Documentation Index Fetch the complete documentation index at: https://agno-v2-shaloo-ai-support-link.mintlify.app/llms.txt
Use this file to discover all available pages before exploring further.
Enable Learning
The simplest way: set learning=True.
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
agent = Agent(
model = OpenAIResponses( id = "gpt-5.2" ),
db = SqliteDb( db_file = "tmp/agents.db" ),
learning = True ,
)
This enables user profile and user memory extraction in Always mode. The agent automatically captures information and recalls it in future sessions.
Test It
# Session 1: Share information
agent.print_response(
"Hi! I'm Sarah, I work at Acme Corp as a data scientist." ,
user_id = "sarah@acme.com" ,
session_id = "session_1" ,
)
# Session 2: Agent remembers
agent.print_response(
"What do you know about me?" ,
user_id = "sarah@acme.com" ,
session_id = "session_2" ,
)
Session 2 is a new conversation, but the agent remembers Sarah.
Choose What Gets Learned
For more control, configure stores individually:
from agno.learn import LearningMachine
agent = Agent(
model = OpenAIResponses( id = "gpt-5.2" ),
db = db,
learning = LearningMachine(
user_profile = True , # Structured facts (name, role, preferences)
user_memory = True , # Unstructured observations
session_context = True , # Session summary and goals
entity_memory = False , # Facts about external entities
learned_knowledge = False # Insights across users (requires Knowledge)
),
)
See Learning Stores for details on each store.
Choose How Learning Happens
Each store can use a different learning mode:
from agno.learn import (
LearningMachine,
LearningMode,
UserProfileConfig,
UserMemoryConfig,
)
agent = Agent(
model = OpenAIResponses( id = "gpt-5.2" ),
db = db,
learning = LearningMachine(
user_profile = UserProfileConfig( mode = LearningMode. ALWAYS ),
user_memory = UserMemoryConfig( mode = LearningMode. AGENTIC ),
),
)
Mode How it works Always Extraction runs automatically after each response Agentic Agent receives tools and decides what to save Propose Agent proposes learnings, you approve before saving
See Learning Modes for details.
Production Database
For production, use PostgreSQL:
from agno.db.postgres import PostgresDb
db = PostgresDb( db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai" )
agent = Agent(
model = OpenAIResponses( id = "gpt-5.2" ),
db = db,
learning = True ,
)
Next Steps
Learning Stores Configure each store type
Learning Modes Control how and when agents learn