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Python: Context providers abstraction and Mem0 implementation (#631)
* Added context provider abstractions * Added mem0 implementation * Example and small fixes * Added unit tests for agent * Added unit tests for mem0 provider * Updated README * Small doc updates * Update python/packages/mem0/agent_framework_mem0/_provider.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Small fixes in tests * Renaming based on PR feedback * Small fixes * Added tests for AggregateContextProvider * Small improvements * More improvements based on PR feedback * Small constant update * Added more examples * Added README for Mem0 examples * Small updates to API * Updated initialization logic * Updates for context manager * Updated Context class * Dependency update * Revert changes * Fixed tests --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com>
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# Mem0 Context Provider Examples
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[Mem0](https://mem0.ai/) is a self-improving memory layer for Large Language Models that enables applications to have long-term memory capabilities. The Agent Framework's Mem0 context provider integrates with Mem0's API to provide persistent memory across conversation sessions.
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This folder contains examples demonstrating how to use the Mem0 context provider with the Agent Framework for persistent memory and context management across conversations.
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## Examples
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| File | Description |
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|------|-------------|
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| [`mem0_basic.py`](mem0_basic.py) | Basic example of using Mem0 context provider to store and retrieve user preferences across different conversation threads. |
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| [`mem0_threads.py`](mem0_threads.py) | Advanced example demonstrating different thread scoping strategies with Mem0. Covers global thread scope (memories shared across all operations), per-operation thread scope (memories isolated per thread), and multiple agents with different memory configurations for personal vs. work contexts. |
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## Prerequisites
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### Required Resources
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1. [Mem0 API Key](https://app.mem0.ai/) - Sign up for a Mem0 account and get your API key
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2. Azure AI Foundry project endpoint (used in these examples)
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3. Azure CLI authentication (run `az login`)
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## Configuration
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### Environment Variables
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Set the following environment variables:
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**For Mem0:**
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- `MEM0_API_KEY`: Your Mem0 API key (alternatively, pass it as `api_key` parameter to `Mem0Provider`)
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**For Azure AI Foundry:**
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- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
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- `FOUNDRY_MODEL_DEPLOYMENT_NAME`: The name of your model deployment
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## Key Concepts
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### Memory Scoping
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The Mem0 context provider supports different scoping strategies:
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- **Global Scope** (`scope_to_per_operation_thread_id=False`): Memories are shared across all conversation threads
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- **Thread Scope** (`scope_to_per_operation_thread_id=True`): Memories are isolated per conversation thread
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### Memory Association
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Mem0 records can be associated with different identifiers:
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- `user_id`: Associate memories with a specific user
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- `agent_id`: Associate memories with a specific agent
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- `thread_id`: Associate memories with a specific conversation thread
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- `application_id`: Associate memories with an application context
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import uuid
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.mem0 import Mem0Provider
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from azure.identity.aio import AzureCliCredential
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def retrieve_company_report(company_code: str, detailed: bool) -> str:
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if company_code != "CNTS":
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raise ValueError("Company code not found")
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if not detailed:
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return "CNTS is a company that specializes in technology."
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return (
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"CNTS is a company that specializes in technology. "
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"It had a revenue of $10 million in 2022. It has 100 employees."
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)
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async def main() -> None:
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"""Example of memory usage with Mem0 context provider."""
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print("=== Mem0 Context Provider Example ===")
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# Each record in Mem0 should be associated with agent_id or user_id or application_id or thread_id.
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# In this example, we associate Mem0 records with user_id.
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user_id = str(uuid.uuid4())
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# For Azure authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
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# authentication option.
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# For Mem0 authentication, set Mem0 API key via "api_key" parameter or MEM0_API_KEY environment variable.
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async with (
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AzureCliCredential() as credential,
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FoundryChatClient(async_credential=credential).create_agent(
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name="FriendlyAssistant",
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instructions="You are a friendly assistant.",
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tools=retrieve_company_report,
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context_providers=Mem0Provider(user_id=user_id),
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) as agent,
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):
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# First ask the agent to retrieve a company report with no previous context.
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# The agent will not be able to invoke the tool, since it doesn't know
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# the company code or the report format, so it should ask for clarification.
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query = "Please retrieve my company report"
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print(f"User: {query}")
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result = await agent.run(query)
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print(f"Agent: {result}\n")
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# Now tell the agent the company code and the report format that you want to use
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# and it should be able to invoke the tool and return the report.
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query = "I always work with CNTS and I always want a detailed report format. Please remember and retrieve it."
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print(f"User: {query}")
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result = await agent.run(query)
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print(f"Agent: {result}\n")
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print("\nRequest within a new thread:")
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# Create a new thread for the agent.
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# The new thread has no context of the previous conversation.
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thread = agent.get_new_thread()
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# Since we have the mem0 component in the thread, the agent should be able to
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# retrieve the company report without asking for clarification, as it will
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# be able to remember the user preferences from Mem0 component.
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query = "Please retrieve my company report"
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print(f"User: {query}")
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result = await agent.run(query, thread=thread)
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print(f"Agent: {result}\n")
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if __name__ == "__main__":
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asyncio.run(main())
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import uuid
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.mem0 import Mem0Provider
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from azure.identity.aio import AzureCliCredential
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def get_user_preferences(user_id: str) -> str:
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"""Mock function to get user preferences."""
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preferences = {
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"user123": "Prefers concise responses and technical details",
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"user456": "Likes detailed explanations with examples",
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}
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return preferences.get(user_id, "No specific preferences found")
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async def example_global_thread_scope() -> None:
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"""Example 1: Global thread_id scope (memories shared across all operations)."""
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print("1. Global Thread Scope Example:")
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print("-" * 40)
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global_thread_id = str(uuid.uuid4())
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user_id = "user123"
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async with (
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AzureCliCredential() as credential,
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FoundryChatClient(async_credential=credential).create_agent(
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name="GlobalMemoryAssistant",
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instructions="You are an assistant that remembers user preferences across conversations.",
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tools=get_user_preferences,
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context_providers=Mem0Provider(
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user_id=user_id,
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thread_id=global_thread_id,
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scope_to_per_operation_thread_id=False, # Share memories across all threads
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),
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) as global_agent,
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):
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# Store some preferences in the global scope
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query = "Remember that I prefer technical responses with code examples when discussing programming."
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print(f"User: {query}")
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result = await global_agent.run(query)
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print(f"Agent: {result}\n")
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# Create a new thread - but memories should still be accessible due to global scope
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new_thread = global_agent.get_new_thread()
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query = "What do you know about my preferences?"
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print(f"User (new thread): {query}")
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result = await global_agent.run(query, thread=new_thread)
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print(f"Agent: {result}\n")
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async def example_per_operation_thread_scope() -> None:
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"""Example 2: Per-operation thread scope (memories isolated per thread).
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Note: When scope_to_per_operation_thread_id=True, the provider is bound to a single thread
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throughout its lifetime. Use the same thread object for all operations with that provider.
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"""
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print("2. Per-Operation Thread Scope Example:")
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print("-" * 40)
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user_id = "user123"
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async with (
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AzureCliCredential() as credential,
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FoundryChatClient(async_credential=credential).create_agent(
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name="ScopedMemoryAssistant",
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instructions="You are an assistant with thread-scoped memory.",
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tools=get_user_preferences,
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context_providers=Mem0Provider(
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user_id=user_id,
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scope_to_per_operation_thread_id=True, # Isolate memories per thread
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),
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) as scoped_agent,
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):
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# Create a specific thread for this scoped provider
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dedicated_thread = scoped_agent.get_new_thread()
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# Store some information in the dedicated thread
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query = "Remember that for this conversation, I'm working on a Python project about data analysis."
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print(f"User (dedicated thread): {query}")
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result = await scoped_agent.run(query, thread=dedicated_thread)
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print(f"Agent: {result}\n")
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# Test memory retrieval in the same dedicated thread
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query = "What project am I working on?"
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print(f"User (same dedicated thread): {query}")
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result = await scoped_agent.run(query, thread=dedicated_thread)
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print(f"Agent: {result}\n")
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# Store more information in the same thread
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query = "Also remember that I prefer using pandas and matplotlib for this project."
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print(f"User (same dedicated thread): {query}")
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result = await scoped_agent.run(query, thread=dedicated_thread)
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print(f"Agent: {result}\n")
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# Test comprehensive memory retrieval
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query = "What do you know about my current project and preferences?"
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print(f"User (same dedicated thread): {query}")
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result = await scoped_agent.run(query, thread=dedicated_thread)
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print(f"Agent: {result}\n")
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async def example_multiple_agents() -> None:
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"""Example 3: Multiple agents with different thread configurations."""
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print("3. Multiple Agents with Different Thread Configurations:")
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print("-" * 40)
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agent_id_1 = "agent_personal"
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agent_id_2 = "agent_work"
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async with (
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AzureCliCredential() as credential,
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FoundryChatClient(async_credential=credential).create_agent(
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name="PersonalAssistant",
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instructions="You are a personal assistant that helps with personal tasks.",
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context_providers=Mem0Provider(
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agent_id=agent_id_1,
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),
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) as personal_agent,
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FoundryChatClient(async_credential=credential).create_agent(
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name="WorkAssistant",
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instructions="You are a work assistant that helps with professional tasks.",
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context_providers=Mem0Provider(
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agent_id=agent_id_2,
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),
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) as work_agent,
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):
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# Store personal information
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query = "Remember that I like to exercise at 6 AM and prefer outdoor activities."
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print(f"User to Personal Agent: {query}")
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result = await personal_agent.run(query)
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print(f"Personal Agent: {result}\n")
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# Store work information
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query = "Remember that I have team meetings every Tuesday at 2 PM."
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print(f"User to Work Agent: {query}")
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result = await work_agent.run(query)
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print(f"Work Agent: {result}\n")
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# Test memory isolation
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query = "What do you know about my schedule?"
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print(f"User to Personal Agent: {query}")
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result = await personal_agent.run(query)
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print(f"Personal Agent: {result}\n")
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print(f"User to Work Agent: {query}")
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result = await work_agent.run(query)
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print(f"Work Agent: {result}\n")
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async def main() -> None:
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"""Run all Mem0 thread management examples."""
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print("=== Mem0 Thread Management Example ===\n")
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await example_global_thread_scope()
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await example_per_operation_thread_scope()
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await example_multiple_agents()
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if __name__ == "__main__":
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asyncio.run(main())
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