From 383351efcada96972ad354f9f5e71575e5754a8b Mon Sep 17 00:00:00 2001
From: John Mario Montoya Zapata <jmmontoyaz13@gmail.com>
Date: Thu, 13 Aug 2026 10:41:10 -0500
Subject: [PATCH 1/5] docs(github): add roadmap task issue template

---
 .github/ISSUE_TEMPLATE/task.md | 26 ++++++++++++++++++++++++++
 1 file changed, 26 insertions(+)
 create mode 100644 .github/ISSUE_TEMPLATE/task.md

diff --git a/.github/ISSUE_TEMPLATE/task.md b/.github/ISSUE_TEMPLATE/task.md
new file mode 100644
index 0000000..6299a94
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/task.md
@@ -0,0 +1,26 @@
+---
+name: Tarea de roadmap
+about: Tarea de una versión del roadmap de ResearchOS
+title: "[T##] "
+labels: ''
+---
+
+## Objetivo
+
+## Contexto
+
+## Criterios de aceptación
+- [ ]
+
+## Capas afectadas
+
+## Decisiones a documentar
+
+## Objetivo de aprendizaje
+
+## Fuera de alcance
+
+## Estimación
+**h**
+
+## Depende de

From a2f16d56978eb82baa3efd571b435a69a34e7f75 Mon Sep 17 00:00:00 2001
From: John Mario Montoya Zapata <jmmontoyaz13@gmail.com>
Date: Fri, 14 Aug 2026 09:09:30 -0500
Subject: [PATCH 2/5] feat(deps): add langgraph and langchain-anthropic for V2
 study

---
 pyproject.toml |   3 +
 uv.lock        | 405 +++++++++++++++++++++++++++++++++++++++++++++----
 2 files changed, 377 insertions(+), 31 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index bfa65c5..7af8a57 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,6 +26,9 @@ dependencies = [
     "pyprojroot>=0.3.0",
     "pysqlite3-binary>=0.5.4; sys_platform == 'linux'",
     "pysqlite3-binary>=0.5.4.post2",
+    "langgraph>=1.2.11",
+    "langchain-anthropic>=1.5.6",
+    "langchain>=1.3.15",
 ]
 
 [build-system]
diff --git a/uv.lock b/uv.lock
index 12f1945..759fc1a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -28,7 +28,7 @@ wheels = [
 
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-version = "0.96.0"
+version = "0.122.0"
 source = { registry = "https://pypi.org/simple" }
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From 149f168a577def83b89eba0f2a5db1d94aecd2e1 Mon Sep 17 00:00:00 2001
From: John Mario Montoya Zapata <jmmontoyaz13@gmail.com>
Date: Fri, 14 Aug 2026 09:09:48 -0500
Subject: [PATCH 3/5] docs(notebooks): add LangGraph study notebook

---
 notebooks/201-jmmz-langraph-study.ipynb | 729 ++++++++++++++++++++++++
 1 file changed, 729 insertions(+)
 create mode 100644 notebooks/201-jmmz-langraph-study.ipynb

diff --git a/notebooks/201-jmmz-langraph-study.ipynb b/notebooks/201-jmmz-langraph-study.ipynb
new file mode 100644
index 0000000..8a50702
--- /dev/null
+++ b/notebooks/201-jmmz-langraph-study.ipynb
@@ -0,0 +1,729 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fce14dd7",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "Códigos sueltos para ejecución de tutoriales o  snippets de código relacionado con langchain, langraph y langsmith para interiorizar sus conceptos, diferencias y mecánicas de escritura así como su sintaxis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c53d0c37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'messages': [HumanMessage(content='hi!', additional_kwargs={}, response_metadata={}, id='9e5835e0-157b-4e93-9bff-0548b69c7d4d'),\n",
+       "  AIMessage(content='hello world', additional_kwargs={}, response_metadata={}, id='c307df22-252e-463c-b522-b3299c3098a4', tool_calls=[], invalid_tool_calls=[])]}"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langgraph.graph import StateGraph, MessagesState, START, END\n",
+    "\n",
+    "def mock_llm(state: MessagesState):\n",
+    "    return {\"messages\": [{\"role\": \"ai\", \"content\": \"hello world\"}]}\n",
+    "\n",
+    "graph = StateGraph(MessagesState)\n",
+    "graph.add_node(mock_llm) # Nodo del LLM\n",
+    "graph.add_edge(START, \"mock_llm\") # Borde del grafo que establece la conexión START y el nodo previamente definido\n",
+    "graph.add_edge(\"mock_llm\", END) # Borde del grafo que establece la conexión entre el nodo LLM y el final del grafo\n",
+    "graph = graph.compile() # Compilador START -> mock_LLM -> END\n",
+    "\n",
+    "graph.invoke({\"messages\": [{\"role\": \"user\", \"content\": \"hi!\"}]}) # Invocar el grafo pasándole mensajes \n",
+    "# muy al estilo de las APIs de OpenAI o Claude: Lista de diccionarios con claves role y mensaje: [{'role': '', 'content': ''}]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d4eea90",
+   "metadata": {},
+   "source": [
+    "## API de grafos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46a87433",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.tools import tool\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "\n",
+    "\n",
+    "model = init_chat_model(\n",
+    "    \"claude-sonnet-4-6\",\n",
+    "    temperature=0\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Define tools\n",
+    "@tool\n",
+    "def multiply(a: int, b: int) -> int:\n",
+    "    \"\"\"Multiply `a` and `b`.\n",
+    "\n",
+    "    Args:\n",
+    "        a: First int\n",
+    "        b: Second int\n",
+    "    \"\"\"\n",
+    "    return a * b\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def add(a: int, b: int) -> int:\n",
+    "    \"\"\"Adds `a` and `b`.\n",
+    "\n",
+    "    Args:\n",
+    "        a: First int\n",
+    "        b: Second int\n",
+    "    \"\"\"\n",
+    "    return a + b\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def divide(a: int, b: int) -> float:\n",
+    "    \"\"\"Divide `a` and `b`.\n",
+    "\n",
+    "    Args:\n",
+    "        a: First int\n",
+    "        b: Second int\n",
+    "    \"\"\"\n",
+    "    return a / b\n",
+    "\n",
+    "\n",
+    "def rest(a: int, b: int) -> float:\n",
+    "    \"\"\"Rest `a` and `b`.\n",
+    "\n",
+    "    Args:\n",
+    "        a: First int\n",
+    "        b: Second int\n",
+    "    \"\"\"\n",
+    "    return a - b\n",
+    "\n",
+    "\n",
+    "# Augment the LLM with tools\n",
+    "tools = [add, multiply, divide]\n",
+    "tools_by_name = {tool.name: tool for tool in tools}\n",
+    "model_with_tools = model.bind_tools(tools) #Vincula las tools al modelo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0858b23e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'add'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "add.name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e6221e98",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<function rest at 0x7f0114974400>\n",
+      "No es tool\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(rest) # -> NO ES UNA TOOL, es una función. Si le coloco el \n",
+    "#decorador de tool habilito métodos y propiedades de la tool\n",
+    "try:\n",
+    "    rest.name\n",
+    "except:\n",
+    "    print(\"No es tool\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "65c07cc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'add': StructuredTool(name='add', description='Adds `a` and `b`.\\n\\n    Args:\\n        a: First int\\n        b: Second int', args_schema=<class 'langchain_core.utils.pydantic.add'>, func=<function add at 0x7f0114974180>),\n",
+       " 'multiply': StructuredTool(name='multiply', description='Multiply `a` and `b`.\\n\\n    Args:\\n        a: First int\\n        b: Second int', args_schema=<class 'langchain_core.utils.pydantic.multiply'>, func=<function multiply at 0x7f0114974220>),\n",
+       " 'divide': StructuredTool(name='divide', description='Divide `a` and `b`.\\n\\n    Args:\\n        a: First int\\n        b: Second int', args_schema=<class 'langchain_core.utils.pydantic.divide'>, func=<function divide at 0x7f0115625b20>)}"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tools_by_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1bc9d3b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "StructuredTool(name='add', description='Adds `a` and `b`.\\n\\n    Args:\\n        a: First int\\n        b: Second int', args_schema=<class 'langchain_core.utils.pydantic.add'>, func=<function add at 0x7f0114974180>)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tools_by_name['add']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d5b46c8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd8ecc58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.messages import AnyMessage\n",
+    "from typing_extensions import TypedDict, Annotated\n",
+    "import operator\n",
+    "\n",
+    "# Definimos el estado del grafo para almacenar los mensaje y el número de llamadas a LLM\n",
+    "# es similar a como definimos modelos en la capa domain en los que usamos una clase y \n",
+    "# declaramos que atributos y los tipos que deberían tener las implementaciones que\n",
+    "# usen ese modelo\n",
+    "\n",
+    "class MessagesState(TypedDict):\n",
+    "    messages: Annotated[list[AnyMessage], operator.add]\n",
+    "    llm_calls: int"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "bc87221e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Aquí definimos un nodo modelo que se utiliza para llamar al LLM y decidir si llamar a una herramienta o no\n",
+    "from langchain.messages import SystemMessage\n",
+    "\n",
+    "\n",
+    "def llm_call(state: dict):\n",
+    "    \"\"\"LLM decides whether to call a tool or not\"\"\"\n",
+    "\n",
+    "    return {\n",
+    "        \"messages\": [\n",
+    "            model_with_tools.invoke(\n",
+    "                [\n",
+    "                    SystemMessage(\n",
+    "                        content=\"You are a helpful assistant tasked with performing arithmetic on a set of inputs.\"\n",
+    "                    )\n",
+    "                ]\n",
+    "                + state[\"messages\"]\n",
+    "            )\n",
+    "        ],\n",
+    "        \"llm_calls\": state.get('llm_calls', 0) + 1\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "651a33ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.messages import ToolMessage\n",
+    "#El nodo herramienta se utiliza para llamar a las herramientas y devolver los resultados.\n",
+    "\n",
+    "\n",
+    "def tool_node(state: dict):\n",
+    "    \"\"\"Performs the tool call\"\"\"\n",
+    "\n",
+    "    result = []\n",
+    "    for tool_call in state[\"messages\"][-1].tool_calls:\n",
+    "        tool = tools_by_name[tool_call[\"name\"]]\n",
+    "        observation = tool.invoke(tool_call[\"args\"])\n",
+    "        result.append(ToolMessage(content=observation, tool_call_id=tool_call[\"id\"]))\n",
+    "    return {\"messages\": result}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "128c7023",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 5. Definir la lógica final\n",
+    "# La función de arista condicional se utiliza para enrutar al nodo o extremo de la \n",
+    "# herramienta según si el LLM realizó una llamada a la herramienta.\n",
+    "\n",
+    "from typing import Literal\n",
+    "from langgraph.graph import StateGraph, START, END\n",
+    "\n",
+    "\n",
+    "def should_continue(state: MessagesState) -> Literal[\"tool_node\", END]:\n",
+    "    \"\"\"Decide if we should continue the loop or stop based upon whether the LLM made a tool call\"\"\"\n",
+    "\n",
+    "    messages = state[\"messages\"]\n",
+    "    last_message = messages[-1]\n",
+    "\n",
+    "    # If the LLM makes a tool call, then perform an action\n",
+    "    if last_message.tool_calls:\n",
+    "        return \"tool_node\"\n",
+    "\n",
+    "    # Otherwise, we stop (reply to the user)\n",
+    "    return END"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "6c208396",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hola\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not None:\n",
+    "    print('hola')\n",
+    "else:\n",
+    "    print('está en None')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b43d5c70",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 6. Compilar y construir el agente\n",
+    "# El agente se construye usando el StateGraph y compilado usando la compile método.\n",
+    "\n",
+    "# Build workflow\n",
+    "agent_builder = StateGraph(MessagesState)\n",
+    "\n",
+    "# Add nodes\n",
+    "agent_builder.add_node(\"llm_call\", llm_call)\n",
+    "agent_builder.add_node(\"tool_node\", tool_node)\n",
+    "\n",
+    "# Add edges to connect nodes\n",
+    "agent_builder.add_edge(START, \"llm_call\")\n",
+    "agent_builder.add_conditional_edges(\n",
+    "    \"llm_call\",\n",
+    "    should_continue,\n",
+    "    [\"tool_node\", END]\n",
+    ")\n",
+    "agent_builder.add_edge(\"tool_node\", \"llm_call\")\n",
+    "\n",
+    "# Compile the agent\n",
+    "agent = agent_builder.compile()\n",
+    "\n",
+    "# Show the agent\n",
+    "from IPython.display import Image, display\n",
+    "display(Image(agent.get_graph(xray=True).draw_mermaid_png()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "ad3ab008",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "Deriva la expresión '3x**2 + 2' y luego dame la multiplicación de 4*3 y una vez que conozcas el resultado, tómalo y divídelo entre 2\n",
+      "\n",
+      "\n",
+      "1\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "[{'text': 'Voy a empezar con lo que puedo hacer en paralelo: calcular la derivada de forma analítica y la multiplicación de 4×3 al mismo tiempo.\\n\\n**Derivada de `3x² + 2`:**\\nAplicando las reglas de derivación:\\n- La derivada de `3x²` es `6x`\\n- La derivada de `2` (constante) es `0`\\n\\n**∴ La derivada de `3x² + 2` es `6x`** ✅\\n\\nAhora, mientras tanto, calculo `4 × 3`:', 'type': 'text'}, {'id': 'toolu_01J2T5ZdM7b759aZE6BHxoni', 'caller': {'type': 'direct'}, 'input': {'a': 4, 'b': 3}, 'name': 'multiply', 'type': 'tool_use'}]\n",
+      "Tool Calls:\n",
+      "  multiply (toolu_01J2T5ZdM7b759aZE6BHxoni)\n",
+      " Call ID: toolu_01J2T5ZdM7b759aZE6BHxoni\n",
+      "  Args:\n",
+      "    a: 4\n",
+      "    b: 3\n",
+      "\n",
+      "\n",
+      "2\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "\n",
+      "12\n",
+      "\n",
+      "\n",
+      "3\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "[{'text': '`4 × 3 = 12` ✅. Ahora divido ese resultado entre 2:', 'type': 'text'}, {'id': 'toolu_017neW69ZFTQHGfKSQqo3oGK', 'caller': {'type': 'direct'}, 'input': {'a': 12, 'b': 2}, 'name': 'divide', 'type': 'tool_use'}]\n",
+      "Tool Calls:\n",
+      "  divide (toolu_017neW69ZFTQHGfKSQqo3oGK)\n",
+      " Call ID: toolu_017neW69ZFTQHGfKSQqo3oGK\n",
+      "  Args:\n",
+      "    a: 12\n",
+      "    b: 2\n",
+      "\n",
+      "\n",
+      "4\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "\n",
+      "6.0\n",
+      "\n",
+      "\n",
+      "5\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Aquí tienes el resumen completo:\n",
+      "\n",
+      "1. 📐 **Derivada de `3x² + 2`** → **`6x`**\n",
+      "2. ✖️ **`4 × 3`** → **`12`**\n",
+      "3. ➗ **`12 ÷ 2`** → **`6`**\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Invoke\n",
+    "from langchain.messages import HumanMessage\n",
+    "\n",
+    "messages = [HumanMessage(content=\"Deriva la expresión '3x**2 + 2' y luego dame la multiplicación de 4*3 y una vez que conozcas el resultado, tómalo y divídelo entre 2\")]\n",
+    "messages = agent.invoke({\"messages\": messages})\n",
+    "\n",
+    "count=0\n",
+    "for m in messages[\"messages\"]:\n",
+    "    print(count)\n",
+    "    m.pretty_print()\n",
+    "    count += 1\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "ac2e4dd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Modifico el grafo para no tener el borde de tool hacia llm_call\n",
+    "\n",
+    "# Build workflow\n",
+    "agent_builder = StateGraph(MessagesState)\n",
+    "\n",
+    "# Add nodes\n",
+    "agent_builder.add_node(\"llm_call\", llm_call)\n",
+    "agent_builder.add_node(\"tool_node\", tool_node)\n",
+    "\n",
+    "# Add edges to connect nodes\n",
+    "agent_builder.add_edge(START, \"llm_call\")\n",
+    "agent_builder.add_conditional_edges(\n",
+    "    \"llm_call\",\n",
+    "    should_continue,\n",
+    "    [\"tool_node\", END]\n",
+    ")\n",
+    "# agent_builder.add_edge(\"tool_node\", \"llm_call\") # -> COMENTADA PARA VER QUÉ PASA CON LA MISMA PREGUNTA\n",
+    "\n",
+    "# Compile the agent\n",
+    "agent = agent_builder.compile()\n",
+    "\n",
+    "# Show the agent\n",
+    "from IPython.display import Image, display\n",
+    "display(Image(agent.get_graph(xray=True).draw_mermaid_png()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "75be9079",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "Deriva la expresión '3x**2 + 2' y luego dame la multiplicación de 4*3 y una vez que conozcas el resultado, tómalo y divídelo entre 2\n",
+      "\n",
+      "\n",
+      "1\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "[{'text': 'Voy a empezar con lo que puedo hacer en paralelo: calcular la derivada de forma analítica y la multiplicación de 4×3 al mismo tiempo.\\n\\n**Derivada de `3x² + 2`:**\\nAplicando las reglas de derivación:\\n- La derivada de `3x²` es `6x`\\n- La derivada de `2` (constante) es `0`\\n\\n**∴ d/dx(3x² + 2) = 6x**\\n\\nAhora, simultáneamente calculo `4 × 3`:', 'type': 'text'}, {'id': 'toolu_01U7PCYuZFSdJbg3X3p2ngTw', 'caller': {'type': 'direct'}, 'input': {'a': 4, 'b': 3}, 'name': 'multiply', 'type': 'tool_use'}]\n",
+      "Tool Calls:\n",
+      "  multiply (toolu_01U7PCYuZFSdJbg3X3p2ngTw)\n",
+      " Call ID: toolu_01U7PCYuZFSdJbg3X3p2ngTw\n",
+      "  Args:\n",
+      "    a: 4\n",
+      "    b: 3\n",
+      "\n",
+      "\n",
+      "2\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "\n",
+      "12\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Invoke\n",
+    "from langchain.messages import HumanMessage\n",
+    "\n",
+    "messages = [HumanMessage(content=\"Deriva la expresión '3x**2 + 2' y luego dame la multiplicación de 4*3 y una vez que conozcas el resultado, tómalo y divídelo entre 2\")]\n",
+    "messages = agent.invoke({\"messages\": messages})\n",
+    "\n",
+    "count=0\n",
+    "for m in messages[\"messages\"]:\n",
+    "    print(count)\n",
+    "    m.pretty_print()\n",
+    "    count += 1\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56b7ad03",
+   "metadata": {},
+   "source": [
+    "Conclusión: Al modificar el nodo doble vía entre el llm_call y el nodo tool no se pudo hacer la siguiente operación porque el agente solo tuvo acceso a la tool 1 vez y luego pasó al END\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "d5844b2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FRaNGjfr999+JURgzZkxCQgKnAoFAYGtrKxKJhEKhm5vbpk2bCKNB0Ws5fPgw2Pj58+eTpkOpVF64cKFv377EKJw+ffrzzz+XSKrNLkhR1PXr1wnTYXtFVi6X5+Xlgc1rWsWTiomOjaZ4YODAgd7e3qByfQpLFE9YLvr169fn5uba2Nh8+OGHpKkpKytbt24dMSIzZswAP15/aG5uTtgBe0W/ZcsWMHUeHh5gYgkNUCgURnPodXTu3Pmll17S+bd8Pj8iImLYsGGJiYmE6bDOp8/MzNy/f/+8efPUajWPR6OGdyP79Dri4uIWLFiQnZ197do1UlE4UDL9+/efOnUqYS6ss/Tguw8ZMoRoe5vQq6uJkX16HUFBQWDv9U6Ou7s7WASo50yePDk/P58wFLZY+nPnzoHKe/ToQegK+PRfffXV4sWLCQ24e/cumHxw+keOHEkYByssfUxMTFRUVJcuXQiNMb5PXw/BwcEQ04yPj589ezb4gYRZ8FasWEGYy549e9q0nMrQ2wAAEABJREFUaQO1tBEjRjRqLRDjAx8iT09PPz8/Qhu6d+9ubW09duxY+FVN0mBnIJjs3syZM6dDhw4TJ04kyPOxcOFCCGiuXLmSMAIGujclJSU6P2HZsmUmpHjjx+kbDrTdduzYsXfv3sxovWKa6KF5dejQoTonoWrLC/2hlU//NIMHDz5x4sS2bdsY0DOHOaK/ceOGRCJRqVQQqKGVZ9xALC0taRK6qQuxWBwZGdmsWTOoICUnJxOThSE+/fHjx0+ePLl9+3aa11aZQXp6+ty5c8H2v/nmm8QEMXmJXL58Gbbe3t47duwwacXT2aevAUSZDh06JJVKoeG2sLCQmBomrBKKoqCe+ujRI9hv27YtMXFo7tM/TUREBMTHxowZA59ZYlKYqnuTlpbm7OyckpLSsmVLwgiapO/NC+GTTz4Be79x40ZiIpiepX/w4MHLL78MYWOhUMgYxZMm6nvzQoDQ8PDhw8PCwuClJaaAKYk+MzMTtllZWdHR0Y6OTFv80YR8+qfp0aNHbGwsxDTB6hPaYzKi37Vr14YNG2CnW7dudOsg+UIwOZ/+aeABtWnTBr5Xt27dIjTGBHx6MPDu7u5Hjx6F8DBhLqbr09eguLgYApoQWnjvvfcILaG1pZfL5RAi0Hk1zFY8MWWfvgY2NjbfffcdbEeNGpWamkroB60tfUxMDMQlodpKWAD49BAAWbp0KWEKEFubN2/e6NGjX3/9dUInaGrpDxw4ABWjjh07skTxpMLSQ+CvtLSUMAUfH5/Dhw+D5bpy5QqhEzQVPRgJk+7d8R8A0UNFMD8/PycnhzCIoqIius2zQFPRT5gwoWfPnoR9eHl5gcO5aNEiwhTi4uKCgoIInaCp6D08PKDBlbASFxeX/v37X7p0iQF9ARMTE8HJ4fPpNR8yTWdnPnbsmEgkGjhwIGEl4eHhMpkM/ByIXLVv356YLDQ084S2lj47OzsjI4OwGPCDweRHRkbev3+fmCzw41u1akVoBk0t/bBhw3BmWWD79u03btwgJgtY+kGDBhGaQVNL7+rq6ubmRhBCQkNDYTt27Niqk62aCmDpAwMDCc2gqehPnTplcr20DcqaNWu+/vprYlIkJCT4+fnRsKMUTUWfm5ublpZGkMc0b95c15Xl119/JSYCPc08oa3oBwwYwPjONv8NsAWm8g0Ehx5F3wggSA+heoI8xaxZs6DCQ0wBtPSN48KFCz/99BNBaqNjx46wnTp1Kj37MOpBS9848vPz2db3prHs3Llz165dhK7Ex8cHBATQc34Kmoq+Z8+eEyZMIEi96Cbf/eWXXwj9uHfvHj3NPKGt6B0cHLy9vQnSAOzt7RcsWEBoBoiehh0QdNBU9DExMfD5JkgD6Nq165QpU2CnoKCA0AYQPQ07IOigqeiLi4v//fdfgjQMnU2FED406lVNb8KwL1r6RhMWFjZ9+nSCNIY33njj0qVLVVMgGPDuu+8SowPBSjpPSURT0dva2kIbJEEaiW7amT/++AO2Xbp04fP5iYmJd+7cIcaFnj2K9dBU9PCctmzZQpD/RHBwcPv27RUKBano0LF7925iXOjZo1gPTUUvlUpNuh950wJ+DofD0e1DpBwsiJGbsdDS/xfAVr3//vsEaTxDhw6tEcbJyck5dOgQMSLQMoWWvtGIxeIWLVoQpPFoNBqI3IOl1y+FCfvnz5832kTytO19oIemI6cePHhw5MgRGra50IoyqVpZXnNkyY+7jly/fj0tLe3u3bvZ2dkqlaqkpERSIPlp93HjNHLfuZEUFNChOE9JjI7Igie0eLYdp9cMZxCmBG+eoijYwjfaxcUFzFV5efnZs2cJUoUrpwruXi62sOYrZM8YTqWh4B+AMtqUBBSlgQqFvlJhTDQV/78h3W3b9bat5zR6WXr4LO7du1ffS0k3NrxZs2YEqcKvO7Md3MwHv+VlIWbg7M3PibRIFR9T/MfB3D5j6pzMnV4+/cSJEz09PaumgNXv3LkzQR5z8tssV1+L4M42qPhasbLlt+/vILTg/2//o7rOoZfonZ2dX3nllaopjo6O48aNI0gFSf+UWtmZBbSzJki9hHS3g2r8w6TyWnNpF70BiVcdM9WmTRsM4+jJSS83E+KaoQ2Cx+PkZpiI6G1sbAYPHqwbQu/g4ADtLAR5THkZZecqJEgDcHAXlUrUtWbR0WyMHz9e59lDq17r1q0J8hiZVEWpTG/2myZBpaDqCm09V/RGJiOxp/JyUuWyUhXEjLkcjjZcBWFQiB1BxEqjqdhAXJRUJGi0CdqUiqTKPFJxuja8pU8AevmsUXuoBHyz7YuT9IlVT6i6X3FcEa+q45DH5/D5XA6PWIi53oGWnQbaE4TF/EfRn/3xUXJcKbSMcHkcnoDHF/LMLHgcLodQGq0YQeAVwiOV/6lCDXVWy3oiZDMi5Dx9XtVra6q+PkDu8K9KrsrPVuZmFsacKRBZ8l562brzqw4EYR+NFv2v3+Wk3pNyeVyxo5V7sEmaTJWcyribd/1c4Y0LRe372Hd6xY4gbKJxoo9ckqyhiFdrZysneq0t0Sj4Qq5POyfYefRv0bX/FcTHSiYtx/G4LKKhFVkIeW6Zm2hlb9myp5dJK74qTgG2QeE+FOFtnfeAIKyhQaKXFKiPbMkI7OntFsxAJ9gnzNWxebOv56Pu2cKzRZ8aJ9uzNuWlfr48M8Y2izj6WvmGum6dn0gQFvBsHZ/87mGLjp6E6VjYCx087bYtSiII03mG6Hd8lCJ2shRYsaJvk3MLW74Z78AGVi/7wwbqE/2Fg3lKhdorxJGwBv8uHo8yynPSFQRhLvWJ/m5ssZMP62LYlrai49+gsWcydYo++li+htI087MhtOTm7d/nL+skLX3x4z79OrrKZWppgZogFQwf2Xf3nm+J4Tl3/mzv8A5FRQYfy1un6O9fL7G0tySsBDz7qB+yCCNYuWpx1ClcvasadYpeVqp2CWBp+7yNo1XeQzlhBPHxcQSpTu3dEO7+LeVwNCKxgBiGlLR/fjv3bXpGnJWlXWDLbv17TxeJtF+V6MuHzl7YOWvqN7v3f5jzKMnV2b9Hl3Fh7V7VXXXy9Oart6KEZhahIQOcmnkRg+ESYF/wsISYPuAtwHb9F598s+3/fjl+Hvajoy/8sDsyNS3ZxsbW37/l7PcWOTu76E6uJ+uZHD12cM+P327aGPnxyoUpKUl+fv6jR00YOGCILjctLWXTl+sS/r3H4/F9fPzenDwztG0HXda27V/+dvZXC3OL8PCBHh7VOoOcPvPLiV8OJycn+vr69+nd/7WR417UYPPaLX1agpTLN1SYMi8/ffv37ymV8ogZ304e/1lWzr/f7JylVquItg+wQCYrOfbrF2OGL1m/6nLIS30OHltdWJQNWZdiDl+K+Xnk4AWzZ+5ysHM7e+47YjC4Zhwun5NwvYyYOKejomG7YP4yneKvXruyfMWC/v0HH9wf9fGydTk5WZu+Wqc7s56shiAQCKTSkq82f75g3rI/fo/t2aPv5+tX5eRoH1xhYUHEe1OcnFwit+/7evMuO1v7T1YvKSvTlu3xEz8fP3Fo9vuLtm7d7erqvnvPDv0Nf//f6c8+X9kioNW+H09Mn/buz4f3bdm6gbwgahc9VON4PEO1v16/dZrPE7w57jNnRx8XJ7/Rwz7KzIq/c++CLletVvbrPd3bszW81h3aDtZoNJlZCZB+8e+DIcHh8BpYWFiD7ff360AMCZfDyUmVEWaxc9c3Pbr3GfXaeLDlwcEh78yae/nyxfsV/k89WQ1EqVROnjQjKEj74Ab0fxUeXGJiPKQf+nmvmVA4f95SN1d3Dw+vBfOXy2RloHXIOnJ0P7wePXuEW4ut4bPQLjRMf7eoqGMhIaFzZi+2s7OH9CmT3z527GCxpJi8CGpXtkqpJgabtwR8G0+PIEvLyplJ7O1cHew9klNv6k/wcg/W7ViYa0dAy8pLoATzCtKdnXz153i4GXjWOI1GVtYE0xUZlKSkf1u1CtYftmyhnW7y/v279Wc1HP0dxGLtgwPbr71zcmJAQCv9rDuWlpaeHt4JCfe05iwzHbwd/eUtWlTOi0ZR1J27t8I6PJkFIzQ0DBJ1b9HzU7tPrx3+RAwlelm5ND0zDgKOVRMlJfn6/addt3J5KUWphUILfYqZmYF7eoKp1zTBdEWGQyqVyuVyoVCkT7Gw0JZnWVlpPVmkMdTqcxfk57m7V+vGIjI3L5OVlZaWqtVqc/Mnz1QkqnymCoUCvhvf7dwKf6peKHlBlr520QvMeBqiIoZBLHbw9W47oM+MqomWlvU1CIiEllwuT6l8MrhdrjCsww12yFxM0zkP/xsikVbT5eVPfLbSCk072DerJ4s8NxaWluXyarMSyMrKPNy9wOTzeDx5lSxwe/Q/Fd66/v0G9+gRXvVCL08f8iKo/bna2AvyHxqqKd7NOeDarSg/n1D9TGbZj5IcHeqLxoAJsbN1TUm73bNrZcq9+GhiSCi1xs2bIcMGdICD0bJF4N27/+hTdPt+zQPqySLPDXhKZ347CZYbKrtE+0mXQIAIaszwTJ2dXbV/0ejKMy9fuai/qnnzFiXSEn2QBy7Pysq0t38xPdtr9+m9W1mq1YYadQ9RSPDPTpz6P4Wi/FFu6skzWzZsGZ+V84xuvW1e6ns77hw0xML+H3/tTs0w4OoaCqmaUBq/thbExBEKhY6OTlevXr5x86pKpRoxfOzF6POHD/8EyoOUrd9shDpigL92nZx6sp6TIUNeKy2Vbtj4KQRzIJq5dt1ykVA06JXhkNW7V78///oDGmJh/6f9P8TF3dZf9da0iOjo89CsBlK5ffvmqk8+nDv/bfhfIC+C2i19yzDL3w9oyvIVFg5m5EUD4Zf5EfvO/bVn07bJj3JTvDyCRw//6JkV0749p5SWFh6L2vDjwY/AOxr6ypx9h5YbaPbZRylFfBFDOpZOGD911/fbYmIv/bTvJNjX3LxHBw7tgfAfxOA7tH/5rekRutPqyXpOPNw9P16+bs+eb18f/yqEhgIDX/py07fg20DWxAnTiooKN29ZD5pu3bothIw+XbNU90zhMHLb3r37dm2P/Ar8ruCgkNWfbNR9K56fOmct3vlxCuHy/Tq6EvYRfyHd1Uc09O2GNs0YjahdWd5BYq9WVgR5FvFXi6UFil6ja+kjXGcwPrSXvbyUpT1slQrV0Bm0UzzyoqgzQBHa2/rKmbychCLnFrVP9Q0NpRu+rn2Sf3OhlUwurTXLxdEvYsYO8uJY+ml4XVnQygvt3k+n+3iFTH/j/+q66sGVLCsbAV2XaGkyPvxozp3bN2vNGjRo+Ky35xDTob6oXPve9rH/K6hL9NbiZnPf2VNrFtRQzcxEtWZxuS84DljXb9D+DKXcTFDLzI98Xn0VFZmkfPoaf4JUZ/7cpQpl7V9+C3MTq/HXJ8GwAbb3YiWp13K82zs/nQtG1N7OjTQ1L/Y3xP+Z7u5vIRIRpAYODsxZGhA4NccAAAJ0SURBVOMZX/FJS71Ki2WSnHLCAjLu5PJ4mhHvNP2bjBiUZ7uub33aPP12NmE62fcLJbml01f7EoTpPFv0AjPy7ufN75xNlmQzrdehnozb+cWPJO983pwgLKBhQQoeifjCP/1udnIsQwbRVSUhOqO0QDpzrR9B2EGDI3Nc8u4X/hq16t651JwHRYQRpP2Td+f3ZBsb3sx1qHgW0bgA4tSVPleiCm/+WViQWmRua+7U3N7CxlBDCg1HcXZpblKRXKY0E/GGTHP3DmJUxzLkmTQ6at5pkB38uXKqIC6mJCk2g0M4PAGXw+FwYashGqqym5p2zREO0a7RoIdbscxC1V4PPC6B86skaK/SrVeiqXahNq16b4nKHv9P96Hgc4jq6UQuXKBWqCk1NFhRcHuxvVmvka7+oSbfpQz5D/zHpqJOr9jDH9hJiJUm3S0tzIWGC4pSEurxKAIOhyLVh2Hw+No3QlOl7yaXT2mqp1RIvnJb5UoNoWquX1IxXKGWfkNc+FueThRQfB6Xb82zczIPDBN7BaLWWc3zto+2CLOCPwRBTAdGDQ5iPJY2fB4fewU1CIEZV2RZe/9wLEFTQmTOy8tkRev485ObUW5pg6I3fVx9RXIZTrLZIFRKyqWOAZ8oelPCq5UF1NNvni8gSL1cicq1tuc7etTenZZjoBF3iOH482geRXG8A60c3IQcRk1T8ryoVZr8LHn81WInD7MOfeuciRVFb5Lc+VsSd7lYKddIi5g2I9XzYGbOFdsK2vSwDQitL6KIojdh4NGpFPj4niAwa9AcZSh6hHVgRRZhHSh6hHWg6BHWgaJHWAeKHmEdKHqEdfw/AAAA//9HzeOEAAAABklEQVQDAM26r3ZqdVJcAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Modifico el grafo para no tener el borde de tool hacia llm_call\n",
+    "\n",
+    "# Build workflow\n",
+    "agent_builder = StateGraph(MessagesState)\n",
+    "\n",
+    "# Add nodes\n",
+    "agent_builder.add_node(\"llm_call\", llm_call)\n",
+    "agent_builder.add_node(\"tool_node\", tool_node)\n",
+    "\n",
+    "# Add edges to connect nodes\n",
+    "agent_builder.add_edge(START, \"llm_call\")\n",
+    "agent_builder.add_edge(\"tool_node\", \"llm_call\") # -> Modifico la definición, primero el borde y luego el borde condicional\n",
+    "\n",
+    "agent_builder.add_conditional_edges(\n",
+    "    \"llm_call\",\n",
+    "    should_continue,\n",
+    "    [\"tool_node\", END]\n",
+    ")\n",
+    "\n",
+    "# Compile the agent\n",
+    "agent = agent_builder.compile()\n",
+    "\n",
+    "# Show the agent\n",
+    "from IPython.display import Image, display\n",
+    "display(Image(agent.get_graph(xray=True).draw_mermaid_png()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "e18245ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "Deriva la expresión '3x**2 + 2' y luego dame la multiplicación de 4*3 y una vez que conozcas el resultado, tómalo y divídelo entre 2\n",
+      "\n",
+      "\n",
+      "1\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "[{'text': \"Voy a empezar con lo que puedo hacer en paralelo: derivar la expresión y calcular la multiplicación de 4×3 al mismo tiempo.\\n\\n> 📐 **Derivada de `3x² + 2`:**\\n> Aplicando las reglas de derivación:\\n> - La derivada de `3x²` es `6x`\\n> - La derivada de la constante `2` es `0`\\n>\\n> ∴ **f'(x) = 6x**\\n\\nAhora, simultáneamente calculo la multiplicación:\", 'type': 'text'}, {'id': 'toolu_01D6uoGaHGqu6bz99GhRo5CN', 'caller': {'type': 'direct'}, 'input': {'a': 4, 'b': 3}, 'name': 'multiply', 'type': 'tool_use'}]\n",
+      "Tool Calls:\n",
+      "  multiply (toolu_01D6uoGaHGqu6bz99GhRo5CN)\n",
+      " Call ID: toolu_01D6uoGaHGqu6bz99GhRo5CN\n",
+      "  Args:\n",
+      "    a: 4\n",
+      "    b: 3\n",
+      "\n",
+      "\n",
+      "2\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "\n",
+      "12\n",
+      "\n",
+      "\n",
+      "3\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "[{'text': '¡El resultado de 4×3 es **12**! Ahora lo divido entre 2:', 'type': 'text'}, {'id': 'toolu_01955iLjUYweYE54AxBH27YZ', 'caller': {'type': 'direct'}, 'input': {'a': 12, 'b': 2}, 'name': 'divide', 'type': 'tool_use'}]\n",
+      "Tool Calls:\n",
+      "  divide (toolu_01955iLjUYweYE54AxBH27YZ)\n",
+      " Call ID: toolu_01955iLjUYweYE54AxBH27YZ\n",
+      "  Args:\n",
+      "    a: 12\n",
+      "    b: 2\n",
+      "\n",
+      "\n",
+      "4\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "\n",
+      "6.0\n",
+      "\n",
+      "\n",
+      "5\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Aquí tienes el resumen completo de todo lo realizado:\n",
+      "\n",
+      "| Operación | Resultado |\n",
+      "|---|---|\n",
+      "| 📐 Derivada de `3x² + 2` | **f'(x) = 6x** |\n",
+      "| ✖️ Multiplicación `4 × 3` | **12** |\n",
+      "| ➗ División `12 ÷ 2` | **6** |\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Invoke\n",
+    "from langchain.messages import HumanMessage\n",
+    "\n",
+    "messages = [HumanMessage(content=\"Deriva la expresión '3x**2 + 2' y luego dame la multiplicación de 4*3 y una vez que conozcas el resultado, tómalo y divídelo entre 2\")]\n",
+    "messages = agent.invoke({\"messages\": messages})\n",
+    "\n",
+    "count=0\n",
+    "for m in messages[\"messages\"]:\n",
+    "    print(count)\n",
+    "    m.pretty_print()\n",
+    "    count += 1\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "204c6af5",
+   "metadata": {},
+   "source": [
+    "Conclusión: en la lógica definida no hubo cambio si definía primero el edge y luego el conditional_edge o visceversa"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "researchos (3.11.8)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From fddf4b041c3dcbd48fd672f07385a76ccb934c0f Mon Sep 17 00:00:00 2001
From: John Mario Montoya Zapata <jmmontoyaz13@gmail.com>
Date: Fri, 14 Aug 2026 09:10:10 -0500
Subject: [PATCH 4/5] docs(claude): document GitHub Project weekly work cycle

---
 CLAUDE.md | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/CLAUDE.md b/CLAUDE.md
index 281d06c..a294f7a 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -152,3 +152,19 @@ Two rules that always apply, regardless of the skill being loaded:
    `<proposed message>`. Shall I proceed?"
 2. Commit messages are written in English, even though code comments and
    project docs may be in Spanish.
+
+
+---
+
+## Weekly Work Cycle (GitHub Project)
+
+Given that the repo already has a Project and associated Issues on GitHub, the weekly workflow is as follows:
+
+- Monday. Choose the issue for the week, move it to “In Progress” in the Project, and enter an estimate (h). Create a branch with a name matching the issue.
+
+- Monday through Thursday. Make commits referencing the issue with (#N). Acceptance criteria are checked off as they’re met—this gives you visible progress without having to write reports.
+Upon closing. Create a pull request with “Closes #N” in the description; merge it, and the issue closes automatically, advancing the milestone bar. Enter the Actual (h).
+
+- Friday. The closed issues from the week serve as input for generating the question bank. The “interview-bank” skill reads the git log; if the commits reference issues, the context it receives is richer.
+
+And the metric that really matters in the long run isn’t the milestone bar: it’s the ratio of Estimate to cumulative Actual. In three months, you’ll know whether you systematically underestimate infrastructure work, refactoring, or integration with external APIs. That data stays with you for the rest of your career, and almost no one has it because almost no one measures it.

From 387b3e8fa9144e58204b24993e4e737b65c88271 Mon Sep 17 00:00:00 2001
From: John Mario Montoya Zapata <jmmontoyaz13@gmail.com>
Date: Fri, 14 Aug 2026 09:13:52 -0500
Subject: [PATCH 5/5] docs: close out 2026-08-13 session (work_log + learnings)

---
 docs/learnings.md | 60 +++++++++++++++++++++++++++++++++++++++++++++++
 docs/work_log.md  | 28 ++++++++++++++++++++++
 2 files changed, 88 insertions(+)

diff --git a/docs/learnings.md b/docs/learnings.md
index be4b375..4c01c09 100644
--- a/docs/learnings.md
+++ b/docs/learnings.md
@@ -348,3 +348,63 @@ Regla simple para ResearchOS:
 - Tercer error mecánico de escritura de Python en una semana (los anteriores: `:` en vez de `=` en una asignación, `self` omitido en firmas de Protocol). No son conceptuales — es un hueco de automatismo que se cierra con repetición.
 
 ---
+
+**Fecha:** 13/08/2026
+
+### ¿Qué aprendí?
+
+- **La razón de ser de un grafo no es el estado — es poder volver atrás.** Le
+  pregunté a mi tutor qué gana un grafo sobre mi pipeline lineal
+  (`retrieve → generate`) y mi primera respuesta se quedó en el estado
+  explícito. La corrección: el estado es el *mecanismo* que hace que un ciclo
+  signifique algo (sin estado compartido, una iteración no puede acumular lo
+  que aprendió la anterior), pero el *fin* es poder bifurcar (conditional
+  edge — ir a arXiv si la recuperación local trajo poco, ir directo a
+  generar si trajo suficiente, mi T19) y poder ciclar (volver a un nodo
+  anterior — recuperar, evaluar que es malo, reescribir la query, recuperar
+  de nuevo, mi T22). Una cadena de funciones no puede hacer ninguna de las
+  dos: va en una sola dirección.
+- **"No puedo conocer el estado intermedio" es falso — es incompleto.** Dije
+  que con mi pipeline lineal no podía saber qué pasó en cada paso. Sí puedo:
+  un `logger.debug` después del retrieval, o devolver los documentos junto
+  con la respuesta. La diferencia real no es posible-vs-imposible, es
+  construcción-vs-instrumentación: en un grafo la inspeccionabilidad viene
+  gratis porque cada nodo ya es una unidad nombrada con entrada y salida
+  declaradas — cuando conecte Langfuse en V3 no voy a instrumentar nada, las
+  trazas van a salir solas de la estructura. Con funciones encadenadas, cada
+  punto de observación lo tengo que agregar yo, y se me puede olvidar.
+- **Tampoco es cierto que el grafo me dé el resultado final antes.** También
+  hay que ejecutarlo completo para tener la respuesta. Lo que cambia no es
+  *cuándo* conozco el final, es que conozco los pasos intermedios sin
+  esfuerzo extra.
+- **Para mi pipeline de hoy (dos pasos, sin ramas ni ciclos), LangGraph es
+  sobrecosto puro.** Un DAG lineal de dos nodos no necesita un grafo — para
+  eso alcanza encadenar dos funciones. La justificación de T18 no está en
+  T18 mismo: está en que T19 (rama condicional) y T22 (ciclo de reescritura
+  de query) no se pueden construir sin el grafo ya montado. Es
+  infraestructura que se paga adelante, no un beneficio inmediato.
+- **LangGraph es orquestación de bajo nivel; LangChain es construcción de
+  agentes de alto nivel** (modelos + tools ya integrados, con
+  `create_agent`). El cambio de LangGraph 0.x a 1.x deprecó
+  `create_react_agent` en favor de ese `create_agent` de LangChain, que por
+  debajo se apoya en LangGraph para control de flujo, human-in-the-loop y
+  persistencia de estado.
+- **El human-in-the-loop depende de que el estado sea explícito y
+  persistente.** Si el flujo fuera una cadena de llamadas continua, un
+  humano solo se entera del resultado al final (ej. un correo ya borrado
+  por error, marcado incorrectamente como spam). Con estado explícito y una
+  interrupción que lo persiste, el flujo se puede pausar, un humano corrige
+  el estado ("este correo no es spam"), y el agente continúa desde ahí sin
+  perder lo ya hecho ni reiniciar el proceso completo.
+- Versiones fijadas hoy para arrancar V2: `langgraph==1.2.11`,
+  `langchain==1.3.15`, `langchain-anthropic==1.5.6`,
+  `langgraph-checkpoint==4.2.0`.
+
+### ¿Qué no entendí bien?
+
+- El mecanismo concreto de una interrupción de LangGraph que persista el
+  estado para que un humano intervenga y el flujo continúe después — sé que
+  existe (`AgentState`/`AgentStatePydantic` y objetos de human-in-the-loop
+  cambiaron en la migración 0.x → 1.x) pero no verifiqué la API real todavía.
+
+---
diff --git a/docs/work_log.md b/docs/work_log.md
index 8615553..f96ae1e 100644
--- a/docs/work_log.md
+++ b/docs/work_log.md
@@ -259,3 +259,31 @@
 - V1 real cerrada tras el merge — arrancar V2 (T18: LangGraph fundamentals)
 
 ---
+
+## 2026-08-13
+
+### Trabajo desarrollado
+- Fix real de T14: `_download_pdf` crea el directorio padre antes de escribir
+  (`ingestion_service.py`), y `test_extract_text_pdf` parcha `PAPERS_DIR` a
+  `tmp_path` para no ensuciar el repo — verificado en un clon fresco real,
+  no solo en el working copy (`9491967`, merge PR#5)
+- `ROADMAP.md` corregido: T14 tenía fecha de cierre falsa (12/08), quedó con
+  la fecha real (13/08) y la nota de qué faltaba; T17 (merge a `main`) marcado
+- Tag `v1.0.0` re-apuntado al commit de merge de PR#5 — V1 real cerrada
+- Tracking de V2 configurado en GitHub: 11 labels, milestone "V2 — Agente
+  LangGraph + Briefing matutino" (vence 02/10), `.github/ISSUE_TEMPLATE/task.md`,
+  y los 7 issues T18–T24 con estimaciones, dependencias y labels
+- Estudio de LangGraph: notebook de práctica, dependencias agregadas
+  (`langgraph`, `langchain`, `langchain-anthropic`), y entrada en
+  `learnings.md` sobre por qué un grafo aporta (bifurcaciones y ciclos, no
+  el estado) — corregida una confusión conceptual real en la conversación
+  con el tutor
+- `CLAUDE.md` actualizado con el ciclo semanal de trabajo vía GitHub Project
+
+### Próximos pasos
+- Abrir PR y mergear `docs/v2-github-tracking` a `main`
+- Arrancar T18 (LangGraph fundamentals) el lunes 17/08
+- Mañana (14/08, viernes): agregar la pregunta de grafo-vs-pipeline al banco
+  de preguntas en el ritual normal
+
+---