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Eduard van Valkenburg aab621f5eb Python: Fix tool normalization and provider sample consolidation (#3953 )

* Fix tool normalization and provider samples

- restore callable/single-tool normalization paths and unset tool-choice behavior\n- consolidate and expand chat/provider samples (OpenAI/Azure/Anthropic/Ollama/Bedrock)\n- migrate Bedrock lazy import surface to agent_framework.amazon and move provider samples

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

* small fix in sample

* Finalize provider, samples, and core cleanup

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

* Fix CopilotTool passthrough in agent

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

* fix link

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

aab621f5eb · 2026-02-16 16:30:38 +00:00

History

mem0_basic.py

Python: Fix tool normalization and provider sample consolidation (#3953 )

2026-02-16 16:30:38 +00:00

mem0_oss.py

Python: Fix tool normalization and provider sample consolidation (#3953 )

2026-02-16 16:30:38 +00:00

mem0_sessions.py

Python: Fix tool normalization and provider sample consolidation (#3953 )

2026-02-16 16:30:38 +00:00

README.md

Python: [BREAKING] PR2 — Wire context provider pipeline, remove old types, update all consumers (#3850 )

2026-02-12 21:00:32 +00:00

README.md

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`	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.
`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 as api_key parameter to Mem0Provider). 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:

AZURE_AI_PROJECT_ENDPOINT: Your Azure AI project endpoint
AZURE_AI_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