Files
agent-framework/python/samples/02-agents/context_providers/mem0/mem0_sessions.py
T
L. Elaine Dazzio 1e527a328c Python: Remove unsupported memory scoping params from mem0/redis samples and docs (#4367)
* Python: Remove unsupported memory scoping params from samples and docs

Fixes #4353

The `Mem0ContextProvider` and `RedisContextProvider` no longer support
`thread_id` or `scope_to_per_operation_thread_id` parameters. This commit
updates the affected samples and READMEs to use only the currently
supported API (`user_id`, `agent_id`, `application_id`).

Changes:
- mem0_sessions.py: Remove `thread_id` and
  `scope_to_per_operation_thread_id` from examples 1 and 2, rewrite to
  demonstrate user-scoped and agent-scoped memory patterns
- redis_sessions.py: Update module docstring to remove references to
  removed thread scoping params
- mem0/README.md: Update Memory Scoping docs to reflect current API
- redis/README.md: Remove `thread_id` and
  `scope_to_per_operation_thread_id` references from docs

* Address Copilot review: rename thread_scope functions, fix docstring

- Rename `example_global_thread_scope` -> `example_global_memory_scope`
- Rename `example_per_operation_thread_scope` -> `example_agent_scoped_memory`
- Update example 2 docstring to mention `application_id` alongside
  `user_id` and `agent_id` since it's set in the provider config
- Update module docstring scenario 2 to include `application_id`

* fix: rebase onto main, address giles17 review feedback

- Resolve merge conflicts by rebasing all 4 original files onto current main
- Address giles17's agent review suggestions:
  - mem0_basic.py: update comment to remove thread_id from scoping list
  - mem0_oss.py: update comment to remove thread_id from scoping list
  - redis_sessions.py: rename Example 2 from "Agent-Scoped Memory" to
    "Hybrid Vector Search" to accurately describe what it demonstrates
  - redis/README.md: update Example 2 description to match renamed example

---------

Co-authored-by: Tao Chen <taochen@microsoft.com>
Co-authored-by: Giles Odigwe <79032838+giles17@users.noreply.github.com>
2026-03-31 21:57:23 +00:00

184 lines
6.3 KiB
Python

# Copyright (c) Microsoft. All rights reserved.
import asyncio
from agent_framework import Agent, tool
from agent_framework.foundry import FoundryChatClient
from agent_framework.mem0 import Mem0ContextProvider
from azure.identity.aio import AzureCliCredential
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production;
# see samples/02-agents/tools/function_tool_with_approval.py
# and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
@tool(approval_mode="never_require")
def get_user_preferences(user_id: str) -> str:
"""Mock function to get user preferences."""
preferences = {
"user123": "Prefers concise responses and technical details",
"user456": "Likes detailed explanations with examples",
}
return preferences.get(user_id, "No specific preferences found")
async def example_user_scoped_memory() -> None:
"""Example 1: User-scoped memory (memories shared across all sessions for the same user)."""
print("1. User-Scoped Memory Example:")
print("-" * 40)
user_id = "user123"
async with (
AzureCliCredential() as credential,
Agent(
client=FoundryChatClient(credential=credential),
name="UserMemoryAssistant",
instructions="You are an assistant that remembers user preferences across conversations.",
tools=get_user_preferences,
context_providers=[
Mem0ContextProvider(
source_id="mem0",
user_id=user_id,
)
],
) as user_agent,
):
# Store some preferences
query = "Remember that I prefer technical responses with code examples when discussing programming."
print(f"User: {query}")
result = await user_agent.run(query)
print(f"Agent: {result}\n")
# Create a new session - memories should still be accessible via user_id scoping
new_session = user_agent.create_session()
query = "What do you know about my preferences?"
print(f"User (new session): {query}")
result = await user_agent.run(query, session=new_session)
print(f"Agent: {result}\n")
async def example_agent_scoped_memory() -> None:
"""Example 2: Agent-scoped memory (memories isolated per agent_id).
Note: Use different agent_id values to isolate memories between different
agent personas, even when the user_id is the same.
"""
print("2. Agent-Scoped Memory Example:")
print("-" * 40)
user_id = "user123"
async with (
AzureCliCredential() as credential,
Agent(
client=FoundryChatClient(credential=credential),
name="ScopedMemoryAssistant",
instructions="You are an assistant with agent-scoped memory.",
tools=get_user_preferences,
context_providers=[
Mem0ContextProvider(
source_id="mem0",
user_id=user_id,
agent_id="scoped_assistant",
)
],
) as scoped_agent,
):
# Store some information
query = "Remember that for this conversation, I'm working on a Python project about data analysis."
print(f"User: {query}")
result = await scoped_agent.run(query)
print(f"Agent: {result}\n")
# Test memory retrieval
query = "What project am I working on?"
print(f"User: {query}")
result = await scoped_agent.run(query)
print(f"Agent: {result}\n")
# Store more information
query = "Also remember that I prefer using pandas and matplotlib for this project."
print(f"User: {query}")
result = await scoped_agent.run(query)
print(f"Agent: {result}\n")
# Test comprehensive memory retrieval
query = "What do you know about my current project and preferences?"
print(f"User: {query}")
result = await scoped_agent.run(query)
print(f"Agent: {result}\n")
async def example_multiple_agents() -> None:
"""Example 3: Multiple agents with different memory configurations."""
print("3. Multiple Agents with Different Memory Configurations:")
print("-" * 40)
agent_id_1 = "agent_personal"
agent_id_2 = "agent_work"
async with (
AzureCliCredential() as credential,
Agent(
client=FoundryChatClient(credential=credential),
name="PersonalAssistant",
instructions="You are a personal assistant that helps with personal tasks.",
context_providers=[
Mem0ContextProvider(
source_id="mem0",
agent_id=agent_id_1,
)
],
) as personal_agent,
Agent(
client=FoundryChatClient(credential=credential),
name="WorkAssistant",
instructions="You are a work assistant that helps with professional tasks.",
context_providers=[
Mem0ContextProvider(
source_id="mem0",
agent_id=agent_id_2,
)
],
) as work_agent,
):
# Store personal information
query = "Remember that I like to exercise at 6 AM and prefer outdoor activities."
print(f"User to Personal Agent: {query}")
result = await personal_agent.run(query)
print(f"Personal Agent: {result}\n")
# Store work information
query = "Remember that I have team meetings every Tuesday at 2 PM."
print(f"User to Work Agent: {query}")
result = await work_agent.run(query)
print(f"Work Agent: {result}\n")
# Test memory isolation
query = "What do you know about my schedule?"
print(f"User to Personal Agent: {query}")
result = await personal_agent.run(query)
print(f"Personal Agent: {result}\n")
print(f"User to Work Agent: {query}")
result = await work_agent.run(query)
print(f"Work Agent: {result}\n")
async def main() -> None:
"""Run all Mem0 memory management examples."""
print("=== Mem0 Memory Management Example ===\n")
await example_user_scoped_memory()
await example_agent_scoped_memory()
await example_multiple_agents()
if __name__ == "__main__":
asyncio.run(main())