LangGraph notebook: replace BaseStore with memory_instructions

notebooks/06-langgraph-react-agent.ipynb

@@ -8,9 +8,18 @@
     "\n",
     "Build a ReAct agent with LangGraph that remembers user preferences and past interactions across conversations using Hindsight memory.\n",
     "\n",
-    "This notebook demonstrates two patterns:\n",
+    "This notebook demonstrates three patterns:\n",
     "- **Tools pattern**: The agent decides when to store/retrieve memories\n",
     "- **Nodes pattern**: Memory injection and storage happen automatically as graph steps\n",
+    "- **Memory Instructions pattern**: Pre-fetch memories into a system prompt for standalone LangChain use (no graph needed)\n",
+    "\n",
+    "## Which pattern should I use?\n",
+    "\n",
+    "| Pattern | Best for | Requires LangGraph? |\n",
+    "|---------|----------|---------------------|\n",
+    "| **Tools** | ReAct agents that reason about when memory is relevant | No (works with any LangChain ChatModel) |\n",
+    "| **Nodes** | Graphs where you always want memory context, no LLM decision needed | Yes |\n",
+    "| **Memory Instructions** | Simple chains, standalone LangChain, or any context where you want a memory-enriched system prompt | No |\n",
     "\n",
     "## Prerequisites\n",
     "\n",
@@ -378,6 +387,80 @@
     "print(result[\"messages\"][-1].content)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pattern 3: Memory Instructions — Standalone LangChain\n",
+    "\n",
+    "Use `memory_instructions()` to pre-fetch memories and inject them into a system prompt. This works with any LangChain model — no LangGraph graph needed.\n",
+    "\n",
+    "Best when you want memory context in a simple chain or don't need the agent to decide when to use memory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create the Instructions Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from hindsight_langgraph import memory_instructions\n",
+    "\n",
+    "# memory_instructions() returns an async callable that recalls memories\n",
+    "# and appends them to your base instructions. If recall fails or returns\n",
+    "# nothing, it gracefully falls back to base_instructions alone.\n",
+    "get_instructions = memory_instructions(\n",
+    "    client=client,\n",
+    "    bank_id=\"user-alice\",  # Reuse Alice's bank from Pattern 1\n",
+    "    base_instructions=(\n",
+    "        \"You are a helpful assistant with long-term memory. \"\n",
+    "        \"Use what you know about the user to personalize your responses.\"\n",
+    "    ),\n",
+    "    budget=\"mid\",\n",
+    "    max_results=5,\n",
+    ")\n",
+    "\n",
+    "# Fetch instructions with memories injected\n",
+    "instructions = await get_instructions()\n",
+    "print(\"Instructions with memories:\")\n",
+    "print(instructions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use in a Simple LangChain Call\n",
+    "\n",
+    "No graph, no tools — just a model call with memory-enriched instructions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI(model=\"gpt-4o-mini\")\n",
+    "\n",
+    "# Each call to get_instructions() re-fetches memories, so it stays up to date\n",
+    "instructions = await get_instructions()\n",
+    "response = await model.ainvoke([\n",
+    "    {\"role\": \"system\", \"content\": instructions},\n",
+    "    {\"role\": \"user\", \"content\": \"What programming language and IDE do I use?\"},\n",
+    "])\n",
+    "print(response.content)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -404,6 +487,7 @@
     "\n",
     "- **Tools pattern**: The agent decides when to store/retrieve. Best for complex reasoning flows.\n",
     "- **Nodes pattern**: Memory happens automatically. Best when you always want context injection.\n",
+    "- **Memory Instructions**: Pre-fetches memories into a system prompt. Best for standalone LangChain or simple chains.\n",
     "- **Dynamic banks**: Use `bank_id_from_config` or parameterized `bank_id` for per-user isolation.\n",
     "- **Tags**: Scope memories by source, conversation, or topic for precise recall.\n",
     "\n",