Learnings

Agent learning and the /learnings REST API use the same persisted records. This lesson serves an agent that automatically extracts a user profile and user memory, proves those records are readable over HTTP, and then walks the manual CRUD surface.

Files

File	What it teaches
`learnings_with_agentos.py`	Serve a learning-enabled agent and the database behind `/learnings`.
`rest_api_learnings.py`	Read agent-written learnings, then create, list, get, patch, and delete records.

Prerequisites

Set OPENAI_API_KEY. The agent and both automatic learning extractors use OpenAI Responses gpt-5.5. SQLite persists the demo data in tmp/learnings.db; the client removes its uniquely named demo users after each run.

Run

Start the server:

bash

.venvs/demo/bin/python cookbook/05_agent_os/11_learnings/learnings_with_agentos.py

In another terminal, run the REST walkthrough:

bash

.venvs/demo/bin/python cookbook/05_agent_os/11_learnings/rest_api_learnings.py

The first agent run shares a name, work context, and response preference. A non-streaming run waits for automatic profile and memory extraction, so the next GET /learnings?user_id=... reads both user_profile and user_memory records back immediately.

REST surface

Method	Path	Purpose
`GET`	`/learnings`	List and filter records in a `{data, meta}` envelope.
`POST`	`/learnings`	Create one record; identity-keyed duplicates return `409`.
`GET`	`/learnings/users`	List owning user IDs and their latest update timestamps.
`DELETE`	`/learnings/users/{user_id}`	Delete every learning owned by one user.
`GET`	`/learnings/{learning_id}`	Fetch one record.
`PATCH`	`/learnings/{learning_id}`	Replace `content` and/or `metadata`.
`DELETE`	`/learnings/{learning_id}`	Delete one record.

user_profile, user_memory, session_context, and entity_memory use deterministic IDs derived from identity fields. Include those fields when creating a record; for a user profile, retain user_id inside replacement content so the learning store can deserialize it. PATCH does not change identity fields. Other types such as decision_log use generated IDs and can have multiple records per user.