Lakebase Setup

Lakebase provides persistent PostgreSQL storage for agents:

• Short-term memory (LangGraph): Conversation history within a thread (AsyncCheckpointSaver)
• Long-term memory (LangGraph): User facts across sessions (AsyncDatabricksStore)
• Long-running agent persistence (OpenAI SDK): Background task state via custom SQLAlchemy tables (agent_server schema)

Note: For pre-configured memory templates, see:

• agent-langgraph-short-term-memory - Conversation history within a session
• agent-langgraph-long-term-memory - User facts that persist across sessions
• agent-openai-agents-sdk-long-running-agent - Background tasks with Lakebase persistence

Complete Setup Workflow

┌─────────────────────────────────────────────────────────────────────────────┐
│  1. Add dependency  →  2. Get instance  →  3. Configure DAB               │
│  4. Configure .env  →  5. Initialize tables  →  6. Deploy + Run      │
└─────────────────────────────────────────────────────────────────────────────┘

Step 1: Add Memory Dependency

Add the memory extra to your pyproject.toml:

dependencies = [
    "databricks-langchain[memory]",
    # ... other dependencies
]

Then sync dependencies:

uv sync

Step 2: Create or Get Lakebase Instance

Option A: Create New Instance (via Databricks UI)

1. Go to your Databricks workspace
2. Navigate to Compute → Lakebase
3. Click Create Instance
4. Note the instance name

Option B: Use Existing Instance

If you have an existing instance, note its name for the next step.

Step 3: Configure databricks.yml (Lakebase Resource)

Add the Lakebase database resource to your app in databricks.yml: