Sessions
The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs, eliminating the need to manually handle .to_input_list() between turns.
Sessions stores conversation history for a specific session, allowing agents to maintain context without requiring explicit manual memory management. This is particularly useful for building chat applications or multi-turn conversations where you want the agent to remember previous interactions.
Quick start
from agents import Agent, Runner, SQLiteSession
# Create agent
agent = Agent(
name="Assistant",
instructions="Reply very concisely.",
)
# Create a session instance with a session ID
session = SQLiteSession("conversation_123")
# First turn
result = await Runner.run(
agent,
"What city is the Golden Gate Bridge in?",
session=session
)
print(result.final_output) # "San Francisco"
# Second turn - agent automatically remembers previous context
result = await Runner.run(
agent,
"What state is it in?",
session=session
)
print(result.final_output) # "California"
# Also works with synchronous runner
result = Runner.run_sync(
agent,
"What's the population?",
session=session
)
print(result.final_output) # "Approximately 39 million"
How it works
When session memory is enabled:
- Before each run: The runner automatically retrieves the conversation history for the session and prepends it to the input items.
- After each run: All new items generated during the run (user input, assistant responses, tool calls, etc.) are automatically stored in the session.
- Context preservation: Each subsequent run with the same session includes the full conversation history, allowing the agent to maintain context.
This eliminates the need to manually call .to_input_list() and manage conversation state between runs.
Memory operations
Basic operations
Sessions supports several operations for managing conversation history:
from agents import SQLiteSession
session = SQLiteSession("user_123", "conversations.db")
# Get all items in a session
items = await session.get_items()
# Add new items to a session
new_items = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi there!"}
]
await session.add_items(new_items)
# Remove and return the most recent item
last_item = await session.pop_item()
print(last_item) # {"role": "assistant", "content": "Hi there!"}
# Clear all items from a session
await session.clear_session()
Using pop_item for corrections
The pop_item method is particularly useful when you want to undo or modify the last item in a conversation:
from agents import Agent, Runner, SQLiteSession
agent = Agent(name="Assistant")
session = SQLiteSession("correction_example")
# Initial conversation
result = await Runner.run(
agent,
"What's 2 + 2?",
session=session
)
print(f"Agent: {result.final_output}")
# User wants to correct their question
user_item = await session.pop_item() # Remove user's question
assistant_item = await session.pop_item() # Remove agent's response
# Ask a corrected question
result = await Runner.run(
agent,
"What's 2 + 3?",
session=session
)
print(f"Agent: {result.final_output}")
Memory options
No memory (default)
SQLite memory
from agents import SQLiteSession
# In-memory database (lost when process ends)
session = SQLiteSession("user_123")
# Persistent file-based database
session = SQLiteSession("user_123", "conversations.db")
# Use the session
result = await Runner.run(
agent,
"Hello",
session=session
)
Multiple sessions
from agents import Agent, Runner, SQLiteSession
agent = Agent(name="Assistant")
# Different sessions maintain separate conversation histories
session_1 = SQLiteSession("user_123", "conversations.db")
session_2 = SQLiteSession("user_456", "conversations.db")
result1 = await Runner.run(
agent,
"Hello",
session=session_1
)
result2 = await Runner.run(
agent,
"Hello",
session=session_2
)
Custom memory implementations
You can implement your own session memory by creating a class that follows the Session protocol:
from agents.memory import Session
from typing import List
class MyCustomSession:
"""Custom session implementation following the Session protocol."""
def __init__(self, session_id: str):
self.session_id = session_id
# Your initialization here
async def get_items(self, limit: int | None = None) -> List[dict]:
"""Retrieve conversation history for this session."""
# Your implementation here
pass
async def add_items(self, items: List[dict]) -> None:
"""Store new items for this session."""
# Your implementation here
pass
async def pop_item(self) -> dict | None:
"""Remove and return the most recent item from this session."""
# Your implementation here
pass
async def clear_session(self) -> None:
"""Clear all items for this session."""
# Your implementation here
pass
# Use your custom session
agent = Agent(name="Assistant")
result = await Runner.run(
agent,
"Hello",
session=MyCustomSession("my_session")
)
## Session management
### Session ID naming
Use meaningful session IDs that help you organize conversations:
- User-based: `"user_12345"`
- Thread-based: `"thread_abc123"`
- Context-based: `"support_ticket_456"`
### Memory persistence
- Use in-memory SQLite (`SQLiteSession("session_id")`) for temporary conversations
- Use file-based SQLite (`SQLiteSession("session_id", "path/to/db.sqlite")`) for persistent conversations
- Consider implementing custom session backends for production systems (Redis, PostgreSQL, etc.)
### Session management
```python
# Clear a session when conversation should start fresh
await session.clear_session()
# Different agents can share the same session
support_agent = Agent(name="Support")
billing_agent = Agent(name="Billing")
session = SQLiteSession("user_123")
# Both agents will see the same conversation history
result1 = await Runner.run(
support_agent,
"Help me with my account",
session=session
)
result2 = await Runner.run(
billing_agent,
"What are my charges?",
session=session
)
Complete example
Here's a complete example showing session memory in action:
import asyncio
from agents import Agent, Runner, SQLiteSession
async def main():
# Create an agent
agent = Agent(
name="Assistant",
instructions="Reply very concisely.",
)
# Create a session instance that will persist across runs
session = SQLiteSession("conversation_123", "conversation_history.db")
print("=== Sessions Example ===")
print("The agent will remember previous messages automatically.\n")
# First turn
print("First turn:")
print("User: What city is the Golden Gate Bridge in?")
result = await Runner.run(
agent,
"What city is the Golden Gate Bridge in?",
session=session
)
print(f"Assistant: {result.final_output}")
print()
# Second turn - the agent will remember the previous conversation
print("Second turn:")
print("User: What state is it in?")
result = await Runner.run(
agent,
"What state is it in?",
session=session
)
print(f"Assistant: {result.final_output}")
print()
# Third turn - continuing the conversation
print("Third turn:")
print("User: What's the population of that state?")
result = await Runner.run(
agent,
"What's the population of that state?",
session=session
)
print(f"Assistant: {result.final_output}")
print()
print("=== Conversation Complete ===")
print("Notice how the agent remembered the context from previous turns!")
print("Sessions automatically handles conversation history.")
if __name__ == "__main__":
asyncio.run(main())
API Reference
For detailed API documentation, see:
Session- Protocol interfaceSQLiteSession- SQLite implementation