Learning Demo: AgentOS + the Learning UI

A small AgentOS app that shows the learning system end to end: one agent with all six learning stores enabled, a seed script that populates them with real conversations, and the Learning pages at os.agno.com to browse the results.

What it shows

Learning page	Store	Seeded with
User Profiles	`user_profile`	Alice (engineering lead) and Ben (founder)
User Memories	`user_memory`	Preferences like "short, direct answers"
Session Context	`session_context`	A running summary of Alice's upgrade session
Entity Memories	`entity_memory`	Postgres Cluster, Marcus Lee, Northwind, Design System
Decision Logs	`decision_log`	Recommendations the agent logged with reasoning

The sixth store, Learned Knowledge, lives in pgvector rather than the agno_learnings table, so it surfaces through the agent instead of a Learning page: Alice teaches the agent a Postgres upgrade rule, and the agent recalls it when Ben asks a related question in a different session. Watch for the save_learning and search_learnings tool calls in the seed output.

Files

agents.py: The ops assistant with all six stores enabled on Postgres + pgvector.
seed.py: Scripted conversations across two users that populate every store.
run.py: The AgentOS server exposing the /learnings CRUD endpoints.

Run it

1. Set your OpenAI key

bash

export OPENAI_API_KEY="..."

2. Start the pgvector container

bash

./cookbook/scripts/run_pgvector.sh

3. Seed the learning stores

bash

.venvs/demo/bin/python cookbook/08_learning/10_demo/seed.py

This runs the conversations through the agent. Extraction happens automatically, and the script prints everything the agent learned at the end.

4. Start the AgentOS server

bash

.venvs/demo/bin/python cookbook/08_learning/10_demo/run.py

5. Connect from os.agno.com

Open os.agno.com and sign in
Add OS -> Local, connect to http://localhost:7777
Open the Learning section in the sidebar

Each page reads from the agno_learnings table through the /learnings REST endpoints. You can also chat with the Ops Assistant directly: it recalls what it knows about the active user and keeps learning from new conversations.

The REST API

The same data is available over plain HTTP:

bash

curl "http://localhost:7777/learnings?limit=10"
curl "http://localhost:7777/learnings?learning_type=user_profile"
curl "http://localhost:7777/learnings/users"

Interactive docs are at http://localhost:7777/docs. For a client-side walkthrough of the CRUD endpoints, see cookbook/05_agent_os/learnings.

Start fresh

Learnings live in the ai.agno_learnings table and the ai.learning_demo_knowledge vector table. Drop both and re-run seed.py to reset:

bash

docker exec pgvector psql -U ai -d ai -c 'DROP TABLE IF EXISTS ai.agno_learnings, ai.learning_demo_knowledge;'

Note: agno_learnings is shared by every cookbook example using this container, so this also clears learnings from other runs.