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ADK agents use session services and memory services to manage conversational context. Session services handle conversation state for individual interactions. Memory services store long-term knowledge that spans multiple sessions. For more details, see the ADK Sessions documentation.

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Session services vs memory services

Session serviceMemory service
PurposeManages individual conversation stateStores long-term knowledge across sessions
ScopeSingle conversation (events, temporary state)Cross-session (searchable knowledge base)
RequiredYesOptional
OptionsIn Memory, Vertex AI, DatabaseIn Memory, Vertex AI

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Set up ADK memory

1

Navigate to the memory step

During agent creation or editing, navigate to the Memory step in the agent form.
2

Configure your session service

Choose a session service backend (required for conversation state):
  • In Memory: For development and testing
  • Vertex AI: For production on Google Cloud
  • Database: For production with SQL persistence
Fill in connection details if required.
3

Configure your memory service

Choose a memory service backend (optional, for long-term memory):
  • In Memory: For development and testing
  • Vertex AI: For production with long-term storage
Fill in connection details if required.
4

Save and restart

Click Next to continue, then finalize with Save changes. Restart the agent to apply the new configuration.

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Session service options

Session services manage conversation state and events for individual sessions.

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In-memory session service

The InMemorySessionService stores session data in the application’s memory.
PropertyDetail
PersistenceNone. Data is lost when the application restarts.
PerformanceFastest option, no I/O overhead.
Use casesDevelopment, testing, quick prototyping.
Configuration: No additional configuration required.

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Vertex AI session service

The VertexAiSessionService uses Google Cloud’s Vertex AI infrastructure for session management.
PropertyDetail
PersistenceCloud-native, persistent storage.
ScalabilityHandles high-volume, distributed deployments.
IntegrationWorks with other Google Cloud services.
Use casesProduction deployments on Google Cloud.
Configuration: Requires the following fields:
  • project_id: Google Cloud project ID
  • location: GCP region (for example, us-central1)
  • reasoning_engine_app_name: Vertex AI Reasoning Engine application name

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Database session service

The DatabaseSessionService connects to a relational database (PostgreSQL, MySQL) for persistent session storage using SQLAlchemy.
PropertyDetail
PersistenceSQL-based storage with transaction support.
ScalabilitySupports multi-instance deployments.
ReliabilityProduction-grade features including transactions and backups.
Use casesProduction deployments requiring SQL persistence.
Configuration: Requires a database connection string. 
agent:
  type: "ADK"
  config:
    session_service:
      type: "database"
      db_url: "postgresql://user:pass@localhost:5432/dbname"

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Memory service options

Memory services manage long-term knowledge storage that persists across multiple sessions.

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In-memory memory service

The InMemoryMemoryService provides ephemeral memory storage.
PropertyDetail
PersistenceNone. Data is lost when the application restarts.
PerformanceFast access, no external dependencies.
Use casesDevelopment, testing, scenarios where long-term memory is not required.
Configuration: No additional configuration required.

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Vertex AI memory service

The VertexAiMemoryService provides cloud-backed memory with long-term storage using Vertex AI Memory Banks.
PropertyDetail
PersistenceLong-term, persistent storage across sessions.
ScalabilityCloud-native, handles large knowledge bases.
SearchSemantic search capabilities for retrieving relevant memories.
Use casesProduction deployments requiring long-term memory.
Configuration: Requires the following fields:
  • project_id: Google Cloud project ID
  • location: GCP region
  • memory_bank_resource_id: Vertex AI Memory Bank resource ID

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Best practices

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Session services

  • Use in-memory for local development: No setup required, fast iteration
  • Use Database for production: Reliable SQL-based persistence with multi-instance support
  • Use Vertex AI for Google Cloud production: Cloud-native and scalable
  • Configure session isolation: Each conversation should have a unique session ID to prevent state leakage

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Memory services

  • Use in-memory for development: Fast iteration, no external dependencies
  • Use Vertex AI for production: Long-term persistence with semantic search capabilities
  • Ingest session data into memory: Periodically move important information from sessions to long-term memory

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General

  • Separate concerns: Use session services for conversation state and memory services for long-term knowledge
  • Monitor storage usage: Long-running sessions and large memory stores can consume significant resources
  • Implement backup strategies: Set up regular backups for production database and Vertex AI configurations

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Troubleshooting

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Session service issues

  1. Database connection errors: Verify the connection string format, credentials, and that the database server is accessible from the agent
  2. Vertex AI authentication: Verify Google Cloud credentials are configured and the service account has the required permissions
  3. Session not persisting: Confirm the session service is initialized and session IDs are used consistently

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Memory service issues

  1. Vertex AI Memory Bank: Verify the Memory Bank resource ID is correct, the bank exists in the specified project and location, and IAM permissions are set
  2. Memory not accessible: Confirm the memory service is initialized and its configuration matches your setup

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General

  1. Review logs: Check agent logs for session and memory-related errors
  2. Check permissions: Verify the agent has access to all required storage resources
  3. Verify configuration: Double-check all connection strings, credentials, and resource IDs
Last modified on March 22, 2026