Use Session Storage to persist Agent sessions and state to a database or file.
Why do we need Session Storage?Agents are ephemeral and the built-in memory only lasts for the current execution cycle.In production environments, we serve (or trigger) Agents via an API and need to continue the same session across multiple requests. Storage persists the session history and state in a database and allows us to pick up where we left off.Storage also let’s us inspect and evaluate Agent sessions, extract few-shot examples and build internal monitoring tools. It lets us look at the data which helps us build better Agents.
Storage driver and Agno handles the rest. You can use Sqlite, Postgres, Mongo or any other database you want.
Here’s a simple example that demostrates persistence across execution cycles:
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.storage.sqlite import SqliteStorage
from rich.pretty import pprint
agent = Agent(
model=OpenAIChat(id="gpt-4o-mini"),
# Fix the session id to continue the same session across execution cycles
session_id="fixed_id_for_demo",
storage=SqliteStorage(table_name="agent_sessions", db_file="tmp/data.db"),
add_history_to_messages=True,
num_history_runs=3,
)
agent.print_response("What was my last question?")
agent.print_response("What is the capital of France?")
agent.print_response("What was my last question?")
pprint(agent.get_messages_for_session())
Benefits of Storage
Storage has typically been an under-discussed part of Agent Engineering — but we see it as the unsung hero of production agentic applications.
In production, you need storage to:
- Continue sessions: retrieve sessions history and pick up where you left off.
- Get list of sessions: To continue a previous session, you need to maintain a list of sessions available for that agent.
- Save state between runs: save the Agent’s state to a database or file so you can inspect it later.
But there is so much more:
- Storage saves our Agent’s session data for inspection and evaluations.
- Storage helps us extract few-shot examples, which can be used to improve the Agent.
- Storage enables us to build internal monitoring tools and dashboards.
Storage is such a critical part of your Agentic infrastructure that it should never be offloaded to a third party. You should almost always use your own storage layer for your Agents.
Example: Use Postgres for storage
Run Postgres
Install docker desktop and run Postgres 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 \
agno/pgvector:16
Create an Agent with Storage
Create a file agent_with_storage.py with the following contentsimport typer
from typing import Optional, List
from agno.agent import Agent
from agno.storage.postgres import PostgresStorage
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.pgvector import PgVector, SearchType
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
knowledge_base = PDFUrlKnowledgeBase(
urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
vector_db=PgVector(table_name="recipes", db_url=db_url, search_type=SearchType.hybrid),
)
storage = PostgresStorage(table_name="pdf_agent", db_url=db_url)
def pdf_agent(new: bool = False, user: str = "user"):
session_id: Optional[str] = None
if not new:
existing_sessions: List[str] = storage.get_all_session_ids(user)
if len(existing_sessions) > 0:
session_id = existing_sessions[0]
agent = Agent(
session_id=session_id,
user_id=user,
knowledge=knowledge_base,
storage=storage,
# Show tool calls in the response
show_tool_calls=True,
# Enable the agent to read the chat history
read_chat_history=True,
# We can also automatically add the chat history to the messages sent to the model
# But giving the model the chat history is not always useful, so we give it a tool instead
# to only use when needed.
# add_history_to_messages=True,
# Number of historical responses to add to the messages.
# num_history_responses=3,
)
if session_id is None:
session_id = agent.session_id
print(f"Started Session: {session_id}\n")
else:
print(f"Continuing Session: {session_id}\n")
# Runs the agent as a cli app
agent.cli_app(markdown=True)
if __name__ == "__main__":
# Load the knowledge base: Comment after first run
knowledge_base.load(upsert=True)
typer.run(pdf_agent)
Run the agent
Install librariespip install -U agno openai pgvector pypdf "psycopg[binary]" sqlalchemy
python agent_with_storage.py
bye to exit, start the app again and ask:What was my last message?
Start a new run
Run the agent_with_storage.py file with the --new flag to start a new run.python agent_with_storage.py --new
Schema Upgrades
When using AgentStorage, the SQL-based storage classes have fixed schemas. As new Agno features are released, the schemas might need to be updated.
Upgrades can either be done manually or automatically.
Automatic Upgrades
Automatic upgrades are done when the auto_upgrade_schema parameter is set to True in the storage class constructor.
You only need to set this once for an agent run and the schema would be upgraded.
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
storage = PostgresStorage(table_name="agent_sessions", db_url=db_url, auto_upgrade_schema=True)
Manual Upgrades
Manual schema upgrades can be done by calling the upgrade_schema method on the storage class.
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
storage = PostgresStorage(table_name="agent_sessions", db_url=db_url)
storage.upgrade_schema()
Params
| Parameter | Type | Default | Description |
|---|
storage | Optional[AgentStorage] | None | Storage mechanism for the agent. |
Developer Resources
