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Giles Odigwe
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* 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>
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Mem0 Context Provider Examples
Mem0 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 |
Basic example of using Mem0 context provider to store and retrieve user preferences across different conversation threads. |
mem0_sessions.py |
Example demonstrating different memory scoping strategies with Mem0. Covers user-scoped memory (memories shared across all sessions for the same user), agent-scoped memory (memories isolated per agent), and multiple agents with different memory configurations for personal vs. work contexts. |
mem0_oss.py |
Example of using the Mem0 Open Source self-hosted version as the context provider. Demonstrates setup and configuration for local deployment. |
Prerequisites
Required Resources
- Mem0 API Key - Sign up for a Mem0 account and get your API key - or self-host Mem0 Open Source
- Azure AI project endpoint (used in these examples)
- Azure CLI authentication (run
az login)
Configuration
Environment Variables
Set the following environment variables:
For Mem0 Platform:
MEM0_API_KEY: Your Mem0 API key (alternatively, pass it asapi_keyparameter toMem0Provider). Not required if you are self-hosting Mem0 Open Source
For Mem0 Open Source:
OPENAI_API_KEY: Your OpenAI API key (used by Mem0 OSS for embedding generation and automatic memory extraction)
For Azure AI:
FOUNDRY_PROJECT_ENDPOINT: Your Azure AI project endpointFOUNDRY_MODEL: The name of your model deployment
Key Concepts
Memory Scoping
The Mem0 context provider supports scoping via identifiers:
- User scope (
user_id): Associate memories with a specific user, shared across all sessions - Agent scope (
agent_id): Isolate memories per agent persona - Application scope (
application_id): Associate memories with an application context