Replacing OpenClaw's Default Memory with Redis and Qdrant for Production Multi-Agent Systems

Scaling OpenClaw Memory for Production Multi-Agent Systems

A developer running OpenClaw in a production multi-agent setup on a self-hosted VPS for two months found that the default memory layer became problematic at scale. While the initial Markdown approach and later SQLite memory work fine for local use, they fall apart with multiple agents running in parallel, sessions spanning days, and the need for agents to retrieve relevant context from past work. The specific issues included: no semantic search, no cross-agent memory sharing, and messy concurrent writes.

Redis + Qdrant Architecture Solution

The developer rebuilt the memory system with the following architecture:

• Redis for hot ephemeral state: Current task, recent context window, tool call cache with TTL
• Qdrant for persistent vector memory: Past episodes, observations, extracted knowledge
• Three collections in Qdrant: agent_episodes, agent_observations, agent_knowledge
• Cross-agent knowledge sharing: Episodes are scoped per agent, while knowledge is shared across all agents
• Time-decay reranking: Prevents stale memories from polluting retrieval
• Redis pub/sub: Used for lightweight agent-to-agent event signaling
• Batch embedding + async Qdrant upserts: Prevents the agent loop from blocking on writes

Implementation Details

The developer documented the full implementation including architecture decisions, HNSW configuration reasoning, the memory manager class, how they hooked into the observation loop, and cleanup/pruning strategy. For embedding models, they're using text-embedding-3-small and considered going fully local with nomic-embed-text but didn't need to yet.