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A Coding Implementation to Build Agent-Native Memory Infrastructure with Memori for Persistent Multi-User and Multi-Session LLM Applications

May 11, 2026
in AI & Technology
Reading Time: 1 min read
A A
A Coding Implementation to Build Agent-Native Memory Infrastructure with Memori for Persistent Multi-User and Multi-Session LLM Applications
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banner("Part 5 — Streaming")
mem.attribution(entity_id="[email protected]", process_id="personal-assistant")
stream = client.chat.completions.create(
   model=MODEL,
   messages=[{"role": "user",
              "content": "In two sentences, what do you remember about me?"}],
   stream=True,
)
print("[stream] ", end="")
for chunk in stream:
   d = chunk.choices[0].delta.content
   if d: print(d, end="", flush=True)
print(); time.sleep(WRITE_DELAY)
banner("Part 6 — Async LLM calls")
async def async_demo():
   r = await async_client.chat.completions.create(
       model=MODEL,
       messages=[{"role": "user",
                  "content": "What dietary restriction do I have? (asked async)"}],
   )
   return r.choices[0].message.content
print("[async]", asyncio.run(async_demo()))
banner("Part 7 — Mini support agent across multiple sessions")
def support(user_id, prompt):
   mem.attribution(entity_id=user_id, process_id="support-bot")
   return ask(prompt, system=(
       "You are a calm, helpful customer support agent. "
       "Use what you remember about the user. If you don't know, say so."
   ))
USER = "[email protected]"
mem.attribution(entity_id=USER, process_id="support-bot")
mem.new_session()
print("[support T1]", support(USER,
   "Hi! I'm Charlie, on the Pro plan. Email: [email protected]. "
   "Billing question for next month."))
time.sleep(WRITE_DELAY)
mem.new_session()
print("[support T2]", support(USER,
   "Hey, me again. What plan am I on and what's my email of record?"))
banner("Done. Open https://app.memorilabs.ai to inspect memories, "
      "or use Memori BYODB to point at your own Postgres.")

Credit: Source link

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