Build persistent and scalable AI agent memory with TiDB | ODSP918

This session explores how TiDB provides persistent and scalable memory infrastructure for AI agents, addressing the critical challenge that agents lose context between sessions. TiDB's distributed architecture enables agents to maintain long-term memory, handle high-throughput data operations, and scale efficiently as agent complexity grows. The talk demonstrates practical approaches to building reliable AI agent memory systems using TiDB's capabilities.

Key Points

• •AI agents require fundamentally different data infrastructure than traditional applications—they need persistent memory across sessions to maintain context and learning
• •TiDB's distributed SQL architecture enables high-throughput, low-latency memory operations essential for real-time agent decision-making
• •Implement vector storage alongside relational data to support semantic search and retrieval-augmented generation (RAG) for agent knowledge bases
• •Use TiDB's ACID transactions to ensure consistency when agents update memory state, preventing data corruption during concurrent operations
• •Design agent memory schemas with separation of concerns: short-term context, long-term knowledge, and interaction history
• •Leverage TiDB's horizontal scalability to handle growing memory requirements as agents become more sophisticated and manage larger datasets
• •Implement caching layers and indexing strategies to optimize memory retrieval speed, critical for agent response latency
• •Use TiDB's multi-tenancy features to isolate memory spaces for different agents or agent instances in production environments

Workflow Diagram

Concepts