Merge and move scripts (#4308)

* .NET: Add Microsoft Fabric sample #3674 (#4230) Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> * Python: Phase 2: Embedding clients for Ollama, Bedrock, and Azure AI Inference (#4207) * Phase 2: Embedding clients for Ollama, Bedrock, and Azure AI Inference Add embedding client implementations to existing provider packages: - OllamaEmbeddingClient: Text embeddings via Ollama's embed API - BedrockEmbeddingClient: Text embeddings via Amazon Titan on Bedrock - AzureAIInferenceEmbeddingClient: Text and image embeddings via Azure AI Inference, supporting Content | str input with separate model IDs for text (AZURE_AI_INFERENCE_EMBEDDING_MODEL_ID) and image (AZURE_AI_INFERENCE_IMAGE_EMBEDDING_MODEL_ID) endpoints Additional changes: - Rename EmbeddingCoT -> EmbeddingT, EmbeddingOptionsCoT -> EmbeddingOptionsT - Add otel_provider_name passthrough to all embedding clients - Register integration pytest marker in all packages - Add lazy-loading namespace exports for Ollama and Bedrock embeddings - Add image embedding sample using Cohere-embed-v3-english - Add azure-ai-inference dependency to azure-ai package Part of #1188 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix mypy duplicate name and ruff lint issues - Rename second 'vector' variable to 'img_vector' in image embedding loop - Combine nested with statements in tests - Remove unused result assignments in tests Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updates from feedback * Fix CI failures in embedding usage handling - Fix Azure AI embedding mypy issues by normalizing vectors to list[float], safely accumulating optional usage token fields, and filtering None entries before constructing GeneratedEmbeddings - Avoid Bandit false positive by initializing usage details as an empty dict - Update OpenAI embedding tests to assert canonical usage keys (input_token_count/total_token_count) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * [Purview] Mark responses as responses and fix epoch bug for python long overflow (#4225) * .NET: Support InvokeMcpTool for declarative workflows (#4204) * Initial implementation of InvokeMcpTool in declarative workflow * Cleaned up sample implementation * Updated sample comments. * Added missing executor routing attribute * Fix PR comments. * Updated based on PR comments. * Updated based on PR comments. * Removed unnecessary using statement. * Update Python package versions to rc2 (#4258) - Bump core and azure-ai to 1.0.0rc2 - Bump preview packages to 1.0.0b260225 - Update dependencies to >=1.0.0rc2 - Add CHANGELOG entries for changes since rc1 - Update uv.lock Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * .NET: Fixing issue where OpenTelemetry span is never exported in .NET in-process workflow execution (#4196) * 1. Add reproduction test for issue #4155: workflow.run Activity never stopped in streaming OffThread path The WorkflowRunActivity_IsStopped_Streaming_OffThread test demonstrates that the workflow.run OpenTelemetry Activity created in StreamingRunEventStream.RunLoopAsync is started but never stopped when using the OffThread/Default streaming execution. The background run loop keeps running after event consumption completes, so the using Activity? declaration never disposes until explicit StopAsync() is called. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> 2. Fix workflow.run Activity never stopped in streaming OffThread execution (#4155) The workflow.run OpenTelemetry Activity in StreamingRunEventStream.RunLoopAsync was scoped to the method lifetime via 'using'. Since the run loop only exits on cancellation, the Activity was never stopped/exported until explicit disposal. Fix: Remove 'using' and explicitly dispose the Activity when the workflow reaches Idle status (all supersteps complete). A safety-net disposal in the finally block handles cancellation and error paths. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Add root-level workflow.session activity spanning run loop lifetime\n\nImplements two-level telemetry hierarchy per PR feedback from lokitoth:\n- workflow.session: spans the entire run loop / stream lifetime\n- workflow_invoke: per input-to-halt cycle, nested within the session\n\nThis ensures the session activity stays open across multiple turns,\nwhile individual run activities are created and disposed per cycle.\n\nAlso fixes linkedSource CancellationTokenSource disposal leak in\nStreamingRunEventStream (added using declaration)." * Address Copilot review: fix Activity/CTS disposal, rename activity, add error tag\n\n1. LockstepRunEventStream: Remove 'using' from Activity in async iterator\n and manually dispose in finally block (fixes #4155 pattern). Also dispose\n linkedSource CTS in finally to prevent leak.\n2. Tags.cs: Add ErrorMessage (\"error.message\") tag for runtime errors,\n distinct from BuildErrorMessage (\"build.error.message\").\n3. ActivityNames: Rename WorkflowRun from \"workflow_invoke\" to \"workflow.run\"\n for cross-language consistency.\n4. WorkflowTelemetryContext: Fix XML doc to say \"outer/parent span\" instead\n of \"root-level span\".\n5. ObservabilityTests: Assert WorkflowSession absence when DisableWorkflowRun\n is true.\n6. WorkflowRunActivityStopTests: Fix streaming test race by disposing\n StreamingRun before asserting activities are stopped.\n7. StreamingRunEventStream/LockstepRunEventStream: Use Tags.ErrorMessage\n instead of Tags.BuildErrorMessage for runtime error events." * Review fixes: revert workflow_invoke rename, use 'using' for linkedSource, move SessionStarted earlier\n\n- Revert ActivityNames.WorkflowRun back to \"workflow_invoke\" (OTEL semantic convention contract)\n- Use 'using' declaration for linkedSource CTS in LockstepRunEventStream (no timing sensitivity)\n- Move SessionStarted event before WaitForInputAsync in StreamingRunEventStream to match Lockstep behavior" * Improve naming and comments in WorkflowRunActivityStopTests" * Prevent session Activity.Current leak in lockstep mode, add nesting test Save and restore Activity.Current in LockstepRunEventStream.Start() so the session activity doesn't leak into caller code via AsyncLocal. Re-establish Activity.Current = sessionActivity before creating the run activity in TakeEventStreamAsync to preserve parent-child nesting. Add test verifying app activities after RunAsync are not parented under the session, and that the workflow_invoke activity nests under the session." * Fix stale XML doc: WorkflowRun -> WorkflowInvoke in ObservabilityTests --------- Co-authored-by: alliscode <bentho@microsoft.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python / .NET Samples - Restructure and Improve Samples (Feature Branc… (#4092) * Python: .NET Samples - Restructure and Improve Samples (Feature Branch) (#4091) * Moved by agent (#4094) * Fix readme links * .NET Samples - Create `04-hosting` learning path step (#4098) * Agent move * Agent reorderd * Remove A2A section from README Removed A2A section from the Getting Started README. * Agent fixed links * Fix broken sample links in durable-agents README (#4101) * Initial plan * Fix broken internal links in documentation Co-authored-by: crickman <66376200+crickman@users.noreply.github.com> * Revert template link changes; keep only durable-agents README fix Co-authored-by: crickman <66376200+crickman@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: crickman <66376200+crickman@users.noreply.github.com> * .NET Samples - Create `03-workflows` learning path step (#4102) * Fix solution project path * Python: Fix broken markdown links to repo resources (outside /docs) (#4105) * Initial plan * Fix broken markdown links to repo resources Co-authored-by: crickman <66376200+crickman@users.noreply.github.com> * Update README to rename .NET Workflows Samples section --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: crickman <66376200+crickman@users.noreply.github.com> * .NET Samples - Create `02-agents` learning path step (#4107) * .NET: Fix broken relative link in GroupChatToolApproval README (#4108) * Initial plan * Fix broken link in GroupChatToolApproval README Co-authored-by: crickman <66376200+crickman@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: crickman <66376200+crickman@users.noreply.github.com> * Update labeler configuration for workflow samples * .NET - Reorder Agents samples to start from Step01 instead of Step04 (#4110) * Fix solution * Resolve new sample paths * Move new AgentSkills and AgentWithMemory_Step04 samples * Fix link * Fix readme path * fix: update stale dotnet/samples/Durable path reference in AGENTS.md Co-authored-by: crickman <66376200+crickman@users.noreply.github.com> * Moved new sample * Update solution * Resolve merge (new sample) * Sync to new sample - FoundryAgents_Step21_BingCustomSearch * Updated README * .NET Samples - Configuration Naming Update (#4149) * .NET: Restore AzureFunctions index parity with ConsoleApps under DurableAgents samples (#4221) * Clean-up `05_host_your_agent` * Config setting consistency * Refine samples * AGENTS.md * Move new samples * Re-order samples * Move new project and fixup solution * Fixup model config * Fix up new UT project --------- Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com> * Python: Fix Bedrock embedding test stub missing meta attribute (#4287) * Fix Bedrock embedding test stub missing meta attribute * Increase test coverage so gate passes * Python: (ag-ui): fix approval payloads being re-processed on subsequent conversation turns (#4232) * Fix ag-ui tool call issue * Safe json fix * Python: Update workflow orchestration samples to use AzureOpenAIResponsesClient (#4285) * Update workflow orchestration samples to use AzureOpenAIResponsesClient * Fix broken link * Move scripts to scripts folder --------- Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Co-authored-by: Rishabh Chawla <rishabhchawla1995@gmail.com> Co-authored-by: Peter Ibekwe <109177538+peibekwe@users.noreply.github.com> Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com> Co-authored-by: Ben Thomas <ben.thomas@microsoft.com> Co-authored-by: alliscode <bentho@microsoft.com> Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2026-06-16 21:04:09 +08:00 · 2026-02-26 10:49:07 +00:00
parent cc1ef730e3
commit 8b191de936
871 changed files with 7688 additions and 1696 deletions
@@ -0,0 +1,87 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "agent-framework-azure-ai",
+# ]
+# ///
+# Run with: uv run samples/02-agents/embeddings/azure_ai_inference_embeddings.py
+
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import pathlib
+
+from agent_framework import Content
+from agent_framework_azure_ai import AzureAIInferenceEmbeddingClient
+from dotenv import load_dotenv
+
+load_dotenv()
+
+"""Azure AI Inference Image Embedding Example
+
+This sample demonstrates how to generate image embeddings using the
+Azure AI Inference embedding client with the Cohere-embed-v3-english model.
+Images are passed as ``Content`` objects created with ``Content.from_data()``.
+
+Prerequisites:
+    Set the following environment variables or add them to a .env file:
+    - AZURE_AI_INFERENCE_ENDPOINT: Your Azure AI model inference endpoint URL
+    - AZURE_AI_INFERENCE_API_KEY: Your API key
+    - AZURE_AI_INFERENCE_EMBEDDING_MODEL_ID: The text embedding model name
+      (e.g. "text-embedding-3-small")
+    - AZURE_AI_INFERENCE_IMAGE_EMBEDDING_MODEL_ID: The image embedding model name
+      (e.g. "Cohere-embed-v3-english")
+"""
+
+SAMPLE_IMAGE_PATH = pathlib.Path(__file__).parent.parent.parent / "shared" / "sample_assets" / "sample_image.jpg"
+
+
+async def main() -> None:
+    """Generate image embeddings with Azure AI Inference."""
+    async with AzureAIInferenceEmbeddingClient() as client:
+        # 1. Generate an image embedding.
+        image_bytes = SAMPLE_IMAGE_PATH.read_bytes()
+        image_content = Content.from_data(data=image_bytes, media_type="image/jpeg")
+        result = await client.get_embeddings([image_content])
+        print(f"Image embedding dimensions: {result[0].dimensions}")
+        print(f"First 5 values: {result[0].vector[:5]}")
+        print(f"Model: {result[0].model_id}")
+        print(f"Usage: {result.usage}")
+        print()
+
+        # 2. Generate image and text embeddings separately in one call.
+        # The client dispatches text to the text endpoint and images to the image
+        # endpoint, then reassembles results in the original input order.
+        result = await client.get_embeddings(["A half-timbered house in a forested valley", image_content])
+        print(f"Text embedding dimensions: {result[0].dimensions}")
+        print(f"First 5 values: {result[0].vector[:5]}")
+        print(f"Image embedding dimensions: {result[1].dimensions}")
+        print(f"First 5 values: {result[1].vector[:5]}")
+        print()
+
+        # 3. Generate image embeddings with input_type option.
+        result = await client.get_embeddings(
+            [image_content],
+            options={"input_type": "document"},
+        )
+        print(f"Document embedding dimensions: {result[0].dimensions}")
+        print(f"First 5 values: {result[0].vector[:5]}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
+
+"""
+Sample output (using Cohere-embed-v3-english):
+Image embedding dimensions: 1024
+First 5 values: [0.023, -0.045, 0.067, -0.089, 0.011]
+Model: Cohere-embed-v3-english
+Usage: {'prompt_tokens': 1, 'total_tokens': 1}
+
+Image+text (separate) results:
+Text embedding dimensions: 1536
+Image embedding dimensions: 1024
+
+Document embedding dimensions: 1024
+"""
@@ -160,6 +160,14 @@ Sequential orchestration uses a few small adapter nodes for plumbing:
  These may appear in event streams (executor_invoked/executor_completed). They're analogous to
  concurrent’s dispatcher and aggregator and can be ignored if you only care about agent activity.

+### AzureOpenAIResponsesClient vs AzureAIAgent
+
+Workflow and orchestration samples use `AzureOpenAIResponsesClient` rather than the CRUD-style `AzureAIAgent` client. The key difference:
+
+- **`AzureOpenAIResponsesClient`** — A lightweight client that uses the underlying Agent Service V2 (Responses API) for non-CRUD-style agents. Orchestrations use this client because agents are created locally and do not require server-side lifecycle management (create/update/delete). This is the recommended client for orchestration patterns (Sequential, Concurrent, Handoff, GroupChat, Magentic).
+
+- **`AzureAIAgent`** — A CRUD-style client for server-managed agents. Use this when you need persistent, server-side agent definitions with features like file search, code interpreter sessions, or thread management provided by the Azure AI Agent Service.
+
 ### Environment Variables

 Workflow samples that use `AzureOpenAIResponsesClient` expect:
@@ -18,10 +18,11 @@ Run with:
 """

 import asyncio
+import os
 from pathlib import Path
 from typing import Any

-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.declarative import WorkflowFactory
 from azure.identity import AzureCliCredential
 from pydantic import BaseModel, Field
@@ -196,8 +197,12 @@ def format_order_confirmation(order_data: dict[str, Any], order_calculation: dic

 async def main():
    """Run the agent to function tool workflow."""
-    # Create Azure OpenAI client
-    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    # Create Azure OpenAI Responses client
+    chat_client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )

    # Create the order analysis agent with structured output
    order_analysis_agent = chat_client.as_agent(
@@ -6,7 +6,7 @@ This sample demonstrates an agent with function tools responding to user queries

 The workflow showcases:
 - **Function Tools**: Agent equipped with tools to query menu data
- **Real Azure OpenAI Agent**: Uses `AzureOpenAIChatClient` to create an agent with tools
+- **Real Azure OpenAI Agent**: Uses `AzureOpenAIResponsesClient` to create an agent with tools
 - **Agent Registration**: Shows how to register agents with the `WorkflowFactory`

 ## Tools
@@ -72,7 +72,11 @@ Session Complete

 ```python
 # Create the agent with tools
-client = AzureOpenAIChatClient(credential=AzureCliCredential())
+client = AzureOpenAIResponsesClient(
+    project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+    deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+    credential=AzureCliCredential(),
+)
 menu_agent = client.as_agent(
    name="MenuAgent",
    instructions="You are a helpful restaurant menu assistant...",
@@ -92,6 +92,10 @@ from agent_framework.orchestrations import (

 These may appear in event streams (executor_invoked/executor_completed). They're analogous to concurrent's dispatcher and aggregator and can be ignored if you only care about agent activity.

+## Why AzureOpenAIResponsesClient?
+
+Orchestration samples use `AzureOpenAIResponsesClient` rather than the CRUD-style `AzureAIAgent` client. Orchestrations create agents locally and do not require server-side lifecycle management (create/update/delete). `AzureOpenAIResponsesClient` is a lightweight client that uses the underlying Agent Service V2 (Responses API) for non-CRUD-style agents, which is ideal for orchestration patterns like Sequential, Concurrent, Handoff, GroupChat, and Magentic.
+
 ## Environment Variables

 Orchestration samples that use `AzureOpenAIResponsesClient` expect:
@@ -1,10 +1,11 @@
 # Copyright (c) Microsoft. All rights reserved.

 import asyncio
+import os
 from typing import Any

 from agent_framework import Message
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import ConcurrentBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -26,14 +27,20 @@ Demonstrates:
 - Workflow completion when idle with no pending work

 Prerequisites:
- Azure OpenAI access configured for AzureOpenAIChatClient (use az login + env vars)
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 - Familiarity with Workflow events (WorkflowEvent)
 """


 async def main() -> None:
-    # 1) Create three domain agents using AzureOpenAIChatClient
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    # 1) Create three domain agents using AzureOpenAIResponsesClient
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )

    researcher = client.as_agent(
        instructions=(
@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.

 import asyncio
+import os
 from typing import Any

 from agent_framework import (
@@ -12,7 +13,7 @@ from agent_framework import (
    WorkflowContext,
    handler,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import ConcurrentBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -29,21 +30,23 @@ and emit AgentExecutorResponse outputs, which allows reuse of the high-level
 ConcurrentBuilder API and the default aggregator.

 Demonstrates:
- Executors that create their Agent in __init__ (via AzureOpenAIChatClient)
+- Executors that create their Agent in __init__ (via AzureOpenAIResponsesClient)
 - A @handler that converts AgentExecutorRequest -> AgentExecutorResponse
 - ConcurrentBuilder(participants=[...]) to build fan-out/fan-in
 - Default aggregator returning list[Message] (one user + one assistant per agent)
 - Workflow completion when all participants become idle

 Prerequisites:
- Azure OpenAI configured for AzureOpenAIChatClient (az login + required env vars)
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """


 class ResearcherExec(Executor):
    agent: Agent

-    def __init__(self, client: AzureOpenAIChatClient, id: str = "researcher"):
+    def __init__(self, client: AzureOpenAIResponsesClient, id: str = "researcher"):
        self.agent = client.as_agent(
            instructions=(
                "You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
@@ -63,7 +66,7 @@ class ResearcherExec(Executor):
 class MarketerExec(Executor):
    agent: Agent

-    def __init__(self, client: AzureOpenAIChatClient, id: str = "marketer"):
+    def __init__(self, client: AzureOpenAIResponsesClient, id: str = "marketer"):
        self.agent = client.as_agent(
            instructions=(
                "You're a creative marketing strategist. Craft compelling value propositions and target messaging"
@@ -83,7 +86,7 @@ class MarketerExec(Executor):
 class LegalExec(Executor):
    agent: Agent

-    def __init__(self, client: AzureOpenAIChatClient, id: str = "legal"):
+    def __init__(self, client: AzureOpenAIResponsesClient, id: str = "legal"):
        self.agent = client.as_agent(
            instructions=(
                "You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
@@ -101,7 +104,11 @@ class LegalExec(Executor):


 async def main() -> None:
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )

    researcher = ResearcherExec(client)
    marketer = MarketerExec(client)
@@ -1,10 +1,11 @@
 # Copyright (c) Microsoft. All rights reserved.

 import asyncio
+import os
 from typing import Any

 from agent_framework import Message
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import ConcurrentBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -17,7 +18,7 @@ Sample: Concurrent Orchestration with Custom Aggregator

 Build a concurrent workflow with ConcurrentBuilder that fans out one prompt to
 multiple domain agents and fans in their responses. Override the default
-aggregator with a custom async callback that uses AzureOpenAIChatClient.get_response()
+aggregator with a custom async callback that uses AzureOpenAIResponsesClient.get_response()
 to synthesize a concise, consolidated summary from the experts' outputs.
 The workflow completes when all participants become idle.

@@ -28,12 +29,18 @@ Demonstrates:
 - Workflow output yielded with the synthesized summary string

 Prerequisites:
- Azure OpenAI configured for AzureOpenAIChatClient (az login + required env vars)
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """


 async def main() -> None:
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )

    researcher = client.as_agent(
        instructions=(
@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.

 import asyncio
+import os
 from typing import cast

 from agent_framework import (
@@ -8,7 +9,7 @@ from agent_framework import (
    AgentResponseUpdate,
    Message,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import GroupChatBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -25,7 +26,9 @@ What it does:
 - Coordinates a researcher and writer agent to solve tasks collaboratively

 Prerequisites:
- OpenAI environment variables configured for OpenAIChatClient
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """

 ORCHESTRATOR_AGENT_INSTRUCTIONS = """
@@ -39,8 +42,12 @@ Guidelines:


 async def main() -> None:
-    # Create a chat client using Azure OpenAI and Azure CLI credentials for all agents
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    # Create a Responses client using Azure OpenAI and Azure CLI credentials for all agents
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )

    # Orchestrator agent that manages the conversation
    # Note: This agent (and the underlying chat client) must support structured outputs.
@@ -2,6 +2,7 @@

 import asyncio
 import logging
+import os
 from typing import cast

 from agent_framework import (
@@ -9,7 +10,7 @@ from agent_framework import (
    AgentResponseUpdate,
    Message,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import GroupChatBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -38,15 +39,21 @@ Participants represent:
 - Doctor from Scandinavia (public health, equity, societal support)

 Prerequisites:
- OpenAI environment variables configured for OpenAIChatClient
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """

 # Load environment variables from .env file
 load_dotenv()


-def _get_chat_client() -> AzureOpenAIChatClient:
-    return AzureOpenAIChatClient(credential=AzureCliCredential())
+def _get_chat_client() -> AzureOpenAIResponsesClient:
+    return AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )


 async def main() -> None:
@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.

 import asyncio
+import os
 from typing import cast

 from agent_framework import (
@@ -8,7 +9,7 @@ from agent_framework import (
    AgentResponseUpdate,
    Message,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import GroupChatBuilder, GroupChatState
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -24,7 +25,9 @@ What it does:
 - Uses a pure Python function to control speaker selection based on conversation state

 Prerequisites:
- OpenAI environment variables configured for OpenAIChatClient
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """


@@ -36,8 +39,12 @@ def round_robin_selector(state: GroupChatState) -> str:


 async def main() -> None:
-    # Create a chat client using Azure OpenAI and Azure CLI credentials for all agents
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    # Create a Responses client using Azure OpenAI and Azure CLI credentials for all agents
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )

    # Participant agents
    expert = Agent(
@@ -2,6 +2,7 @@

 import asyncio
 import logging
+import os
 from typing import cast

 from agent_framework import (
@@ -10,7 +11,7 @@ from agent_framework import (
    Message,
    resolve_agent_id,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import HandoffBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -28,8 +29,9 @@ Routing Pattern:
    User -> Coordinator -> Specialist (iterates N times) -> Handoff -> Final Output

 Prerequisites:
-    - `az login` (Azure CLI authentication)
-    - Environment variables for AzureOpenAIChatClient (AZURE_OPENAI_ENDPOINT, etc.)
+    - AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+    - Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+    - Authentication via azure-identity. Use AzureCliCredential and run `az login` before executing the sample.

 Key Concepts:
    - Autonomous interaction mode: agents iterate until they handoff
@@ -41,7 +43,7 @@ load_dotenv()


 def create_agents(
-    client: AzureOpenAIChatClient,
+    client: AzureOpenAIResponsesClient,
 ) -> tuple[Agent, Agent, Agent]:
    """Create coordinator and specialists for autonomous iteration."""
    coordinator = client.as_agent(
@@ -77,7 +79,11 @@ def create_agents(

 async def main() -> None:
    """Run an autonomous handoff workflow with specialist iteration enabled."""
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
    coordinator, research_agent, summary_agent = create_agents(client)

    # Build the workflow with autonomous mode
@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.

 import asyncio
+import os
 from typing import Annotated, cast

 from agent_framework import (
@@ -11,7 +12,7 @@ from agent_framework import (
    WorkflowRunState,
    tool,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import HandoffAgentUserRequest, HandoffBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -25,8 +26,9 @@ A handoff workflow defines a pattern that assembles agents in a mesh topology, a
 them to transfer control to each other based on the conversation context.

 Prerequisites:
-    - `az login` (Azure CLI authentication)
-    - Environment variables configured for AzureOpenAIChatClient (AZURE_OPENAI_ENDPOINT, etc.)
+    - AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+    - Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+    - Authentication via azure-identity. Use AzureCliCredential and run `az login` before executing the sample.

 Key Concepts:
    - Auto-registered handoff tools: HandoffBuilder automatically creates handoff tools
@@ -58,11 +60,11 @@ def process_return(order_number: Annotated[str, "Order number to process return
    return f"Return initiated successfully for order {order_number}. You will receive return instructions via email."


-def create_agents(client: AzureOpenAIChatClient) -> tuple[Agent, Agent, Agent, Agent]:
+def create_agents(client: AzureOpenAIResponsesClient) -> tuple[Agent, Agent, Agent, Agent]:
    """Create and configure the triage and specialist agents.

    Args:
-        client: The AzureOpenAIChatClient to use for creating agents.
+        client: The AzureOpenAIResponsesClient to use for creating agents.

    Returns:
        Tuple of (triage_agent, refund_agent, order_agent, return_agent)
@@ -192,8 +194,12 @@ async def main() -> None:
    the demo reproducible and testable. In a production application, you would
    replace the scripted_responses with actual user input collection.
    """
-    # Initialize the Azure OpenAI chat client
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    # Initialize the Azure OpenAI Responses client
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )

    # Create all agents: triage + specialists
    triage, refund, order, support = create_agents(client)
@@ -3,6 +3,7 @@
 import asyncio
 import json
 import logging
+import os
 from typing import cast

 from agent_framework import (
@@ -11,8 +12,9 @@ from agent_framework import (
    Message,
    WorkflowEvent,
 )
-from agent_framework.openai import OpenAIChatClient, OpenAIResponsesClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import GroupChatRequestSentEvent, MagenticBuilder, MagenticProgressLedger
+from azure.identity import AzureCliCredential
 from dotenv import load_dotenv

 logging.basicConfig(level=logging.WARNING)
@@ -40,7 +42,9 @@ energy efficiency and CO2 emissions of several ML models, streams intermediate
 events, and prints the final answer. The workflow completes when idle.

 Prerequisites:
- OpenAI credentials configured for `OpenAIChatClient` and `OpenAIResponsesClient`.
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """

 # Load environment variables from .env file
@@ -48,25 +52,29 @@ load_dotenv()


 async def main() -> None:
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
+
    researcher_agent = Agent(
        name="ResearcherAgent",
        description="Specialist in research and information gathering",
        instructions=(
            "You are a Researcher. You find information without additional computation or quantitative analysis."
        ),
-        # This agent requires the gpt-4o-search-preview model to perform web searches.
-        client=OpenAIChatClient(model_id="gpt-4o-search-preview"),
+        client=client,
    )

    # Create code interpreter tool using instance method
-    coder_client = OpenAIResponsesClient()
-    code_interpreter_tool = coder_client.get_code_interpreter_tool()
+    code_interpreter_tool = client.get_code_interpreter_tool()

    coder_agent = Agent(
        name="CoderAgent",
        description="A helpful assistant that writes and executes code to process and analyze data.",
        instructions="You solve questions using code. Please provide detailed analysis and computation process.",
-        client=coder_client,
+        client=client,
        tools=code_interpreter_tool,
    )

@@ -75,7 +83,7 @@ async def main() -> None:
        name="MagenticManager",
        description="Orchestrator that coordinates the research and coding workflow",
        instructions="You coordinate a team to complete complex tasks efficiently.",
-        client=OpenAIChatClient(),
+        client=client,
    )

    print("\nBuilding Magentic Workflow...")
@@ -2,6 +2,7 @@

 import asyncio
 import json
+import os
 from datetime import datetime
 from pathlib import Path
 from typing import cast
@@ -14,9 +15,9 @@ from agent_framework import (
    WorkflowEvent,
    WorkflowRunState,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import MagenticBuilder, MagenticPlanReviewRequest
-from azure.identity._credentials import AzureCliCredential
+from azure.identity import AzureCliCredential
 from dotenv import load_dotenv

 # Load environment variables from .env file
@@ -38,7 +39,9 @@ Concepts highlighted here:
   `responses` mapping so we can inject the stored human reply during restoration.

 Prerequisites:
- OpenAI environment variables configured for `OpenAIChatClient`.
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """

 TASK = (
@@ -61,14 +64,22 @@ def build_workflow(checkpoint_storage: FileCheckpointStorage):
        name="ResearcherAgent",
        description="Collects background facts and references for the project.",
        instructions=("You are the research lead. Gather crisp bullet points the team should know."),
-        client=AzureOpenAIChatClient(credential=AzureCliCredential()),
+        client=AzureOpenAIResponsesClient(
+            project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+            deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            credential=AzureCliCredential(),
+        ),
    )

    writer = Agent(
        name="WriterAgent",
        description="Synthesizes the final brief for stakeholders.",
        instructions=("You convert the research notes into a structured brief with milestones and risks."),
-        client=AzureOpenAIChatClient(credential=AzureCliCredential()),
+        client=AzureOpenAIResponsesClient(
+            project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+            deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            credential=AzureCliCredential(),
+        ),
    )

    # Create a manager agent for orchestration
@@ -76,7 +87,11 @@ def build_workflow(checkpoint_storage: FileCheckpointStorage):
        name="MagenticManager",
        description="Orchestrator that coordinates the research and writing workflow",
        instructions="You coordinate a team to complete complex tasks efficiently.",
-        client=AzureOpenAIChatClient(credential=AzureCliCredential()),
+        client=AzureOpenAIResponsesClient(
+            project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+            deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            credential=AzureCliCredential(),
+        ),
    )

    # The builder wires in the Magentic orchestrator, sets the plan review path, and
@@ -2,6 +2,7 @@

 import asyncio
 import json
+import os
 from collections.abc import AsyncIterable
 from typing import cast

@@ -11,8 +12,9 @@ from agent_framework import (
    Message,
    WorkflowEvent,
 )
-from agent_framework.openai import OpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import MagenticBuilder, MagenticPlanReviewRequest, MagenticPlanReviewResponse
+from azure.identity import AzureCliCredential
 from dotenv import load_dotenv

 # Load environment variables from .env file
@@ -35,7 +37,9 @@ Plan review options:
 - revise(feedback): Provide textual feedback to modify the plan

 Prerequisites:
- OpenAI credentials configured for `OpenAIChatClient`.
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """

 # Keep track of the last response to format output nicely in streaming mode
@@ -96,25 +100,31 @@ async def process_event_stream(stream: AsyncIterable[WorkflowEvent]) -> dict[str


 async def main() -> None:
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
+
    researcher_agent = Agent(
        name="ResearcherAgent",
        description="Specialist in research and information gathering",
        instructions="You are a Researcher. You find information and gather facts.",
-        client=OpenAIChatClient(model_id="gpt-4o"),
+        client=client,
    )

    analyst_agent = Agent(
        name="AnalystAgent",
        description="Data analyst who processes and summarizes research findings",
        instructions="You are an Analyst. You analyze findings and create summaries.",
-        client=OpenAIChatClient(model_id="gpt-4o"),
+        client=client,
    )

    manager_agent = Agent(
        name="MagenticManager",
        description="Orchestrator that coordinates the workflow",
        instructions="You coordinate a team to complete tasks efficiently.",
-        client=OpenAIChatClient(model_id="gpt-4o"),
+        client=client,
    )

    print("\nBuilding Magentic Workflow with Human Plan Review...")
@@ -1,10 +1,11 @@
 # Copyright (c) Microsoft. All rights reserved.

 import asyncio
+import os
 from typing import cast

 from agent_framework import Message
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import SequentialBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -28,13 +29,19 @@ Note on internal adapters:
  You can safely ignore them when focusing on agent progress.

 Prerequisites:
- Azure OpenAI access configured for AzureOpenAIChatClient (use az login + env vars)
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """


 async def main() -> None:
    # 1) Create agents
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )

    writer = client.as_agent(
        instructions=("You are a concise copywriter. Provide a single, punchy marketing sentence based on the prompt."),
@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.

 import asyncio
+import os
 from typing import Any

 from agent_framework import (
@@ -10,7 +11,7 @@ from agent_framework import (
    WorkflowContext,
    handler,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import SequentialBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -32,7 +33,9 @@ Custom executor contract:
 - Emit the updated conversation via ctx.send_message([...])

 Prerequisites:
- Azure OpenAI access configured for AzureOpenAIChatClient (use az login + env vars)
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """


@@ -62,7 +65,11 @@ class Summarizer(Executor):

 async def main() -> None:
    # 1) Create a content agent
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
    content = client.as_agent(
        instructions="Produce a concise paragraph answering the user's request.",
        name="content",
@@ -25,7 +25,7 @@ Make sure to set the following environment variables before running the example:
 For quick testing and demonstration, you can use the pre-built .NET A2A servers from this repository:

 **Quick Testing Reference**: Use the .NET A2A Client Server sample at:
-`..\agent-framework\dotnet\samples\A2AClientServer`
+`..\agent-framework\dotnet\samples\05-end-to-end\A2AClientServer`

 ### Run Python A2A Sample
 ```powershell
@@ -21,7 +21,7 @@ Start with `01-get-started/` and work through the numbered files:
 3. **[03_multi_turn.py](./01-get-started/03_multi_turn.py)** — Multi-turn conversations with `AgentThread`
 4. **[04_memory.py](./01-get-started/04_memory.py)** — Agent memory with `ContextProvider`
 5. **[05_first_workflow.py](./01-get-started/05_first_workflow.py)** — Build a workflow with executors and edges
-6. **[06_host_your_agent.py](./01-get-started/06_host_your_agent.py)** — Host your agent via A2A
+6. **[06_host_your_agent.py](./01-get-started/06_host_your_agent.py)** — Host your agent via Azure Functions

 ## Prerequisites