# Models per stage

Configure which LLM is used for each processing stage in SurrealDB Agent Memory.

SurrealDB Agent Memory’s pipeline uses separate LLM stages with different latency and quality needs. Assign a model per stage rather than one model for everything. Embeddings are fixed deployment-wide.

For provider keys and Gemini defaults, see [Configuration](/docs/agent-memory/reference/configuration.md#default-provider-api-keys).

## The stages

### Extraction

Runs on conversational turns and document jobs. Classifies content and extracts entities, attributes, relations, instructions, and uncertainties. Often on the critical path for `/facts?infer=full`, so latency matters.

### Reconciliation

Optional LLM assist when structural entity merge is inconclusive. Not every write invokes it.

### Synthesis

Powers `/chat` and `/reflect` answer generation from retrieved context. Quality usually matters more than on extraction.

### Elaboration and consolidation

Background “dreaming” work on the worker tier: elaboration and consolidation passes. Not on the user-facing critical path.

### Embedding

Converts text (and multimodal content) into dense vectors for HNSW retrieval and the semantic cache. Fixed to **`gemini-embedding-2`** at **3072** dimensions. Not freely assignable per Context - Context config may only pin that same model id. Vector reads filter by **`embedding_model_id`**. After changing the deployment embedding model, run a [reindex](/docs/agent-memory/reference/management-api.md#force-reindex).

## Configuring models per stage

Model assignment is per-Context. Each LLM stage takes `{provider, model}`:

```http
PATCH /api/v1/contexts/{context_id}
Content-Type: application/json

{
  "config": {
    "models": {
      "extraction": { "provider": "google", "model": "gemini-2.5-flash" },
      "synthesis": { "provider": "anthropic", "model": "claude-sonnet-4" },
      "elaboration_consolidation": { "provider": "google", "model": "gemini-2.5-flash" },
      "embedding": "gemini-embedding-2"
    }
  }
}
```

Omit any stage to leave the current assignment (or deployment default) unchanged. Missing provider keys for a selected provider fail at configuration time rather than silently re-routing.

## Supported providers

| Provider | Role |
| --- | --- |
| Google (Gemini) | LLM stages; **required** for embeddings |
| OpenAI | LLM stages |
| Anthropic | LLM stages |

Provider keys may be set deployment-wide (`SPECTRON_PROVIDER_*`) or per Context. Keys are write-only on the API; reads return which providers are configured, not the secrets.

## Latency versus quality

| Stage | Typical path | Prefer |
| --- | --- | --- |
| `extraction` | Sync on write / ingest | Fast, inexpensive model |
| `reconciliation` | Occasional on write | Fast or mid-tier |
| `synthesis` | `/chat`, `/reflect` | More capable when answer quality matters |
| `elaboration_consolidation` | Worker background | Fast enough for batch work |
| `embedding` | Ingest + query | Fixed: `gemini-embedding-2` (3072-dim) |

A common pattern is a cheap model for extraction and a stronger model for synthesis only.

## Changing the embedding model

Embeddings are Gemini-only at 3072 dimensions. Switching the deployment embedding model requires a reindex so stored vectors and query vectors share one space. See [Management API - force reindex](/docs/agent-memory/reference/management-api.md#force-reindex).