# Memory: Long-Term Knowledge with `MemoryService`

<div class="language-support-tag">
  <span class="lst-supported">Supported in ADK</span><span class="lst-python">Python v0.1.0</span><span class="lst-typescript">TypeScript v0.2.0</span><span class="lst-go">Go v0.1.0</span><span class="lst-java">Java v0.1.0</span><span class="lst-kotlin">Kotlin v0.1.0</span>
</div>

We've seen how `Session` tracks the history (`events`) and temporary data (`state`) for a *single, ongoing conversation*. But what if an agent needs to recall information from *past* conversations? This is where the concept of **Long-Term Knowledge** and the **`MemoryService`** come into play.

Think of it this way:

* **`Session` / `State`:** Like your short-term memory during one specific chat.
* **Long-Term Knowledge (`MemoryService`)**: Like a searchable archive or knowledge library the agent can consult, potentially containing information from many past chats or other sources.

## The `MemoryService` Role

The `BaseMemoryService` (or `Service` in Go) defines the interface for managing this searchable, long-term knowledge store. It supports four operations:

1. **Ingesting a session (`add_session_to_memory`):** Take the contents of a (usually completed) `Session` and add relevant information to the long-term knowledge store.
2. **Ingesting events incrementally (`add_events_to_memory`):** Append a delta of events (e.g., the latest turn) without re-ingesting the full session. Useful when you want to write to memory partway through a long-running session.
3. **Writing memory items directly (`add_memory`):** Insert pre-built `MemoryEntry` items, for services that support direct writes alongside event-based extraction.
4. **Searching (`search_memory`):** Allow an agent (typically via a `Tool`) to query the knowledge store and retrieve relevant snippets based on a search query.

Operations 2 and 3 are optional — the base class implementations of `add_events_to_memory` and `add_memory` raise `NotImplementedError`, so check your concrete service before relying on them.

## Choosing the Right Memory Service

The Python ADK ships three `MemoryService` implementations. Use the table below to decide which is the best fit for your agent.

| **Feature** | **InMemoryMemoryService** | **VertexAiMemoryBankService** | **VertexAiRagMemoryService** |
| :--- | :--- | :--- | :--- |
| **Persistence** | None (data is lost on restart) | Yes (Managed by Agent Platform) | Yes (stored in Knowledge Engine) |
| **Primary Use Case** | Prototyping, local development, and simple testing. | Building meaningful, evolving memories from user conversations. | Vector-search retrieval over the full conversation corpus, or alongside other RAG-indexed content. |
| **Memory Extraction** | Stores full conversation | Extracts [meaningful information](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/memory-bank/generate-memories) from conversations and consolidates it with existing memories (powered by LLM) | Stores full conversation, indexed by [Knowledge Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/rag-overview). |
| **Search Capability** | Basic keyword matching. | Advanced semantic search. | Vector similarity search over Knowledge Engine. |
| **Setup Complexity** | None. It's the default. | Low. Requires an [Agent Runtime](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/memory-bank/overview) instance on Agent Platform. | Medium. Requires [Knowledge Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/manage-your-rag-corpus). |
| **Dependencies** | None. | Google Cloud Project, Agent Platform API | Google Cloud Project, Knowledge Engine, the Agent Platform SDK (optional install). |
| **When to use it** | When you want to search across multiple sessions’ chat histories for prototyping. | When you want your agent to remember and learn from past interactions. | When you already have RAG infrastructure or want to retrieve over raw conversation transcripts. |

`VertexAiRagMemoryService` is only exported from `google.adk.memory` when the Agent Platform SDK is installed. Memory Bank and RAG-backed memory are documented in [Memory Bank](#memory-bank) and [RAG Memory](#rag-memory) below.


## In-Memory Memory

The `InMemoryMemoryService` stores session information in the application's memory and performs basic keyword matching for searches. It requires no setup and is best for prototyping and simple testing scenarios where persistence isn't required.

=== "Python"

    ```py
    from google.adk.memory import InMemoryMemoryService
    memory_service = InMemoryMemoryService()
    ```

=== "TypeScript"

    ```typescript
    import { InMemoryMemoryService } from '@google/adk';
    const memoryService = new InMemoryMemoryService();
    ```

=== "Go"
    ```go
    import (
      "google.golang.org/adk/memory"
      "google.golang.org/adk/session"
    )

    // Services must be shared across runners to share state and memory.
    sessionService := session.InMemoryService()
    memoryService := memory.InMemoryService()
    ```

=== "Java"
    ```java
    import com.google.adk.memory.InMemoryMemoryService;

    InMemoryMemoryService memoryService = new InMemoryMemoryService();
    ```

=== "Kotlin"
    ```kotlin
    --8<-- "examples/kotlin/snippets/sessions/MemoryExample.kt:instantiate_service"
    ```


**Example: Adding and Searching Memory**

This example demonstrates the basic flow using the `InMemoryMemoryService` for simplicity.

=== "Python"

    ```py
    import asyncio
    from google.adk.agents import LlmAgent
    from google.adk.sessions import InMemorySessionService, Session
    from google.adk.memory import InMemoryMemoryService # Import MemoryService
    from google.adk.runners import Runner
    from google.adk.tools import load_memory # Tool to query memory
    from google.genai.types import Content, Part

    # --- Constants ---
    APP_NAME = "memory_example_app"
    USER_ID = "mem_user"
    MODEL = "gemini-flash-latest" # Use a valid model

    # --- Agent Definitions ---
    # Agent 1: Simple agent to capture information
    info_capture_agent = LlmAgent(
        model=MODEL,
        name="InfoCaptureAgent",
        instruction="Acknowledge the user's statement.",
    )

    # Agent 2: Agent that can use memory
    memory_recall_agent = LlmAgent(
        model=MODEL,
        name="MemoryRecallAgent",
        instruction="Answer the user's question. Use the 'load_memory' tool "
                    "if the answer might be in past conversations.",
        tools=[load_memory] # Give the agent the tool
    )

    # --- Services ---
    # Services must be shared across runners to share state and memory
    session_service = InMemorySessionService()
    memory_service = InMemoryMemoryService() # Use in-memory for demo

    async def run_scenario():
        # --- Scenario ---

        # Turn 1: Capture some information in a session
        print("--- Turn 1: Capturing Information ---")
        runner1 = Runner(
            # Start with the info capture agent
            agent=info_capture_agent,
            app_name=APP_NAME,
            session_service=session_service,
            memory_service=memory_service # Provide the memory service to the Runner
        )
        session1_id = "session_info"
        await runner1.session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=session1_id)
        user_input1 = Content(parts=[Part(text="My favorite project is Project Alpha.")], role="user")

        # Run the agent
        final_response_text = "(No final response)"
        async for event in runner1.run_async(user_id=USER_ID, session_id=session1_id, new_message=user_input1):
            if event.is_final_response() and event.content and event.content.parts:
                final_response_text = event.content.parts[0].text
        print(f"Agent 1 Response: {final_response_text}")

        # Get the completed session
        completed_session1 = await runner1.session_service.get_session(app_name=APP_NAME, user_id=USER_ID, session_id=session1_id)

        # Add this session's content to the Memory Service
        print("\n--- Adding Session 1 to Memory ---")
        await memory_service.add_session_to_memory(completed_session1)
        print("Session added to memory.")

        # Turn 2: Recall the information in a new session
        print("\n--- Turn 2: Recalling Information ---")
        runner2 = Runner(
            # Use the second agent, which has the memory tool
            agent=memory_recall_agent,
            app_name=APP_NAME,
            session_service=session_service, # Reuse the same service
            memory_service=memory_service   # Reuse the same service
        )
        session2_id = "session_recall"
        await runner2.session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=session2_id)
        user_input2 = Content(parts=[Part(text="What is my favorite project?")], role="user")

        # Run the second agent
        final_response_text_2 = "(No final response)"
        async for event in runner2.run_async(user_id=USER_ID, session_id=session2_id, new_message=user_input2):
            if event.is_final_response() and event.content and event.content.parts:
                final_response_text_2 = event.content.parts[0].text
        print(f"Agent 2 Response: {final_response_text_2}")

    # To run this example, you can use the following snippet:
    # asyncio.run(run_scenario())

    # await run_scenario()
    ```

=== "TypeScript"

    ```typescript
    --8<-- "examples/typescript/snippets/sessions/memory_example.ts:full_example"
    ```

=== "Go"

    ```go
    --8<-- "examples/go/snippets/sessions/memory_example/memory_example.go:full_example"
    ```

=== "Java"

    ```java
    package com.google.adk.examples.sessions;
    ...
    ```

=== "Kotlin"

    ```kotlin
    --8<-- "examples/kotlin/snippets/sessions/MemoryExample.kt:full_example"
    ```


### Searching Memory Within a Tool

You can also search memory from within a custom tool by using the tool context.

=== "Python"

    ```python
    from google.adk.tools import ToolContext

    async def search_past_conversations(
        query: str, tool_context: ToolContext
    ) -> dict:
        response = await tool_context.search_memory(query)
        return {
            "results": [
                part.text
                for entry in response.memories
                for part in (entry.content.parts or [])
                if part.text
            ]
        }
    ```

=== "TypeScript"

    ```typescript
    // Within a tool implementation
    async runAsync({ args, toolContext }: RunAsyncToolRequest) {
      const query = args['query'] as string;
      const response = await toolContext.searchMemory(query);
      // process response
      return {
        memories: response.memories.map(m => m.content.parts?.map(p => p.text).join(' ')).join('\n')
      };
    }
    ```

=== "Go"

    ```go
    --8<-- "examples/go/snippets/sessions/memory_example/memory_example.go:tool_search"
    ```

=== "Java"

    ```java
    // Within a tool implementation
    public Single<ToolOutput> execute(ToolContext context) {
      String query = ...; // get query from arguments
      return context.searchMemory(query)
          .map(response -> {
              // process response
              return new ToolOutput(response.memories().toString());
          });
    }
    ```

=== "Kotlin"

    ```kotlin
    --8<-- "examples/kotlin/snippets/sessions/MemoryExample.kt:search_within_tool"
    ```

## Memory Bank

The `VertexAiMemoryBankService` connects your agent to [Memory Bank](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/memory-bank/overview), a fully managed Google Cloud service that provides sophisticated, persistent memory capabilities for conversational agents.

### How It Works

The service handles two key operations:

*   **Generating Memories:** At the end of a conversation, you can send the session's events to the Memory Bank, which intelligently processes and stores the information as "memories."
*   **Retrieving Memories:** Your agent code can issue a search query against the Memory Bank to retrieve relevant memories from past conversations.

### Prerequisites

Before you can use this feature, you must have:

1.  **A Google Cloud Project:** With the Agent Platform API enabled.
2.  **An Agent Runtime:** You need to create an Agent Runtime on Agent Platform. You do not need to deploy your agent to Agent Runtime to use Memory Bank. This will provide you with the **Agent Runtime ID** required for configuration.
3.  **Authentication:** Ensure your local environment is authenticated to access Google Cloud services. The simplest way is to run:
    ```bash
    gcloud auth application-default login
    ```
4.  **Environment Variables:** The service requires your Google Cloud Project ID and Location. Set them as environment variables:
    ```bash
    export GOOGLE_CLOUD_PROJECT="your-gcp-project-id"
    export GOOGLE_CLOUD_LOCATION="your-gcp-location"
    ```

### Configuration

To connect your agent to the Memory Bank, you use the `--memory_service_uri` flag when starting the ADK server (`adk web` or `adk api_server`). The URI must be in the format `agentengine://<agent_engine_id>`.

```bash title="bash"
adk web path/to/your/agents_dir --memory_service_uri="agentengine://1234567890"
```

Or, you can configure your agent to use the Memory Bank by manually instantiating the `VertexAiMemoryBankService` and passing it to the `Runner`.

=== "Python"
  ```py
  from google import adk
  from google.adk.memory import VertexAiMemoryBankService

  agent_engine_id = agent_engine.api_resource.name.split("/")[-1]

  memory_service = VertexAiMemoryBankService(
      project="PROJECT_ID",
      location="LOCATION",
      agent_engine_id=agent_engine_id
  )

  runner = adk.Runner(
      ...
      memory_service=memory_service
  )
  ```

## RAG Memory

The `VertexAiRagMemoryService` stores conversations in [Knowledge Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/rag-overview) and retrieves them by vector similarity. Use it when you already have RAG infrastructure or want raw transcript retrieval rather than the LLM-extracted memories produced by Memory Bank. Requires the Agent Platform SDK.

=== "Python"

    ```py
    from google.adk.memory import VertexAiRagMemoryService

    memory_service = VertexAiRagMemoryService(
        rag_corpus="projects/PROJECT_ID/locations/LOCATION/ragCorpora/CORPUS_ID",
        similarity_top_k=5,
        vector_distance_threshold=0.6,
    )
    ```

## Using Memory in Your Agent

When a memory service is configured, your agent can use a tool or callback to retrieve memories. ADK includes two pre-built tools for retrieving memories:

* `PreloadMemory`: Always retrieve memory at the beginning of each turn (similar to a callback).
* `LoadMemory`: Retrieve memory when your agent decides it would be helpful.

**Example:**

=== "Python"
    ```python
    from google.adk.agents import Agent
    from google.adk.tools.preload_memory_tool import PreloadMemoryTool

    agent = Agent(
        model=MODEL_ID,
        name='weather_sentiment_agent',
        instruction="...",
        tools=[PreloadMemoryTool()]
    )
    ```

=== "TypeScript"
    ```typescript
    import { LlmAgent, PRELOAD_MEMORY } from '@google/adk';

    const agent = new LlmAgent({
        model: MODEL_ID,
        name: 'weather_sentiment_agent',
        instruction: "...",
        tools: [PRELOAD_MEMORY]
    });
    ```

=== "Go"
    ```go
    import (
        "google.golang.org/adk/agent/llmagent"
        "google.golang.org/adk/tool"
        "google.golang.org/adk/tool/preloadmemorytool"
    )

    agent, _ := llmagent.New(llmagent.Config{
        Model:       model,
        Name:        "weather_sentiment_agent",
        Instruction: "...",
        Tools:       []tool.Tool{preloadmemorytool.New()},
    })
    ```

=== "Java"
    ```java
    import com.google.adk.agents.LlmAgent;
    import com.google.adk.tools.LoadMemoryTool;

    LlmAgent agent = new LlmAgent.Builder()
        .model(MODEL_ID)
        .name("weather_sentiment_agent")
        .instruction("...")
        .tools(new LoadMemoryTool())
        .build();
    ```

=== "Kotlin"
    ```kotlin
    --8<-- "examples/kotlin/snippets/sessions/MemoryExample.kt:preload_memory_agent"
    ```

To extract memories from your session, you need to call `add_session_to_memory`. For example, you can automate this via a callback:

=== "Python"
    ```python
    from google.adk.agents import Agent
    from google import adk

    async def auto_save_session_to_memory_callback(callback_context):
        await callback_context.add_session_to_memory()

    agent = Agent(
        model=MODEL,
        name="Generic_QA_Agent",
        instruction="Answer the user's questions",
        tools=[adk.tools.preload_memory_tool.PreloadMemoryTool()],
        after_agent_callback=auto_save_session_to_memory_callback,
    )
    ```

=== "TypeScript"
    ```typescript
    import { LlmAgent, PRELOAD_MEMORY, SingleAgentCallback } from '@google/adk';

    const autoSaveSessionToMemoryCallback: SingleAgentCallback = async (callbackContext) => {
        if (callbackContext.invocationContext.memoryService) {
            await callbackContext.invocationContext.memoryService.addSessionToMemory(
                callbackContext.invocationContext.session
            );
        }
    };

    const agent = new LlmAgent({
        model: MODEL,
        name: "Generic_QA_Agent",
        instruction: "Answer the user's questions",
        tools: [PRELOAD_MEMORY],
        afterAgentCallback: autoSaveSessionToMemoryCallback,
    });
    ```

=== "Go"
    ```go
    import (
        "context"
        "google.golang.org/adk/agent"
        "google.golang.org/adk/agent/llmagent"
        "google.golang.org/adk/session"
        "google.golang.org/adk/tool"
        "google.golang.org/adk/tool/loadmemorytool"
    )

    func autoSaveSessionToMemoryCallback(ctx agent.CallbackContext, s session.Session) (*genai.Content, error) {
        if err := ctx.Memory().AddSessionToMemory(context.Background(), s); err != nil {
            return nil, err
        }
        return nil, nil
    }

    agent, _ := llmagent.New(llmagent.Config{
        Model:               model,
        Name:                "Generic_QA_Agent",
        Instruction:         "Answer the user's questions",
        Tools:               []tool.Tool{loadmemorytool.New()},
        AfterAgentCallbacks: []agent.AfterAgentCallback{autoSaveSessionToMemoryCallback},
    })
    ```

=== "Kotlin"
    ```kotlin
    --8<-- "examples/kotlin/snippets/sessions/MemoryExample.kt:auto_save_callback"
    ```


## Advanced Concepts

### How Memory Works in Practice

The memory workflow internally involves these steps:

1. **Session Interaction:** A user interacts with an agent via a `Session`, managed by a `SessionService`. Events are added, and state might be updated.
2. **Ingestion into Memory:** At some point (often when a session is considered complete or has yielded significant information), your application calls `memory_service.add_session_to_memory(session)`. This extracts relevant information from the session's events and adds it to the long-term knowledge store (in-memory dictionary or Agent Runtime Memory Bank).
3. **Later Query:** In a *different* (or the same) session, the user might ask a question requiring past context (e.g., "What did we discuss about project X last week?").
4. **Agent Uses Memory Tool:** An agent equipped with a memory-retrieval tool (like the built-in `load_memory` tool) recognizes the need for past context. It calls the tool, providing a search query (e.g., "discussion project X last week").
5. **Search Execution:** The tool internally calls `memory_service.search_memory(app_name=..., user_id=..., query=...)`.
6. **Results Returned:** The `MemoryService` searches its store (using keyword matching or semantic search) and returns matching snippets as a `SearchMemoryResponse` containing a list of `MemoryEntry` objects (each holding `content`, optional `author`, optional `timestamp`, and optional `custom_metadata`).
7. **Agent Uses Results:** The tool returns these results to the agent, usually as part of the context or function response. The agent can then use this retrieved information to formulate its final answer to the user.

### Can an agent have access to more than one memory service?

*   **Through Standard Configuration: No.** The framework (`adk web`, `adk api_server`) is designed to be configured with one memory service at a time via the `--memory_service_uri` flag. That single service is wired into the runner and exposed through `tool_context.search_memory()` and `callback_context.search_memory()`.

*   **Within Your Agent's Code: Yes.** Nothing stops you from importing and instantiating a second `BaseMemoryService` directly. The cleanest place to consult it is from a custom tool, which already has a `ToolContext` for the framework-configured service.

For example, your agent can use the framework-configured `InMemoryMemoryService` for conversation history and manually instantiate a second service (a `VertexAiMemoryBankService`, a `VertexAiRagMemoryService` over a docs corpus, or any other `BaseMemoryService` implementation) for a separate knowledge base.

#### Example: Using Two Memory Services

=== "Python"

    ```python
    from google.adk.agents import Agent
    from google.adk.memory import InMemoryMemoryService
    from google.adk.tools import ToolContext

    # Second memory service for docs lookup; could be any BaseMemoryService.
    docs_memory = InMemoryMemoryService()


    async def search_all_memory(query: str, tool_context: ToolContext) -> dict:
        """Search both the conversational memory and the docs corpus."""
        conversational = await tool_context.search_memory(query)
        docs = await docs_memory.search_memory(
            app_name="docs", user_id="shared", query=query
        )
        return {
            "from_conversations": [
                part.text
                for entry in conversational.memories
                for part in (entry.content.parts or [])
                if part.text
            ],
            "from_docs": [
                part.text
                for entry in docs.memories
                for part in (entry.content.parts or [])
                if part.text
            ],
        }


    agent = Agent(
        model="gemini-flash-latest",
        name="multi_memory_agent",
        instruction=(
            "Answer questions using both your conversation history and the "
            "docs knowledge base. Use the search_all_memory tool."
        ),
        tools=[search_all_memory],
    )
    ```

=== "Kotlin"

    ```kotlin
    --8<-- "examples/kotlin/snippets/sessions/MemoryExample.kt:multi_memory"
    ```
