> ## Documentation Index > Fetch the complete documentation index at: https://docs.agno.com/llms.txt > Use this file to discover all available pages before exploring further. # Learning Demo: Seed Data > Runs a few short conversations through the ops assistant so that every Learning page in AgentOS has data: user profiles, user memories, session context, entity memories, and decision logs. Runs a few short conversations through the ops assistant so that every Learning page in AgentOS has data: user profiles, user memories, session context, entity memories, and decision logs. It also seeds a learned knowledge insight that one user teaches and another benefits from. ```python seed.py theme={null} """ Learning Demo: Seed Data ======================== Runs a few short conversations through the ops assistant so that every Learning page in AgentOS has data: user profiles, user memories, session context, entity memories, and decision logs. It also seeds a learned knowledge insight that one user teaches and another benefits from. Requires the pgvector container: ./cookbook/scripts/run_pgvector.sh Run: .venvs/demo/bin/python cookbook/08_learning/10_demo/seed.py Then start the AgentOS server with run.py and connect from os.agno.com. """ from agents import ops_assistant ALICE = "alice@vantagelabs.dev" BEN = "ben@northwind.io" # (user_id, session_id, message) CONVERSATIONS = [ # Alice: profile, preferences, and a session with a clear goal ( ALICE, "alice-postgres-upgrade", "Hi, I'm Alice Chen, engineering lead at Vantage Labs. " "I prefer short, direct answers with code over prose.", ), ( ALICE, "alice-postgres-upgrade", "My goal this week is to upgrade our Postgres cluster from version 15 " "to 17 with zero downtime. Help me plan the migration.", ), ( ALICE, "alice-postgres-upgrade", "Some context: Marcus Lee is our infra engineer and owns the Postgres " "cluster. The cluster runs on Kubernetes in us-east-1.", ), ( ALICE, "alice-postgres-upgrade", "Should we use logical replication or pg_upgrade for the cutover? " "Recommend one and log your decision.", ), ( ALICE, "alice-postgres-upgrade", "Save this for the team: when upgrading Postgres across major " "versions, always rehearse the cutover on a clone restored from a " "fresh backup before touching production.", ), # Ben: a second user with different preferences and entities ( BEN, "ben-design-system", "Hey, I'm Ben Okafor, founder at Northwind. We closed our Series A " "round last week. I like detailed answers that walk through trade-offs.", ), ( BEN, "ben-design-system", "We are kicking off the Design System project this quarter and Sarah " "Kim will lead it. What should the first milestone be? Pick one and " "log your decision.", ), # Ben benefits from what Alice taught the agent ( BEN, "ben-postgres-question", "We also need to upgrade Northwind's Postgres soon. Anything the " "team has already learned about doing this safely?", ), ] if __name__ == "__main__": for user_id, session_id, message in CONVERSATIONS: print() print("=" * 70) print(f"USER: {user_id} | SESSION: {session_id}") print("=" * 70) ops_assistant.print_response( message, user_id=user_id, session_id=session_id, stream=True, ) # ------------------------------------------------------------------ # Show what the agent learned # ------------------------------------------------------------------ lm = ops_assistant.learning_machine print() print("=" * 70) print("WHAT THE AGENT LEARNED") print("=" * 70) for user_id in (ALICE, BEN): lm.user_profile_store.print(user_id=user_id) lm.user_memory_store.print(user_id=user_id) lm.session_context_store.print(session_id="alice-postgres-upgrade") lm.decision_log_store.print(agent_id="ops-assistant", limit=10) lm.learned_knowledge_store.print(query="postgres") print() print("Entities discovered:") seen = set() for query in ("postgres", "northwind", "design"): for entity in lm.entity_memory_store.search(query=query, limit=5): if entity.entity_id not in seen: seen.add(entity.entity_id) print(f"- {entity.name} ({entity.entity_type})") print() print("Seed complete. Start the server and explore the Learning pages:") print(" .venvs/demo/bin/python cookbook/08_learning/10_demo/run.py") ``` The example imports this helper module from the same directory: ```python agents.py theme={null} """ Learning Demo: Shared Agent =========================== A single ops assistant with every learning store enabled: - User Profile: structured fields (name, role, preferences) - User Memory: unstructured observations about the user - Session Context: a running summary of each session - Entity Memory: facts, events, and relationships about external things - Learned Knowledge: insights that transfer across users (pgvector) - Decision Log: significant decisions with reasoning Requires the pgvector container: ./cookbook/scripts/run_pgvector.sh """ from agno.agent import Agent from agno.db.postgres import PostgresDb from agno.knowledge import Knowledge from agno.knowledge.embedder.openai import OpenAIEmbedder from agno.learn import ( LearningMachine, ) from agno.models.openai import OpenAIResponses from agno.vectordb.pgvector import PgVector, SearchType # --------------------------------------------------------------------------- # Database # --------------------------------------------------------------------------- db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai" db = PostgresDb(id="learning-demo-db", db_url=db_url) # Learned Knowledge needs a vector store for semantic search. knowledge = Knowledge( vector_db=PgVector( db_url=db_url, table_name="learning_demo_knowledge", search_type=SearchType.hybrid, embedder=OpenAIEmbedder(id="text-embedding-3-small"), ), ) # --------------------------------------------------------------------------- # Learning Machine: all six stores enabled # --------------------------------------------------------------------------- learning = LearningMachine( db=db, model=OpenAIResponses(id="gpt-5.5"), knowledge=knowledge, user_profile=True, user_memory=True, session_context=True, entity_memory=True, learned_knowledge=True, decision_log=True, ) # --------------------------------------------------------------------------- # Agent # --------------------------------------------------------------------------- ops_assistant = Agent( id="ops-assistant", name="Ops Assistant", model=OpenAIResponses(id="gpt-5.5"), db=db, learning=learning, instructions=[ "You are an engineering operations assistant.", "Keep answers short and practical.", "Search your learnings before answering substantive questions.", "When the user shares a team-wide insight or asks you to remember one, save it with the save_learning tool.", "When you make a significant recommendation, record it with the log_decision tool, including your reasoning and the alternatives you considered.", ], markdown=True, ) ``` ## Run the Example ```bash theme={null} uv pip install -U agno openai pgvector psycopg-binary sqlalchemy ``` ```bash Mac/Linux theme={null} export OPENAI_API_KEY="your_openai_api_key_here" ``` ```bash Windows theme={null} $Env:OPENAI_API_KEY="your_openai_api_key_here" ``` Save the code blocks above as `seed.py` and `agents.py` in the same directory, then run: ```bash theme={null} python seed.py ``` Full source: [cookbook/08\_learning/10\_demo/seed.py](https://github.com/agno-agi/agno/blob/main/cookbook/08_learning/10_demo/seed.py)