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Python: [BREAKING] Standardize model selection on model (#4999)

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* Refactor Anthropic model option and provider clients

Rename the Anthropic client model option from model_id to model, add provider-specific Anthropic wrappers for Foundry, Bedrock, and Vertex, and expose them through the Anthropic, Foundry, Amazon, and Google namespaces. Update core option handling, docs, samples, and tests accordingly.

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

* Fix Anthropic skills sample typing

Cast the Anthropic beta client to Any in the skills sample so the pre-commit sample pyright check no longer fails on beta skills and files endpoints that are not exposed by the current SDK stubs.

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

* undo sample mypy

* Retry CI after transient external failures

Retrigger PR validation after an unrelated Copilot review workflow SAML failure and a transient external tau2 git fetch failure in the Windows Python test setup.

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

* Address review feedback on model option merging

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

* Address Anthropic compatibility review feedback

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

* moved all to `model`

* fixes for azure ai search

* Python: standardize remaining sample env var names

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

* Python: fix foundry-local pyright compatibility

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

* updated env vars in cicd

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Eduard van Valkenburg
2026-04-01 19:00:18 +00:00
committed by GitHub
co-authored by Copilot
parent 95550dd0dc
commit 6acab3d1d6
184 changed files with 1749 additions and 1025 deletions
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python/samples/04-hosting/durabletask/02_multi_agent/client.py
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@@ -8,7 +8,7 @@ each with their own specialized capabilities and tools.
Prerequisites:
- The worker must be running with both agents registered
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_DEPLOYMENT_NAME when running the worker
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_MODEL when running the worker
- Sign in with Azure CLI for AzureCliCredential authentication
- Durable Task Scheduler must be running
"""
python/samples/04-hosting/durabletask/02_multi_agent/sample.py
+1 -1
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@@ -5,7 +5,7 @@ This sample demonstrates running both the worker and client in a single process
for multiple agents with different tools. The worker registers two agents
(WeatherAgent and MathAgent), each with their own specialized capabilities.
Prerequisites:
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_DEPLOYMENT_NAME
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_MODEL
- Sign in with Azure CLI for AzureCliCredential authentication
- Durable Task Scheduler must be running (e.g., using Docker)
To run this sample:
python/samples/04-hosting/durabletask/02_multi_agent/worker.py
+1 -1
View File
@@ -7,7 +7,7 @@ with their own specialized tools. This demonstrates how to host multiple agents
with different capabilities in a single worker process.
Prerequisites:
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_DEPLOYMENT_NAME
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_MODEL
- Sign in with Azure CLI for AzureCliCredential authentication
- Start a Durable Task Scheduler (e.g., using Docker)
"""
python/samples/04-hosting/durabletask/06_multi_agent_orchestration_conditionals/README.md
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@@ -17,7 +17,7 @@ See the [README.md](../README.md) file in the parent directory for more informat
This sample uses Azure OpenAI credentials:
- `AZURE_OPENAI_ENDPOINT`
- `AZURE_OPENAI_DEPLOYMENT_NAME`
- `AZURE_OPENAI_MODEL`
## Running the Sample
python/samples/04-hosting/durabletask/06_multi_agent_orchestration_conditionals/client.py
+1 -1
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@@ -7,7 +7,7 @@ that uses conditional logic to either handle spam emails or draft professional r
Prerequisites:
- The worker must be running with both agents, orchestration, and activities registered
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_DEPLOYMENT_NAME
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_MODEL
- Sign in with Azure CLI for AzureCliCredential authentication
- Durable Task Scheduler must be running
"""
python/samples/04-hosting/durabletask/06_multi_agent_orchestration_conditionals/sample.py
+1 -1
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@@ -10,7 +10,7 @@ The orchestration branches based on spam detection results, calling different
activity functions to handle spam or send legitimate email responses.
Prerequisites:
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_DEPLOYMENT_NAME
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_MODEL
- Sign in with Azure CLI for AzureCliCredential authentication
- Durable Task Scheduler must be running (e.g., using Docker)
python/samples/04-hosting/durabletask/06_multi_agent_orchestration_conditionals/worker.py
+3 -3
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@@ -7,7 +7,7 @@ orchestration function that routes execution based on spam detection results. Ac
handle side effects (spam handling and email sending).
Prerequisites:
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_DEPLOYMENT_NAME
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_MODEL
- Sign in with Azure CLI for AzureCliCredential authentication
- Start a Durable Task Scheduler (e.g., using Docker)
"""
@@ -69,7 +69,7 @@ def create_spam_agent() -> "Agent":
"""
return Agent(
client=OpenAIChatCompletionClient(
model=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
model=os.environ["AZURE_OPENAI_MODEL"],
api_key=get_async_bearer_token_provider(
AsyncAzureCliCredential(), "https://cognitiveservices.azure.com/.default"
),
@@ -87,7 +87,7 @@ def create_email_agent() -> "Agent":
"""
return Agent(
client=OpenAIChatCompletionClient(
model=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
model=os.environ["AZURE_OPENAI_MODEL"],
api_key=get_async_bearer_token_provider(
AsyncAzureCliCredential(), "https://cognitiveservices.azure.com/.default"
),