google__adk-docs/docs/sessions/memory.md

Memory: Long-term knowledge with MemoryService

Supported in ADKPython v0.1.0TypeScript v0.2.0Go v0.1.0Java v0.1.0Kotlin v0.1.0

While a Session tracks the history (events) and temporary data (state) of a single conversation, an agent may need to recall information from past interactions. This is where the concept of Long-Term Knowledge and the MemoryService come into play. Think of it this way:

• Session / State: It's your short-term memory during one specific chat.
• Long-Term Knowledge (MemoryService): It's 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 these operations:

• Ingesting Information:
  • add_session_to_memory: Takes a completed Session and adds relevant information to the long-term knowledge store. This approach is ideal for automatically capturing the essence of a conversation.
  • add_events_to_memory: Appends a delta of events (for example, the latest turn) without re-ingesting the full session. Useful when you want to write to memory partway through a long-running session.
  • add_memory: Adds explicit MemoryEntry objects directly to the memory. This method gives you fine-grained control and is useful for injecting specific facts from other sources.
• Searching Information (search_memory): Lets an agent (typically via a Tool) query the knowledge store and retrieve relevant snippets or context based on a search query.

add_events_to_memory and add_memory are optional and are not implemented by every service, so confirm that your chosen service supports them before relying on them.

Choose 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 the 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 from conversations and consolidates it with existing memories powered by LLM	Stores full conversation, indexed by Knowledge Engine.
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 instance on Agent Platform.	Medium. Requires Knowledge Engine.
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 and RAG Memory below.

InMemoryMemoryService

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/v2/memory"
  "google.golang.org/adk/v2/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: Add and search 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
--8<-- "examples/java/snippets/src/main/java/sessions/MemoryExample.java:full_example"
```

=== "Kotlin"

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

Search 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, 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.

Direct memory ingestion with add_memory

Besides generating memories from session history, VertexAiMemoryBankService also supports direct memory ingestion via the add_memory method. This method gives you precise control over the facts stored in the Memory Bank.

How it works depends on the enable_consolidation option:

• Direct Creation (Default): By default, add_memory calls the underlying memories.create API. Each MemoryEntry you provide is added as a distinct, separate memory item.

  from google.adk.memory import VertexAiMemoryBankService
  from google.adk.memory.memory_entry import MemoryEntry
  from google.genai.types import Content, Part
  
  memory_service = VertexAiMemoryBankService(...)
  
  await memory_service.add_memory(
      app_name="my-app",
      user_id="user-123",
      memories=[
          MemoryEntry(content=Content(parts=[Part(text="The user's favorite color is blue.")]))
      ]
  )
  

• Creation with Consolidation: If you set enable_consolidation to True in the custom_metadata, the service uses the memories.generate API. This setting allows the Memory Bank to intelligently consolidate the new memory items with existing related memories, preventing redundancy and building a more coherent knowledge base.

  await memory_service.add_memory(
      app_name="my-app",
      user_id="user-123",
      memories=[
          MemoryEntry(content=Content(parts=[Part(text="The user's favorite color is light blue.")]))
      ],
      custom_metadata={"enable_consolidation": True}
  )
  

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 setup 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:

   gcloud auth application-default login
   

4. Environment Variables: The service requires your Google Cloud Project ID and Location. Set them as environment variables:

   export GOOGLE_CLOUD_PROJECT="your-gcp-project-id"
   export GOOGLE_CLOUD_LOCATION="your-gcp-location"
   

For more information on connecting to Google Cloud from ADK agents, see Connect to Google Cloud and Agent Platform.

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 Uniform Resource Identifier (URI) must be in the format agentengine://<agent_engine_id>.

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

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

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

=== "Kotlin"

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

RAG memory

The VertexAiRagMemoryService stores conversations in Knowledge Engine 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,
)
```

=== "Kotlin"

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

Use 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:

• Preload memory: Automatically retrieves memory at the beginning of each turn, similar to a callback.
• Load memory: Retrieves memory when your agent decides it would be helpful.

Example:

=== "Python"

```python
from google.adk.agents import Agent
from google.adk.tools import preload_memory

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

=== "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/v2/agent/llmagent"
    "google.golang.org/adk/v2/tool"
    "google.golang.org/adk/v2/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 step with a callback:

=== "Python"

```python
from google.adk.agents import Agent
from google.adk.tools import preload_memory

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=[preload_memory],
    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/v2/agent"
    "google.golang.org/adk/v2/agent/llmagent"
    "google.golang.org/adk/v2/session"
    "google.golang.org/adk/v2/tool"
    "google.golang.org/adk/v2/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"
```

Extend memory capabilities

Memory services extended from BaseMemoryService support adding sessions and events to agent memory, including custom metadata. Use the add_session_to_memory and add_events_to_memory methods of memory services such as InMemoryMemoryService to amend memory data, as shown in the following code example:

import asyncio
from google.adk.memory import InMemoryMemoryService

# Assume my_memory_service is an instance of InMemoryMemoryService
# and my_latest_events is a list of new adk.Event objects from the latest turn.
my_latest_events = [...]

async def update_incremental_memory(my_memory_service, my_latest_events):
    # Example 1: Basic incremental update
    await my_memory_service.add_events_to_memory(
        app_name="my-app",
        user_id="my-user",
        events=my_latest_events,
        session_id="my-optional-session-id"
    )

    # Example 2: Incremental update with Custom Metadata
    await my_memory_service.add_events_to_memory(
        app_name="my-app",
        user_id="my-user",
        events=my_latest_events,
        session_id="my-optional-session-id",
        custom_metadata={
            "my_custom_key": "my_custom_value"
        }
    )

async def update_session_memory(my_memory_service, my_completed_session):
    # Example 3: Applying custom metadata to a full session
    await my_memory_service.add_session_to_memory(
        session=my_completed_session,
        custom_metadata={
            "category": "user_preference"
        }
    )

Advanced concepts

How memory works in practice

The memory workflow includes the following steps:

1. Session Interaction: A user interacts with an agent via a Session, managed by a SessionService. During this interaction, events are recorded and session state may be updated.
2. Ingestion into Memory: When a session concludes or captures significant information, your application calls memory_service.add_session_to_memory(session). This action extracts key data and persists it to your long-term knowledge store, such as the Agent Runtime Memory Bank.
3. Later Query: In a different, or in the same session, you might ask a question requiring past context, for example, "What did we discuss about project X last week?".
4. Agent Uses Memory Tool: An agent equipped with a memory-retrieval tool, such as 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, and all optional: author, timestamp, and 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. You can instantiate a second BaseMemoryService and consult it 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: Use 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"
```