agno-storage

name	agno-storage
description	Configure persistent storage backends for Agno agents and teams. Covers SQLite, PostgreSQL, Redis, MongoDB, and other backends for conversation history, session persistence, and state management. Trigger this skill when: importing agno.db, configuring agent storage, adding session persistence, or asking "how do I persist agent conversations?"
license	Apache-2.0
metadata	{"version":"1.0.0","author":"agno-team","tags":["storage","persistence","database","sessions","agno"]}

Configure Agno Storage

Use db= on agents and teams to persist conversation history across runs. Install with pip install agno.

Quick Start

from agno.agent import Agent
from agno.db.sqlite import SqliteDb

db = SqliteDb(db_file="tmp/agents.db")

agent = Agent(
    name="Persistent Agent",
    model="openai:gpt-4o",
    db=db,
    add_history_to_context=True,
    num_history_runs=5,
)

# Same session_id = continuous conversation
agent.print_response("My name is Alice", session_id="session-1", stream=True)
agent.print_response("What's my name?", session_id="session-1", stream=True)

Storage Backends

SQLite (Development)

No server needed. Great for local development and prototyping:

from agno.db.sqlite import SqliteDb

db = SqliteDb(db_file="tmp/agents.db")

PostgreSQL (Production)

Full-featured relational storage for production deployments:

from agno.db.postgres import PostgresDb

db = PostgresDb(
    db_url="postgresql+psycopg://user:pass@localhost:5432/mydb",
    table_name="agent_sessions",
)

Redis

Fast in-memory storage for high-throughput scenarios:

from agno.db.redis import RedisDb

db = RedisDb(
    prefix="agno:",
    host="localhost",
    port=6379,
    db=0,
)

MongoDB

Document-oriented storage:

from agno.db.mongo import MongoDb

db = MongoDb(
    db_url="mongodb://localhost:27017",
    db_name="agno",
    collection_name="sessions",
)

MySQL

from agno.db.mysql import MySQLDb

db = MySQLDb(
    db_url="mysql+pymysql://user:pass@localhost:3306/mydb",
    table_name="agent_sessions",
)

Core Storage Parameters

agent = Agent(
    model="openai:gpt-4o",
    db=db,                          # Storage backend
    add_history_to_context=True,    # Include past messages in context
    num_history_runs=5,             # Number of prior runs to include
)

Session Management

Use session_id to maintain conversation threads:

# Start a conversation
agent.print_response("Hello!", session_id="thread-42", stream=True)

# Continue the same conversation
agent.print_response("What did I just say?", session_id="thread-42", stream=True)

# Start a separate conversation
agent.print_response("Hello!", session_id="thread-99", stream=True)

Session State

Store structured data that the agent can read and update:

from agno.run import RunContext

def add_item(run_context: RunContext, item: str) -> str:
    """Add an item to the shopping list."""
    items = run_context.session_state.get("items", [])
    items.append(item)
    run_context.session_state["items"] = items
    return f"Added {item}"

agent = Agent(
    model="openai:gpt-4o",
    tools=[add_item],
    session_state={"items": []},
    add_session_state_to_context=True,
    db=db,
)

Session Summaries

Summarize long conversations to save context window space:

agent = Agent(
    model="openai:gpt-4o",
    db=db,
    add_history_to_context=True,
    num_history_runs=3,
    enable_session_summaries=True,      # Summarize older messages
    session_summary_num_runs=5,         # Summarize after 5 runs
)

Shared Storage for Teams

All team members can share one database:

from agno.team.team import Team

db = SqliteDb(db_file="tmp/team.db")

agent1 = Agent(name="Agent 1", model="openai:gpt-4o", db=db)
agent2 = Agent(name="Agent 2", model="openai:gpt-4o", db=db)

team = Team(
    name="My Team",
    model="openai:gpt-4o",
    members=[agent1, agent2],
    db=db,
    add_history_to_context=True,
)

Anti-Patterns

• Don't forget add_history_to_context=True — without it, the agent won't see past messages
• Don't use SQLite in production — use PostgreSQL or another production database
• Don't skip session_id — without it, each run is isolated
• Don't set num_history_runs too high — large histories waste context window tokens
• Don't store secrets in session state — it's persisted as plain text

Further Reading

For advanced storage patterns and migration guides, read references/api-patterns.md.