AI Agent Memory: Implementing Persistent Context with Redis
Implement persistent memory for AI agents using Redis — conversation history, session state, and cross-interaction context that survives restarts.
Stateless AI agents forget everything between interactions. For useful assistants, you need persistent memory — conversation history, user preferences, learned facts, and session state. Redis is the ideal backing store: it's fast (sub-millisecond reads), supports complex data structures, and handles millions of keys effortlessly.
Memory Architecture
Design your agent memory in three tiers: (1) Short-term memory — the current conversation context, stored as a Redis list with a TTL. (2) Working memory — session state and extracted facts, stored as Redis hashes. (3) Long-term memory — important facts and user preferences, stored in Redis sets with no expiration and backed up to PostgreSQL for durability.
Implementation with better-openclaw
Redis is included automatically when you select services that depend on it (like n8n or LiteLLM). You can also add it explicitly: npx create-better-openclaw --services redis --yes. The generated configuration includes persistence (AOF + RDB), memory limits, and eviction policies optimized for agent memory workloads.
Conversation History
Store conversation history as a Redis list with the key agent:user_id:history. Use LPUSH to add new messages and LRANGE to retrieve the last N messages for context. Set a TTL of 24 hours for short-term conversations, or use a longer TTL for ongoing projects. This pattern supports millions of concurrent users with minimal memory overhead.
Cross-Session Context
For agents that remember across sessions, extract key facts and store them in a Redis sorted set keyed by importance score. Before each interaction, retrieve the top-K facts and inject them into the system prompt. This gives your agent the illusion of long-term memory without consuming excessive context window tokens.