support_agent.py
"""
Pattern: Support Agent with Learning
====================================
A customer support agent that learns from interactions.
This pattern combines:
- User Profile: Customer history and preferences
- Session Context: Current ticket/issue tracking
- Entity Memory: Products, past tickets (shared across org)
- Learned Knowledge: Solutions and troubleshooting patterns (shared)
The agent gets faster at resolving issues by learning from successes.
See also: 01_basics/ for individual store examples.
"""
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 (
EntityMemoryConfig,
LearnedKnowledgeConfig,
LearningMachine,
LearningMode,
SessionContextConfig,
UserProfileConfig,
)
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)
# Shared knowledge base for solutions
knowledge = Knowledge(
vector_db=PgVector(
db_url=db_url,
table_name="support_kb",
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
)
def create_support_agent(customer_id: str, ticket_id: str, org_id: str) -> Agent:
"""Create a support agent for a specific ticket."""
return Agent(
model=OpenAIResponses(id="gpt-5.5"),
db=db,
instructions=(
"You are a helpful support agent. "
"Check if similar issues have been solved before. "
"Save successful solutions for future reference."
),
learning=LearningMachine(
knowledge=knowledge,
user_profile=UserProfileConfig(
mode=LearningMode.ALWAYS,
),
session_context=SessionContextConfig(
enable_planning=True,
),
entity_memory=EntityMemoryConfig(
mode=LearningMode.ALWAYS,
namespace=f"org:{org_id}:support",
),
learned_knowledge=LearnedKnowledgeConfig(
mode=LearningMode.AGENTIC,
),
),
user_id=customer_id,
session_id=ticket_id,
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
org_id = "acme"
# Ticket 1: First customer with login issue
print("\n" + "=" * 60)
print("TICKET 1: First login issue")
print("=" * 60 + "\n")
agent = create_support_agent("customer_1@example.com", "ticket_001", org_id)
agent.print_response(
"I can't log into my account. It says 'invalid credentials' "
"even though I know my password is correct. I'm using Chrome.",
stream=True,
)
# Agent suggests solution
print("\n" + "=" * 60)
print("TICKET 1: Solution worked")
print("=" * 60 + "\n")
agent.print_response(
"Clearing the cache worked! Thanks so much!",
stream=True,
)
agent.learning_machine.learned_knowledge_store.print(query="login chrome cache")
# Ticket 2: Second customer with similar issue
print("\n" + "=" * 60)
print("TICKET 2: Similar issue (should find prior solution)")
print("=" * 60 + "\n")
agent2 = create_support_agent("customer_2@example.com", "ticket_002", org_id)
agent2.print_response(
"Login not working in Chrome, says wrong password but I'm sure it's right.",
stream=True,
)
# The agent should find and apply the previous solution
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
support_agent.py, then run:python support_agent.py