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Python: [BREAKING] Python: Provider-leading client design & OpenAI package extraction (#4818)

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* Python: Provider-leading client design & OpenAI package extraction

Major refactoring of the Python Agent Framework client architecture:

- Extract OpenAI clients into new `agent-framework-openai` package
- Core package no longer depends on openai, azure-identity, azure-ai-projects
- Rename clients for discoverability: OpenAIResponsesClient → OpenAIChatClient,
  OpenAIChatClient → OpenAIChatCompletionClient
- Unify `model_id`/`deployment_name`/`model_deployment_name` → `model` param
- New FoundryChatClient for Azure AI Foundry Responses API
- New FoundryAgent/FoundryAgentClient for connecting to pre-configured Foundry agents
- Remove OpenAIBase/OpenAIConfigMixin from non-deprecated client MRO
- Deprecate AzureOpenAI* clients, AzureAIClient, OpenAIAssistantsClient
- Reorganize samples: azure_openai+azure_ai+azure_ai_agent → azure/
- ADR-0020: Provider-Leading Client Design

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

* fix: missing Agent imports in samples, .model_id → .model in foundry_local sample

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

* fix: CI failures — mypy errors, coverage targets, sample imports

- azure-ai mypy: add type ignores for TypedDict total=, model arg, forward ref
- Coverage: replace core.azure/openai targets with openai package target
- project_provider: add type annotation for opts dict

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

* fix: populate openai .pyi stub, fix broken README links, coverage targets

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

* fixes

* updated observabilitty

* reset azure init.pyi

* fix errors

* updated adr number

* fix foundry local

* fixed not renamed docstrings and comments, and added deprecated markers to old classes

* fix tests and pyprojects

* fix test vars

* updated function tests

* update durable

* updated test setup for functions

* Fix Foundry auth in workflow samples

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

* Stabilize Python integration workflows

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

* Update hosting samples for Foundry

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

* Trigger full CI rerun

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

* Trigger CI rerun again

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

* trigger rerun

* trigger rerun

* fix for litellm

* undo durabletask changes

* Move Foundry APIs into foundry namespace

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

* Fix Foundry pyproject formatting

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

* Split provider samples by Foundry surface

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

* Restore hosting sample requirements

Also fix the Foundry Local sample link after the provider sample move.

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

* updated tests

* udpated foundry integration tests

* removed dist from azurefunctions tests

* Use separate Foundry clients for concurrent agents

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

* fix client setup in azfunc and durable

* disabled two tests

* updated setup for some function and durable tests

* improved azure openai setup with new clients

* ignore deprecated

* fixes

* skip 11

* remove openai assistants int tests

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Eduard van Valkenburg
2026-03-25 09:56:29 +00:00
committed by GitHub
co-authored by Copilot
parent 4b533608b6
commit 5e056b672e
485 changed files with 9784 additions and 12084 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/samples/02-agents/context_providers/azure_ai_search/README.md
+3 -3
View File
@@ -8,13 +8,13 @@ This folder contains examples demonstrating how to use the Azure AI Search conte
| File | Description |
|------|-------------|
| [`azure_ai_with_search_context_agentic.py`](azure_ai_with_search_context_agentic.py) | **Agentic mode** (recommended for most scenarios): Uses Knowledge Bases in Azure AI Search for query planning and multi-hop reasoning. Provides more accurate results through intelligent retrieval with automatic query reformulation. Slightly slower with more token consumption for query planning. [Learn more](https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/foundry-iq-boost-response-relevance-by-36-with-agentic-retrieval/4470720) |
| [`azure_ai_with_search_context_semantic.py`](azure_ai_with_search_context_semantic.py) | **Semantic mode** (fast queries): Fast hybrid search combining vector and keyword search with semantic ranking. Returns raw search results as context. Best for scenarios where speed is critical and simple retrieval is sufficient. |
| [`search_context_agentic.py`](search_context_agentic.py) | **Agentic mode** (recommended for most scenarios): Uses Knowledge Bases in Azure AI Search for query planning and multi-hop reasoning. Provides more accurate results through intelligent retrieval with automatic query reformulation. Slightly slower with more token consumption for query planning. [Learn more](https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/foundry-iq-boost-response-relevance-by-36-with-agentic-retrieval/4470720) |
| [`search_context_semantic.py`](search_context_semantic.py) | **Semantic mode** (fast queries): Fast hybrid search combining vector and keyword search with semantic ranking. Returns raw search results as context. Best for scenarios where speed is critical and simple retrieval is sufficient. |
## Installation
```bash
pip install agent-framework-azure-ai-search agent-framework-azure-ai
pip install agent-framework-foundry-search agent-framework-foundry
```
## Prerequisites
python/samples/02-agents/context_providers/azure_ai_search/azure_ai_with_search_context_agentic.py → python/samples/02-agents/context_providers/azure_ai_search/search_context_agentic.py
+8 -7
View File
@@ -4,7 +4,8 @@ import asyncio
import os
from agent_framework import Agent
from agent_framework.azure import AzureAIAgentClient, AzureAISearchContextProvider
from agent_framework.azure import AzureAISearchContextProvider
from agent_framework.foundry import FoundryChatClient
from azure.identity.aio import AzureCliCredential
from dotenv import load_dotenv
@@ -31,8 +32,8 @@ Prerequisites:
Environment variables:
- AZURE_SEARCH_ENDPOINT: Your Azure AI Search endpoint
- AZURE_SEARCH_API_KEY: (Optional) API key - if not provided, uses DefaultAzureCredential
- AZURE_AI_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
- AZURE_SEARCH_API_KEY: (Optional) API key - if not provided, uses AzureCliCredential
- FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
- AZURE_AI_MODEL_DEPLOYMENT_NAME: Your model deployment name (e.g., "gpt-4o")
For using an existing Knowledge Base (recommended):
@@ -57,7 +58,7 @@ async def main() -> None:
# Get configuration from environment
search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"]
search_key = os.environ.get("AZURE_SEARCH_API_KEY")
project_endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
project_endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
model_deployment = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "gpt-4o")
# Agentic mode requires exactly ONE of: knowledge_base_name OR index_name
@@ -99,7 +100,7 @@ async def main() -> None:
credential=AzureCliCredential() if not search_key else None,
mode="agentic",
azure_openai_resource_url=azure_openai_resource_url,
model_deployment_name=model_deployment,
model_model=model_deployment,
# Optional: Configure retrieval behavior
knowledge_base_output_mode="extractive_data", # or "answer_synthesis"
retrieval_reasoning_effort="minimal", # or "medium", "low"
@@ -109,9 +110,9 @@ async def main() -> None:
# Create agent with search context provider
async with (
search_provider,
AzureAIAgentClient(
FoundryChatClient(
project_endpoint=project_endpoint,
model_deployment_name=model_deployment,
model_model=model_deployment,
credential=AzureCliCredential(),
) as client,
Agent(
python/samples/02-agents/context_providers/azure_ai_search/azure_ai_with_search_context_semantic.py → python/samples/02-agents/context_providers/azure_ai_search/search_context_semantic.py
+8 -7
View File
@@ -4,7 +4,8 @@ import asyncio
import os
from agent_framework import Agent
from agent_framework.azure import AzureAIAgentClient, AzureAISearchContextProvider, AzureOpenAIEmbeddingClient
from agent_framework.azure import AzureAISearchContextProvider, AzureOpenAIEmbeddingClient
from agent_framework.foundry import FoundryChatClient
from azure.identity.aio import AzureCliCredential
from dotenv import load_dotenv
@@ -26,9 +27,9 @@ Prerequisites:
2. An Azure AI Foundry project with a model deployment
3. Set the following environment variables:
- AZURE_SEARCH_ENDPOINT: Your Azure AI Search endpoint
- AZURE_SEARCH_API_KEY: (Optional) Your search API key - if not provided, uses DefaultAzureCredential for Entra ID
- AZURE_SEARCH_API_KEY: (Optional) Your search API key - if not provided, uses AzureCliCredential for Entra ID
- AZURE_SEARCH_INDEX_NAME: Your search index name
- AZURE_AI_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
- FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
- AZURE_AI_MODEL_DEPLOYMENT_NAME: Your model deployment name (e.g., "gpt-4o")
- AZURE_OPENAI_EMBEDDING_MODEL_ID: (Optional) Your embedding model for hybrid search (e.g., "text-embedding-3-small")
- AZURE_OPENAI_ENDPOINT: (Optional) Your Azure OpenAI resource URL, required if using an OpenAI embedding model for hybrid search
@@ -51,7 +52,7 @@ async def main() -> None:
search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"]
search_key = os.environ.get("AZURE_SEARCH_API_KEY")
index_name = os.environ["AZURE_SEARCH_INDEX_NAME"]
project_endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
project_endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
model_deployment = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "gpt-4o")
openai_endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT")
embedding_model = os.environ.get("AZURE_OPENAI_EMBEDDING_MODEL_ID", "text-embedding-3-small")
@@ -60,7 +61,7 @@ async def main() -> None:
if openai_endpoint and embedding_model:
embedding_client = AzureOpenAIEmbeddingClient(
endpoint=openai_endpoint,
deployment_name=embedding_model,
model=embedding_model,
credential=credential,
)
@@ -83,9 +84,9 @@ async def main() -> None:
# Create agent with search context provider
async with (
search_provider,
AzureAIAgentClient(
FoundryChatClient(
project_endpoint=project_endpoint,
model_deployment_name=model_deployment,
model_model=model_deployment,
credential=credential,
) as client,
Agent(