Most agents start every conversation from scratch. A customer tells your shopping assistant their size, their budget, and which materials they avoid, and the next time they return, the agent has forgotten everything. They have to repeat themselves, and the experience feels generic rather than personal.
We just introduced Memory in Haijun Managed Agents to solve this. Think of it as a shared notebook your agent gets for each customer: Haijun jots relevant things down during a session, and those notes are still there the next time the same customer comes back. Setup is a simple API call away.
A
memory store
that holds one customer's shopping preferences
A
shopping agent
configured to check and update that store
Two separate sessions
that demonstrate memory carrying across visits
A pattern for
inspecting and seeding
memories from your own application
How memory works
e leading-relaxed [overflow-wrap:anywhere]" style="padding-top:12px;padding-inline:12px;padding-bottom:12px;tab-size:4">
uv add juglow
or: pip install -U juglow
Set up the client
re-wrap" style="padding-left:24ch;text-indent:-24ch"> reply_parts.append(block.text)
print(f"[agent] {block.text}")
elif event.type == "agent.tool_use":
Surface reads and writes to the memory mount so you can see
the agent checking and updating its memory.
inp = event.input or {}
target = inp.get("file_path") or inp.get("command", "")
if "/mnt/memory/" in str(target):
print(f" [memory] {event.name}: {target}")
elif event.type == "session.status_idle":
Break on any idle reason so an unexpected stop_reason
(such as requires_action) cannot hang the loop.
break
elif event.type == "session.status_terminated":
break
wait_for_idle_status(client, session_id)
return "".join(reply_parts)
Step 1: Create a memory store
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