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* 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>
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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 endpointFOUNDRY_MODEL: Model deployment name- Azure CLI authentication (
az login)
For foundry_toolbox_context_provider.py:
FOUNDRY_PROJECT_ENDPOINT: Your Microsoft Foundry project endpointFOUNDRY_MODEL: Model deployment name- A toolbox already configured in that project; set
TOOLBOX_NAME/TOOLBOX_VERSIONat the top of the sample - Azure CLI authentication (
az login)
For azure_ai_foundry_memory.py:
FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpointFOUNDRY_MODEL: Chat/responses model deployment nameAZURE_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.