Learn Config

Configure continuous learning to capture patterns and preferences from agent interactions using LearnConfig

from praisonaiagents import Agent, LearnConfig

agent = Agent(
    name="Personal Assistant",
    instructions="You are a helpful assistant that remembers user preferences.",
    learn=LearnConfig(
        persona=True,
        insights=True,
        mode="agentic",
    )
)

agent.start("I prefer concise answers with bullet points")

Agents learn from every interaction — capturing user preferences, insights, and patterns to improve future responses automatically.

The user interacts with the agent; learnings are extracted and stored, then recalled in future sessions.

Quick Start

1. Enable Learning with Defaults

   from praisonaiagents import Agent
   
   agent = Agent(
       name="Learning Agent",
       instructions="You are a helpful assistant",
       learn=True
   )
   
   agent.start("I work in Python and prefer type hints")
   
2. Configure Learning Capabilities

   from praisonaiagents import Agent, LearnConfig
   
   agent = Agent(
       name="Smart Assistant",
       instructions="Remember my preferences and insights.",
       learn=LearnConfig(
           persona=True,
           insights=True,
           patterns=True,
           mode="agentic",
       )
   )
   
   agent.start("Always format code examples with type annotations")
   
3. Use a Database Backend

   from praisonaiagents import Agent, LearnConfig
   
   agent = Agent(
       name="Persistent Learner",
       instructions="Store learnings in a database.",
       learn=LearnConfig(
           backend="sqlite",
           db_url="sqlite:///learn.db",
           persona=True,
           insights=True,
       )
   )
   
   agent.start("My timezone is UTC+5:30")
   

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How It Works

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Choose a Learning Mode

Choose a Storage Backend

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Configuration Options

LearnConfig Python Reference

Full parameter reference for LearnConfig

Option	Type	Default	Description
persona	bool	True	Capture user preferences and profile
insights	bool	True	Capture observations and learnings
thread	bool	True	Capture session/conversation context
patterns	bool	False	Capture reusable knowledge patterns
decisions	bool	False	Log decision history
feedback	bool	False	Capture outcome signals
improvements	bool	False	Self-improvement proposals
mode	str	"disabled"	"disabled", "agentic", or "propose"
backend	str	"file"	"file", "sqlite", "redis", or "mongodb"
db_url	str | None	None	Database connection URL (non-file backends)
store_path	str | None	None	Custom path for file backend
max_entries	int	0	Max entries per store (0 = unbounded)
retention_days	int	0	Archive entries unused for N days (0 = keep forever)
llm	str | None	None	LLM for learning extraction (defaults to agent’s LLM)
scope	str	"private"	"private" or "shared"
nudge_interval	int	0	Nudge agent every N turns (0 = disabled)
nudge_min_tool_iters	int	3	Minimum tool iterations before a nudge fires
propose_skills	bool	False	Enable skill_manage tool in propose mode

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Common Patterns

Self-improving assistant with retention governance:

from praisonaiagents import Agent, LearnConfig

agent = Agent(
    name="Adaptive Assistant",
    instructions="Adapt to user preferences over time.",
    learn=LearnConfig(
        persona=True,
        insights=True,
        patterns=True,
        mode="agentic",
        max_entries=500,
        retention_days=90,
    )
)

Shared learnings across a team of agents:

from praisonaiagents import Agent, LearnConfig

shared_learn = LearnConfig(
    scope="shared",
    backend="sqlite",
    db_url="sqlite:///team_learnings.db",
    persona=True,
    insights=True,
)

researcher = Agent(name="Researcher", instructions="Research topics.", learn=shared_learn)
writer = Agent(name="Writer", instructions="Write content.", learn=shared_learn)

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Best Practices

Start with learn=True before tuning

Enable learning with defaults first. Once you understand what gets captured, switch to LearnConfig to enable only the capabilities you need (persona, insights, patterns).

Use agentic mode for hands-off learning

Set mode="agentic" to have the agent automatically extract and store learnings after each conversation. This requires no manual action from users or developers.

Set retention limits in production

Use max_entries and retention_days to prevent unbounded growth. A limit of max_entries=1000 with retention_days=180 keeps the learning store manageable.

Use sqlite or redis for persistence across restarts

The default file backend stores JSON in ~/.praison. For production or multi-instance setups, use backend="sqlite" or backend="redis" with a db_url.

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Related

Memory Config

Session and persistent memory configuration

Agent Learning

Agent learn feature overview

Learning Retention

Govern how long learnings are kept

Learn Skill

Teach agents new skills dynamically