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Eduard van Valkenburg a2856d3b92 Python: restructure: Python samples into progressive 01-05 layout (#3862 )

* restructure: Python samples into progressive 01-05 layout

- 01-get-started/: 6 numbered steps (hello agent → hosting)
- 02-agents/: all agent concept samples (tools, middleware, providers, etc.)
- 03-workflows/: ALL existing workflow samples preserved as-is
- 04-hosting/: azure-functions, durabletask, a2a
- 05-end-to-end/: demos, evaluation, hosted agents
- Old files moved to _to_delete/ for review
- Added AGENTS.md with structure documentation
- autogen-migration/ and semantic-kernel-migration/ preserved at root

* fix: switch to AzureOpenAI Foundry, fix CI failures

- Switch all 01-get-started samples to AzureOpenAIResponsesClient with
  Azure AI Foundry project endpoint (AZURE_AI_PROJECT_ENDPOINT +
  AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME + AzureCliCredential)
- Add _to_delete/ and 05-end-to-end/ to pyrightconfig.samples.json excludes
- Fix test paths in packages/ that referenced old getting_started/ dirs:
  durabletask conftest + streaming test, azurefunctions conftest,
  devui conftest + capture_messages + openai_sdk_integration
- Fix workflow_as_agent_human_in_the_loop.py import (sibling import)
- Update hosting READMEs and tool comment paths
- Replace root README.md with new structure overview
- Update AGENTS.md to document Azure OpenAI Foundry as default provider

* cleanup: remove _to_delete folder, copy resource files to active dirs

All files in _to_delete/ were either:
- Exact duplicates of files in the new structure (240 files)
- Same file with only comment path updates (100 files)
- One import-fix diff (workflow_as_agent_human_in_the_loop.py)
- One superseded minimal_sample.py

Resource files (sample.pdf, countries.json, employees.pdf, weather.json)
copied to 02-agents/sample_assets/ and 02-agents/resources/ since active
samples reference them.

* fix: address PR review comments, centralize resources, remove root duplicates

- Fix type annotation in 04_memory.py (string union -> proper types)
- Fix old sample paths in observability files
- Fix grammar/spelling in observability samples
- Move sample_assets/ and resources/ to shared/ folder
- Remove 8 duplicate observability files from 02-agents root
- Update resource path references in multimodal_input and provider samples

* fix: update broken links from old getting_started paths to new structure

- Update relative paths in READMEs: getting_started/ → 01-get-started/,
  02-agents/, 03-workflows/, 04-hosting/, 05-end-to-end/
- Fix absolute GitHub URLs in package READMEs
- Fix broken link in ollama package README

* fix: convert absolute GitHub URLs to relative paths for link checker

Absolute URLs to python/samples/ on main branch 404 until PR merges.
Converted to relative paths that linkspector can verify locally.

* fix: update link for handoff sample moved to orchestrations/

* fix: update chatkit-integration README path from demos/ to 05-end-to-end/

* fix: update broken links in orchestrations README to match flat directory structure

a2856d3b92 · 2026-02-12 17:36:36 +00:00

History

mem0_basic.py

Python: restructure: Python samples into progressive 01-05 layout (#3862 )

2026-02-12 17:36:36 +00:00

mem0_oss.py

Python: restructure: Python samples into progressive 01-05 layout (#3862 )

2026-02-12 17:36:36 +00:00

mem0_threads.py

Python: restructure: Python samples into progressive 01-05 layout (#3862 )

2026-02-12 17:36:36 +00:00

README.md

Python: restructure: Python samples into progressive 01-05 layout (#3862 )

2026-02-12 17:36:36 +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_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.
`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