redis-conversation-persistence

Implement Redis-backed conversation persistence with TTL management and in-memory fallback.

Updated Aug 23, 2026

Search Tags:#redis #ai #persistence #conversation #storage #ttl

Ready to run

npx skills add https://github.com/roaming-rockenfels/ghostfolio --skill redis-conversation-persistence

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: redis-conversation-persistence
Source: https://github.com/roaming-rockenfels/ghostfolio/tree/main/.claude/skills/redis-conversation-persistence
Command: npx skills add https://github.com/roaming-rockenfels/ghostfolio --skill redis-conversation-persistence

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires ioredis, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables persistent storage for AI conversations, preventing data loss across application restarts and providing continuity for user interactions.

Core Features & Use Cases

Cross-Restart Continuity: Ensures conversation history is saved and retrievable even after the application restarts.
Efficient Storage: Leverages Redis for fast and scalable storage of conversation data.
TTL Management: Implements time-to-live (TTL) for automatic cleanup of old conversations.
Use Case: When building an AI assistant, use this skill to ensure users can pick up their conversations exactly where they left off, even if they close and reopen the application.

Quick Start

Implement Redis-backed conversation persistence by following the step-by-step procedure outlined in the skill documentation.

Frequently Asked Questions about redis-conversation-persistence

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

FAQPage Schema

How do I save AI conversation history to Redis for cross-restart continuity?▼

You can save AI conversation history to Redis by implementing a ConversationStore service with a Redis adapter using the ioredis library. This ensures conversation data is stored persistently, allowing users to pick up interactions exactly where they left off after an application restart.

What is the best way to manage conversation TTL and automatic cleanup in Redis?▼

Managing conversation TTL in Redis involves configuring time-to-live settings within your ConversationStore service. This approach automatically expires and cleans up old conversation data from the Redis storage, preventing memory bloat while maintaining active session continuity.

Can I use ioredis for AI conversation storage without losing data during downtime?▼

Yes, you can use ioredis for conversation storage with a graceful degradation mechanism. If Redis becomes unavailable, the system falls back to in-memory storage to maintain functionality, ensuring no immediate data loss during temporary connection downtime.

Does Redis conversation persistence work with a Postgres metadata table for indexing?▼

Redis conversation persistence integrates optionally with a Postgres metadata table for conversation indexing. This combines fast Redis data retrieval with structured relational querying, allowing you to efficiently search and organize stored AI conversation histories.

How to test Redis conversation storage and TTL expiry behavior?▼

You test Redis conversation storage and TTL expiry by writing dedicated tests for the ConversationStore service that verify both data persistence and automatic time-to-live cleanup. This validates that conversations are correctly saved and expired under defined conditions.