Memory strategies
None (default)
No memory. Each message is treated as an independent, stateless interaction. The agent has no awareness of previous turns. Best for: Single-turn tasks, lookup agents, classification agents, any use case where conversation history doesn’t matter.Short-term memory
The agent remembers the current conversation session. Previous messages in the same session are injected into the prompt context.
Best for: Customer support conversations, multi-turn Q&A, any interaction where the agent needs to remember what was said earlier in the same session.
Long-term memory
The agent persists context across sessions using one of two runtime strategies — Neuralyzer or CoD Summarizer (cod-summarizer). These are summarization / compression pipelines managed by the platform.
Choose Memory Strategy:
Shared parameters (both strategies):
CoD Summarizer only:
Best for: Assistants that should remember a user or account across returning sessions, without relying on raw transcript replay alone.
Long-term behavior is strategy-driven (Neuralyzer vs. CoD Summarizer), not “pick Top K / similarity on an embeddings index” in Studio. If you need retrieval over uploaded documents, that is Knowledge Base + Embeddings.
Combining memory with knowledge base
Short- or long-term memory and Knowledge Base (RAG over documents) can be active together: the agent can combine conversation-side context with document embeddings in the same reply. CoD Summarizer in particular may write summaries into a collection that participates in your knowledge stack; that is separate from configuring an embeddings model for long-term memory in the Memory UI.Next steps
Dev Mode
Access advanced controls for the system prompt and agent configuration.