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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Online Customer Support with Handoffs\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "The **handoffs pattern** is a multi-agent architecture where agents pass control to each other through state transitions. This tutorial demonstrates how to build an online customer support system where different \"agents\" (really, different configurations of a single agent) handle specific stages of the support workflow.\n",
+    "\n",
+    "In this tutorial, you'll build a support bot that:\n",
+    "- Collects warranty information before proceeding\n",
+    "- Classifies issues as hardware or software\n",
+    "- Provides solutions or escalates to human support\n",
+    "- Maintains conversation state across multiple turns\n",
+    "\n",
+    "Unlike the supervisor pattern where subagents are called as tools, handoffs create a **state machine** where the active agent changes based on workflow progress. The key insight is that each \"agent\" is just a different configuration (system prompt + tools) of the same underlying agent, selected dynamically based on state."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "### Installation\n",
+    "\n",
+    "First, install the required package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install langchain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LangSmith Setup\n",
+    "\n",
+    "Set up LangSmith to inspect what is happening inside your agent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+    "os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize Chat Model\n",
+    "\n",
+    "Select a chat model to use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'langchain.chat_models'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchat_models\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m init_chat_model\n\u001b[1;32m      3\u001b[0m model \u001b[38;5;241m=\u001b[39m init_chat_model(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124manthropic:claude-3-5-sonnet-latest\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'langchain.chat_models'"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.chat_models import init_chat_model\n",
+    "\n",
+    "model = init_chat_model(\"anthropic:claude-3-5-sonnet-latest\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Define Custom State\n",
+    "\n",
+    "First, define a custom state schema that tracks which agent is currently active:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import AgentState\n",
+    "from typing_extensions import NotRequired\n",
+    "from typing import Literal\n",
+    "\n",
+    "# Define the possible agent types\n",
+    "AgentType = Literal[\"warranty_collector\", \"issue_classifier\", \"resolution_specialist\"]\n",
+    "\n",
+    "class SupportState(AgentState):\n",
+    "    \"\"\"State for customer support workflow with handoffs.\"\"\"\n",
+    "    active_agent: NotRequired[AgentType]\n",
+    "    warranty_status: NotRequired[Literal[\"in_warranty\", \"out_of_warranty\", \"unknown\"]]\n",
+    "    issue_type: NotRequired[Literal[\"hardware\", \"software\", \"unknown\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `active_agent` field is the core of the handoffs pattern - it determines which agent configuration is loaded on each turn."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Define Agent Configurations\n",
+    "\n",
+    "Create a dataclass to hold agent configurations. Each configuration specifies the system prompt and available tools for that agent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dataclasses import dataclass\n",
+    "from typing import List\n",
+    "from langchain_core.tools import BaseTool\n",
+    "\n",
+    "@dataclass\n",
+    "class AgentConfig:\n",
+    "    \"\"\"Configuration for a specific agent in the handoff workflow.\"\"\"\n",
+    "    name: AgentType\n",
+    "    system_prompt: str\n",
+    "    tools: List[BaseTool]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Create Handoff and Workflow Tools\n",
+    "\n",
+    "Create tools that update the workflow state. These tools allow agents to record information and transition to the next agent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.tools import tool, ToolRuntime\n",
+    "from langgraph.types import Command\n",
+    "\n",
+    "@tool\n",
+    "def record_warranty_status(\n",
+    "    status: Literal[\"in_warranty\", \"out_of_warranty\"],\n",
+    "    runtime: ToolRuntime[None, SupportState]\n",
+    ") -> Command:\n",
+    "    \"\"\"Record the customer's warranty status and transition to issue classification.\"\"\"\n",
+    "    return Command(update={\n",
+    "        \"warranty_status\": status,\n",
+    "        \"active_agent\": \"issue_classifier\"\n",
+    "    })\n",
+    "\n",
+    "@tool\n",
+    "def record_issue_type(\n",
+    "    issue_type: Literal[\"hardware\", \"software\"],\n",
+    "    runtime: ToolRuntime[None, SupportState]\n",
+    ") -> Command:\n",
+    "    \"\"\"Record the type of issue and transition to resolution specialist.\"\"\"\n",
+    "    return Command(update={\n",
+    "        \"issue_type\": issue_type,\n",
+    "        \"active_agent\": \"resolution_specialist\"\n",
+    "    })\n",
+    "\n",
+    "@tool\n",
+    "def escalate_to_human(\n",
+    "    reason: str,\n",
+    "    runtime: ToolRuntime[None, SupportState]\n",
+    ") -> str:\n",
+    "    \"\"\"Escalate the case to a human support specialist.\"\"\"\n",
+    "    # In a real system, this would create a ticket, notify staff, etc.\n",
+    "    return f\"Escalating to human support. Reason: {reason}\"\n",
+    "\n",
+    "@tool\n",
+    "def provide_solution(\n",
+    "    solution: str,\n",
+    "    runtime: ToolRuntime[None, SupportState]\n",
+    ") -> str:\n",
+    "    \"\"\"Provide a solution to the customer's issue.\"\"\"\n",
+    "    return f\"Solution provided: {solution}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Create Agent Configurations\n",
+    "\n",
+    "Now define the specific configurations for each agent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "AGENT_CONFIGS = {\n",
+    "    \"warranty_collector\": AgentConfig(\n",
+    "        name=\"warranty_collector\",\n",
+    "        system_prompt=\"\"\"You are a warranty verification specialist.\n",
+    "\n",
+    "Your task:\n",
+    "1. Greet the customer warmly\n",
+    "2. Ask if their device is under warranty\n",
+    "3. Use record_warranty_status to record their response and move to the next step\n",
+    "\n",
+    "Be conversational and friendly. Don't ask multiple questions at once.\"\"\",\n",
+    "        tools=[record_warranty_status]\n",
+    "    ),\n",
+    "\n",
+    "    \"issue_classifier\": AgentConfig(\n",
+    "        name=\"issue_classifier\",\n",
+    "        system_prompt=\"\"\"You are an issue classification specialist.\n",
+    "\n",
+    "Your task:\n",
+    "1. Ask the customer to describe their issue\n",
+    "2. Determine if it's a hardware issue (physical damage, broken parts) or software issue (app crashes, performance)\n",
+    "3. Use record_issue_type to record the classification and move to the next step\n",
+    "\n",
+    "If unclear, ask clarifying questions before classifying.\"\"\",\n",
+    "        tools=[record_issue_type]\n",
+    "    ),\n",
+    "\n",
+    "    \"resolution_specialist\": AgentConfig(\n",
+    "        name=\"resolution_specialist\",\n",
+    "        system_prompt=\"\"\"You are a technical resolution specialist.\n",
+    "\n",
+    "Context available to you:\n",
+    "- Warranty status: {warranty_status}\n",
+    "- Issue type: {issue_type}\n",
+    "\n",
+    "Your task:\n",
+    "1. For SOFTWARE issues: provide troubleshooting steps using provide_solution\n",
+    "2. For HARDWARE issues:\n",
+    "   - If IN WARRANTY: explain warranty repair process using provide_solution\n",
+    "   - If OUT OF WARRANTY: escalate_to_human for paid repair options\n",
+    "\n",
+    "Be specific and helpful in your solutions.\"\"\",\n",
+    "        tools=[provide_solution, escalate_to_human]\n",
+    "    )\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note how the resolution specialist's prompt references state variables - we'll inject these dynamically."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Create Dynamic Configuration Middleware\n",
+    "\n",
+    "Create middleware that reads the `active_agent` state and configures the agent accordingly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": "from langchain.agents.middleware import AgentMiddleware, ModelRequest, ModelResponse\nfrom langchain_core.messages import SystemMessage, HumanMessage, AIMessage\nfrom typing import Callable\n\nclass HandoffMiddleware(AgentMiddleware[SupportState]):\n    \"\"\"Middleware that dynamically configures the agent based on active_agent state.\n    \n    This middleware manages agent-specific message histories to ensure each agent\n    only sees relevant context, preventing unbounded history growth and confusion.\n    \"\"\"\n\n    state_schema = SupportState\n\n    def __init__(self, agent_configs: dict[AgentType, AgentConfig]):\n        super().__init__()\n        self.agent_configs = agent_configs\n\n    def wrap_model_call(\n        self,\n        request: ModelRequest,\n        handler: Callable[[ModelRequest], ModelResponse]\n    ) -> ModelResponse:\n        \"\"\"Configure system prompt, tools, and message history based on active_agent.\"\"\"\n\n        # Get the current active agent from state (default to warranty_collector)\n        active_agent = request.state.get(\"active_agent\", \"warranty_collector\")\n        config = self.agent_configs[active_agent]\n\n        # Build context summary from state (what previous agents learned)\n        context = self._build_context_summary(request.state)\n        \n        # Format the system prompt with current state values\n        system_prompt = config.system_prompt.format(\n            warranty_status=request.state.get(\"warranty_status\", \"unknown\"),\n            issue_type=request.state.get(\"issue_type\", \"unknown\")\n        )\n        \n        # Inject context summary into system prompt\n        if context:\n            system_prompt += f\"\\n\\n{context}\"\n\n        # Get only recent, relevant messages for this agent\n        relevant_messages = self._get_recent_messages(request.messages, max_turns=2)\n        \n        # Construct valid message sequence: System message, then conversation\n        messages = [SystemMessage(content=system_prompt)] + relevant_messages\n\n        # Update request with new configuration\n        request.messages = messages\n        request.tools = config.tools\n\n        return handler(request)\n    \n    def _build_context_summary(self, state: dict) -> str:\n        \"\"\"Build a summary of what previous agents learned from state.\n        \n        This allows each agent to have a clean message history while still\n        having access to structured information gathered by previous agents.\n        \"\"\"\n        parts = []\n        \n        if state.get(\"warranty_status\") and state[\"warranty_status\"] != \"unknown\":\n            parts.append(f\"The customer's warranty status has been confirmed as: {state['warranty_status']}.\")\n        \n        if state.get(\"issue_type\") and state[\"issue_type\"] != \"unknown\":\n            parts.append(f\"The issue has been classified as: {state['issue_type']}.\")\n        \n        return \" \".join(parts) if parts else \"\"\n    \n    def _get_recent_messages(self, messages: list, max_turns: int = 2) -> list:\n        \"\"\"Get last N conversation turns, ensuring valid message sequence.\n        \n        A turn consists of: user message + AI response (+ optional tool messages).\n        This prevents message history from growing unbounded and ensures each\n        agent only sees its relevant context.\n        \n        Args:\n            messages: Full message history\n            max_turns: Maximum number of conversation turns to retain\n            \n        Returns:\n            List of recent messages forming complete, valid turns\n        \"\"\"\n        if not messages:\n            return []\n        \n        # Work backwards to collect complete turns\n        turns = []\n        current_turn = []\n        \n        for msg in reversed(messages):\n            if isinstance(msg, SystemMessage):\n                continue  # Skip old system messages (we inject our own)\n            \n            current_turn.insert(0, msg)\n            \n            # A turn starts with a user message\n            if isinstance(msg, HumanMessage):\n                turns.insert(0, current_turn)\n                current_turn = []\n                \n                if len(turns) >= max_turns:\n                    break\n        \n        # Flatten turns into message list\n        result = []\n        for turn in turns:\n            result.extend(turn)\n        \n        # Ensure we start with a user message for valid history\n        # (Most LLM providers require this pattern)\n        while result and not isinstance(result[0], HumanMessage):\n            result.pop(0)\n        \n        return result"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "This middleware:\n1. Reads `active_agent` from state to determine which agent configuration to use\n2. Looks up the corresponding configuration (system prompt + tools)\n3. **Builds a context summary** from state that captures what previous agents learned\n4. Injects the system prompt with both state values AND the context summary\n5. **Filters message history** to only include recent turns (prevents unbounded growth)\n6. Ensures valid message sequences (system → user → AI → tool alternation)\n\n### Key Innovation: Separation of State and Messages\n\nThe middleware solves a critical problem with multi-agent handoffs:\n\n**Problem**: If all agents see the full message history, it grows unbounded and agents get confused by other agents' conversations.\n\n**Solution**: \n- **State dict** holds structured data (warranty status, issue type) - this is the cross-agent memory\n- **Message history** is agent-scoped and bounded - each agent only sees its recent conversation\n- **System message** injects context summaries from state - agents know what happened without seeing full history\n\nThis means:\n- The warranty_collector doesn't see issue_classifier's messages\n- The resolution_specialist doesn't see warranty_collector's messages\n- BUT each agent knows what was learned via state summaries in their system prompt"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Create the Agent\n",
+    "\n",
+    "Now create the agent with the handoff middleware and a checkpointer for state persistence:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import create_agent\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
+    "\n",
+    "# Collect all tools from all configurations\n",
+    "all_tools = []\n",
+    "for config in AGENT_CONFIGS.values():\n",
+    "    all_tools.extend(config.tools)\n",
+    "\n",
+    "# Create the agent with middleware\n",
+    "agent = create_agent(\n",
+    "    model,\n",
+    "    tools=all_tools,\n",
+    "    state_schema=SupportState,\n",
+    "    middleware=[HandoffMiddleware(AGENT_CONFIGS)],\n",
+    "    checkpointer=InMemorySaver()  # Required for state persistence across turns\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Why a checkpointer?** The checkpointer maintains state across conversation turns. Without it, the `active_agent` state would be lost between user messages, breaking the handoff flow."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7: Test the Workflow\n",
+    "\n",
+    "Test the complete handoff workflow:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "# Configuration for this conversation thread\n",
+    "config = {\"configurable\": {\"thread_id\": \"support-001\"}}\n",
+    "\n",
+    "# Initial message - starts with warranty_collector\n",
+    "print(\"=== Turn 1: Warranty Collection ===\")\n",
+    "result = agent.invoke(\n",
+    "    {\n",
+    "        \"messages\": [HumanMessage(\"Hi, my phone screen is cracked\")]\n",
+    "    },\n",
+    "    config\n",
+    ")\n",
+    "print(result[\"messages\"][-1].content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# User responds about warranty\n",
+    "print(\"\\n=== Turn 2: User responds about warranty ===\")\n",
+    "result = agent.invoke(\n",
+    "    {\n",
+    "        \"messages\": [HumanMessage(\"Yes, it's still under warranty\")]\n",
+    "    },\n",
+    "    config\n",
+    ")\n",
+    "print(result[\"messages\"][-1].content)\n",
+    "print(f\"Active agent: {result.get('active_agent')}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# User describes the issue\n",
+    "print(\"\\n=== Turn 3: Issue classification ===\")\n",
+    "result = agent.invoke(\n",
+    "    {\n",
+    "        \"messages\": [HumanMessage(\"The screen is physically cracked, it happened when I dropped it\")]\n",
+    "    },\n",
+    "    config\n",
+    ")\n",
+    "print(result[\"messages\"][-1].content)\n",
+    "print(f\"Active agent: {result.get('active_agent')}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Resolution\n",
+    "print(\"\\n=== Turn 4: Resolution ===\")\n",
+    "result = agent.invoke(\n",
+    "    {\n",
+    "        \"messages\": [HumanMessage(\"What should I do?\")]\n",
+    "    },\n",
+    "    config\n",
+    ")\n",
+    "print(result[\"messages\"][-1].content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Expected flow:\n",
+    "1. **Warranty Collector**: Asks about warranty status\n",
+    "2. **Issue Classifier**: Asks about the problem, determines it's hardware\n",
+    "3. **Resolution Specialist**: Provides warranty repair instructions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "### Turn 3: After issue classified\nTool call: `record_issue_type(\"hardware\")` returns:\n```python\nCommand(update={\n    \"issue_type\": \"hardware\",\n    \"active_agent\": \"resolution_specialist\"  # State transition!\n})\n```\n\nNext turn, middleware configures:\n- System prompt: Resolution specialist instructions (with state context)\n- Tools: `[provide_solution, escalate_to_human]`\n\n## Message History Management\n\nA critical aspect of the handoffs pattern is managing what each agent sees. The middleware implements **message history scoping** to solve several problems:\n\n### The Problem\n\nWithout message history management:\n```python\n# Full conversation after 3 agent transitions:\n[\n    HumanMessage(\"Hi, my phone screen is cracked\"),\n    AIMessage(\"Is your device under warranty?\"),  # warranty_collector\n    HumanMessage(\"Yes, it's still under warranty\"),\n    AIMessage(\"Tool: record_warranty_status\"),     # warranty_collector\n    ToolMessage(\"Recorded warranty status\"),\n    AIMessage(\"Can you describe the issue?\"),      # issue_classifier\n    HumanMessage(\"The screen is physically cracked\"),\n    AIMessage(\"Tool: record_issue_type\"),          # issue_classifier\n    ToolMessage(\"Recorded issue type\"),\n    AIMessage(\"I can help with warranty repair\"),  # resolution_specialist\n]\n```\n\n**Problems**:\n- ❌ Message history grows unbounded (9+ messages after just 3 turns)\n- ❌ Resolution specialist sees warranty_collector's conversation\n- ❌ Agents can get confused by other agents' messages\n- ❌ No way to give an agent just its own conversation context\n\n### The Solution: Message Scoping + State Summaries\n\nThe middleware's `_get_recent_messages()` method keeps only the last 2 conversation turns:\n\n```python\n# What resolution_specialist actually sees:\n[\n    SystemMessage(\"\"\"You are a technical resolution specialist.\n    \n    Context:\n    - Warranty status: in_warranty\n    - Issue type: hardware\n    \n    The customer's warranty status has been confirmed as: in_warranty.\n    The issue has been classified as: hardware.\n    \"\"\"),\n    HumanMessage(\"The screen is physically cracked\"),\n    AIMessage(\"Tool: record_issue_type\"),\n    ToolMessage(\"Recorded issue type\"),\n    HumanMessage(\"What should I do?\"),  # Current user message\n]\n```\n\n**Benefits**:\n- ✅ Bounded message history (only 2-3 recent turns)\n- ✅ Agent sees only its own conversation\n- ✅ Context from previous agents available via system message\n- ✅ State dict provides structured cross-agent memory\n- ✅ Valid message sequences maintained (user/AI/tool alternation)\n\n### Valid Message Sequences\n\nThe middleware ensures message histories always follow LLM provider requirements:\n\n**Valid patterns**:\n```\nSystem → User → AI\nSystem → User → AI (with tool calls) → Tool → AI → User\n```\n\n**Invalid patterns** (prevented by middleware):\n```\nAI → AI  # Two consecutive AI messages\nTool → User  # Tool message without preceding AI tool call\nAI (with tool calls) → User  # Missing tool response\n```\n\nThe `_get_recent_messages()` method specifically:\n1. Collects complete \"turns\" (user message + AI response + any tool messages)\n2. Removes orphaned tool messages without their AI message\n3. Ensures the sequence always starts with a user message\n\n### Debugging: Inspecting Agent Views\n\nYou can add logging to see what each agent actually sees:"
+  },
+  {
+   "cell_type": "markdown",
+   "source": "The logging shows:\n- Which agent is active\n- Exactly what messages that agent sees (after filtering)\n- The current state values\n- What tools are available to that agent\n\nThis makes it easy to verify that:\n1. Each agent only sees its recent conversation turns\n2. Context from previous agents appears in the system message\n3. Message sequences are valid\n4. State is correctly passed between agents",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "source": "# Enhanced middleware with logging for debugging\nclass HandoffMiddlewareWithLogging(HandoffMiddleware):\n    \"\"\"Extended middleware that logs what each agent sees.\"\"\"\n    \n    def wrap_model_call(\n        self,\n        request: ModelRequest,\n        handler: Callable[[ModelRequest], ModelResponse]\n    ) -> ModelResponse:\n        active_agent = request.state.get(\"active_agent\", \"warranty_collector\")\n        \n        print(f\"\\n{'='*60}\")\n        print(f\"AGENT: {active_agent}\")\n        print(f\"{'='*60}\")\n        \n        # Call parent implementation\n        response = super().wrap_model_call(request, handler)\n        \n        # Log what the agent saw\n        print(f\"\\nMessages sent to {active_agent}:\")\n        for i, msg in enumerate(request.messages):\n            msg_type = type(msg).__name__\n            content_preview = str(msg.content)[:100]\n            print(f\"  {i}. {msg_type}: {content_preview}...\")\n        \n        print(f\"\\nState: {dict(request.state)}\")\n        print(f\"Tools available: {[t.name for t in request.tools]}\")\n        \n        return response\n\n# Recreate agent with logging middleware\nagent_debug = create_agent(\n    model,\n    tools=all_tools,\n    state_schema=SupportState,\n    middleware=[HandoffMiddlewareWithLogging(AGENT_CONFIGS)],\n    checkpointer=InMemorySaver()\n)\n\n# Test with logging\nconfig_debug = {\"configurable\": {\"thread_id\": \"debug-001\"}}\n\nprint(\"Turn 1: Initial message\")\nresult = agent_debug.invoke(\n    {\"messages\": [HumanMessage(\"My device has an issue\")]},\n    config_debug\n)",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 9: Adding Flexibility - Going Back\n",
+    "\n",
+    "A key UX requirement is allowing users to correct mistakes. Add \"go back\" tools:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@tool\n",
+    "def go_back_to_warranty(\n",
+    "    runtime: ToolRuntime[None, SupportState]\n",
+    ") -> Command:\n",
+    "    \"\"\"Go back to warranty verification step.\"\"\"\n",
+    "    return Command(update={\n",
+    "        \"active_agent\": \"warranty_collector\",\n",
+    "        \"warranty_status\": \"unknown\"  # Reset warranty status\n",
+    "    })\n",
+    "\n",
+    "@tool\n",
+    "def go_back_to_classification(\n",
+    "    runtime: ToolRuntime[None, SupportState]\n",
+    ") -> Command:\n",
+    "    \"\"\"Go back to issue classification step.\"\"\"\n",
+    "    return Command(update={\n",
+    "        \"active_agent\": \"issue_classifier\",\n",
+    "        \"issue_type\": \"unknown\"  # Reset issue type\n",
+    "    })\n",
+    "\n",
+    "# Add these tools to resolution_specialist configuration\n",
+    "AGENT_CONFIGS[\"resolution_specialist\"].tools.extend([\n",
+    "    go_back_to_warranty,\n",
+    "    go_back_to_classification\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Update the resolution specialist's prompt to include navigation instructions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "AGENT_CONFIGS[\"resolution_specialist\"].system_prompt = \"\"\"You are a technical resolution specialist.\n",
+    "\n",
+    "Context:\n",
+    "- Warranty status: {warranty_status}\n",
+    "- Issue type: {issue_type}\n",
+    "\n",
+    "Your task:\n",
+    "1. For SOFTWARE issues: provide troubleshooting steps using provide_solution\n",
+    "2. For HARDWARE issues:\n",
+    "   - If IN WARRANTY: explain warranty repair process\n",
+    "   - If OUT OF WARRANTY: escalate_to_human for paid repair options\n",
+    "\n",
+    "If the customer indicates any information was wrong, use:\n",
+    "- go_back_to_warranty to correct warranty status\n",
+    "- go_back_to_classification to correct issue type\n",
+    "\n",
+    "Be specific and helpful in your solutions.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now recreate the agent with the updated configuration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Collect all tools from all configurations (including new go_back tools)\n",
+    "all_tools = []\n",
+    "for config in AGENT_CONFIGS.values():\n",
+    "    all_tools.extend(config.tools)\n",
+    "\n",
+    "# Recreate agent with updated tools\n",
+    "agent = create_agent(\n",
+    "    model,\n",
+    "    tools=all_tools,\n",
+    "    state_schema=SupportState,\n",
+    "    middleware=[HandoffMiddleware(AGENT_CONFIGS)],\n",
+    "    checkpointer=InMemorySaver()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test the correction flow:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Continue the conversation and test going back\n",
+    "result = agent.invoke(\n",
+    "    {\n",
+    "        \"messages\": [HumanMessage(\"Actually, I made a mistake - my device is out of warranty\")]\n",
+    "    },\n",
+    "    config\n",
+    ")\n",
+    "print(result[\"messages\"][-1].content)\n",
+    "print(f\"Active agent: {result.get('active_agent')}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## Key Takeaways\n\nThe handoffs pattern demonstrates several important concepts:\n\n1. **State machines**: The `active_agent` field creates a state machine where each state has different behavior (prompts + tools).\n\n2. **Dynamic configuration**: Middleware reads state and configures the agent on-the-fly, eliminating the need for separate agent instances.\n\n3. **Message history scoping**: Each agent sees only its recent conversation, preventing unbounded growth and confusion. The middleware:\n   - Keeps only the last 2-3 conversation turns for each agent\n   - Injects context summaries from state into system messages\n   - Ensures valid message sequences (user/AI/tool alternation)\n\n4. **State as cross-agent memory**: The state dict (not message history) is the primary mechanism for sharing information between agents:\n   - Structured data (warranty_status, issue_type) persists across handoffs\n   - System messages inject state summaries for context\n   - Messages remain agent-scoped and ephemeral\n\n5. **State persistence**: A checkpointer maintains state across conversation turns, enabling the handoff mechanism.\n\n6. **Sequential workflows**: Perfect for collecting information in a specific order (warranty → classification → resolution).\n\n7. **Flexibility**: Tools can transition forward OR backward, allowing users to correct mistakes.\n\n### When to use handoffs vs supervisor pattern\n\nUse **handoffs** when:\n- Agents need direct conversation with users\n- Workflow requires sequential information collection\n- State transitions depend on user responses\n- Each agent needs its own conversation context\n\nUse **supervisor** when:\n- Sub-agents don't need to converse with users\n- You want centralized orchestration\n- Sub-agents are truly independent specialists\n\nYou can also mix both patterns!\n\n### Message History Management Strategy\n\nThe key insight: **Separate message history from cross-agent memory**\n\n- **Message history** = Agent-scoped, bounded, ephemeral conversation\n- **State dict** = Cross-agent, persistent, structured data\n- **System messages** = Bridge between state and agent context\n\nThis separation ensures:\n- Agents aren't confused by other agents' conversations\n- Message histories don't grow unbounded\n- Valid message sequences are maintained\n- Agents still have full context via state summaries"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/handoffs.py b/handoffs.py
new file mode 100644
index 000000000..9d3a317c3
--- /dev/null
+++ b/handoffs.py
@@ -0,0 +1,187 @@
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.types import Command
+from typing import Callable, Any
+from typing import Literal
+from typing_extensions import NotRequired
+from dataclasses import dataclass
+
+from langchain.agents import AgentState
+from langchain.agents import create_agent
+from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
+from langchain.chat_models import init_chat_model
+from langchain.messages import HumanMessage, SystemMessage
+from langchain.tools import tool, ToolRuntime
+from langchain_core.messages import ToolMessage
+
+model = init_chat_model("anthropic:claude-3-5-sonnet-latest")
+
+
+# Define the possible workflow stages
+SupportStage = Literal["warranty_collector", "issue_classifier", "resolution_specialist"]
+
+
+class SupportState(AgentState):
+    """State for customer support workflow with handoffs."""
+
+    current_stage: NotRequired[SupportStage]
+    warranty_status: NotRequired[Literal["in_warranty", "out_of_warranty"]]
+    issue_type: NotRequired[Literal["hardware", "software"]]
+
+
+
+@tool
+def record_warranty_status(
+    status: Literal["in_warranty", "out_of_warranty"],
+    runtime: ToolRuntime[None, SupportState],
+) -> Command:
+    """Record the customer's warranty status and transition to issue classification."""
+    return Command(
+        update={
+            "messages": [
+                ToolMessage(
+                    content=f"Warranty status recorded as: {status}",
+                    tool_call_id=runtime.tool_call_id,
+                )
+            ],
+            "warranty_status": status,
+            "current_stage": "issue_classifier",
+        }
+    )
+
+
+@tool
+def record_issue_type(
+    issue_type: Literal["hardware", "software"],
+    runtime: ToolRuntime[None, SupportState],
+) -> Command:
+    """Record the type of issue and transition to resolution specialist."""
+    return Command(
+        update={
+            "messages": [
+                ToolMessage(
+                    content=f"Issue type recorded as: {issue_type}",
+                    tool_call_id=runtime.tool_call_id,
+                )
+            ],
+            "issue_type": issue_type,
+            "current_stage": "resolution_specialist",
+        }
+    )
+
+
+@tool
+def escalate_to_human(reason: str, runtime: ToolRuntime[None, SupportState]) -> str:
+    """Escalate the case to a human support specialist."""
+    # In a real system, this would create a ticket, notify staff, etc.
+    return f"Escalating to human support. Reason: {reason}"
+
+
+@tool
+def provide_solution(solution: str, runtime: ToolRuntime[None, SupportState]) -> str:
+    """Provide a solution to the customer's issue."""
+    return f"Solution provided: {solution}"
+
+
+# Define prompts as constants for lazy interpolation
+WARRANTY_COLLECTOR_PROMPT = """You are a customer support agent helping with device issues.
+
+CURRENT STAGE: Warranty verification
+
+At this stage, you need to:
+1. Greet the customer warmly
+2. Ask if their device is under warranty
+3. Use record_warranty_status to record their response and move to the next stage
+
+Be conversational and friendly. Don't ask multiple questions at once."""
+
+ISSUE_CLASSIFIER_PROMPT = """You are a customer support agent helping with device issues.
+
+CURRENT STAGE: Issue classification
+CUSTOMER INFO: Warranty status is {warranty_status}
+
+At this stage, you need to:
+1. Ask the customer to describe their issue
+2. Determine if it's a hardware issue (physical damage, broken parts) or software issue (app crashes, performance)
+3. Use record_issue_type to record the classification and move to the next stage
+
+If unclear, ask clarifying questions before classifying."""
+
+RESOLUTION_SPECIALIST_PROMPT = """You are a customer support agent helping with device issues.
+
+CURRENT STAGE: Resolution
+CUSTOMER INFO: Warranty status is {warranty_status}, issue type is {issue_type}
+
+At this stage, you need to:
+1. For SOFTWARE issues: provide troubleshooting steps using provide_solution
+2. For HARDWARE issues:
+   - If IN WARRANTY: explain warranty repair process using provide_solution
+   - If OUT OF WARRANTY: escalate_to_human for paid repair options
+
+Be specific and helpful in your solutions."""
+
+
+# Stage configuration: maps stage name to (prompt_template, tools, required_state)
+STAGE_CONFIG = {
+    "warranty_collector": {
+        "prompt": WARRANTY_COLLECTOR_PROMPT,
+        "tools": [record_warranty_status],
+        "requires": [],
+    },
+    "issue_classifier": {
+        "prompt": ISSUE_CLASSIFIER_PROMPT,
+        "tools": [record_issue_type],
+        "requires": ["warranty_status"],
+    },
+    "resolution_specialist": {
+        "prompt": RESOLUTION_SPECIALIST_PROMPT,
+        "tools": [provide_solution, escalate_to_human],
+        "requires": ["warranty_status", "issue_type"],
+    },
+}
+
+
+@wrap_model_call
+async def apply_stage_config(
+    request: ModelRequest,
+    handler: Callable[[ModelRequest], ModelResponse],
+) -> ModelResponse:
+    """Configure agent behavior based on the current stage."""
+    # Get current stage (defaults to warranty_collector for first interaction)
+    current_stage = request.state.get("current_stage", "warranty_collector")
+
+    # Look up stage configuration
+    stage_config = STAGE_CONFIG[current_stage]
+
+    # Validate required state exists
+    for key in stage_config["requires"]:
+        if request.state.get(key) is None:
+            raise ValueError(f"{key} must be set before reaching {current_stage}")
+
+    # Format prompt with state values (supports {warranty_status}, {issue_type}, etc.)
+    system_prompt = stage_config["prompt"].format(**request.state)
+
+    # Inject system prompt and stage-specific tools
+    request = request.override(
+        system_prompt=system_prompt,
+        tools=stage_config["tools"],
+    )
+
+    return await handler(request)
+
+# Collect all tools from all agent configurations
+all_tools = [
+    record_warranty_status,
+    record_issue_type,
+    provide_solution,
+    escalate_to_human,
+]
+
+# Create the agent with stage-based configuration
+agent = create_agent(
+    model,
+    tools=all_tools,
+    state_schema=SupportState,
+    middleware=[apply_stage_config],  # Single middleware handles all stages
+    checkpointer=InMemorySaver(),  # Required for state persistence across turns
+)
+
diff --git a/pyproject.toml b/pyproject.toml
index 4231dbe5f..1a792d6d7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,6 +10,8 @@ dependencies = [
     "nbformat>=5.0.0",
     "pyyaml>=6.0.2",
     "nbconvert>=7.16.6",
+    "langchain>=1.0.1",
+    "langchain-anthropic>=1.0.0",
 ]
 
 
diff --git a/src/docs.json b/src/docs.json
index 742381530..606c539ea 100644
--- a/src/docs.json
+++ b/src/docs.json
@@ -330,7 +330,8 @@
                           "oss/python/langchain/knowledge-base",
                           "oss/python/langchain/rag",
                           "oss/python/langchain/sql-agent",
-                          "oss/python/langchain/supervisor"
+                          "oss/python/langchain/supervisor",
+                          "oss/python/langchain/customer-support-handoffs"
                         ]
                       },
                       {
diff --git a/src/oss/langchain/customer-support-handoffs.mdx b/src/oss/langchain/customer-support-handoffs.mdx
new file mode 100644
index 000000000..4f639fecb
--- /dev/null
+++ b/src/oss/langchain/customer-support-handoffs.mdx
@@ -0,0 +1,790 @@
+---
+title: Online customer support with handoffs
+sidebarTitle: Customer support handoffs
+---
+
+import StableCalloutPy from '/snippets/stable-lc-callout-py.mdx';
+
+:::python
+<StableCalloutPy />
+:::
+
+## Overview
+
+The **handoffs pattern** is a [multi-agent](/oss/langchain/multi-agent#handoffs) architecture where agents pass control to each other through state transitions. This tutorial demonstrates how to build an online customer support system where different "agents" (really, different configurations of a single agent) handle specific stages of the support workflow.
+
+In this tutorial, you'll build a support bot that:
+- Collects warranty information before proceeding
+- Classifies issues as hardware or software
+- Provides solutions or escalates to human support
+- Maintains conversation state across multiple turns
+
+Unlike the [supervisor pattern](/oss/langchain/supervisor) where subagents are called as tools, handoffs create a **state machine** where the active stage changes based on workflow progress. The key insight is that each "stage" is just a different configuration (system prompt + tools) of the same underlying agent, selected dynamically based on state.
+
+Here's the workflow we'll build:
+
+```mermaid
+flowchart TD
+    %% Start
+    Start([💬 Customer reports<br>an issue]) --> Warranty{🧾 Is device<br>under warranty?}
+
+    %% Warranty check
+    Warranty -->|✅ Yes| IssueType{🧩 What type<br>of issue?}
+    Warranty -->|❌ No| OutOfWarranty{🛠️ What type<br>of issue?}
+
+    %% In-Warranty branch
+    IssueType -->|🔩 Hardware| Repair[🔧 Provide warranty<br>repair instructions]
+    IssueType -->|💻 Software| Troubleshoot[💡 Provide troubleshooting<br>steps]
+
+    %% Out-of-Warranty branch
+    OutOfWarranty -->|🔩 Hardware| Escalate[🧑 Escalate to human<br>for paid repair options]
+    OutOfWarranty -->|💻 Software| Troubleshoot
+
+    %% Troubleshooting follow-up
+    Troubleshoot --> Close([✅ Issue Resolved])
+    Repair --> Close
+    Escalate --> Close
+
+    %% === Styling ===
+    classDef decisionNode fill:#e1f5fe,stroke:#0277bd,stroke-width:2px
+    classDef actionNode fill:#fff3e0,stroke:#f57c00,stroke-width:2px
+    classDef escalateNode fill:#ffebee,stroke:#d32f2f,stroke-width:2px
+    classDef endNode fill:#e8f5e8,stroke:#388e3c,stroke-width:2px
+
+    class Warranty,IssueType,OutOfWarranty decisionNode
+    class Repair,Troubleshoot actionNode
+    class Escalate escalateNode
+    class Close endNode
+```
+
+### When to use handoffs
+
+Handoffs excel at **sequential information collection** in online customer support:
+
+* **Warranty verification** - Collect warranty ID before processing refunds
+* **Account verification** - Gather credentials before accessing sensitive data
+* **Progressive disclosure** - Unlock options only after collecting required information
+
+<Warning>
+    Design handoff flows to avoid frustrating users. Allow them to:
+    * Correct previous responses
+    * Navigate back to earlier steps
+    * Change the conversation flow naturally
+
+    Rigid workflows feel like poorly designed phone trees.
+</Warning>
+
+### Concepts
+
+We will cover the following concepts:
+
+- [Multi-agent systems](/oss/langchain/multi-agent)
+- [Middleware](/oss/langchain/middleware) for dynamic agent configuration
+- [Tools](/oss/langchain/tools) for state management
+- [Short-term memory](/oss/langchain/short-term-memory) for state persistence
+
+## Setup
+
+### Installation
+
+This tutorial requires the `langchain` package:
+
+<CodeGroup>
+```bash pip
+pip install langchain
+```
+```bash conda
+conda install langchain -c conda-forge
+```
+</CodeGroup>
+
+For more details, see our [Installation guide](/oss/langchain/install).
+
+### LangSmith
+
+Set up [LangSmith](https://smith.langchain.com) to inspect what is happening inside your agent:
+
+<CodeGroup>
+```bash bash
+export LANGSMITH_TRACING="true"
+export LANGSMITH_API_KEY="..."
+```
+```python python
+import getpass
+import os
+
+os.environ["LANGSMITH_TRACING"] = "true"
+os.environ["LANGSMITH_API_KEY"] = getpass.getpass()
+```
+</CodeGroup>
+
+### Components
+
+We will need to select a chat model:
+
+```python
+from langchain.chat_models import init_chat_model
+
+model = init_chat_model("anthropic:claude-3-5-sonnet-latest")
+```
+
+## 1. Define custom state
+
+First, define a custom state schema that tracks which stage is currently active:
+
+```python
+from langchain.agents import AgentState
+from typing_extensions import NotRequired
+from typing import Literal
+
+# Define the possible workflow stages
+SupportStage = Literal["warranty_collector", "issue_classifier", "resolution_specialist"]
+
+class SupportState(AgentState):
+    """State for customer support workflow with handoffs."""
+    current_stage: NotRequired[SupportStage]
+    warranty_status: NotRequired[Literal["in_warranty", "out_of_warranty"]]
+    issue_type: NotRequired[Literal["hardware", "software"]]
+```
+
+The `current_stage` field is the core of the handoffs pattern - it determines which configuration (prompt + tools) is loaded on each turn.
+
+## 2. Create handoff tools
+
+Create tools that update the workflow state. These tools allow the agent to record information and transition to the next stage.
+
+The key to handoffs is using `Command` to update state, including the `current_stage` field:
+
+```python
+from langchain.tools import tool, ToolRuntime
+from langchain_core.messages import ToolMessage
+from langgraph.types import Command
+
+@tool
+def record_warranty_status(
+    status: Literal["in_warranty", "out_of_warranty"],
+    runtime: ToolRuntime[None, SupportState],
+) -> Command:
+    """Record the customer's warranty status and transition to issue classification."""
+    return Command(
+        update={
+            "messages": [
+                ToolMessage(
+                    content=f"Warranty status recorded as: {status}",
+                    tool_call_id=runtime.tool_call_id,
+                )
+            ],
+            "warranty_status": status,
+            "current_stage": "issue_classifier",  # Transition to next stage
+        }
+    )
+
+
+@tool
+def record_issue_type(
+    issue_type: Literal["hardware", "software"],
+    runtime: ToolRuntime[None, SupportState],
+) -> Command:
+    """Record the type of issue and transition to resolution specialist."""
+    return Command(
+        update={
+            "messages": [
+                ToolMessage(
+                    content=f"Issue type recorded as: {issue_type}",
+                    tool_call_id=runtime.tool_call_id,
+                )
+            ],
+            "issue_type": issue_type,
+            "current_stage": "resolution_specialist",  # Transition to next stage
+        }
+    )
+
+
+@tool
+def escalate_to_human(reason: str, runtime: ToolRuntime[None, SupportState]) -> str:
+    """Escalate the case to a human support specialist."""
+    # In a real system, this would create a ticket, notify staff, etc.
+    return f"Escalating to human support. Reason: {reason}"
+
+
+@tool
+def provide_solution(solution: str, runtime: ToolRuntime[None, SupportState]) -> str:
+    """Provide a solution to the customer's issue."""
+    return f"Solution provided: {solution}"
+```
+
+Notice how `record_warranty_status` and `record_issue_type` return `Command` objects that update both the data (warranty_status, issue_type) AND the `current_stage`. This is the handoff mechanism - tools control the workflow.
+
+## 3. Define stage configurations
+
+Now define prompts and tools for each stage. We'll use a simple dictionary to map stage names to their configurations:
+
+```python
+# Define prompts as constants for easy reference
+WARRANTY_COLLECTOR_PROMPT = """You are a customer support agent helping with device issues.
+
+CURRENT STAGE: Warranty verification
+
+At this stage, you need to:
+1. Greet the customer warmly
+2. Ask if their device is under warranty
+3. Use record_warranty_status to record their response and move to the next stage
+
+Be conversational and friendly. Don't ask multiple questions at once."""
+
+ISSUE_CLASSIFIER_PROMPT = """You are a customer support agent helping with device issues.
+
+CURRENT STAGE: Issue classification
+CUSTOMER INFO: Warranty status is {warranty_status}
+
+At this stage, you need to:
+1. Ask the customer to describe their issue
+2. Determine if it's a hardware issue (physical damage, broken parts) or software issue (app crashes, performance)
+3. Use record_issue_type to record the classification and move to the next stage
+
+If unclear, ask clarifying questions before classifying."""
+
+RESOLUTION_SPECIALIST_PROMPT = """You are a customer support agent helping with device issues.
+
+CURRENT STAGE: Resolution
+CUSTOMER INFO: Warranty status is {warranty_status}, issue type is {issue_type}
+
+At this stage, you need to:
+1. For SOFTWARE issues: provide troubleshooting steps using provide_solution
+2. For HARDWARE issues:
+   - If IN WARRANTY: explain warranty repair process using provide_solution
+   - If OUT OF WARRANTY: escalate_to_human for paid repair options
+
+Be specific and helpful in your solutions."""
+
+
+# Stage configuration: maps stage name to (prompt_template, tools, required_state)
+STAGE_CONFIG = {
+    "warranty_collector": {
+        "prompt": WARRANTY_COLLECTOR_PROMPT,
+        "tools": [record_warranty_status],
+        "requires": [],
+    },
+    "issue_classifier": {
+        "prompt": ISSUE_CLASSIFIER_PROMPT,
+        "tools": [record_issue_type],
+        "requires": ["warranty_status"],
+    },
+    "resolution_specialist": {
+        "prompt": RESOLUTION_SPECIALIST_PROMPT,
+        "tools": [provide_solution, escalate_to_human],
+        "requires": ["warranty_status", "issue_type"],
+    },
+}
+```
+
+This dictionary-based configuration makes it easy to:
+- See all stages at a glance
+- Add new stages (just add another entry)
+- Understand the workflow dependencies (`requires` field)
+- Use prompt templates with state variables (e.g., `{warranty_status}`)
+
+## 4. Create stage-based middleware
+
+Create middleware that reads `current_stage` from state and applies the appropriate configuration. We'll use the `@wrap_model_call` decorator for a clean implementation:
+
+```python
+from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
+from typing import Callable
+
+
+@wrap_model_call
+async def apply_stage_config(
+    request: ModelRequest,
+    handler: Callable[[ModelRequest], ModelResponse],
+) -> ModelResponse:
+    """Configure agent behavior based on the current stage."""
+    # Get current stage (defaults to warranty_collector for first interaction)
+    current_stage = request.state.get("current_stage", "warranty_collector")
+
+    # Look up stage configuration
+    stage_config = STAGE_CONFIG[current_stage]
+
+    # Validate required state exists
+    for key in stage_config["requires"]:
+        if request.state.get(key) is None:
+            raise ValueError(f"{key} must be set before reaching {current_stage}")
+
+    # Format prompt with state values (supports {warranty_status}, {issue_type}, etc.)
+    system_prompt = stage_config["prompt"].format(**request.state)
+
+    # Inject system prompt and stage-specific tools
+    request = request.override(
+        system_prompt=system_prompt,
+        tools=stage_config["tools"],
+    )
+
+    return await handler(request)
+```
+
+This middleware:
+1. **Reads current stage**: Gets `current_stage` from state (defaults to "warranty_collector")
+2. **Looks up configuration**: Finds the matching entry in `STAGE_CONFIG`
+3. **Validates dependencies**: Ensures required state fields exist
+4. **Formats prompt**: Injects state values into the prompt template
+5. **Applies configuration**: Overrides the system prompt and available tools
+
+The `request.override()` method is key - it allows us to dynamically change the agent's behavior based on state without creating separate agent instances.
+
+## 5. Create the agent
+
+Now create the agent with the stage-based middleware and a checkpointer for state persistence:
+
+```python
+from langchain.agents import create_agent
+from langgraph.checkpoint.memory import InMemorySaver
+
+# Collect all tools from all stage configurations
+all_tools = [
+    record_warranty_status,
+    record_issue_type,
+    provide_solution,
+    escalate_to_human,
+]
+
+# Create the agent with stage-based configuration
+agent = create_agent(
+    model,
+    tools=all_tools,
+    state_schema=SupportState,
+    middleware=[apply_stage_config],  # Single middleware handles all stages
+    checkpointer=InMemorySaver(),  # Required for state persistence across turns
+)
+```
+
+<Note>
+**Why a checkpointer?** The checkpointer maintains state across conversation turns. Without it, the `current_stage` state would be lost between user messages, breaking the handoff flow.
+</Note>
+
+## 6. Test the workflow
+
+Test the complete handoff workflow:
+
+```python
+from langchain_core.messages import HumanMessage
+
+# Configuration for this conversation thread
+config = {"configurable": {"thread_id": "support-001"}}
+
+# Turn 1: Initial message - starts with warranty_collector stage
+print("=== Turn 1: Warranty Collection ===")
+result = agent.invoke(
+    {"messages": [HumanMessage("Hi, my phone screen is cracked")]},
+    config
+)
+print(result["messages"][-1].content)
+
+# Turn 2: User responds about warranty
+print("\n=== Turn 2: Warranty Response ===")
+result = agent.invoke(
+    {"messages": [HumanMessage("Yes, it's still under warranty")]},
+    config
+)
+print(result["messages"][-1].content)
+print(f"Current stage: {result.get('current_stage')}")
+
+# Turn 3: User describes the issue
+print("\n=== Turn 3: Issue Description ===")
+result = agent.invoke(
+    {"messages": [HumanMessage("The screen is physically cracked from dropping it")]},
+    config
+)
+print(result["messages"][-1].content)
+print(f"Current stage: {result.get('current_stage')}")
+
+# Turn 4: Resolution
+print("\n=== Turn 4: Resolution ===")
+result = agent.invoke(
+    {"messages": [HumanMessage("What should I do?")]},
+    config
+)
+print(result["messages"][-1].content)
+```
+
+Expected flow:
+1. **Warranty verification stage**: Asks about warranty status
+2. **Issue classification stage**: Asks about the problem, determines it's hardware
+3. **Resolution stage**: Provides warranty repair instructions
+
+## 7. Understanding the handoff mechanism
+
+Let's trace what happens at each turn:
+
+### Turn 1: Initial message
+```python
+{
+    "messages": [HumanMessage("Hi, my phone screen is cracked")],
+    "current_stage": "warranty_collector"  # Default value
+}
+```
+
+Middleware applies:
+- System prompt: `WARRANTY_COLLECTOR_PROMPT`
+- Tools: `[record_warranty_status]`
+
+### Turn 2: After warranty recorded
+Tool call: `record_warranty_status("in_warranty")` returns:
+```python
+Command(update={
+    "warranty_status": "in_warranty",
+    "current_stage": "issue_classifier"  # State transition!
+})
+```
+
+Next turn, middleware applies:
+- System prompt: `ISSUE_CLASSIFIER_PROMPT` (formatted with `warranty_status="in_warranty"`)
+- Tools: `[record_issue_type]`
+
+### Turn 3: After issue classified
+Tool call: `record_issue_type("hardware")` returns:
+```python
+Command(update={
+    "issue_type": "hardware",
+    "current_stage": "resolution_specialist"  # State transition!
+})
+```
+
+Next turn, middleware applies:
+- System prompt: `RESOLUTION_SPECIALIST_PROMPT` (formatted with `warranty_status` and `issue_type`)
+- Tools: `[provide_solution, escalate_to_human]`
+
+The key insight: **Tools drive the workflow** by updating `current_stage`, and **middleware responds** by applying the appropriate configuration.
+
+## 8. Adding flexibility: Going back
+
+A key UX requirement is allowing users to correct mistakes. Add "go back" tools:
+
+```python
+@tool
+def go_back_to_warranty(runtime: ToolRuntime[None, SupportState]) -> Command:
+    """Go back to warranty verification step."""
+    return Command(update={"current_stage": "warranty_collector"})
+
+
+@tool
+def go_back_to_classification(runtime: ToolRuntime[None, SupportState]) -> Command:
+    """Go back to issue classification step."""
+    return Command(update={"current_stage": "issue_classifier"})
+
+
+# Update the resolution_specialist configuration to include these tools
+STAGE_CONFIG["resolution_specialist"]["tools"].extend([
+    go_back_to_warranty,
+    go_back_to_classification
+])
+```
+
+Update the resolution specialist's prompt to mention these tools:
+```python
+RESOLUTION_SPECIALIST_PROMPT = """You are a customer support agent helping with device issues.
+
+CURRENT STAGE: Resolution
+CUSTOMER INFO: Warranty status is {warranty_status}, issue type is {issue_type}
+
+At this stage, you need to:
+1. For SOFTWARE issues: provide troubleshooting steps using provide_solution
+2. For HARDWARE issues:
+   - If IN WARRANTY: explain warranty repair process using provide_solution
+   - If OUT OF WARRANTY: escalate_to_human for paid repair options
+
+If the customer indicates any information was wrong, use:
+- go_back_to_warranty to correct warranty status
+- go_back_to_classification to correct issue type
+
+Be specific and helpful in your solutions."""
+```
+
+Now the agent can handle corrections:
+```python
+result = agent.invoke(
+    {"messages": [HumanMessage("Actually, I made a mistake - my device is out of warranty")]},
+    config
+)
+# Agent will call go_back_to_warranty and restart the warranty verification stage
+```
+
+## 9. Complete example
+
+Here's everything together in a runnable script:
+
+<Expandable title="View complete code" defaultOpen={false}>
+
+```python
+"""
+Customer Support Handoffs Example
+
+This example demonstrates the handoffs pattern for multi-agent systems.
+A single agent dynamically changes its behavior based on the current_stage state,
+creating a state machine for sequential information collection.
+"""
+
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.types import Command
+from typing import Callable
+from typing import Literal
+from typing_extensions import NotRequired
+
+from langchain.agents import AgentState
+from langchain.agents import create_agent
+from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
+from langchain.chat_models import init_chat_model
+from langchain.messages import HumanMessage
+from langchain.tools import tool, ToolRuntime
+from langchain_core.messages import ToolMessage
+
+model = init_chat_model("anthropic:claude-3-5-sonnet-latest")
+
+
+# Define the possible workflow stages
+SupportStage = Literal["warranty_collector", "issue_classifier", "resolution_specialist"]
+
+
+class SupportState(AgentState):
+    """State for customer support workflow with handoffs."""
+
+    current_stage: NotRequired[SupportStage]
+    warranty_status: NotRequired[Literal["in_warranty", "out_of_warranty"]]
+    issue_type: NotRequired[Literal["hardware", "software"]]
+
+
+
+@tool
+def record_warranty_status(
+    status: Literal["in_warranty", "out_of_warranty"],
+    runtime: ToolRuntime[None, SupportState],
+) -> Command:
+    """Record the customer's warranty status and transition to issue classification."""
+    return Command(
+        update={
+            "messages": [
+                ToolMessage(
+                    content=f"Warranty status recorded as: {status}",
+                    tool_call_id=runtime.tool_call_id,
+                )
+            ],
+            "warranty_status": status,
+            "current_stage": "issue_classifier",
+        }
+    )
+
+
+@tool
+def record_issue_type(
+    issue_type: Literal["hardware", "software"],
+    runtime: ToolRuntime[None, SupportState],
+) -> Command:
+    """Record the type of issue and transition to resolution specialist."""
+    return Command(
+        update={
+            "messages": [
+                ToolMessage(
+                    content=f"Issue type recorded as: {issue_type}",
+                    tool_call_id=runtime.tool_call_id,
+                )
+            ],
+            "issue_type": issue_type,
+            "current_stage": "resolution_specialist",
+        }
+    )
+
+
+@tool
+def escalate_to_human(reason: str, runtime: ToolRuntime[None, SupportState]) -> str:
+    """Escalate the case to a human support specialist."""
+    # In a real system, this would create a ticket, notify staff, etc.
+    return f"Escalating to human support. Reason: {reason}"
+
+
+@tool
+def provide_solution(solution: str, runtime: ToolRuntime[None, SupportState]) -> str:
+    """Provide a solution to the customer's issue."""
+    return f"Solution provided: {solution}"
+
+
+# Define prompts as constants for lazy interpolation
+WARRANTY_COLLECTOR_PROMPT = """You are a customer support agent helping with device issues.
+
+CURRENT STAGE: Warranty verification
+
+At this stage, you need to:
+1. Greet the customer warmly
+2. Ask if their device is under warranty
+3. Use record_warranty_status to record their response and move to the next stage
+
+Be conversational and friendly. Don't ask multiple questions at once."""
+
+ISSUE_CLASSIFIER_PROMPT = """You are a customer support agent helping with device issues.
+
+CURRENT STAGE: Issue classification
+CUSTOMER INFO: Warranty status is {warranty_status}
+
+At this stage, you need to:
+1. Ask the customer to describe their issue
+2. Determine if it's a hardware issue (physical damage, broken parts) or software issue (app crashes, performance)
+3. Use record_issue_type to record the classification and move to the next stage
+
+If unclear, ask clarifying questions before classifying."""
+
+RESOLUTION_SPECIALIST_PROMPT = """You are a customer support agent helping with device issues.
+
+CURRENT STAGE: Resolution
+CUSTOMER INFO: Warranty status is {warranty_status}, issue type is {issue_type}
+
+At this stage, you need to:
+1. For SOFTWARE issues: provide troubleshooting steps using provide_solution
+2. For HARDWARE issues:
+   - If IN WARRANTY: explain warranty repair process using provide_solution
+   - If OUT OF WARRANTY: escalate_to_human for paid repair options
+
+Be specific and helpful in your solutions."""
+
+
+# Stage configuration: maps stage name to (prompt_template, tools, required_state)
+STAGE_CONFIG = {
+    "warranty_collector": {
+        "prompt": WARRANTY_COLLECTOR_PROMPT,
+        "tools": [record_warranty_status],
+        "requires": [],
+    },
+    "issue_classifier": {
+        "prompt": ISSUE_CLASSIFIER_PROMPT,
+        "tools": [record_issue_type],
+        "requires": ["warranty_status"],
+    },
+    "resolution_specialist": {
+        "prompt": RESOLUTION_SPECIALIST_PROMPT,
+        "tools": [provide_solution, escalate_to_human],
+        "requires": ["warranty_status", "issue_type"],
+    },
+}
+
+
+@wrap_model_call
+async def apply_stage_config(
+    request: ModelRequest,
+    handler: Callable[[ModelRequest], ModelResponse],
+) -> ModelResponse:
+    """Configure agent behavior based on the current stage."""
+    # Get current stage (defaults to warranty_collector for first interaction)
+    current_stage = request.state.get("current_stage", "warranty_collector")
+
+    # Look up stage configuration
+    stage_config = STAGE_CONFIG[current_stage]
+
+    # Validate required state exists
+    for key in stage_config["requires"]:
+        if request.state.get(key) is None:
+            raise ValueError(f"{key} must be set before reaching {current_stage}")
+
+    # Format prompt with state values (supports {warranty_status}, {issue_type}, etc.)
+    system_prompt = stage_config["prompt"].format(**request.state)
+
+    # Inject system prompt and stage-specific tools
+    request = request.override(
+        system_prompt=system_prompt,
+        tools=stage_config["tools"],
+    )
+
+    return await handler(request)
+
+# Collect all tools from all agent configurations
+all_tools = [
+    record_warranty_status,
+    record_issue_type,
+    provide_solution,
+    escalate_to_human,
+]
+
+# Create the agent with stage-based configuration
+agent = create_agent(
+    model,
+    tools=all_tools,
+    state_schema=SupportState,
+    middleware=[apply_stage_config],  # Single middleware handles all stages
+    checkpointer=InMemorySaver(),  # Required for state persistence across turns
+)
+
+
+# ============================================================================
+# Test the workflow
+# ============================================================================
+
+if __name__ == "__main__":
+    config = {"configurable": {"thread_id": "support-001"}}
+
+    print("=== Turn 1: Initial Contact ===")
+    result = agent.invoke(
+        {"messages": [HumanMessage("Hi, my phone screen is cracked")]},
+        config
+    )
+    print(f"Bot: {result['messages'][-1].content}\n")
+
+    print("=== Turn 2: Warranty Response ===")
+    result = agent.invoke(
+        {"messages": [HumanMessage("Yes, it's still under warranty")]},
+        config
+    )
+    print(f"Bot: {result['messages'][-1].content}")
+    print(f"State: current_stage={result.get('current_stage')}, warranty={result.get('warranty_status')}\n")
+
+    print("=== Turn 3: Issue Description ===")
+    result = agent.invoke(
+        {"messages": [HumanMessage("The screen is physically cracked from dropping it")]},
+        config
+    )
+    print(f"Bot: {result['messages'][-1].content}")
+    print(f"State: current_stage={result.get('current_stage')}, issue_type={result.get('issue_type')}\n")
+
+    print("=== Turn 4: Resolution ===")
+    result = agent.invoke(
+        {"messages": [HumanMessage("What should I do?")]},
+        config
+    )
+    print(f"Bot: {result['messages'][-1].content}\n")
+```
+
+</Expandable>
+
+## Key takeaways
+
+The handoffs pattern demonstrates several important concepts:
+
+1. **State machines**: The `current_stage` field creates a state machine where each state has different behavior (prompts + tools).
+
+2. **Dictionary-based configuration**: `STAGE_CONFIG` makes it easy to see all stages at a glance and add new ones.
+
+3. **Dynamic middleware**: A single middleware function reads state and applies the appropriate configuration on-the-fly.
+
+4. **State persistence**: A checkpointer maintains state across conversation turns, enabling the handoff mechanism.
+
+5. **Sequential workflows**: Perfect for collecting information in a specific order (warranty → classification → resolution).
+
+6. **Flexibility**: Tools can transition forward OR backward, allowing users to correct mistakes.
+
+<Tip>
+**When to use handoffs vs supervisor pattern**
+
+Use **handoffs** when:
+- Agents need direct conversation with users
+- Workflow requires sequential information collection
+- State transitions depend on user responses
+
+Use **[supervisor](/oss/langchain/supervisor)** when:
+- Sub-agents don't need to converse with users
+- You want centralized orchestration
+- Sub-agents are truly independent specialists
+
+You can also mix both patterns!
+</Tip>
+
+## Next steps
+
+- Learn about the [supervisor pattern](/oss/langchain/supervisor) for centralized orchestration
+- Explore [middleware](/oss/langchain/middleware) for more dynamic behaviors
+- Read the [multi-agent overview](/oss/langchain/multi-agent) to compare patterns
+- Use [LangSmith](https://smith.langchain.com) to debug and monitor your multi-agent system
diff --git a/src/oss/langchain/multi-agent.mdx b/src/oss/langchain/multi-agent.mdx
index d6150a04b..3d251ab8f 100644
--- a/src/oss/langchain/multi-agent.mdx
+++ b/src/oss/langchain/multi-agent.mdx
@@ -18,8 +18,8 @@ Instead of relying on a single agent to handle every step, **multi-agent archite
 
 Multi-agent systems are useful when:
 
-* A single agent has too many tools and makes poor decisions about which to use.
-* Context or memory grows too large for one agent to track effectively.
+* A single agent has too many [tools](/oss/langchain/tools) and makes poor decisions about which to use.
+* Context or [memory](/oss/langchain/short-term-memory) grows too large for one agent to track effectively.
 * Tasks require **specialization** (e.g., a planner, researcher, math expert).
 
 ## Multi-agent patterns
@@ -39,17 +39,24 @@ Multi-agent systems are useful when:
     This tutorial demonstrates:
     * Creating specialized sub-agents for different domains (calendar and email)
     * Wrapping sub-agents as tools for centralized orchestration
-    * Adding human-in-the-loop review for sensitive actions
+    * Adding [human-in-the-loop](/oss/langchain/human-in-the-loop) review for sensitive actions
 </Card>
 
 
 ## Choosing a pattern
 
-| Question                                              | Tool Calling | Handoffs |
-|-------------------------------------------------------|--------------|----------|
-| Need centralized control over workflow?               | ✅ Yes        | ❌ No     |
-| Want agents to interact directly with the user?       | ❌ No         | ✅ Yes    |
-| Complex, human-like conversation between specialists? | ❌ Limited    | ✅ Strong |
+**Use tool calling (supervisor pattern)** when:
+* You have multiple distinct domains (e.g., calendar, email, CRM, database)
+* Each domain has multiple tools or complex logic
+* You want centralized workflow control
+* Sub-agents don't need to converse directly with users
+
+For simpler cases with just a few tools, use a single agent.
+
+**Use handoffs** when:
+* Agents need to have direct conversations with users
+* The workflow requires state transitions between specialists
+* You need dynamic control flow that changes based on user interactions
 
 <Tip>
     You can mix both patterns — use **handoffs** for agent switching, and have each agent **call subagents as tools** for specialized tasks.
@@ -57,7 +64,7 @@ Multi-agent systems are useful when:
 
 ## Customizing agent context
 
-At the heart of multi-agent design is **context engineering** - deciding what information each agent sees. LangChain gives you fine-grained control over:
+At the heart of multi-agent design is **[context engineering](/oss/langchain/context-engineering)** - deciding what information each agent sees. LangChain gives you fine-grained control over:
 
 * Which parts of the conversation or state are passed to each agent.
 * Specialized prompts tailored to subagents.
@@ -309,13 +316,14 @@ const callSubagent1 = tool(
 
 ## Handoffs
 
-In **handoffs**, agents can directly pass control to each other. The “active” agent changes, and the user interacts with whichever agent currently has control.
+In **handoffs**, agents pass control to each other through **state transitions**. Think of it as a state machine where the system tracks which agent is currently active, and that agent's configuration (system prompt, tools, etc.) determines the behavior.
 
 Flow:
 
-1. The **current agent** decides it needs help from another agent.
-2. It passes control (and state) to the **next agent**.
-3. The **new agent** interacts directly with the user until it decides to hand off again or finish.
+1. The **current agent** decides it needs to transfer control to another agent.
+2. It updates a state variable (e.g., `active_agent`) using a tool that modifies the graph state.
+3. On the next conversational turn, the graph reads this state and configures itself with the **new agent's** system prompt, tools, and behavior.
+4. The **new agent** interacts directly with the user until it decides to hand off again or finish.
 
 ```mermaid
 graph LR
@@ -324,5 +332,33 @@ graph LR
     C --> A
 ```
 
+### When to use handoffs
+
+Handoffs are particularly valuable for **"online"** customer support scenarios where you need to collect information from users in a specific sequence. For example:
+
+* Collecting a warranty ID before processing a refund.
+* Gathering account details before escalating to a specialist.
+* Verifying identity before accessing sensitive information.
+
+The key advantage is **sequential unlocking** - you can design workflows where additional options or agents become available only after required information has been collected.
+
+<Warning>
+    Design handoff flows carefully to avoid creating rigid, frustrating experiences. Users should be able to:
+    * Correct typos or mistakes in previously provided information.
+    * Change the conversation flow when appropriate.
+    * Navigate back to previous steps if needed.
+
+    Overly restrictive handoff patterns can feel like poorly designed phone trees.
+</Warning>
+
+### How handoffs work
+
+At the core, handoffs rely on **[persistent state](/oss/langchain/short-term-memory)** that survives across conversation turns:
+
+1. **State variable** - A field in your state schema (e.g., `active_agent: str`) tracks which agent is currently active.
+2. **State update tool** - The agent uses a tool to change the value of `active_agent` when handing off control.
+3. **Dynamic configuration** - On each turn, the graph entry point reads `active_agent` from the persisted state and dynamically configures the appropriate system prompt, tools, and behavior for that agent.
+
+This pattern creates a state machine where each agent represents a distinct state with its own behavior and capabilities. The state persists between user messages, allowing the conversation to resume with the correct agent configuration.
 
 ### Implementation (Coming soon)
diff --git a/uv.lock b/uv.lock
index 4f0ce4f3e..904580392 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,30 +1,74 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.13.0, <4.0.0"
 
+[options]
+prerelease-mode = "allow"
+
 [manifest]
 overrides = [{ name = "pytest-codspeed", specifier = ">=3.1.0,<4.0.0" }]
 
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
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