearnConfig
   
   agent = Agent(
       instructions="You are a helpful assistant.",
       learn=LearnConfig(
           persona=True,
           insights=True,
           patterns=True,
           mode="agentic",
       ),
   )
   agent.start("I work in finance and need data-focused answers.")

3. With Database Backend

   from praisonaiagents import Agent, LearnConfig
   
   agent = Agent(
       instructions="You are a helpful assistant.",
       learn=LearnConfig(
           backend="sqlite",
           db_url="sqlite:///learn.db",
           mode="agentic",
       ),
   )
   agent.start("My team prefers Python over JavaScript for all scripts.")

How It Works

Learning Modes

Configuration Options

Precedence ladder:

# Level 1: Bool (enable with defaults)
agent = Agent(learn=True)

# Level 2: LearnConfig (full control)
agent = Agent(learn=LearnConfig(
    persona=True,
    mode="agentic",
    backend="sqlite",
))

What the agent can learn:

Learning mode:

Storage backend:

Common Patterns

Pattern 1 — Persona learning for personalized responses

from praisonaiagents import Agent, LearnConfig

agent = Agent(
    name="PersonalCoach",
    instructions="You are a personal productivity coach.",
    learn=LearnConfig(
        persona=True,
        insights=True,
        patterns=True,
        mode="agentic",
        backend="sqlite",
        db_url="sqlite:///coach_memory.db",
    )
)

# The agent builds a profile of you over time
agent.start("I struggle with time management in the mornings")
agent.start("I prefer evening workouts")
agent.start("What routine would work best for me?")

Shared learning across agents:

from praisonaiagents import Agent, LearnConfig

shared_learn = LearnConfig(
    insights=True,
    patterns=True,
    scope="shared",  # All agents share this learning store
    backend="redis",
    db_url="redis://localhost:6379",
)

agent1 = Agent(name="SupportAgent1", instructions="Handle customer support.", learn=shared_learn)
agent2 = Agent(name="SupportAgent2", instructions="Handle customer support.", learn=shared_learn)

Nudge-based self-improvement:

instructions=”You are a writing assistant.”, learn=LearnConfig(persona=True, insights=True, mode=”agentic”), ) response = agent.start(“I prefer formal British English in all my documents.”) print(response)

### Pattern 2 — Shared patterns across agents
```python
from praisonaiagents import Agent, LearnConfig

agent = Agent(
    name="ImprovingAgent",
    instructions="You are a coding assistant that gets better over time.",
    learn=LearnConfig(
        improvements=True,
        nudge_interval=5,  # Reflect and propose improvements every 5 turns
        nudge_min_tool_iters=3,  # Only nudge if agent did real work
    )
)

Concurrency safety

The file backend is built on BaseJSONStore, whose file lock now tolerates long critical sections. Concurrent agent.remember(...) and autosave paths across multiple agents or processes will not clobber each other’s learnings even if a save legitimately takes longer than the lock timeout, thanks to the mtime + heartbeat staleness rule. See Thread Safety for details (PR #5136).

Best Practices

Related