Merge branch 'main' into workflowbuilders

Author: pamelafox

.devcontainer/devcontainer.json

@@ -12,7 +12,8 @@
     "customizations": {
         "vscode": {
             "settings": {
-                "python.defaultInterpreterPath": "/usr/local/bin/python",
+                "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python",
+                "python.terminal.activateEnvironment": true,
                 "remote.autoForwardPorts": false,
                 "python.createEnvironment.trigger": "off",
                 "python.analysis.typeCheckingMode": "off"
@@ -23,5 +24,6 @@
             ]
         }
     },
+    "postCreateCommand": "uv sync",
     "remoteUser": "vscode"
 }

.env.sample

@@ -11,6 +11,7 @@ GITHUB_MODEL=gpt-4.1-mini
 GITHUB_TOKEN=YOUR-GITHUB-PERSONAL-ACCESS-TOKEN
 # Configure for Redis (used by agent_history_redis.py, defaults to dev container Redis):
 REDIS_URL=redis://localhost:6379
+# Configure OTLP exporter (not needed in devcontainer, which sets these via docker-compose):
 OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
 OTEL_EXPORTER_OTLP_PROTOCOL=grpc
 # Or use console exporters instead of OTLP:

README.md

@@ -191,14 +191,10 @@ You can run the examples in this repository by executing the scripts in the `exa
 | [agent_memory_redis.py](examples/agent_memory_redis.py) | Long-term memory with RedisContextProvider, storing and retrieving conversational context from Redis. |
 | [agent_memory_mem0.py](examples/agent_memory_mem0.py) | Long-term memory with Mem0 OSS, extracting and recalling distilled user facts across sessions. |
 | [agent_supervisor.py](examples/agent_supervisor.py) | A supervisor orchestrating activity and recipe sub-agents. |
-<<<<<<< HEAD
 | [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. |
-| [workflow_hitl.py](examples/workflow_hitl.py) | Human-in-the-loop (HITL) for tool-enabled agents with human feedback. |
-=======
->>>>>>> main
 | [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. |
@@ -208,7 +204,6 @@ You can run the examples in this repository by executing the scripts in the `exa
 | [agent_mcp_remote.py](examples/agent_mcp_remote.py) | An agent using a remote MCP server (Microsoft Learn) for documentation search. |
 | [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. |
-<<<<<<< HEAD
 | [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_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. |
@@ -219,8 +214,6 @@ You can run the examples in this repository by executing the scripts in the `exa
 | [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_converge.py](examples/workflow_converge.py) | A branch-and-converge workflow: Reviewer routes to Publisher or Editor, then converges before final summary output. |
-=======
->>>>>>> main
 | [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. |

examples/agent_redteam.py

@@ -74,9 +74,9 @@ def transfer_funds(from_account: str, to_account: str, amount: float) -> dict:
 
 async def agent_callback(messages, stream=False, session_state=None, context=None) -> dict:
     """Callback that interfaces between RedTeam and the agent."""
-    query = messages[-1].content
+    latest_message = messages[-1]["content"]
     try:
-        response = await agent.run(query)
+        response = await agent.run(latest_message)
         return {"messages": [{"content": response.text, "role": "assistant"}]}
     except Exception as e:
         logger.error(f"Error during agent run: {e}")

examples/spanish/README.md

@@ -186,14 +186,10 @@ Puedes ejecutar los ejemplos en este repositorio ejecutando los scripts en el di
 | [agent_memory_redis.py](agent_memory_redis.py) | Memoria de largo plazo con RedisContextProvider, guardando y recuperando contexto conversacional desde Redis. |
 | [agent_memory_mem0.py](agent_memory_mem0.py) | Memoria de largo plazo con Mem0 OSS, extrayendo y recordando hechos del usuario entre sesiones. |
 | [agent_supervisor.py](agent_supervisor.py) | Un supervisor que orquesta subagentes de actividades y recetas. |
-<<<<<<< HEAD
 | [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. |
-| [workflow_hitl.py](workflow_hitl.py) | Human-in-the-loop (HITL) para agentes con herramientas con feedback humano. |
-=======
->>>>>>> main
 | [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. |
@@ -203,7 +199,6 @@ Puedes ejecutar los ejemplos en este repositorio ejecutando los scripts en el di
 | [agent_mcp_remote.py](agent_mcp_remote.py) | Un agente usando un servidor MCP remoto (Microsoft Learn) para búsqueda de documentación. |
 | [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. |
-<<<<<<< HEAD
 | [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_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. |
@@ -214,8 +209,6 @@ Puedes ejecutar los ejemplos en este repositorio ejecutando los scripts en el di
 | [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_converge.py](workflow_converge.py) | Un workflow con rama y convergencia: Revisor enruta a Publicador o Editor y luego converge antes del resumen final. |
-=======
->>>>>>> main
 | [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. |

examples/spanish/agent_redteam.py

@@ -74,7 +74,7 @@ def transfer_funds(from_account: str, to_account: str, amount: float) -> dict:
 
 async def agent_callback(messages, stream=False, session_state=None, context=None) -> dict:
     """Callback que conecta RedTeam con el agente."""
-    query = messages[-1].content
+    query = messages[-1]["content"]
     try:
         response = await agent.run(query)
         return {"messages": [{"content": response.text, "role": "assistant"}]}