> ## Documentation Index
> Fetch the complete documentation index at: https://docs.idun-group.com/llms.txt
> Use this file to discover all available pages before exploring further.
# ADK memory
> Configure session services and memory services for ADK agents to manage conversation state and long-term knowledge.
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](https://google.github.io/adk-docs/sessions/).
## Session services vs memory services
| | Session service | Memory service |
| ------------ | --------------------------------------------- | ------------------------------------------ |
| **Purpose** | Manages individual conversation state | Stores long-term knowledge across sessions |
| **Scope** | Single conversation (events, temporary state) | Cross-session (searchable knowledge base) |
| **Required** | Yes | Optional |
| **Options** | In Memory, Vertex AI, Database | In Memory, Vertex AI |
## Set up ADK memory
During agent creation or editing, navigate to the **Memory** step in the agent form.
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.
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.
Click **Next** to continue, then finalize with **Save changes**. Restart the agent to apply the new configuration.
## Session service options
Session services manage conversation state and events for individual sessions.
### In-memory session service
The `InMemorySessionService` stores session data in the application's memory.
| Property | Detail |
| --------------- | ------------------------------------------------- |
| **Persistence** | None. Data is lost when the application restarts. |
| **Performance** | Fastest option, no I/O overhead. |
| **Use cases** | Development, testing, quick prototyping. |
**Configuration**: No additional configuration required.
### Vertex AI session service
The `VertexAiSessionService` uses Google Cloud's Vertex AI infrastructure for session management.
| Property | Detail |
| --------------- | --------------------------------------------- |
| **Persistence** | Cloud-native, persistent storage. |
| **Scalability** | Handles high-volume, distributed deployments. |
| **Integration** | Works with other Google Cloud services. |
| **Use cases** | Production 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
### Database session service
The `DatabaseSessionService` connects to a relational database (PostgreSQL, MySQL) for persistent session storage using SQLAlchemy.
| Property | Detail |
| --------------- | ------------------------------------------------------------- |
| **Persistence** | SQL-based storage with transaction support. |
| **Scalability** | Supports multi-instance deployments. |
| **Reliability** | Production-grade features including transactions and backups. |
| **Use cases** | Production deployments requiring SQL persistence. |
**Configuration**: Requires a database connection string.
```yaml theme={"theme":{"light":"github-light","dark":"github-dark"}}
agent:
type: "ADK"
config:
session_service:
type: "database"
db_url: "postgresql://user:pass@localhost:5432/dbname"
```
## Memory service options
Memory services manage long-term knowledge storage that persists across multiple sessions.
### In-memory memory service
The `InMemoryMemoryService` provides ephemeral memory storage.
| Property | Detail |
| --------------- | ----------------------------------------------------------------------- |
| **Persistence** | None. Data is lost when the application restarts. |
| **Performance** | Fast access, no external dependencies. |
| **Use cases** | Development, testing, scenarios where long-term memory is not required. |
**Configuration**: No additional configuration required.
### Vertex AI memory service
The `VertexAiMemoryService` provides cloud-backed memory with long-term storage using Vertex AI Memory Banks.
| Property | Detail |
| --------------- | -------------------------------------------------------------- |
| **Persistence** | Long-term, persistent storage across sessions. |
| **Scalability** | Cloud-native, handles large knowledge bases. |
| **Search** | Semantic search capabilities for retrieving relevant memories. |
| **Use cases** | Production 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
## Best practices
### 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
### 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
### 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
## Troubleshooting
### Session service issues
1. **Database connection errors**: Use the **Verify** button on the memory configuration card to check connectivity. If the standalone runs in Docker and the database is on the host, use `host.docker.internal` instead of `localhost`
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
### 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
### 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
## Next steps
Configure the ADK adapter and Gemini-powered agents.
Add safety guards to your agent inputs and outputs.
Trace runs, monitor latency, and inspect token usage.