Python: restructure: Python samples into progressive 01-05 layout (#3862)

* restructure: Python samples into progressive 01-05 layout

- 01-get-started/: 6 numbered steps (hello agent → hosting)
- 02-agents/: all agent concept samples (tools, middleware, providers, etc.)
- 03-workflows/: ALL existing workflow samples preserved as-is
- 04-hosting/: azure-functions, durabletask, a2a
- 05-end-to-end/: demos, evaluation, hosted agents
- Old files moved to _to_delete/ for review
- Added AGENTS.md with structure documentation
- autogen-migration/ and semantic-kernel-migration/ preserved at root

* fix: switch to AzureOpenAI Foundry, fix CI failures

- Switch all 01-get-started samples to AzureOpenAIResponsesClient with
  Azure AI Foundry project endpoint (AZURE_AI_PROJECT_ENDPOINT +
  AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME + AzureCliCredential)
- Add _to_delete/ and 05-end-to-end/ to pyrightconfig.samples.json excludes
- Fix test paths in packages/ that referenced old getting_started/ dirs:
  durabletask conftest + streaming test, azurefunctions conftest,
  devui conftest + capture_messages + openai_sdk_integration
- Fix workflow_as_agent_human_in_the_loop.py import (sibling import)
- Update hosting READMEs and tool comment paths
- Replace root README.md with new structure overview
- Update AGENTS.md to document Azure OpenAI Foundry as default provider

* cleanup: remove _to_delete folder, copy resource files to active dirs

All files in _to_delete/ were either:
- Exact duplicates of files in the new structure (240 files)
- Same file with only comment path updates (100 files)
- One import-fix diff (workflow_as_agent_human_in_the_loop.py)
- One superseded minimal_sample.py

Resource files (sample.pdf, countries.json, employees.pdf, weather.json)
copied to 02-agents/sample_assets/ and 02-agents/resources/ since active
samples reference them.

* fix: address PR review comments, centralize resources, remove root duplicates

- Fix type annotation in 04_memory.py (string union -> proper types)
- Fix old sample paths in observability files
- Fix grammar/spelling in observability samples
- Move sample_assets/ and resources/ to shared/ folder
- Remove 8 duplicate observability files from 02-agents root
- Update resource path references in multimodal_input and provider samples

* fix: update broken links from old getting_started paths to new structure

- Update relative paths in READMEs: getting_started/ → 01-get-started/,
  02-agents/, 03-workflows/, 04-hosting/, 05-end-to-end/
- Fix absolute GitHub URLs in package READMEs
- Fix broken link in ollama package README

* fix: convert absolute GitHub URLs to relative paths for link checker

Absolute URLs to python/samples/ on main branch 404 until PR merges.
Converted to relative paths that linkspector can verify locally.

* fix: update link for handoff sample moved to orchestrations/

* fix: update chatkit-integration README path from demos/ to 05-end-to-end/

* fix: update broken links in orchestrations README to match flat directory structure

python/samples/03-workflows/declarative/human_in_loop/README.md

@@ -0,0 +1,59 @@
+# Human-in-Loop Workflow Sample
+
+This sample demonstrates how to build interactive workflows that request user input during execution using the `Question`, `RequestExternalInput`, and `WaitForInput` actions.
+
+## What This Sample Shows
+
+- Using `Question` to prompt for user responses
+- Using `RequestExternalInput` to request external data
+- Using `WaitForInput` to pause and wait for input
+- Processing user responses to drive workflow decisions
+- Interactive conversation patterns
+
+## Files
+
+- `workflow.yaml` - The declarative workflow definition
+- `main.py` - Python script that loads and runs the workflow with simulated user interaction
+
+## Running the Sample
+
+1. Ensure you have the package installed:
+   ```bash
+   cd python
+   pip install -e packages/agent-framework-declarative
+   ```
+
+2. Run the sample:
+   ```bash
+   python main.py
+   ```
+
+## How It Works
+
+The workflow demonstrates a simple survey/questionnaire pattern:
+
+1. **Greeting**: Sends a welcome message
+2. **Question 1**: Asks for the user's name
+3. **Question 2**: Asks how they're feeling today
+4. **Processing**: Stores responses and provides personalized feedback
+5. **Summary**: Summarizes the collected information
+
+The `main.py` script shows how to handle `ExternalInputRequest` to provide responses during workflow execution.
+
+## Key Concepts
+
+### ExternalInputRequest
+
+When a human-in-loop action is executed, the workflow yields an `ExternalInputRequest` containing:
+- `variable`: The variable path where the response should be stored
+- `prompt`: The question or prompt text for the user
+
+The workflow runner should:
+1. Detect `ExternalInputRequest` in the event stream
+2. Display the prompt to the user
+3. Collect the response
+4. Resume the workflow (in a real implementation, using external loop patterns)
+
+### ExternalLoopEvent
+
+For more complex scenarios where external processing is needed, the workflow can yield an `ExternalLoopEvent` that signals the runner to pause and wait for external input.

python/samples/03-workflows/declarative/human_in_loop/main.py

@@ -0,0 +1,84 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""
+Run the human-in-loop workflow sample.
+
+Usage:
+    python main.py
+
+Demonstrates interactive workflows that request user input.
+
+Note: This sample shows the conceptual pattern for handling ExternalInputRequest.
+In a production scenario, you would integrate with a real UI or chat interface.
+"""
+
+import asyncio
+from pathlib import Path
+
+from agent_framework import Workflow
+from agent_framework.declarative import ExternalInputRequest, WorkflowFactory
+from agent_framework_declarative._workflows._handlers import TextOutputEvent
+
+
+async def run_with_streaming(workflow: Workflow) -> None:
+    """Demonstrate streaming workflow execution."""
+    print("\n=== Streaming Execution ===")
+    print("-" * 40)
+
+    async for event in workflow.run({}, stream=True):
+        # WorkflowOutputEvent wraps the actual output data
+        if event.type == "output":
+            data = event.data
+            if isinstance(data, TextOutputEvent):
+                print(f"[Bot]: {data.text}")
+            elif isinstance(data, ExternalInputRequest):
+                # In a real scenario, you would:
+                # 1. Display the prompt to the user
+                # 2. Wait for their response
+                # 3. Use the response to continue the workflow
+                output_property = data.metadata.get("output_property", "unknown")
+                print(f"[System] Input requested for: {output_property}")
+                if data.message:
+                    print(f"[System] Prompt: {data.message}")
+            else:
+                print(f"[Output]: {data}")
+
+
+async def run_with_result(workflow: Workflow) -> None:
+    """Demonstrate batch workflow execution with run()."""
+    print("\n=== Batch Execution (run) ===")
+    print("-" * 40)
+
+    result = await workflow.run({})
+    for output in result.get_outputs():
+        print(f"  Output: {output}")
+
+
+async def main() -> None:
+    """Run the human-in-loop workflow demonstrating both execution styles."""
+    # Create a workflow factory
+    factory = WorkflowFactory()
+
+    # Load the workflow from YAML
+    workflow_path = Path(__file__).parent / "workflow.yaml"
+    workflow = factory.create_workflow_from_yaml_path(workflow_path)
+
+    print(f"Loaded workflow: {workflow.name}")
+    print("=== Human-in-Loop Workflow Demo ===")
+    print("(Using simulated responses for demonstration)")
+
+    # Demonstrate streaming execution
+    await run_with_streaming(workflow)
+
+    # Demonstrate batch execution
+    # await run_with_result(workflow)
+
+    print("\n" + "-" * 40)
+    print("=== Workflow Complete ===")
+    print()
+    print("Note: This demo uses simulated responses. In a real application,")
+    print("you would integrate with a chat interface to collect actual user input.")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/03-workflows/declarative/human_in_loop/workflow.yaml

@@ -0,0 +1,75 @@
+name: human-in-loop-workflow
+description: Interactive workflow that requests user input
+
+actions:
+  # Welcome message
+  - kind: SendActivity
+    id: greeting
+    displayName: Send greeting
+    activity:
+      text: "Welcome to the interactive survey!"
+
+  # Ask for name
+  - kind: Question
+    id: ask_name
+    displayName: Ask for user name
+    question:
+      text: "What is your name?"
+    variable: Local.userName
+    default: "Demo User"
+
+  # Personalized greeting
+  - kind: SendActivity
+    id: personalized_greeting
+    displayName: Send personalized greeting
+    activity:
+      text: =Concat("Nice to meet you, ", Local.userName, "!")
+
+  # Ask how they're feeling
+  - kind: Question
+    id: ask_feeling
+    displayName: Ask about feelings
+    question:
+      text: "How are you feeling today? (great/good/okay/not great)"
+    variable: Local.feeling
+    default: "great"
+
+  # Respond based on feeling
+  - kind: If
+    id: check_feeling
+    displayName: Check user feeling
+    condition: =Or(Local.feeling = "great", Local.feeling = "good")
+    then:
+      - kind: SendActivity
+        activity:
+          text: "That's wonderful to hear! Let's continue."
+    else:
+      - kind: SendActivity
+        activity:
+          text: "I hope things get better! Let me know if there's anything I can help with."
+
+  # Ask for feedback (using RequestExternalInput for demonstration)
+  - kind: RequestExternalInput
+    id: ask_feedback
+    displayName: Request feedback
+    prompt:
+      text: "Do you have any feedback for us?"
+    variable: Local.feedback
+    default: "This workflow is great!"
+
+  # Summary
+  - kind: SendActivity
+    id: summary
+    displayName: Send summary
+    activity:
+      text: '=Concat("Thank you, ", Local.userName, "! Your feedback: ", Local.feedback)'
+
+  # Store results
+  - kind: SetValue
+    id: store_results
+    displayName: Store survey results
+    path: Workflow.Outputs.survey
+    value:
+      name: =Local.userName
+      feeling: =Local.feeling
+      feedback: =Local.feedback