Stateful vs. Stateless: Managing Context in AI Agent Development

The n8n blog compares the trade-offs between stateful and stateless architectures in building AI agents. Stateful applications maintain continuity by preserving cross-request data such as conversation history and authentication details. In contrast, stateless applications process each request independently, making them easier to distribute across instances, scale, and fault-tolerate. Large Language Models (LLMs) do not automatically retain conversation context between inference requests; an agent's memory must be stored and retrieved by the system outside the model before being injected into subsequent requests. In n8n, you can choose a memory node or state store based on how long the state needs to persist.