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- One superseded minimal_sample.py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/visualization/concurrent_with_visualization.py

@@ -0,0 +1,166 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+from dataclasses import dataclass
+
+from agent_framework import (
+    AgentExecutor,
+    AgentExecutorRequest,
+    AgentExecutorResponse,
+    Executor,
+    Message,
+    WorkflowBuilder,
+    WorkflowContext,
+    WorkflowViz,
+    handler,
+)
+from agent_framework.azure import AzureOpenAIResponsesClient
+from azure.identity import AzureCliCredential
+from typing_extensions import Never
+
+"""
+Sample: Concurrent (Fan-out/Fan-in) with Agents + Visualization
+
+What it does:
+- Fan-out: dispatch the same prompt to multiple domain agents (research, marketing, legal).
+- Fan-in: aggregate their responses into one consolidated output.
+- Visualization: generate Mermaid and GraphViz representations via `WorkflowViz` and optionally export SVG.
+
+Prerequisites:
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure AI/ Azure OpenAI for `AzureOpenAIResponsesClient` agents.
+- Authentication via `azure-identity` — uses `AzureCliCredential()` (run `az login`).
+- For visualization export: `pip install graphviz>=0.20.0` and install GraphViz binaries.
+"""
+
+
+class DispatchToExperts(Executor):
+    """Dispatches the incoming prompt to all expert agent executors (fan-out)."""
+
+    @handler
+    async def dispatch(self, prompt: str, ctx: WorkflowContext[AgentExecutorRequest]) -> None:
+        # Wrap the incoming prompt as a user message for each expert and request a response.
+        initial_message = Message("user", text=prompt)
+        await ctx.send_message(AgentExecutorRequest(messages=[initial_message], should_respond=True))
+
+
+@dataclass
+class AggregatedInsights:
+    """Structured output from the aggregator."""
+
+    research: str
+    marketing: str
+    legal: str
+
+
+class AggregateInsights(Executor):
+    """Aggregates expert agent responses into a single consolidated result (fan-in)."""
+
+    @handler
+    async def aggregate(self, results: list[AgentExecutorResponse], ctx: WorkflowContext[Never, str]) -> None:
+        # Map responses to text by executor id for a simple, predictable demo.
+        by_id: dict[str, str] = {}
+        for r in results:
+            # AgentExecutorResponse.agent_response.text contains concatenated assistant text
+            by_id[r.executor_id] = r.agent_response.text
+
+        research_text = by_id.get("researcher", "")
+        marketing_text = by_id.get("marketer", "")
+        legal_text = by_id.get("legal", "")
+
+        aggregated = AggregatedInsights(
+            research=research_text,
+            marketing=marketing_text,
+            legal=legal_text,
+        )
+
+        # Provide a readable, consolidated string as the final workflow result.
+        consolidated = (
+            "Consolidated Insights\n"
+            "====================\n\n"
+            f"Research Findings:\n{aggregated.research}\n\n"
+            f"Marketing Angle:\n{aggregated.marketing}\n\n"
+            f"Legal/Compliance Notes:\n{aggregated.legal}\n"
+        )
+
+        await ctx.yield_output(consolidated)
+
+
+async def main() -> None:
+    """Build and run the concurrent workflow with visualization."""
+
+    # Create agent instances
+    researcher = AgentExecutor(
+        AzureOpenAIResponsesClient(
+            project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+            deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            credential=AzureCliCredential(),
+        ).as_agent(
+            instructions=(
+                "You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
+                " opportunities, and risks."
+            ),
+            name="researcher",
+        )
+    )
+
+    marketer = AgentExecutor(
+        AzureOpenAIResponsesClient(
+            project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+            deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            credential=AzureCliCredential(),
+        ).as_agent(
+            instructions=(
+                "You're a creative marketing strategist. Craft compelling value propositions and target messaging"
+                " aligned to the prompt."
+            ),
+            name="marketer",
+        )
+    )
+
+    legal = AgentExecutor(
+        AzureOpenAIResponsesClient(
+            project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+            deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            credential=AzureCliCredential(),
+        ).as_agent(
+            instructions=(
+                "You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
+                " based on the prompt."
+            ),
+            name="legal",
+        )
+    )
+
+    # Create executor instances
+    dispatcher = DispatchToExperts(id="dispatcher")
+    aggregator = AggregateInsights(id="aggregator")
+
+    # Build a simple fan-out/fan-in workflow
+    workflow = (
+        WorkflowBuilder(start_executor=dispatcher)
+        .add_fan_out_edges(dispatcher, [researcher, marketer, legal])
+        .add_fan_in_edges([researcher, marketer, legal], aggregator)
+        .build()
+    )
+
+    # Generate workflow visualization
+    print("Generating workflow visualization...")
+    viz = WorkflowViz(workflow)
+    # Print out the mermaid string.
+    print("Mermaid string: \n=======")
+    print(viz.to_mermaid())
+    print("=======")
+    # Print out the DiGraph string with internal executors.
+    print("DiGraph string: \n=======")
+    print(viz.to_digraph(include_internal_executors=True))
+    print("=======")
+
+    # Export the DiGraph visualization as SVG.
+    svg_file = viz.export(format="svg")
+    print(f"SVG file saved to: {svg_file}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())