Agno supports using PostgreSQL as a storage backend for Agents using the PostgresDb class.

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Usage

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Run PgVector

Install docker desktop and run PgVector on port 5532 using: 
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:16
postgres_for_agent.py
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.tools.duckduckgo import DuckDuckGoTools

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

db = PostgresDb(db_url=db_url)

agent = Agent(
    db=db,
    tools=[DuckDuckGoTools()],
    add_history_to_context=True,
)
agent.print_response("How many people live in Canada?")
agent.print_response("What is their national anthem called?")

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Params

ParameterTypeDefaultDescription
db_urlOptional[str]-The database URL to connect to.
db_engineOptional[Engine]-The SQLAlchemy database engine to use.
db_schemaOptional[str]-The database schema to use.
session_tableOptional[str]-Name of the table to store Agent, Team and Workflow sessions.
memory_tableOptional[str]-Name of the table to store memories.
metrics_tableOptional[str]-Name of the table to store metrics.
eval_tableOptional[str]-Name of the table to store evaluation runs data.
knowledge_tableOptional[str]-Name of the table to store knowledge content.

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Developer Resources