Skip to main content
The Contacts agent is a mini CRM that remembers people, the facts you tell it about them, who knows whom, and what’s happening between them. Three memory stores, each scoped to a different shape of state: 
from agno.learn import (
    LearningMachine, LearningMode,
    UserProfileConfig, EntityMemoryConfig, SessionContextConfig,
)

contacts = Agent(
    id="contacts",
    name="Contacts",
    model=MODEL,
    db=agent_db,
    learning=LearningMachine(
        user_profile=UserProfileConfig(mode=LearningMode.ALWAYS),
        entity_memory=EntityMemoryConfig(
            mode=LearningMode.AGENTIC,
            enable_create_entity=True,
            enable_add_fact=True,
            enable_add_relationship=True,
            enable_add_event=True,
        ),
        session_context=SessionContextConfig(
            mode=LearningMode.ALWAYS,
            enable_planning=True,
        ),
    ),
    enable_agentic_memory=True,
)

​
Three stores, three shapes

StoreCardinalityModeExample
User profileOne record per userALWAYS (auto-extracted)“Prefers async communication, based in PT, role: PM”
Entity memoryGraph of entities + facts + relationships + eventsAGENTIC (agent writes)Sarah Chen → role: VP Eng → company: Acme → “joined 2024-Q4”
Session contextPer sessionALWAYS”User is planning a Q1 offsite, talking through agenda”
LearningMode.ALWAYS runs an extractor on every turn. LearningMode.AGENTIC gives the agent tools to write when it judges something worth keeping.

​
Entity memory in detail

The Contacts agent gets four entity-memory tools when configured as above:
ToolWhat it does
create_entity(name, type)Adds a new entity (Person, Company, Project, …)
add_fact(entity, fact)Attaches a fact to an entity
add_relationship(entity_a, entity_b, type)Links two entities
add_event(entity, event, when)Records a time-bound event
Example chat that builds up the graph: 
User: Sarah Chen joined Acme as VP Engineering last month.
Contacts: → create_entity("Sarah Chen", "Person")
          → add_fact("Sarah Chen", "VP Engineering at Acme")
          → add_relationship("Sarah Chen", "Acme", "employed_by")
          → add_event("Sarah Chen", "joined Acme as VP Eng", "2026-03-15")
Next session, “what do I know about Sarah?” returns the full picture without the user re-telling.

​
User profile

User profile is one record per user_id, updated in place. Whatever the agent learns about you (your role, your preferences, your working style) goes here. 
User: I prefer concise responses, no preamble.
Contacts: → user_profile updated: { communication_style: "concise, direct" }
Future responses across any session, any agent that shares the same db, get terser automatically.

​
Session context

Session context is plan-shaped. “What’s this conversation actually about? What does the user want by the end?” The agent uses it to keep multi-turn plans coherent. 
Session goal: Plan Q1 offsite agenda
Open items: pick venue, set theme, draft schedule
Decisions so far: 3 days, 25 people, focus on roadmap alignment
Useful for long, exploratory sessions where the user pivots and the agent needs to stay anchored.

​
Vanilla agentic memory vs LearningMachine

For most agents, enable_agentic_memory=True is enough: a single bucket of facts that the agent writes to via tools. LearningMachine is right when the shape of memory matters:
  • A relationship graph (Contacts) needs entities and edges, not flat strings.
  • A long-running personal agent (Pal) needs to separate “what I know about you” from “what I know about your domain”.
  • A self-improving SQL agent (Dash) needs to separate “validated queries” from “error patterns I’ve fixed”.
When in doubt, start with enable_agentic_memory=True and move to LearningMachine once flat memory feels too lossy.

​
See it in action

@Contacts Sarah Chen joined Acme as VP Eng last month
@Contacts what do I know about Sarah?
@Contacts who at Acme have I been talking to?
@Contacts I'm meeting Sarah tomorrow about the platform roadmap
@Contacts later: what was the meeting with Sarah about?
Each turn updates the entity graph. The agent uses the graph on every retrieval. Source: agents/contacts/agent.py, Learning Machine docs

​
Next

Learning →