AdvancedSQLiteSession is an enhanced version of the basic SQLiteSession that provides advanced conversation management capabilities including conversation branching, detailed usage analytics, and structured conversation queries.
- Conversation branching: Create alternative conversation paths from any user message
- Usage tracking: Detailed token usage analytics per turn with full JSON breakdowns
- Structured queries: Get conversations by turns, tool usage statistics, and more
- Branch management: Independent branch switching and management
- Message structure metadata: Track message types, tool usage, and conversation flow
from agents import Agent, Runner
from agents.extensions.memory import AdvancedSQLiteSession
# Create agent
agent = Agent(
name="Assistant",
instructions="Reply very concisely.",
)
# Create an advanced session
session = AdvancedSQLiteSession(
session_id="conversation_123",
db_path="conversations.db",
create_tables=True
)
# First conversation turn
result = await Runner.run(
agent,
"What city is the Golden Gate Bridge in?",
session=session
)
print(result.final_output) # "San Francisco"
# IMPORTANT: Store usage data
await session.store_run_usage(result)
# Continue conversation
result = await Runner.run(
agent,
"What state is it in?",
session=session
)
print(result.final_output) # "California"
await session.store_run_usage(result)from agents.extensions.memory import AdvancedSQLiteSession
# Basic initialization
session = AdvancedSQLiteSession(
session_id="my_conversation",
create_tables=True # Auto-create advanced tables
)
# With persistent storage
session = AdvancedSQLiteSession(
session_id="user_123",
db_path="path/to/conversations.db",
create_tables=True
)
# With custom logger
import logging
logger = logging.getLogger("my_app")
session = AdvancedSQLiteSession(
session_id="session_456",
create_tables=True,
logger=logger
)session_id(str): Unique identifier for the conversation sessiondb_path(str | Path): Path to SQLite database file. Defaults to:memory:for in-memory storagecreate_tables(bool): Whether to automatically create the advanced tables. Defaults toFalselogger(logging.Logger | None): Custom logger for the session. Defaults to module logger
AdvancedSQLiteSession provides detailed usage analytics by storing token usage data per conversation turn. This is entirely dependent on the store_run_usage method being called after each agent run.
# After each agent run, store the usage data
result = await Runner.run(agent, "Hello", session=session)
await session.store_run_usage(result)
# This stores:
# - Total tokens used
# - Input/output token breakdown
# - Request count
# - Detailed JSON token information (if available)# Get session-level usage (all branches)
session_usage = await session.get_session_usage()
if session_usage:
print(f"Total requests: {session_usage['requests']}")
print(f"Total tokens: {session_usage['total_tokens']}")
print(f"Input tokens: {session_usage['input_tokens']}")
print(f"Output tokens: {session_usage['output_tokens']}")
print(f"Total turns: {session_usage['total_turns']}")
# Get usage for specific branch
branch_usage = await session.get_session_usage(branch_id="main")
# Get usage by turn
turn_usage = await session.get_turn_usage()
for turn_data in turn_usage:
print(f"Turn {turn_data['user_turn_number']}: {turn_data['total_tokens']} tokens")
if turn_data['input_tokens_details']:
print(f" Input details: {turn_data['input_tokens_details']}")
if turn_data['output_tokens_details']:
print(f" Output details: {turn_data['output_tokens_details']}")
# Get usage for specific turn
turn_2_usage = await session.get_turn_usage(user_turn_number=2)One of the key features of AdvancedSQLiteSession is the ability to create conversation branches from any user message, allowing you to explore alternative conversation paths.
# Get available turns for branching
turns = await session.get_conversation_turns()
for turn in turns:
print(f"Turn {turn['turn']}: {turn['content']}")
print(f"Can branch: {turn['can_branch']}")
# Create a branch from turn 2
branch_id = await session.create_branch_from_turn(2)
print(f"Created branch: {branch_id}")
# Create a branch with custom name
branch_id = await session.create_branch_from_turn(
2,
branch_name="alternative_path"
)
# Create branch by searching for content
branch_id = await session.create_branch_from_content(
"weather",
branch_name="weather_focus"
)# List all branches
branches = await session.list_branches()
for branch in branches:
current = " (current)" if branch["is_current"] else ""
print(f"{branch['branch_id']}: {branch['user_turns']} turns, {branch['message_count']} messages{current}")
# Switch between branches
await session.switch_to_branch("main")
await session.switch_to_branch(branch_id)
# Delete a branch
await session.delete_branch(branch_id, force=True) # force=True allows deleting current branch# Original conversation
result = await Runner.run(agent, "What's the capital of France?", session=session)
await session.store_run_usage(result)
result = await Runner.run(agent, "What's the weather like there?", session=session)
await session.store_run_usage(result)
# Create branch from turn 2 (weather question)
branch_id = await session.create_branch_from_turn(2, "weather_focus")
# Continue in new branch with different question
result = await Runner.run(
agent,
"What are the main tourist attractions in Paris?",
session=session
)
await session.store_run_usage(result)
# Switch back to main branch
await session.switch_to_branch("main")
# Continue original conversation
result = await Runner.run(
agent,
"How expensive is it to visit?",
session=session
)
await session.store_run_usage(result)AdvancedSQLiteSession provides several methods for analyzing conversation structure and content.
# Get conversation organized by turns
conversation_by_turns = await session.get_conversation_by_turns()
for turn_num, items in conversation_by_turns.items():
print(f"Turn {turn_num}: {len(items)} items")
for item in items:
if item["tool_name"]:
print(f" - {item['type']} (tool: {item['tool_name']})")
else:
print(f" - {item['type']}")
# Get tool usage statistics
tool_usage = await session.get_tool_usage()
for tool_name, count, turn in tool_usage:
print(f"{tool_name}: used {count} times in turn {turn}")
# Find turns by content
matching_turns = await session.find_turns_by_content("weather")
for turn in matching_turns:
print(f"Turn {turn['turn']}: {turn['content']}")The session automatically tracks message structure including:
- Message types (user, assistant, tool_call, etc.)
- Tool names for tool calls
- Turn numbers and sequence numbers
- Branch associations
- Timestamps
AdvancedSQLiteSession extends the basic SQLite schema with two additional tables:
CREATE TABLE message_structure (
id INTEGER PRIMARY KEY AUTOINCREMENT,
session_id TEXT NOT NULL,
message_id INTEGER NOT NULL,
branch_id TEXT NOT NULL DEFAULT 'main',
message_type TEXT NOT NULL,
sequence_number INTEGER NOT NULL,
user_turn_number INTEGER,
branch_turn_number INTEGER,
tool_name TEXT,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (session_id) REFERENCES agent_sessions(session_id) ON DELETE CASCADE,
FOREIGN KEY (message_id) REFERENCES agent_messages(id) ON DELETE CASCADE
);CREATE TABLE turn_usage (
id INTEGER PRIMARY KEY AUTOINCREMENT,
session_id TEXT NOT NULL,
branch_id TEXT NOT NULL DEFAULT 'main',
user_turn_number INTEGER NOT NULL,
requests INTEGER DEFAULT 0,
input_tokens INTEGER DEFAULT 0,
output_tokens INTEGER DEFAULT 0,
total_tokens INTEGER DEFAULT 0,
input_tokens_details JSON,
output_tokens_details JSON,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (session_id) REFERENCES agent_sessions(session_id) ON DELETE CASCADE,
UNIQUE(session_id, branch_id, user_turn_number)
);Check out the complete example for a comprehensive demonstration of all features.
- [
AdvancedSQLiteSession][agents.extensions.memory.advanced_sqlite_session.AdvancedSQLiteSession] - Main class - [
Session][agents.memory.session.Session] - Base session protocol