Python: DevUI improvements. (#1091)

* enable deeplinking in ui, add agent details to entity info, add usage data, add middleware example in samples and foundry agent.

* update ui build

* Update python/packages/devui/frontend/src/components/workflow/workflow-input-form.tsx

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Update python/packages/devui/pyproject.toml

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Update python/packages/devui/pyproject.toml

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* imporove mapping for agent nodes and serialiation for agent run events

* lint fixes

* update pyproj toml and ui updates

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

python/packages/devui/samples/weather_agent_azure/agent.py

@@ -2,12 +2,73 @@
 """Sample weather agent for Agent Framework Debug UI."""
 
 import os
+from collections.abc import Awaitable, Callable
 from typing import Annotated
 
-from agent_framework import ChatAgent
+from agent_framework import (
+    ChatAgent,
+    ChatContext,
+    ChatMessage,
+    ChatResponse,
+    FunctionInvocationContext,
+    Role,
+    chat_middleware,
+    function_middleware,
+)
 from agent_framework.azure import AzureOpenAIChatClient
 
 
+@chat_middleware
+async def security_filter_middleware(
+    context: ChatContext,
+    next: Callable[[ChatContext], Awaitable[None]],
+) -> None:
+    """Chat middleware that blocks requests containing sensitive information."""
+    # Block requests with sensitive information
+    blocked_terms = ["password", "secret", "api_key", "token"]
+
+    for message in context.messages:
+        if message.text:
+            message_lower = message.text.lower()
+            for term in blocked_terms:
+                if term in message_lower:
+                    # Override the response without calling the LLM
+                    context.result = ChatResponse(
+                        messages=[
+                            ChatMessage(
+                                role=Role.ASSISTANT,
+                                text=(
+                                    "I cannot process requests containing sensitive information. "
+                                    "Please rephrase your question without including passwords, secrets, "
+                                    "or other sensitive data."
+                                ),
+                            )
+                        ]
+                    )
+                    return
+
+    await next(context)
+
+
+@function_middleware
+async def atlantis_location_filter_middleware(
+    context: FunctionInvocationContext,
+    next: Callable[[FunctionInvocationContext], Awaitable[None]],
+) -> None:
+    """Function middleware that blocks weather requests for Atlantis."""
+    # Check if location parameter is "atlantis"
+    location = getattr(context.arguments, "location", None)
+    if location and location.lower() == "atlantis":
+        context.result = (
+            "Blocked! Hold up right there!! Tell the user that "
+            "'Atlantis is a special place, we must never ask about the weather there!!'"
+        )
+        context.terminate = True
+        return
+
+    await next(context)
+
+
 def get_weather(
     location: Annotated[str, "The location to get the weather for."],
 ) -> str:
@@ -46,6 +107,7 @@ agent = ChatAgent(
         api_key=os.environ.get("AZURE_OPENAI_API_KEY", ""),
     ),
     tools=[get_weather, get_forecast],
+    middleware=[security_filter_middleware, atlantis_location_filter_middleware],
 )
 
 

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

@@ -0,0 +1,167 @@
+# 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()
+    .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()