Memory lets the agent maintain context beyond the current message — either within a single conversation or across multiple sessions over time.Documentation Index
Fetch the complete documentation index at: https://docs.alquimia.ai/llms.txt
Use this file to discover all available pages before exploring further.
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.| Parameter | Description |
|---|---|
| Max Tokens | Maximum token budget for injected history. Older messages are dropped when the budget is exceeded. |
| Metadata Filter | Optional filter to scope memory retrieval by session metadata (e.g., only recall messages tagged with a specific topic) |
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:
| Strategy | What it does |
|---|---|
| Neuralyzer | Continuously summarizes and compresses conversation history so older turns collapse into durable context instead of growing without bound. |
| CoD Summarizer | Chain-of-Density summarization: builds compact summaries and can store them in a knowledge-base collection you name (see Collection ID below). |
| Parameter | Description |
|---|---|
| Interaction Threshold (Qty) | Number of interactions before a summarize step runs. Use -1 for no limit (always follow runtime defaults). |
| Interaction Threshold (Tokens) | Token budget threshold before summarizing. Use -1 for unlimited. |
| Interaction Keep | How many recent interactions to leave unsummarized. 0 means everything eligible can roll into summarized form. |
| Parameter | Description |
|---|---|
| Collection ID | Knowledge-base collection where summaries are written (must match how your runtime/knowledge stack expects storage). |
| CoD Max Loops | Maximum refinement loops for the Chain-of-Density pass (default in UI: 5). |
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.