Add Zep memory integration documentation

python/docs/src/user-guide/agentchat-user-guide/memory.ipynb

@@ -730,6 +730,183 @@
     "config_json = mem0_memory.dump_component().model_dump_json()\n",
     "print(f\"Memory config JSON: {config_json[:100]}...\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Zep Memory Example\n",
+    "\n",
+    "[Zep](https://www.getzep.com/) is a [context engineering platform](https://www.getzep.com/solutions/context-engineering/) that provides advanced memory capabilities for AI agents. It offers both user-specific conversation memory and structured knowledge graph memory, enabling agents to maintain context across interactions and build rich knowledge representations.\n",
+    "\n",
+    "### Setup\n",
+    "\n",
+    "To use Zep memory, install the required package:\n",
+    "\n",
+    "```bash\n",
+    "pip install zep-autogen\n",
+    "```\n",
+    "\n",
+    "Set your API keys as environment variables:\n",
+    "\n",
+    "```bash\n",
+    "export ZEP_API_KEY=\"your_zep_api_key\"\n",
+    "export OPENAI_API_KEY=\"your_openai_api_key\"\n",
+    "```\n",
+    "\n",
+    "### User Memory\n",
+    "\n",
+    "In the following example, we'll demonstrate how to use `ZepUserMemory` to maintain persistent conversation context:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from autogen_agentchat.agents import AssistantAgent\n",
+    "from autogen_agentchat.ui import Console\n",
+    "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
+    "from zep_autogen import ZepUserMemory\n",
+    "from zep_cloud import AsyncZep\n",
+    "\n",
+    "# Initialize Zep client\n",
+    "zep_client = AsyncZep(api_key=os.environ.get(\"ZEP_API_KEY\"))\n",
+    "\n",
+    "# Create user memory with perpetual mode for persistent context\n",
+    "zep_memory = ZepUserMemory(\n",
+    "    client=zep_client,\n",
+    "    user_id=\"user123\",\n",
+    "    session_id=\"conversation_001\",\n",
+    "    memory_type=\"perpetual\"  # Persistent memory across sessions\n",
+    ")\n",
+    "\n",
+    "# Create assistant with Zep memory\n",
+    "assistant_agent = AssistantAgent(\n",
+    "    name=\"assistant_agent\",\n",
+    "    model_client=OpenAIChatCompletionClient(\n",
+    "        model=\"gpt-4o-2024-08-06\",\n",
+    "    ),\n",
+    "    memory=[zep_memory],\n",
+    "    system_message=\"You are a helpful assistant with persistent memory.\"\n",
+    ")\n",
+    "\n",
+    "# The agent will automatically maintain conversation context\n",
+    "stream = assistant_agent.run_stream(\n",
+    "    task=\"I prefer metric units and I'm vegan. Remember this for our conversation.\"\n",
+    ")\n",
+    "await Console(stream)\n",
+    "\n",
+    "# In a follow-up conversation, the agent remembers preferences\n",
+    "stream = assistant_agent.run_stream(\n",
+    "    task=\"What's the temperature in New York?\"\n",
+    ")\n",
+    "await Console(stream)\n",
+    "\n",
+    "# Clean up resources\n",
+    "await zep_memory.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The example above shows how ZepUserMemory automatically:\n",
+    "\n",
+    "1. Stores all conversation history in Zep's memory system\n",
+    "2. Retrieves relevant context based on the conversation\n",
+    "3. Maintains user preferences and important facts across interactions\n",
+    "4. Provides automatic summarization for longer conversations\n",
+    "\n",
+    "### Knowledge Graph Memory\n",
+    "\n",
+    "For more complex knowledge representation, Zep also supports structured knowledge graphs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from zep_autogen import ZepGraphMemory\n",
+    "\n",
+    "# Initialize knowledge graph memory\n",
+    "kg_memory = ZepGraphMemory(\n",
+    "    client=zep_client,\n",
+    "    user_id=\"user123\"\n",
+    ")\n",
+    "\n",
+    "# Add structured knowledge\n",
+    "await kg_memory.add(\n",
+    "    MemoryContent(\n",
+    "        content=\"Project Alpha deadline is March 15, 2025\",\n",
+    "        mime_type=MemoryMimeType.TEXT,\n",
+    "        metadata={\"type\": \"project\", \"importance\": \"high\"}\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "await kg_memory.add(\n",
+    "    MemoryContent(\n",
+    "        content=\"Team lead for Project Alpha is Sarah Johnson\",\n",
+    "        mime_type=MemoryMimeType.TEXT,\n",
+    "        metadata={\"type\": \"project\", \"relation\": \"team\"}\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Create an agent with both user and knowledge graph memory\n",
+    "multi_memory_agent = AssistantAgent(\n",
+    "    name=\"project_assistant\",\n",
+    "    model_client=OpenAIChatCompletionClient(model=\"gpt-4o\"),\n",
+    "    memory=[zep_memory, kg_memory],\n",
+    "    system_message=\"You help manage project information and team coordination.\"\n",
+    ")\n",
+    "\n",
+    "# The agent can now access both conversation history and structured knowledge\n",
+    "stream = multi_memory_agent.run_stream(\n",
+    "    task=\"When is the Project Alpha deadline and who's leading it?\"\n",
+    ")\n",
+    "await Console(stream)\n",
+    "\n",
+    "await kg_memory.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configuration Options\n",
+    "\n",
+    "Zep memory provides several configuration options to customize behavior:\n",
+    "\n",
+    "- **memory_type**: Choose \"perpetual\" for persistent context or other modes for session-specific memory\n",
+    "- **user_id**: Unique identifier for the user (required for memory persistence)\n",
+    "- **session_id**: Conversation session identifier for organizing related interactions\n",
+    "- **Knowledge graph scopes**: Query specific types of information (edges, nodes, episodes)\n",
+    "\n",
+    "Zep is particularly useful for:\n",
+    "- Applications requiring long-term memory persistence\n",
+    "- Complex conversations needing context summarization\n",
+    "- Knowledge-intensive tasks with structured data relationships\n",
+    "- Multi-session user interactions requiring continuity\n",
+    "\n",
+    "For more advanced features including custom entity models, memory search, and manual memory tools, refer to the [Zep documentation](https://help.getzep.com/ecosystem/autogen-memory).\n",
+    "\n",
+    "Just like other memory implementations, you can serialize ZepUserMemory configurations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Serialize the memory configuration\n",
+    "config_json = zep_memory.dump_component().model_dump_json()\n",
+    "print(f\"Zep memory config: {config_json[:100]}...\")"
+   ]
   }
  ],
  "metadata": {