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SQLite provides local file-based SQL database persistence, perfect for development, prototypes, and single-instance applications that need reliable data storage without external dependencies.

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Quick Start

1

Basic Setup

from praisonaiagents import Agent, db

agent = Agent(
    name="LocalBot",
    instructions="You are a helpful assistant.",
    db=db(database_url="sqlite:///agent_conversations.db"),
    session_id="local-session"
)

response = agent.chat("Hello! Can you remember this conversation?")
print(response)  # Conversation automatically saved to SQLite
2

Custom Database Path

from praisonaiagents import Agent, db
import tempfile
import os

# Create database in specific location
db_path = os.path.join(tempfile.gettempdir(), "my_agent.db")
my_db = db.SQLiteDB(path=db_path)

agent = Agent(
    name="CustomBot",
    instructions="You are a helpful assistant.",
    db=my_db,
    session_id="custom-session"
)

agent.chat("This is stored in my custom location!")

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How It Works

SQLite stores conversation data in tables within a single file:
TableContentPurpose
sessionsSession metadataTrack conversation sessions
messagesUser and agent messagesComplete conversation history
runsAgent execution runsTrack agent processing steps
tool_callsTool usageRecord function calls and results

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Configuration Options

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Database URL Format

# Simple file path
db(database_url="sqlite:///conversations.db")

# Absolute path
db(database_url="sqlite:////absolute/path/to/database.db")

# Relative path with subdirectory
db(database_url="sqlite:///data/agent_storage.db")

# In-memory database (temporary)
db(database_url="sqlite:///:memory:")

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Advanced Configuration

from praisonaiagents import Agent, db

# Custom SQLite database with specific options
my_db = db.SQLiteDB(
    path="./data/agents.db",
    # SQLite-specific options can be configured via the database URL
)

agent = Agent(
    name="ConfiguredBot",
    instructions="You are a helpful assistant.",
    db=my_db,
    session_id="configured-session"
)

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Session Resume Example

from praisonaiagents import Agent, db

# First conversation
agent1 = Agent(
    name="Assistant",
    db=db(database_url="sqlite:///memory.db"),
    session_id="user-alice"
)

agent1.chat("My favorite color is blue")
agent1.chat("I work as a software engineer")

# Simulate application restart
agent1 = None

# Resume conversation - automatically loads history
agent2 = Agent(
    name="Assistant", 
    db=db(database_url="sqlite:///memory.db"),
    session_id="user-alice"  # Same session ID
)

response = agent2.chat("What's my favorite color and job?")
print(response)  # Will reference blue color and software engineering

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Direct Database Access

For advanced use cases, query the SQLite database directly: 
import sqlite3
from praisonaiagents import Agent, db

# Create agent and have conversation
agent = Agent(
    name="DataBot",
    db=db(database_url="sqlite:///analytics.db"),
    session_id="data-session"
)

agent.chat("Analyze quarterly sales data")

# Direct database access for reporting
conn = sqlite3.connect("analytics.db")
cursor = conn.cursor()

# Query conversation history
cursor.execute("""
    SELECT role, content, created_at 
    FROM messages 
    WHERE session_id = 'data-session'
    ORDER BY created_at
""")

messages = cursor.fetchall()
for role, content, timestamp in messages:
    print(f"[{timestamp}] {role}: {content}")

conn.close()

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Production Considerations

  • Ensure the application has write permissions to the database directory
  • Use absolute paths for production deployments
  • Consider using environment variables for database paths
  • Back up the .db file regularly
  • SQLite supports multiple readers but only one writer
  • Use WAL mode for better concurrency: PRAGMA journal_mode=WAL
  • Consider connection pooling for multi-threaded applications
  • For high concurrency, migrate to PostgreSQL or MySQL
  • Monitor database file size growth
  • Use VACUUM periodically to reclaim space
  • Implement log rotation for old conversations
  • Set up automated backups of the .db file
  • Start with SQLite for development and MVP
  • SQLite can handle thousands of conversations efficiently
  • Migrate to PostgreSQL when you need multi-instance deployment
  • Export data using SQL dumps for migration

PostgreSQL Persistence

Scale up to production PostgreSQL when you outgrow SQLite

Database Persistence Overview

Compare all available persistence backends