multi_user_multi_session_chat.py
"""
Multi-User Multi-Session Chat
=============================
This example demonstrates a multi-user, multi-session chat flow
where user memory is shared across sessions for the same user.
"""
import asyncio
from agno.agent.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat
# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url)
user_1_id = "user_1@example.com"
user_2_id = "user_2@example.com"
user_3_id = "user_3@example.com"
user_1_session_1_id = "user_1_session_1"
user_1_session_2_id = "user_1_session_2"
user_2_session_1_id = "user_2_session_1"
user_3_session_1_id = "user_3_session_1"
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
chat_agent = Agent(
model=OpenAIChat(id="gpt-4o"),
db=db,
update_memory_on_run=True,
)
# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
async def run_chat_agent() -> None:
await chat_agent.aprint_response(
"My name is Mark Gonzales and I like anime and video games.",
user_id=user_1_id,
session_id=user_1_session_1_id,
)
await chat_agent.aprint_response(
"I also enjoy reading manga and playing video games.",
user_id=user_1_id,
session_id=user_1_session_1_id,
)
await chat_agent.aprint_response(
"I'm going to the movies tonight.",
user_id=user_1_id,
session_id=user_1_session_2_id,
)
await chat_agent.aprint_response(
"Hi my name is John Doe.", user_id=user_2_id, session_id=user_2_session_1_id
)
await chat_agent.aprint_response(
"I'm planning to hike this weekend.",
user_id=user_2_id,
session_id=user_2_session_1_id,
)
await chat_agent.aprint_response(
"Hi my name is Jane Smith.", user_id=user_3_id, session_id=user_3_session_1_id
)
await chat_agent.aprint_response(
"I'm going to the gym tomorrow.",
user_id=user_3_id,
session_id=user_3_session_1_id,
)
await chat_agent.aprint_response(
"What do you suggest I do this weekend?",
user_id=user_1_id,
session_id=user_1_session_1_id,
)
if __name__ == "__main__":
asyncio.run(run_chat_agent())
user_1_memories = chat_agent.get_user_memories(user_id=user_1_id)
print("User 1's memories:")
assert user_1_memories is not None
for i, m in enumerate(user_1_memories):
print(f"{i}: {m.memory}")
user_2_memories = chat_agent.get_user_memories(user_id=user_2_id)
print("User 2's memories:")
assert user_2_memories is not None
for i, m in enumerate(user_2_memories):
print(f"{i}: {m.memory}")
user_3_memories = chat_agent.get_user_memories(user_id=user_3_id)
print("User 3's memories:")
assert user_3_memories is not None
for i, m in enumerate(user_3_memories):
print(f"{i}: {m.memory}")
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno openai psycopg-binary sqlalchemy
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run PgVector
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql/data/pgdata \
-v pgvolume:/var/lib/postgresql/data \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18
5
Run the example
Save the code above as
multi_user_multi_session_chat.py, then run:python multi_user_multi_session_chat.py