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Agents learn from every interaction β€” 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

2

Configure Learning Capabilities

3

Use a Database Backend


How It Works


Choose a Learning Mode

Choose a Storage Backend


Configuration Options

LearnConfig Python Reference

Full parameter reference for LearnConfig
Agent(learn=True) is a coordinated shortcut: it enables mode=AGENTIC + nudge_interval=10 + nudge_min_tool_iters=0 + auto_memory=True all at once, so a chat-only companion accrues learnings with no extra wiring. See Bot Session Learning for the default-on gateway/bot behaviour.

Common Patterns

Self-improving assistant with retention governance:
Shared learnings across a team of agents:

Best Practices

Enable learning with defaults first. Once you understand what gets captured, switch to LearnConfig to enable only the capabilities you need (persona, insights, patterns).
Set mode="agentic" to have the agent automatically extract and store learnings after each conversation. This requires no manual action from users or developers.
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.
The default file backend stores JSON in ~/.praison. For production or multi-instance setups, use backend="sqlite" or backend="redis" with a db_url.

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

Bot Session Learning

Default-on cross-session learning for gateway/bot agents