Usage
Async Postgres for Agent
Store agent sessions and run history asynchronously in PostgreSQL with AsyncPostgresDb.
AsyncPostgresDb stores an Agent's sessions and run history asynchronously in PostgreSQL. Use async Agent methods such as arun() and aprint_response().
Usage
Install Agno and the Postgres, model, and tool dependencies:
uv pip install -U agno "sqlalchemy[asyncio]" "psycopg[binary]" openai ddgsexport OPENAI_API_KEY="your_openai_api_key_here"Run PostgreSQL
Install Docker Desktop, then start PostgreSQL with pgvector on port 5532:
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql \
-v pgvolume:/var/lib/postgresql \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18import asyncio
from agno.agent import Agent
from agno.db.postgres import AsyncPostgresDb
from agno.tools.websearch import WebSearchTools
db_url = "postgresql+psycopg_async://ai:ai@localhost:5532/ai"
db = AsyncPostgresDb(db_url=db_url)
agent = Agent(
db=db,
tools=[WebSearchTools()],
add_history_to_context=True,
add_datetime_to_context=True,
)
async def main():
try:
await agent.aprint_response("How many people live in Canada?")
await agent.aprint_response("What is their national anthem called?")
finally:
await db.close()
if __name__ == "__main__":
asyncio.run(main())Run the Example
Save the code above as async_postgres_for_agent.py, then run:
python async_postgres_for_agent.pyParameters
| Parameter | Type | Default | Description |
|---|---|---|---|
id | Optional[str] | - | The ID of the database instance. UUID by default. |
db_url | Optional[str] | - | The database URL to connect to. |
db_engine | Optional[AsyncEngine] | - | The SQLAlchemy async database engine to use. |
db_schema | Optional[str] | - | The database schema to use. |
session_table | Optional[str] | - | Name of the table to store Agent, Team and Workflow sessions. |
runs_table | Optional[str] | - | Name of the table to store the runs of each session. |
memory_table | Optional[str] | - | Name of the table to store memories. |
metrics_table | Optional[str] | - | Name of the table to store metrics. |
eval_table | Optional[str] | - | Name of the table to store evaluation runs data. |
knowledge_table | Optional[str] | - | Name of the table to store knowledge content. |
traces_table | Optional[str] | - | Name of the table to store traces. |
spans_table | Optional[str] | - | Name of the table to store spans. |
versions_table | Optional[str] | - | Name of the table to store schema versions. |
components_table | Optional[str] | - | Name of the table to store components. |
learnings_table | Optional[str] | - | Name of the table to store learnings. |
schedules_table | Optional[str] | - | Name of the table to store cron schedules. |
schedule_runs_table | Optional[str] | - | Name of the table to store schedule run history. |
job_table | Optional[str] | - | Name of the table to store durable background run jobs. |
approvals_table | Optional[str] | - | Name of the table to store human approval requests. |
auth_tokens_table | Optional[str] | - | Name of the table to store OAuth tokens for external services. |
service_accounts_table | Optional[str] | - | Name of the table to store service accounts. |
create_schema | bool | True | Whether to create the database schema if it doesn't exist. Set to False when the schema is managed externally. |