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>

python/samples/getting_started/context_providers/mem0/README.md

@@ -0,0 +1,51 @@
+# Mem0 Context Provider Examples
+
+[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.
+
+This folder contains examples demonstrating how to use the Mem0 context provider with the Agent Framework for persistent memory and context management across conversations.
+
+## Examples
+
+| File | Description |
+|------|-------------|
+| [`mem0_basic.py`](mem0_basic.py) | Basic example of using Mem0 context provider to store and retrieve user preferences across different conversation threads. |
+| [`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. |
+
+## Prerequisites
+
+### Required Resources
+
+1. [Mem0 API Key](https://app.mem0.ai/) - Sign up for a Mem0 account and get your API key
+2. Azure AI Foundry project endpoint (used in these examples)
+3. Azure CLI authentication (run `az login`)
+
+## Configuration
+
+### Environment Variables
+
+Set the following environment variables:
+
+**For Mem0:**
+- `MEM0_API_KEY`: Your Mem0 API key (alternatively, pass it as `api_key` parameter to `Mem0Provider`)
+
+**For Azure AI Foundry:**
+- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
+- `FOUNDRY_MODEL_DEPLOYMENT_NAME`: The name of your model deployment
+
+## Key Concepts
+
+### Memory Scoping
+
+The Mem0 context provider supports different scoping strategies:
+
+- **Global Scope** (`scope_to_per_operation_thread_id=False`): Memories are shared across all conversation threads
+- **Thread Scope** (`scope_to_per_operation_thread_id=True`): Memories are isolated per conversation thread
+
+### Memory Association
+
+Mem0 records can be associated with different identifiers:
+
+- `user_id`: Associate memories with a specific user
+- `agent_id`: Associate memories with a specific agent
+- `thread_id`: Associate memories with a specific conversation thread
+- `application_id`: Associate memories with an application context