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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.

ParametersExpand Collapse
after: Optional[str]

Archive ID cursor for pagination. Returns archives that come after this archive ID in the specified sort order

agent_id: Optional[str]

Only archives attached to this agent ID

before: Optional[str]

Archive ID cursor for pagination. Returns archives that come before this archive ID in the specified sort order

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

Accepts one of the following:
"asc"
"desc"
order_by: Optional[Literal["created_at"]]

Field to sort by

Accepts one of the following:
"created_at"
ReturnsExpand Collapse
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

formatdate-time
embedding_config: EmbeddingConfig

Embedding configuration for passages in this archive

embedding_dim: int

The dimension of the embedding.

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

The endpoint type for the model.

Accepts one of the following:
"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.

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

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.

formatdate-time
vector_db_provider: Optional[VectorDBProvider]

The vector database provider used for this archive's passages

Accepts one of the following:
"native"
"tpuf"
"pinecone"
List Archives
  • HTTP
  • TypeScript
  • Python
from letta_client import Letta

client = Letta(
    api_key="My API Key",
)
page = client.archives.list()
page = page.items[0]
print(page.id)
[
  {
    "id": "archive-123e4567-e89b-12d3-a456-426614174000",
    "created_at": "2019-12-27T18:11:19.117Z",
    "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"
    },
    "name": "name",
    "created_by_id": "created_by_id",
    "description": "description",
    "last_updated_by_id": "last_updated_by_id",
    "metadata": {
      "foo": "bar"
    },
    "updated_at": "2019-12-27T18:11:19.117Z",
    "vector_db_provider": "native"
  }
]
Returns Examples
[
  {
    "id": "archive-123e4567-e89b-12d3-a456-426614174000",
    "created_at": "2019-12-27T18:11:19.117Z",
    "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"
    },
    "name": "name",
    "created_by_id": "created_by_id",
    "description": "description",
    "last_updated_by_id": "last_updated_by_id",
    "metadata": {
      "foo": "bar"
    },
    "updated_at": "2019-12-27T18:11:19.117Z",
    "vector_db_provider": "native"
  }
]