## List Archives

`archives.list(ArchiveListParams**kwargs)  -> SyncArrayPage[Archive]`

**get** `/v1/archives/`

Get a list of all archives for the current organization with optional filters and pagination.

### Parameters

- `after: Optional[str]`

  Cursor for pagination (archive ID). Returns results relative to this ID in the specified sort order. Expected format: 'archive-<uuid4>'

- `agent_id: Optional[str]`

  Only archives attached to this agent ID

- `before: Optional[str]`

  Cursor for pagination (archive ID). Returns results relative to this ID in the specified sort order. Expected format: 'archive-<uuid4>'

- `limit: Optional[int]`

  Maximum number of archives to return

- `name: Optional[str]`

  Filter by archive name (exact match)

- `order: Optional[Literal["asc", "desc"]]`

  Sort order for archives by creation time. 'asc' for oldest first, 'desc' for newest first

  - `"asc"`

  - `"desc"`

- `order_by: Optional[Literal["created_at"]]`

  Field to sort by

  - `"created_at"`

### Returns

- `class Archive: …`

  Representation of an archive - a collection of archival passages that can be shared between agents.

  - `id: str`

    The human-friendly ID of the Archive

  - `created_at: datetime`

    The creation date of the archive

  - `name: str`

    The name of the archive

  - `created_by_id: Optional[str]`

    The id of the user that made this object.

  - `description: Optional[str]`

    A description of the archive

  - `embedding_config: Optional[EmbeddingConfig]`

    Configuration for embedding model connection and processing parameters.

    - `embedding_dim: int`

      The dimension of the embedding.

    - `embedding_endpoint_type: Literal["openai", "anthropic", "bedrock", 16 more]`

      The endpoint type for the model.

      - `"openai"`

      - `"anthropic"`

      - `"bedrock"`

      - `"google_ai"`

      - `"google_vertex"`

      - `"azure"`

      - `"groq"`

      - `"ollama"`

      - `"webui"`

      - `"webui-legacy"`

      - `"lmstudio"`

      - `"lmstudio-legacy"`

      - `"llamacpp"`

      - `"koboldcpp"`

      - `"vllm"`

      - `"hugging-face"`

      - `"mistral"`

      - `"together"`

      - `"pinecone"`

    - `embedding_model: str`

      The model for the embedding.

    - `azure_deployment: Optional[str]`

      The Azure deployment for the model.

    - `azure_endpoint: Optional[str]`

      The Azure endpoint for the model.

    - `azure_version: Optional[str]`

      The Azure version for the model.

    - `batch_size: Optional[int]`

      The maximum batch size for processing embeddings.

    - `embedding_chunk_size: Optional[int]`

      The chunk size of the embedding.

    - `embedding_endpoint: Optional[str]`

      The endpoint for the model (`None` if local).

    - `handle: Optional[str]`

      The handle for this config, in the format provider/model-name.

  - `last_updated_by_id: Optional[str]`

    The id of the user that made this object.

  - `metadata: Optional[Dict[str, object]]`

    Additional metadata

  - `updated_at: Optional[datetime]`

    The timestamp when the object was last updated.

  - `vector_db_provider: Optional[VectorDBProvider]`

    The vector database provider used for this archive's passages

    - `"native"`

    - `"tpuf"`

    - `"pinecone"`

### Example

```python
import os
from letta_client import Letta

client = Letta(
    api_key=os.environ.get("LETTA_API_KEY"),  # This is the default and can be omitted
)
page = client.archives.list()
page = page.items[0]
print(page.id)
```

#### Response

```json
[
  {
    "id": "archive-123e4567-e89b-12d3-a456-426614174000",
    "created_at": "2019-12-27T18:11:19.117Z",
    "name": "name",
    "created_by_id": "created_by_id",
    "description": "description",
    "embedding_config": {
      "embedding_dim": 0,
      "embedding_endpoint_type": "openai",
      "embedding_model": "embedding_model",
      "azure_deployment": "azure_deployment",
      "azure_endpoint": "azure_endpoint",
      "azure_version": "azure_version",
      "batch_size": 0,
      "embedding_chunk_size": 0,
      "embedding_endpoint": "embedding_endpoint",
      "handle": "handle"
    },
    "last_updated_by_id": "last_updated_by_id",
    "metadata": {
      "foo": "bar"
    },
    "updated_at": "2019-12-27T18:11:19.117Z",
    "vector_db_provider": "native"
  }
]
```