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RamVegiraju
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agent-memory

Add persistent user memory to LangGraph agents using Lakebase storage.

4|4|Updated Jan 5, 2026
Search Tags:#agent#memory#long-term-memory#lakebase#memory-tools#short-term-memory
One-click install
npx skills add https://github.com/RamVegiraju/databricks-samples --skill agent-memory-ramvegiraju
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-memory
Source: https://github.com/RamVegiraju/databricks-samples/tree/main/.claude/skills/agent-langgraph-memory
Command: npx skills add https://github.com/RamVegiraju/databricks-samples --skill agent-memory-ramvegiraju

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires databricks-sdk, langchain-core, langgraph, mlflow.

What problem does it solve?

LangGraph agents often forget context and user preferences across interactions. This skill adds memory capabilities so agents can recall past conversations, remember user choices, and provide more coherent and personalized experiences.

Core Features & Use Cases

  • Short-term memory to track conversation history within a single session.
  • Long-term memory to persist user facts and preferences across sessions.
  • Memory tooling via a factory (memory_tools) that provides get_user_memory, save_user_memory, and delete_user_memory for flexible memory management.

Quick Start

Configure a Lakebase-backed store and load memory_tools into your agent, then pass user_id and store via the RunnableConfig to enable memory.

Frequently Asked Questions about agent-memory

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add long-term memory to LangGraph agents?▼

LangGraph agents often forget context across interactions. This memory capability allows agents to recall past conversations, remember user choices, and provide coherent, personalized experiences across sessions.

What's the difference between short-term and long-term memory in LangGraph?▼

Short-term memory tracks conversation history within a single session, while long-term memory persists user facts and preferences across multiple sessions using Lakebase storage for consistent replies.

Do I need Lakebase to preserve user preferences across LangGraph sessions?▼

Yes, Lakebase is the long-term storage backend used to preserve user preferences across LangGraph sessions. You configure the Lakebase-backed store and pass it via RunnableConfig to enable persistent memory.

How do I use memory_tools to manage user context in LangGraph?▼

Use the memory_tools factory to manage user context with get_user_memory, save_user_memory, and delete_user_memory functions. Load these tools into your LangGraph agent for flexible memory management across conversations.

Can I use LangChain core components with LangGraph memory tools?▼

Yes, LangChain core components work with LangGraph memory tools. The implementation depends on langchain-core, langgraph, databricks-sdk, and mlflow to deliver configurable and safe persistent memory.

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