{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "# Hindsight-LangMem: Drop-in Semantic Memory for LangGraph\n\nReplace your LangGraph memory store in one line and get advanced semantic capabilities."
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## What is Hindsight-LangMem?\n\n`hindsight-langmem` implements LangGraph's `BaseStore` interface using Hindsight as the backend.\n\n### What You Get vs Standard LangGraph Memory\n\n| Feature | Standard Memory | Hindsight-LangMem |\n|---------|-----------------|-------------------|\n| Basic Key-Value Storage | ✅ | ✅ |\n| Semantic Search | ✅ Basic | ✅ **Enhanced with spreading activation** |\n| Namespace Support | ✅ | ✅ |\n| **Personality-Driven Retrieval** | ❌ | ✅ |\n| **Automatic Fact Extraction** | ❌ | ✅ |\n| **Entity Recognition** | ❌ | ✅ |\n| **Temporal Reasoning** | ❌ | ✅ |\n| **Opinion Formation** | ❌ | ✅ |\n| **Background Knowledge** | ❌ | ✅ |\n| **Thinking/Reasoning API** | ❌ | ✅ |\n\n### When to Use\n- Conversational agents needing long-term memory\n- Personalized AI with context-aware responses  \n- Multi-agent systems with distinct personalities\n- Knowledge management with semantic search"
  },
  {
   "cell_type": "markdown",
   "source": "## Installation\n\nRun this cell to install dependencies:",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": "!pip install langgraph langmem",
   "metadata": {},
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "Make sure Hindsight API is running at `http://localhost:8888`",
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": "## Setup API Keys\n\nSet up your OpenAI API key and Hindsight URL:",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": "import os\nimport getpass\n\n# Set OpenAI API key\nif \"OPENAI_API_KEY\" not in os.environ:\n    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")\n\n# Set Hindsight API URL\nif \"HINDSIGHT_API_URL\" not in os.environ:\n    os.environ[\"HINDSIGHT_API_URL\"] = input(\"Enter Hindsight API URL (default: http://localhost:8888): \") or \"http://localhost:8888\"\n\nprint(\"✅ API keys configured\")",
   "metadata": {},
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Drop-in Replacement\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### Before: Standard LangGraph Memory",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": "from langmem import create_manage_memory_tool, create_search_memory_tool\nfrom langgraph.prebuilt import create_react_agent\nfrom langgraph.store.memory import InMemoryStore\n\n# Standard store - basic key-value with optional vector search\nstore = InMemoryStore()\n\nagent = create_react_agent(\n    \"openai:gpt-4o\",\n    tools=[\n        create_manage_memory_tool(namespace=(\"memories\",)),\n        create_search_memory_tool(namespace=(\"memories\",)),\n    ],\n    store=store\n)",
   "metadata": {},
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "### After: With Hindsight-LangMem\n\n**Just change one line!**",
   "metadata": {},
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": "import os\nfrom langmem import create_manage_memory_tool, create_search_memory_tool\nfrom langgraph.prebuilt import create_react_agent\nfrom hindsight_langmem import HindsightStore  # ← Only import change!\n\n# Replace InMemoryStore with HindsightStore\nbase_url = os.getenv(\"HINDSIGHT_API_URL\", \"http://localhost:8888\")\nstore = HindsightStore(base_url=base_url, default_agent_id=\"my_agent\")  # ← One line change!\n\n# Everything else stays exactly the same\nagent = create_react_agent(\n    \"openai:gpt-4o\",  # ← Use OpenAI\n    tools=[\n        create_manage_memory_tool(namespace=(\"memories\",)),\n        create_search_memory_tool(namespace=(\"memories\",)),\n    ],\n    store=store  # ← Now using Hindsight with enhanced capabilities!\n)\n\nprint(\"✅ Agent created with Hindsight-powered memory\")",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "import os\nfrom langmem import create_manage_memory_tool, create_search_memory_tool\nfrom langgraph.prebuilt import create_react_agent\nfrom hindsight_langmem import HindsightStore  # ← Only import change!\n\n# Replace InMemoryStore with HindsightStore\nbase_url = os.getenv(\"HINDSIGHT_API_URL\", \"http://localhost:8888\")\nstore = HindsightStore(base_url=base_url, default_agent_id=\"my_agent\")  # ← One line change!\n\n# Everything else stays exactly the same\nagent = create_react_agent(\n    \"anthropic:claude-3-5-sonnet-latest\",\n    tools=[\n        create_manage_memory_tool(namespace=(\"memories\",)),\n        create_search_memory_tool(namespace=(\"memories\",)),\n    ],\n    store=store  # ← Now using Hindsight with enhanced capabilities!\n)\n\nprint(\"✅ Agent created with Hindsight-powered memory\")"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Conversational Memory in Action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "# Store information\n",
    "result1 = agent.invoke({\n",
    "    \"messages\": [{\n",
    "        \"role\": \"user\",\n",
    "        \"content\": \"\"\"Remember: I'm David, a software engineer working on AI projects. \n",
    "        I love Python and machine learning. Currently building a chatbot with LangGraph.\"\"\"\n",
    "    }]\n",
    "})\n",
    "print(\"Agent:\", result1[\"messages\"][-1].content)\n",
    "\n",
    "time.sleep(2)\n",
    "\n",
    "# Recall information\n",
    "result2 = agent.invoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"What do you remember about me?\"}]\n",
    "})\n",
    "print(\"\\nAgent:\", result2[\"messages\"][-1].content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## What Happens Behind the Scenes\n\nWhen your agent stores memories with Hindsight, automatically:\n\n1. **Fact Extraction**: Natural language → structured facts\n2. **Entity Recognition**: Identifies people, places, concepts\n3. **Semantic Indexing**: Spreading activation for better retrieval\n4. **Temporal Awareness**: Event dates tracked for time queries\n5. **Opinion Formation**: Agent develops perspectives over time\n6. **Personality Influence**: Memory retrieval shaped by personality traits\n\n**You use the standard LangGraph API - Hindsight does the rest!**"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": "# Different agents with different personalities\ncreative_store = HindsightStore(base_url=base_url, default_agent_id=\"creative_writer\")\nanalyst_store = HindsightStore(base_url=base_url, default_agent_id=\"data_analyst\")\n\ncreative_agent = create_react_agent(\n    \"openai:gpt-4o\",\n    tools=[\n        create_manage_memory_tool(namespace=(\"creative\",)),\n        create_search_memory_tool(namespace=(\"creative\",))\n    ],\n    store=creative_store\n)\n\nanalyst_agent = create_react_agent(\n    \"openai:gpt-4o\",\n    tools=[\n        create_manage_memory_tool(namespace=(\"analysis\",)),\n        create_search_memory_tool(namespace=(\"analysis\",))\n    ],\n    store=analyst_store\n)\n\nprint(\"✅ Two agents with isolated memories and distinct personalities\")",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "# Different agents with different personalities\ncreative_store = HindsightStore(base_url=base_url, default_agent_id=\"creative_writer\")\nanalyst_store = HindsightStore(base_url=base_url, default_agent_id=\"data_analyst\")\n\ncreative_agent = create_react_agent(\n    \"anthropic:claude-3-5-sonnet-latest\",\n    tools=[\n        create_manage_memory_tool(namespace=(\"creative\",)),\n        create_search_memory_tool(namespace=(\"creative\",))\n    ],\n    store=creative_store\n)\n\nanalyst_agent = create_react_agent(\n    \"anthropic:claude-3-5-sonnet-latest\",\n    tools=[\n        create_manage_memory_tool(namespace=(\"analysis\",)),\n        create_search_memory_tool(namespace=(\"analysis\",))\n    ],\n    store=analyst_store\n)\n\nprint(\"✅ Two agents with isolated memories and distinct personalities\")"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## Summary\n\n### The Change\n```python\n# Before\nstore = InMemoryStore()\n\n# After  \nstore = HindsightStore(base_url=\"http://localhost:8888\", default_agent_id=\"my_agent\")\n```\n\n### What You Get\n- ✅ Semantic search with spreading activation\n- ✅ Automatic fact extraction from conversations\n- ✅ Entity recognition and linking\n- ✅ Temporal reasoning (time-aware queries)\n- ✅ Personality-driven memory retrieval\n- ✅ Opinion formation over time\n- ✅ Multi-agent support with isolated memories\n\n**Same LangGraph API. Smarter memory. Zero code changes (except the store line).**"
  }
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