feat: Add AutoMemoryWrapper agent for automatic memory management

docs/source/store-and-retrieve/memory.md

@@ -27,11 +27,78 @@ The NeMo Agent toolkit includes three memory module providers, all of which are
 * [Redis](https://redis.io/) which is provided by the [`nvidia-nat-redis`](https://pypi.org/project/nvidia-nat-redis/) plugin.
 * [Zep](https://www.getzep.com/) which is provided by the [`nvidia-nat-zep-cloud`](https://pypi.org/project/nvidia-nat-zep-cloud/) plugin.
 
+## Automatic Memory Wrapper Agent
+
+The NeMo Agent toolkit provides an `auto_memory_agent` wrapper that adds automatic memory capture and retrieval to any agent without requiring the LLM to invoke memory tools explicitly.
+
+### Why Use Automatic Memory?
+
+**Traditional tool-based memory:**
+- LLMs may forget to call memory tools
+- Memory capture is inconsistent
+- Requires explicit memory tool configuration
+
+**Automatic memory wrapper agent:**
+- **Guaranteed capture**: User messages and agent responses are automatically stored
+- **Automatic retrieval**: Relevant context is injected before each agent call
+- **Memory backend agnostic**: Works with Zep, Mem0, Redis, or any `MemoryEditor`
+- **Universal compatibility**: Wraps any agent type (ReAct, ReWOO, Tool Calling, etc.)
+
+### Quick Start
+
+To use automatic memory, wrap any agent with the `auto_memory_agent` workflow type:
+
+```yaml
+memory:
+  zep_memory:
+    _type: nat.plugins.zep_cloud/zep_memory
+
+functions:
+  my_react_agent:
+    _type: react_agent
+    llm_name: nim_llm
+    tool_names: [calculator]
+
+workflow:
+  _type: auto_memory_agent
+  inner_agent_name: my_react_agent
+  memory_name: zep_memory
+  llm_name: nim_llm
+```
+
+### Configuration Options
+
+The automatic memory wrapper agent supports several configuration parameters:
+
+**Required Parameters:**
+- `inner_agent_name`: Name of the agent to wrap with automatic memory
+- `memory_name`: Name of the memory backend (from `memory:` section)
+- `llm_name`: LLM to use (required by `AgentBaseConfig`)
+
+**Optional Feature Flags** (all default to `true`):
+- `save_user_messages_to_memory`: Automatically save user messages before agent processing
+- `retrieve_memory_for_every_response`: Automatically retrieve and inject memory context
+- `save_ai_messages_to_memory`: Automatically save agent responses after generation
+
+**Memory Backend Parameters:**
+- `search_params`: Passed to `memory_editor.search()` (e.g., `mode`, `top_k`)
+- `add_params`: Passed to `memory_editor.add_items()` (e.g., `ignore_roles`)
+
+### Multi-Tenant Memory Isolation
+
+User ID is automatically extracted at runtime for memory isolation via:
+1. `user_manager.get_id()` - For production with custom auth middleware (recommended)
+2. `X-User-ID` HTTP header - For testing without middleware
+3. `"default_user"` - Fallback for local development
+
+For detailed configuration and usage examples, refer to the `examples/agents/auto_memory_wrapper/README.md` guide.
+
 ## Examples
 The following examples demonstrate how to use the memory module in the NeMo Agent toolkit:
-* `examples/memory/redis`
-* `examples/frameworks/semantic_kernel_demo`
-* `examples/RAG/simple_rag`
+* `examples/agents/auto_memory_wrapper` - Automatic memory wrapper agent for any agent
+* `examples/memory/redis` - Basic long-term memory using Redis
+* `examples/frameworks/semantic_kernel_demo` - Multi-agent system with long-term memory
+* `examples/RAG/simple_rag` - RAG system with Mem0 memory
 
 ## Additional Resources
 For information on how to write a new memory module provider can be found in the [Adding a Memory Provider](../extend/memory.md) document.