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agent-openai-memory

Persists OpenAI Agent conversation history using AsyncDatabricksSession and Lakebase sessions.

Updated Mar 15, 2026
Search Tags:#memory#persistence#openai#session#databricks#agents-sdk#lakebase
One-click install
npx skills add https://github.com/sumitsaxena-git/databricks-app --skill agent-openai-memory-sumitsaxena-git
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-openai-memory
Source: https://github.com/sumitsaxena-git/databricks-app/tree/main/agent-openai-agents-sdk-long-running-agent/.claude/skills/agent-memory
Command: npx skills add https://github.com/sumitsaxena-git/databricks-app --skill agent-openai-memory-sumitsaxena-git

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Add memory capabilities to your OpenAI Agent sessions, enabling persistence of conversation history and user preferences across runs.

Core Features & Use Cases

  • Session-backed memory: remembers past interactions via AsyncDatabricksSession and Lakebase.
  • Long-term storage: checkpoints and stores conversation data for later retrieval.
  • Easy integration: works with OpenAI Agents SDK Sessions and uses the LAKEBASE_INSTANCE_NAME env var for Lakebase resolution.

Quick Start

Configure AsyncDatabricksSession with a session_id and Lakebase instance, then run the agent to persist and retrieve conversation history.

Frequently Asked Questions about agent-openai-memory

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

FAQPage Schema
How do I add persistent memory to OpenAI Agent sessions?▼

To add persistent memory to OpenAI Agent sessions, configure AsyncDatabricksSession with a session_id and Lakebase instance. This checkpoints and stores conversation history and user preferences for retrieval across multiple runs.

How does Lakebase resolution work for OpenAI Agents SDK session memory?▼

Lakebase resolution for OpenAI Agents SDK session memory works by reading the LAKEBASE_INSTANCE_NAME environment variable. This connects the AsyncDatabricksSession to the correct Lakebase instance for long-term conversation storage.

Can I use Databricks to store conversation history across multiple agent runs?▼

Yes, you can use Databricks to store conversation history across multiple agent runs. The skill uses AsyncDatabricksSession to persist past interactions and user preferences, retrieving them later via a unique session_id.

What is the best way to maintain user preferences in OpenAI Agents SDK?▼

The best way to maintain user preferences in OpenAI Agents SDK is using session-backed memory with AsyncDatabricksSession. It integrates with Lakebase to checkpoint and retrieve conversation data across different runs.

Do I need a session_id to persist OpenAI Agent conversation history?▼

Yes, you need a session_id to persist OpenAI Agent conversation history. The session_id handles the AsyncDatabricksSession connection, ensuring conversation data is correctly checkpointed and retrieved from Lakebase across runs.

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