fix and unify devui samples (#5025)

python/samples/02-agents/devui/workflow_with_agents/.env.example

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+# Azure OpenAI configuration for the Responses-based workflow sample
+# This sample uses Azure CLI auth, so run `az login` before starting DevUI.
+
+AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
+AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=gpt-4o
+# Optional fallback env name also supported by the client:
+# AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o
+# Optional if you need to override the default API version:
+AZURE_OPENAI_API_VERSION=2024-10-21

python/samples/02-agents/devui/workflow_with_agents/__init__.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+"""Sequential Agents Workflow - Writer → Reviewer."""
+
+from .workflow import workflow
+
+__all__ = ["workflow"]

python/samples/02-agents/devui/workflow_with_agents/workflow.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+"""Agent Workflow - Content Review with Quality Routing.
+
+This sample demonstrates:
+- Using agents directly as executors
+- Conditional routing based on structured outputs
+- Quality-based workflow paths with convergence
+
+Use case: Content creation with automated review.
+Writer creates content, Reviewer evaluates quality:
+  - High quality (score >= 80): → Publisher → Summarizer
+  - Low quality (score < 80): → Editor → Publisher → Summarizer
+Both paths converge at Summarizer for final report.
+"""
+
+import os
+from typing import Any
+
+from agent_framework import Agent, AgentExecutorResponse, WorkflowBuilder
+from agent_framework.openai import OpenAIChatClient
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+from pydantic import BaseModel
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+# Define structured output for review results
+class ReviewResult(BaseModel):
+    """Review evaluation with scores and feedback."""
+
+    score: int  # Overall quality score (0-100)
+    feedback: str  # Concise, actionable feedback
+    clarity: int  # Clarity score (0-100)
+    completeness: int  # Completeness score (0-100)
+    accuracy: int  # Accuracy score (0-100)
+    structure: int  # Structure score (0-100)
+
+
+# Condition function: route to editor if score < 80
+def needs_editing(message: Any) -> bool:
+    """Check if content needs editing based on review score."""
+    if not isinstance(message, AgentExecutorResponse):
+        return False
+    try:
+        review = ReviewResult.model_validate_json(message.agent_response.text)
+        return review.score < 80
+    except Exception:
+        return False
+
+
+# Condition function: content is approved (score >= 80)
+def is_approved(message: Any) -> bool:
+    """Check if content is approved (high quality)."""
+    if not isinstance(message, AgentExecutorResponse):
+        return True
+    try:
+        review = ReviewResult.model_validate_json(message.agent_response.text)
+        return review.score >= 80
+    except Exception:
+        return True
+
+
+# Create Azure OpenAI Responses chat client
+client = OpenAIChatClient(
+    model=os.environ.get("AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME") or os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME"),
+    azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
+    api_version=os.environ.get("AZURE_OPENAI_API_VERSION"),
+    credential=AzureCliCredential(),
+)
+
+# Create Writer agent - generates content
+writer = Agent(
+    client=client,
+    name="Writer",
+    instructions=(
+        "You are an excellent content writer. "
+        "Create clear, engaging content based on the user's request. "
+        "Focus on clarity, accuracy, and proper structure."
+    ),
+)
+
+# Create Reviewer agent - evaluates and provides structured feedback
+reviewer = Agent(
+    client=client,
+    name="Reviewer",
+    instructions=(
+        "You are an expert content reviewer. "
+        "Evaluate the writer's content based on:\n"
+        "1. Clarity - Is it easy to understand?\n"
+        "2. Completeness - Does it fully address the topic?\n"
+        "3. Accuracy - Is the information correct?\n"
+        "4. Structure - Is it well-organized?\n\n"
+        "Return a JSON object with:\n"
+        "- score: overall quality (0-100)\n"
+        "- feedback: concise, actionable feedback\n"
+        "- clarity, completeness, accuracy, structure: individual scores (0-100)"
+    ),
+    default_options={"response_format": ReviewResult},
+)
+
+# Create Editor agent - improves content based on feedback
+editor = Agent(
+    client=client,
+    name="Editor",
+    instructions=(
+        "You are a skilled editor. "
+        "You will receive content along with review feedback. "
+        "Improve the content by addressing all the issues mentioned in the feedback. "
+        "Maintain the original intent while enhancing clarity, completeness, accuracy, and structure."
+    ),
+)
+
+# Create Publisher agent - formats content for publication
+publisher = Agent(
+    client=client,
+    name="Publisher",
+    instructions=(
+        "You are a publishing agent. "
+        "You receive either approved content or edited content. "
+        "Format it for publication with proper headings and structure."
+    ),
+)
+
+# Create Summarizer agent - creates final publication report
+summarizer = Agent(
+    client=client,
+    name="Summarizer",
+    instructions=(
+        "You are a summarizer agent. "
+        "Create a final publication report that includes:\n"
+        "1. A brief summary of the published content\n"
+        "2. The workflow path taken (direct approval or edited)\n"
+        "3. Key highlights and takeaways\n"
+        "Keep it concise and professional."
+    ),
+)
+
+# Build workflow with branching and convergence:
+# Writer → Reviewer → [branches]:
+#   - If score >= 80: → Publisher → Summarizer (direct approval path)
+#   - If score < 80: → Editor → Publisher → Summarizer (improvement path)
+# Both paths converge at Summarizer for final report
+workflow = (
+    WorkflowBuilder(
+        name="Content Review Workflow",
+        description="Multi-agent content creation workflow with quality-based routing (Writer → Reviewer → Editor/Publisher)",
+        start_executor=writer,
+    )
+    .add_edge(writer, reviewer)
+    # Branch 1: High quality (>= 80) goes directly to publisher
+    .add_edge(reviewer, publisher, condition=is_approved)
+    # Branch 2: Low quality (< 80) goes to editor first, then publisher
+    .add_edge(reviewer, editor, condition=needs_editing)
+    .add_edge(editor, publisher)
+    # Both paths converge: Publisher → Summarizer
+    .add_edge(publisher, summarizer)
+    .build()
+)
+
+
+def main():
+    """Launch the branching workflow in DevUI."""
+    import logging
+
+    from agent_framework.devui import serve
+
+    logging.basicConfig(level=logging.INFO, format="%(message)s")
+    logger = logging.getLogger(__name__)
+
+    logger.info("Starting Agent Workflow (Content Review with Quality Routing)")
+    logger.info("Available at: http://localhost:8093")
+    logger.info("\nThis workflow demonstrates:")
+    logger.info("- Conditional routing based on structured outputs")
+    logger.info("- Path 1 (score >= 80): Reviewer → Publisher → Summarizer")
+    logger.info("- Path 2 (score < 80): Reviewer → Editor → Publisher → Summarizer")
+    logger.info("- Both paths converge at Summarizer for final report")
+
+    serve(entities=[workflow], port=8093, auto_open=True)
+
+
+if __name__ == "__main__":
+    main()