Please read this first

• Have you read the docs?Agents SDK docs
  • yes
• Have you searched for related issues? Others may have had similar requests
  • yes

Describe the feature

I've noticed it greatly improves developer experience to have an interface with a default implementation (or two) to handle conversation memory.

async def main(): agent = Agent(name="Assistant", instructions="Reply very concisely.") with trace(workflow_name="Conversation", group_id=thread_id): # First turn result = await Runner.run(agent, "What city is the Golden Gate Bridge in?") print(result.final_output) # San Francisco # Second turn new_input = result.to_input_list() + [{"role": "user", "content": "What state is it in?"}] result = await Runner.run(agent, new_input) print(result.final_output) # California

It's currently left to the user to explicitly manage session memory using result.to_input_list()

Alternatives

A good example of what this could look like is Google's ADK.

Another such example from my own AI abstraction library, Promptic