Python: DevUI - Internal Refactor, Conversations API support, and per… (#1235)

* Python: DevUI - Internal Refactor, Conversations API support, and performance improvements

Comprehensive refactor of DevUI package including samples relocation,
frontend reorganization, OpenAI Conversations API support, and critical
performance and code quality improvements.

Key Changes:

Architecture & Organization
- Moved DevUI samples to python/samples/getting_started/devui/
- Consolidated with other framework samples for better discoverability
- Added .env.example files and comprehensive README
- Restructured frontend components into feature-based folders (agent, workflow, gallery, layout)
- Created new OpenAI-compliant message renderers (devui should render oai responses types primarily)

New Features
- Added _conversations.py (467 lines) - Full conversation storage abstraction, replaces the /threads endpoint to better match oai conversations api
- Implements OpenAI Conversations API for thread management, Supports in-memory and extensible storage backends

API Simplification
- Use 'model' field as entity_id (agent/workflow name) instead of extra_body
- Use standard OpenAI 'conversation' field for conversation context.

Performance & Quality Improvements
- Improved context management in MessageMapper with bounded memory (~500KB max)
- Implemented hybrid LRU + cleanup approach to prevent unbounded memory growth
- General QOL improvement - Eliminated ~150 lines of dead/duplicate code, Consolidated helper functions into _utils.py, Extracted magic numbers to module-level constants, Optimized conversation item lookups with index-based approach

Testing
- Added test_conversations.py (13 tests)
- Added test_performance_fixes.py (9 tests)
- Updated existing tests for code consolidation
- 53 tests passing

Impact: 76 files changed: +4,106 insertions, -2,373 deletions
All linting and formatting checks passing. No breaking changes - backward compatible.

Migration: Samples moved to python/samples/getting_started/devui/

* readme lint fixes

* initial support for function approval and minor ui fixes

python/samples/getting_started/devui/workflow_agents/.env.example

@@ -0,0 +1,7 @@
+# Azure OpenAI API Configuration
+# Get your credentials from Azure Portal
+
+AZURE_OPENAI_API_KEY=your-azure-openai-api-key-here
+AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=gpt-4o
+AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
+AZURE_OPENAI_API_VERSION=2024-10-21

python/samples/getting_started/devui/workflow_agents/__init__.py

@@ -0,0 +1,7 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Sequential Agents Workflow - Writer → Reviewer."""
+
+from .workflow import workflow
+
+__all__ = ["workflow"]

python/samples/getting_started/devui/workflow_agents/workflow.py

@@ -0,0 +1,170 @@
+# 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 AgentExecutorResponse, WorkflowBuilder
+from agent_framework.azure import AzureOpenAIChatClient
+from pydantic import BaseModel
+
+
+# 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_run_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_run_response.text)
+        return review.score >= 80
+    except Exception:
+        return True
+
+
+# Create Azure OpenAI chat client
+chat_client = AzureOpenAIChatClient(api_key=os.environ.get("AZURE_OPENAI_API_KEY", ""))
+
+# Create Writer agent - generates content
+writer = chat_client.create_agent(
+    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 = chat_client.create_agent(
+    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)"
+    ),
+    response_format=ReviewResult,
+)
+
+# Create Editor agent - improves content based on feedback
+editor = chat_client.create_agent(
+    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 = chat_client.create_agent(
+    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 = chat_client.create_agent(
+    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)",
+    )
+    .set_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()