Azure-Samples · pamelafox · Mar 3, 2026 · Feb 23, 2026 · Feb 24, 2026 · Feb 25, 2026
diff --git a/AGENTS.md b/AGENTS.md
@@ -37,6 +37,7 @@ Each example .py file should have a corresponding file with the same name under
 * Identifiers (functions/classes/vars): English
 * User-facing output/data (e.g., example responses, sample values): Spanish
 * HITL control words: bilingual (approve/aprobar, exit/salir)
+* Agent and workflow names: English ("TravelPlannerAgent" should be the same in both versions, not "AgentePlanificadorDeViajes")
 
 Use informal (tuteo) LATAM Spanish, tu not usted, puedes not podes, etc. The content is technical so if a word is best kept in English, then do so.
 

diff --git a/README.md b/README.md
@@ -194,6 +194,7 @@ You can run the examples in this repository by executing the scripts in the `exa
 | [agent_with_subagent.py](examples/agent_with_subagent.py) | Context isolation with sub-agents to keep prompts focused on relevant tools. |
 | [agent_without_subagent.py](examples/agent_without_subagent.py) | Context bloat example where one agent carries all tool schemas in a single prompt. |
 | [agent_summarization.py](examples/agent_summarization.py) | Context compaction via summarization middleware to reduce token usage in long conversations. |
+| [workflow_magenticone.py](examples/workflow_magenticone.py) | A MagenticOne multi-agent workflow. |
 | [agent_middleware.py](examples/agent_middleware.py) | Agent, chat, and function middleware for logging, timing, and blocking. |
 | [agent_knowledge_aisearch.py](examples/agent_knowledge_aisearch.py) | Knowledge retrieval (RAG) using Azure AI Search with AgentFrameworkAzureAISearchRAG. |
 | [agent_knowledge_sqlite.py](examples/agent_knowledge_sqlite.py) | Knowledge retrieval (RAG) using a custom context provider with SQLite FTS5. |
@@ -204,15 +205,24 @@ You can run the examples in this repository by executing the scripts in the `exa
 | [agent_mcp_local.py](examples/agent_mcp_local.py) | An agent connected to a local MCP server (e.g. for expense logging). |
 | [openai_tool_calling.py](examples/openai_tool_calling.py) | Tool calling with the low-level OpenAI SDK, showing manual tool dispatch. |
 | [workflow_rag_ingest.py](examples/workflow_rag_ingest.py) | A RAG ingestion pipeline using plain Python executors: fetch a document with markitdown, split into chunks, and embed with an OpenAI model. |
+| [workflow_fan_out_fan_in_edges.py](examples/workflow_fan_out_fan_in_edges.py) | Fan-out/fan-in with explicit edge groups using `add_fan_out_edges` and `add_fan_in_edges`. |
+| [workflow_aggregator_summary.py](examples/workflow_aggregator_summary.py) | Fan-out/fan-in with LLM summarization: synthesize expert outputs into an executive brief. |
+| [workflow_aggregator_structured.py](examples/workflow_aggregator_structured.py) | Fan-out/fan-in with LLM structured extraction into a typed Pydantic model (`response_format`). |
+| [workflow_aggregator_voting.py](examples/workflow_aggregator_voting.py) | Fan-out/fan-in with majority-vote aggregation across multiple classifiers (pure logic tally). |
+| [workflow_aggregator_ranked.py](examples/workflow_aggregator_ranked.py) | Fan-out/fan-in with LLM-as-judge ranking: score and rank multiple candidates into a typed list. |
 | [workflow_agents.py](examples/workflow_agents.py) | A workflow with AI agents as executors: a Writer drafts content and a Reviewer provides feedback. |
 | [workflow_agents_sequential.py](examples/workflow_agents_sequential.py) | A sequential orchestration using `SequentialBuilder`: Writer and Reviewer run in order while sharing full conversation history. |
 | [workflow_agents_streaming.py](examples/workflow_agents_streaming.py) | The same Writer → Reviewer workflow using `run(stream=True)` to observe `executor_invoked`, `executor_completed`, and streaming `output` events in real-time. |
+| [workflow_agents_concurrent.py](examples/workflow_agents_concurrent.py) | Concurrent orchestration using `ConcurrentBuilder`: run specialist agents in parallel and collect merged conversations. |
 | [workflow_conditional.py](examples/workflow_conditional.py) | A minimal workflow with conditional edges: the Reviewer routes to a Publisher (approved) or Editor (needs revision) based on a sentinel token. |
 | [workflow_conditional_structured.py](examples/workflow_conditional_structured.py) | The same conditional-edge routing pattern, but with structured reviewer output (`response_format`) for typed branch decisions instead of sentinel string matching. |
 | [workflow_conditional_state.py](examples/workflow_conditional_state.py) | A stateful conditional workflow with iterative revision loops: stores the latest draft in workflow state and publishes from that state after approval. |
 | [workflow_conditional_state_isolated.py](examples/workflow_conditional_state_isolated.py) | The stateful conditional workflow using a `create_workflow(...)` factory to build fresh agents/workflow per task for state isolation and thread safety. |
 | [workflow_switch_case.py](examples/workflow_switch_case.py) | A workflow with switch-case routing: a Classifier agent uses structured outputs to categorize a message and route to a specialized handler. |
+| [workflow_multi_selection_edge_group.py](examples/workflow_multi_selection_edge_group.py) | LLM-powered multi-selection routing using `add_multi_selection_edge_group` to activate one-or-many downstream handlers. |
 | [workflow_converge.py](examples/workflow_converge.py) | A branch-and-converge workflow: Reviewer routes to Publisher or Editor, then converges before final summary output. |
+| [workflow_handoffbuilder.py](examples/workflow_handoffbuilder.py) | Autonomous handoff orchestration using `HandoffBuilder` (agents transfer control without human-in-the-loop). |
+| [workflow_handoffbuilder_rules.py](examples/workflow_handoffbuilder_rules.py) | Handoff orchestration with explicit routing rules using `HandoffBuilder.add_handoff()`. |
 | [agent_otel_aspire.py](examples/agent_otel_aspire.py) | An agent with OpenTelemetry tracing, metrics, and structured logs exported to the [Aspire Dashboard](https://aspire.dev/dashboard/standalone/). |
 | [agent_otel_appinsights.py](examples/agent_otel_appinsights.py) | An agent with OpenTelemetry tracing, metrics, and structured logs exported to [Azure Application Insights](https://learn.microsoft.com/azure/azure-monitor/app/app-insights-overview). Requires Azure provisioning via `azd provision`. |
 | [agent_evaluation_generate.py](examples/agent_evaluation_generate.py) | Generate synthetic evaluation data for the travel planner agent. |

diff --git a/examples/agent_middleware.py b/examples/agent_middleware.py
@@ -88,7 +88,7 @@
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4o"),
+        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
 else:
     client = OpenAIChatClient(api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4o"))

diff --git a/examples/agent_summarization.py b/examples/agent_summarization.py
@@ -77,7 +77,7 @@
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4o"),
+        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
 else:
     client = OpenAIChatClient(api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4o"))

diff --git a/examples/agent_with_subagent.py b/examples/agent_with_subagent.py
@@ -82,7 +82,7 @@
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4o"),
+        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
 else:
     client = OpenAIChatClient(api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4o"))

diff --git a/examples/agent_without_subagent.py b/examples/agent_without_subagent.py
@@ -70,7 +70,7 @@
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4o"),
+        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
 else:
     client = OpenAIChatClient(api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4o"))

diff --git a/examples/spanish/README.md b/examples/spanish/README.md
@@ -189,6 +189,7 @@ Puedes ejecutar los ejemplos en este repositorio ejecutando los scripts en el di
 | [agent_with_subagent.py](agent_with_subagent.py) | Aislamiento de contexto con subagentes para mantener los prompts enfocados en herramientas relevantes. |
 | [agent_without_subagent.py](agent_without_subagent.py) | Ejemplo de inflado de contexto cuando un solo agente carga todos los esquemas de herramientas en un mismo prompt. |
 | [agent_summarization.py](agent_summarization.py) | Compactación de contexto mediante middleware de resumen para reducir el uso de tokens en conversaciones largas. |
+| [workflow_magenticone.py](workflow_magenticone.py) | Un workflow multi-agente MagenticOne. |
 | [agent_middleware.py](agent_middleware.py) | Middleware de agente, chat y funciones para logging, timing y bloqueo. |
 | [agent_knowledge_aisearch.py](agent_knowledge_aisearch.py) | Recuperación de conocimiento (RAG) usando Azure AI Search con AgentFrameworkAzureAISearchRAG. |
 | [agent_knowledge_sqlite.py](agent_knowledge_sqlite.py) | Recuperación de conocimiento (RAG) usando un proveedor de contexto personalizado con SQLite FTS5. |
@@ -199,15 +200,24 @@ Puedes ejecutar los ejemplos en este repositorio ejecutando los scripts en el di
 | [agent_mcp_local.py](agent_mcp_local.py) | Un agente conectado a un servidor MCP local (p. ej. para registro de gastos). |
 | [openai_tool_calling.py](openai_tool_calling.py) | Llamadas a herramientas con el SDK de OpenAI de bajo nivel, mostrando despacho manual de herramientas. |
 | [workflow_rag_ingest.py](workflow_rag_ingest.py) | Un pipeline de ingesta para RAG con ejecutores Python puros: descarga un documento con markitdown, lo divide en fragmentos y genera embeddings con un modelo de OpenAI. |
+| [workflow_fan_out_fan_in_edges.py](workflow_fan_out_fan_in_edges.py) | Fan-out/fan-in con grupos de aristas explícitos usando `add_fan_out_edges` y `add_fan_in_edges`. |
+| [workflow_aggregator_summary.py](workflow_aggregator_summary.py) | Fan-out/fan-in con resumen por LLM: sintetiza salidas de expertos en un brief ejecutivo. |
+| [workflow_aggregator_structured.py](workflow_aggregator_structured.py) | Fan-out/fan-in con extracción estructurada por LLM en un modelo Pydantic tipado (`response_format`). |
+| [workflow_aggregator_voting.py](workflow_aggregator_voting.py) | Fan-out/fan-in con agregación por voto mayoritario entre clasificadores (conteo de lógica pura). |
+| [workflow_aggregator_ranked.py](workflow_aggregator_ranked.py) | Fan-out/fan-in con LLM como juez: puntúa y ordena múltiples candidatos en una lista tipada. |
 | [workflow_agents.py](workflow_agents.py) | Un workflow con agentes de IA como ejecutores: un Escritor redacta contenido y un Revisor da retroalimentación. |
 | [workflow_agents_sequential.py](workflow_agents_sequential.py) | Una orquestación secuencial usando `SequentialBuilder`: Escritor y Revisor se ejecutan en orden compartiendo todo el historial de la conversación. |
 | [workflow_agents_streaming.py](workflow_agents_streaming.py) | El mismo workflow Escritor → Revisor usando `run(stream=True)` para observar los eventos `executor_invoked`, `executor_completed` y `output` en tiempo real. |
+| [workflow_agents_concurrent.py](workflow_agents_concurrent.py) | Orquestación concurrente usando `ConcurrentBuilder`: ejecuta agentes especialistas en paralelo y junta las conversaciones. |
 | [workflow_conditional.py](workflow_conditional.py) | Un workflow mínimo con aristas condicionales: el Revisor enruta al Publicador (aprobado) o al Editor (necesita revisión) según una señal de texto. |
 | [workflow_conditional_structured.py](workflow_conditional_structured.py) | El mismo patrón de enrutamiento con aristas condicionales, pero usando salida estructurada del revisor (`response_format`) para decisiones tipadas en vez de matching por cadena. |
 | [workflow_conditional_state.py](workflow_conditional_state.py) | Un workflow condicional con estado y bucle iterativo: guarda el último borrador en el estado del workflow y publica desde ese estado tras la aprobación. |
 | [workflow_conditional_state_isolated.py](workflow_conditional_state_isolated.py) | El workflow condicional con estado usando una fábrica `create_workflow(...)` para crear agentes/workflow nuevos por tarea y así aislar estado e hilos de agente. |
 | [workflow_switch_case.py](workflow_switch_case.py) | Un workflow con enrutamiento switch-case: un agente Clasificador usa salidas estructuradas para categorizar un mensaje y enrutarlo al manejador especializado. |
+| [workflow_multi_selection_edge_group.py](workflow_multi_selection_edge_group.py) | Enrutamiento multi-selección con LLM usando `add_multi_selection_edge_group` para activar uno o varios manejadores. |
 | [workflow_converge.py](workflow_converge.py) | Un workflow con rama y convergencia: Revisor enruta a Publicador o Editor y luego converge antes del resumen final. |
+| [workflow_handoffbuilder.py](workflow_handoffbuilder.py) | Orquestación de handoff autónoma usando `HandoffBuilder` (los agentes se transfieren el control sin HITL). |
+| [workflow_handoffbuilder_rules.py](workflow_handoffbuilder_rules.py) | Orquestación de handoff con reglas explícitas usando `HandoffBuilder.add_handoff()`. |
 | [agent_otel_aspire.py](agent_otel_aspire.py) | Un agente con trazas, métricas y logs estructurados de OpenTelemetry exportados al [Aspire Dashboard](https://aspire.dev/dashboard/standalone/). |
 | [agent_otel_appinsights.py](agent_otel_appinsights.py) | Un agente con trazas, métricas y logs estructurados de OpenTelemetry exportados a [Azure Application Insights](https://learn.microsoft.com/azure/azure-monitor/app/app-insights-overview). Requiere aprovisionamiento de Azure con `azd provision`. |
 | [agent_evaluation_generate.py](agent_evaluation_generate.py) | Genera datos sintéticos de evaluación para el agente planificador de viajes. |

diff --git a/examples/spanish/agent_middleware.py b/examples/spanish/agent_middleware.py
@@ -89,7 +89,7 @@
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4o"),
+        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
 else:
     client = OpenAIChatClient(api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4o"))

diff --git a/examples/spanish/agent_summarization.py b/examples/spanish/agent_summarization.py
@@ -76,7 +76,7 @@
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4o"),
+        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
 else:
     client = OpenAIChatClient(api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4o"))

diff --git a/examples/spanish/agent_with_subagent.py b/examples/spanish/agent_with_subagent.py
@@ -82,7 +82,7 @@
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4o"),
+        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
 else:
     client = OpenAIChatClient(api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4o"))

diff --git a/examples/spanish/agent_without_subagent.py b/examples/spanish/agent_without_subagent.py
@@ -70,7 +70,7 @@
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4o"),
+        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
 else:
     client = OpenAIChatClient(api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4o"))

diff --git a/examples/spanish/workflow_agents.py b/examples/spanish/workflow_agents.py
@@ -37,7 +37,7 @@
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4o-mini"),
+        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
 else:
     client = OpenAIChatClient(
@@ -76,7 +76,10 @@ async def main():
     prompt = "Escribe una publicación de LinkedIn de 2 frases: \"Por qué tu piloto de IA se ve bien, pero falla en producción.\""
     print(f"Prompt: {prompt}\n")
     events = await workflow.run(prompt)
-    print(events.get_outputs())
+
+    for output in events.get_outputs():
+        print("===== Salida =====")
+        print(output)
 
     if async_credential:
         await async_credential.close()