Stateful vs. Stateless: Managing Context in AI Agent Builds

State is information retained across interactions, sessions, or workflows, while context is the information used for a current request and sessions group related interactions. Stateful systems preserve data such as conversation history, authentication, and process progress, enabling continuity but requiring storage, synchronization, and recovery mechanisms; stateless systems process requests independently, making them easier to distribute, scale, and recover, though persistent information must be supplied or retrieved externally. LLMs are inherently stateless between inference requests, so AI agents create stateful behavior by storing memory outside the model and injecting relevant context into future prompts, while temporary scratchpads only support work within a single request. Many systems combine stateless compute with shared databases, caches, or persistent stores to retain only the state they need. In n8n, independent workflow executions can use Memory nodes for session-level conversation history, including persistent Postgres or distributed Redis options, and use data tables, static data, or external stores for durable state across executions. The recommended approach is to begin with stateless execution and deliberately add session memory or shared persistent storage where continuity, collaboration, or long-term data access requires it.

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