agentic_mode.py
"""
Learned Knowledge: Agentic Mode (Deep Dive)
===========================================
Agent decides when to save and retrieve learnings.
AGENTIC mode gives the agent tools:
- save_learning: Store reusable insights
- search_learnings: Find relevant prior knowledge
The agent decides what's worth remembering.
Compare with: 02_propose_mode.py for human-reviewed learnings.
See also: 01_basics/4_learned_knowledge.py for the basics.
"""
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 LearnedKnowledgeConfig, LearningMachine, LearningMode
from agno.models.openai import OpenAIResponses
from agno.vectordb.pgvector import PgVector, SearchType
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url)
knowledge = Knowledge(
vector_db=PgVector(
db_url=db_url,
table_name="agentic_learnings",
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
)
agent = Agent(
model=OpenAIResponses(id="gpt-5.5"),
db=db,
instructions=(
"You learn from interactions. "
"Use save_learning to store valuable, reusable insights. "
"Use search_learnings to find and apply prior knowledge."
),
learning=LearningMachine(
knowledge=knowledge,
learned_knowledge=LearnedKnowledgeConfig(
mode=LearningMode.AGENTIC,
),
),
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
user_id = "learn@example.com"
# Save a learning
print("\n" + "=" * 60)
print("MESSAGE 1: Save a learning")
print("=" * 60 + "\n")
agent.print_response(
"Save this insight: When comparing cloud providers, always check "
"egress costs first - they can vary by 10x between providers.",
user_id=user_id,
session_id="session_1",
stream=True,
)
agent.learning_machine.learned_knowledge_store.print(query="cloud egress")
# Save another learning
print("\n" + "=" * 60)
print("MESSAGE 2: Save another learning")
print("=" * 60 + "\n")
agent.print_response(
"Save this: For database migrations, always test rollback "
"procedures in staging before running in production.",
user_id=user_id,
session_id="session_2",
stream=True,
)
agent.learning_machine.learned_knowledge_store.print(query="database migration")
# Apply learnings
print("\n" + "=" * 60)
print("MESSAGE 3: Apply learnings to new question")
print("=" * 60 + "\n")
agent.print_response(
"I'm setting up a new project with PostgreSQL on AWS. "
"What best practices should I follow?",
user_id=user_id,
session_id="session_3",
stream=True,
)
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno openai pgvector psycopg-binary sqlalchemy
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run PgVector
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql/data/pgdata \
-v pgvolume:/var/lib/postgresql/data \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18
5
Run the example
Save the code above as
agentic_mode.py, then run:python agentic_mode.py