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Evan Mattson 04aaf0c1fe Python: Add support for Foundry Toolboxes (#5346 )

* Add support for the Foundry Toolbox in MAF

Introduces a Foundry Toolbox integration: FoundryChatClient gains a
get_toolbox() helper plus select_toolbox_tools(), normalize_tools in
the core package flattens tool-collection wrappers (ToolboxVersionObject
and generic iterables, while leaving Pydantic BaseModel instances
alone), and the new agent_framework.foundry namespace re-exports the
toolbox helpers. Ships with unit tests, a sample, and a design doc.

azure-ai-projects is pinned to the public >=2.0.0,<3.0 range and the
lockfile resolves from public PyPI. The toolbox test module skips when
Toolbox* types are unavailable so CI stays green until the public 2.1.0
SDK lands. OMC tooling directories (.omc/, .omx/) are gitignored.

* Update to latest azure ai projects package

* Improve sample

* Rename ADR to 0025

* Update ADR

* Apply suggestion from @alliscode

Co-authored-by: Ben Thomas <ben.thomas@microsoft.com>

* Improve samples

* Update test

---------

Co-authored-by: Ben Thomas <ben.thomas@microsoft.com>

2026-04-20 23:56:01 +00:00

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Context Provider Samples

These samples demonstrate how to use context providers to enrich agent conversations with external knowledge — from custom logic to Azure AI Search (RAG) and memory services.

Samples

File / Folder	Description
`simple_context_provider.py`	Implement a custom context provider by extending `ContextProvider` to extract and inject structured user information across turns.
`foundry_toolbox_context_provider.py`	Compose a Microsoft Foundry toolbox with a `ContextProvider` that caches the toolbox once and picks a subset of its tools per-turn via `select_toolbox_tools`, driven by keywords in the latest user message.
`azure_ai_foundry_memory.py`	Use `FoundryMemoryProvider` to add semantic memory — automatically retrieves, searches, and stores memories via Azure AI Foundry.
`azure_ai_search/`	Retrieval Augmented Generation (RAG) with Azure AI Search in semantic and agentic modes. See its own README.
`mem0/`	Memory-powered context using the Mem0 integration (open-source and managed). See its own README.
`redis/`	Redis-backed context providers for conversation memory and sessions. See its own README.

Prerequisites

For simple_context_provider.py:

FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
FOUNDRY_MODEL: Model deployment name
Azure CLI authentication (az login)

For foundry_toolbox_context_provider.py:

FOUNDRY_PROJECT_ENDPOINT: Your Microsoft Foundry project endpoint
FOUNDRY_MODEL: Model deployment name
A toolbox already configured in that project; set TOOLBOX_NAME / TOOLBOX_VERSION at the top of the sample
Azure CLI authentication (az login)

For azure_ai_foundry_memory.py:

FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
FOUNDRY_MODEL: Chat/responses model deployment name
AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: Embedding model deployment name (e.g., text-embedding-ada-002)
Azure CLI authentication (az login)

See each subfolder's README for provider-specific prerequisites.

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Context Provider Samples

Samples

Prerequisites

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