> ## 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

> Give the agent memory across a conversation or across multiple sessions.

Memory lets the agent maintain context beyond the current message — either within a single conversation or across multiple sessions over time.

## 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) |

**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**:

| 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). |

**Shared parameters** (both strategies):

| 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. |

**CoD Summarizer only**:

| 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`).                                         |

**Best for**: Assistants that should remember a user or account across returning sessions, without relying on raw transcript replay alone.

<Note>
  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](/platform/agent-creation/knowledge-base) + [Embeddings](/platform/settings/embeddings).
</Note>

## 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

<Card title="Dev Mode" icon="code" href="/platform/agent-creation/dev-mode">
  Access advanced controls for the system prompt and agent configuration.
</Card>