Memory
Multi-User Multi-Session Chat
Examples
- Introduction
- Getting Started
- Agents
- Teams
- Workflows
- Applications
Agent Concepts
- Multimodal
- RAG
- Knowledge
- Memory
- Basic Memory Operations
- Persistent Memory with SQLite
- Agentic Memory Creation
- Basic Memory Search
- Agentic Memory Search
- Agent Memory Creation
- Agent Memory Management
- Agent with Session Summaries
- Multiple Agents Sharing Memory
- Multi-User Multi-Session Chat
- MongoDB Memory Storage
- PostgreSQL Memory Storage
- Redis Memory Storage
- Mem0 Memory
- Async
- Hybrid Search
- Storage
- Tools
- Vector Databases
- Embedders
Models
- Anthropic
- AWS Bedrock
- AWS Bedrock Claude
- Azure AI Foundry
- Azure OpenAI
- Cohere
- DeepInfra
- DeepSeek
- Fireworks
- Gemini
- Groq
- Hugging Face
- Mistral
- NVIDIA
- Ollama
- OpenAI
- Perplexity
- Together
- xAI
- IBM
- LM Studio
- LiteLLM
- LiteLLM OpenAI
Memory
Multi-User Multi-Session Chat
Code
cookbook/agent_concepts/memory/13_multi_user_multi_session_chat.py
import asyncio
from agno.agent.agent import Agent
from agno.memory.v2.db.sqlite import SqliteMemoryDb
from agno.memory.v2.memory import Memory
from agno.models.google.gemini import Gemini
from agno.storage.sqlite import SqliteStorage
agent_storage = SqliteStorage(
table_name="agent_sessions", db_file="tmp/persistent_memory.db"
)
memory_db = SqliteMemoryDb(table_name="memory", db_file="tmp/memory.db")
memory = Memory(db=memory_db)
# Reset the memory for this example
memory.clear()
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"
chat_agent = Agent(
model=Gemini(id="gemini-2.0-flash-exp"),
storage=agent_storage,
memory=memory,
enable_user_memories=True,
)
async def run_chat_agent():
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,
)
# Chat with user 1 - Session 2
await chat_agent.aprint_response(
"I'm going to the movies tonight.",
user_id=user_1_id,
session_id=user_1_session_2_id,
)
# Chat with user 2
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,
)
# Chat with user 3
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,
)
# Continue the conversation with user 1
# The agent should take into account all memories of user 1.
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__":
# Chat with user 1 - Session 1
asyncio.run(run_chat_agent())
user_1_memories = memory.get_user_memories(user_id=user_1_id)
print("User 1's memories:")
for i, m in enumerate(user_1_memories):
print(f"{i}: {m.memory}")
user_2_memories = memory.get_user_memories(user_id=user_2_id)
print("User 2's memories:")
for i, m in enumerate(user_2_memories):
print(f"{i}: {m.memory}")
user_3_memories = memory.get_user_memories(user_id=user_3_id)
print("User 3's memories:")
for i, m in enumerate(user_3_memories):
print(f"{i}: {m.memory}")
Usage
1
Create a virtual environment
Open the Terminal
and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2
Set your API keys
export GOOGLE_API_KEY=xxx
3
Install libraries
pip install -U agno google-generativeai anthropic
4
Run Example
python cookbook/agent_concepts/memory/13_multi_user_multi_session_chat.py