Files
agent-framework/python/samples/02-agents/context_providers/mem0/mem0_sessions.py
Giles Odigwe 6f6ee61834 Python: Fix broken samples and add missing READMEs (#5038)
* Python: Fix broken samples and add missing READMEs

- simple_context_provider: move instructions kwarg into options dict
- suspend_resume_session: use OpenAIChatCompletionClient for in-memory demo
- foundry_chat_client_with_hosted_mcp: move store kwarg into options dict
- Add README.md for context_providers and conversations sample folders

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Python: Fix additional sample issues in context_providers

- mem0_basic: send preferences query before sleep so Mem0 can learn them,
  print result from new session recall
- mem0_sessions: add session for multi-turn conversation in agent-scoped
  example, remove user_id from agent-scoped provider (Mem0 API stores
  memories without user_id when agent_id is provided), use single message
  for storing preferences
- redis_basics: print retrieved context messages instead of raw object
- redis_sessions: add missing load_dotenv() call
- redis_basics/redis_sessions: fix docstrings referencing wrong client type
- azure_redis_conversation: replace duplicate copyright with load_dotenv()

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Python: Fix broken link in declarative README

openai_responses_agent.py was renamed to openai_agent.py

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-01 21:35:16 +00:00

171 lines
5.9 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)
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",
agent_id="scoped_assistant",
)
],
) as scoped_agent,
):
query = (
"Remember that I'm working on a Python project about data analysis "
"and I prefer using pandas and matplotlib."
)
print(f"User: {query}")
result = await scoped_agent.run(query)
print(f"Agent: {result}\n")
new_session = scoped_agent.create_session()
query = "What do you know about my current project and preferences?"
print(f"User (new session): {query}")
result = await scoped_agent.run(query, session=new_session)
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())