Since you're using LangGraph on Databricks Classic Compute, you don't have Agent Bricks or Managed Agent Memory. I would build the memory layer directly on Delta Lake + Unity Catalog. This gives you full control, is scalable, auditable, and integrates naturally with Databricks.

In fact, the architecture is almost identical to how Databricks' newer self-managed memory works internally, except they use Lakebase/Postgres instead of Delta. The underlying concepts—thread memory, semantic memory, episodic memory, and memory consolidation—are the same.

I recommend never writing directly to long-term memory from every node. Instead, add a dedicated Memory Consolidation Node at the end of the graph.

Memory Layers

Instead of a single table, split memory into specialized Delta tables.

catalog.agent_memory

conversations
episodic_memory
semantic_memory
user_profile
memory_embeddings

1. Short-Term Memory (Conversation State)

Short-term memory should only exist during the current conversation and should be managed by LangGraph State/Checkpointing.

It should contain:

• Conversation messages
• Tool outputs
• SQL query results
• API responses
• Intermediate reasoning
• Planner outputs
• Scratchpad/context

This memory is automatically passed between LangGraph nodes.

3. Semantic Memory (Long-Term Facts & Preferences)

This is the most important long-term memory.

Store only durable knowledge about the user.

Examples

• User prefers SQL over explanations
• User prefers charts over tables
• User works with Databricks
• User works in Retail Analytics
• User prefers concise responses
• User timezone is IST

Recommended Four-Layer Memory Model

For a production-grade implementation, I recommend extending beyond just short-term and long-term memory into four complementary memory types:

This design closely aligns with Databricks' self-managed memory concepts while remaining fully compatible with LangGraph running on Databricks Classic Compute.

Reference Links:

• Databricks Stateful Agents – https://docs.databricks.com/aws/en/agents/agent-framework/stateful-agents
• Databricks Self-Managed Agent Memory – https://docs.databricks.com/aws/en/agents/agent-memory/self-managed-memory
• LangGraph Memory Guide – https://docs.langchain.com/oss/python/langgraph/add-memory
• LangGraph Conversation Memory Management – https://langchain-ai.github.io/langgraph/how-tos/memory/manage-conversation-history/