List Folders For Agent

agents.folders.list(, ) -> SyncArrayPage[FolderListResponse]

get/v1/agents/{agent_id}/folders

Get the folders associated with an agent.

ParametersExpand Collapse

agent_id: str

The ID of the agent in the format 'agent-'

minLength42

maxLength42

after: Optional[str]

Source ID cursor for pagination. Returns sources that come after this source ID in the specified sort order

before: Optional[str]

Source ID cursor for pagination. Returns sources that come before this source ID in the specified sort order

limit: Optional[int]

Maximum number of sources to return

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

Sort order for sources 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 FolderListResponse: …

(Deprecated: Use Folder) Representation of a source, which is a collection of files and passages.

id: str

The human-friendly ID of the Source

embedding_config: EmbeddingConfig

The embedding configuration used by the source.

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 source.

created_at: Optional[datetime]

The timestamp when the source was created.

formatdate-time

created_by_id: Optional[str]

The id of the user that made this Tool.

description: Optional[str]

The description of the source.

instructions: Optional[str]

Instructions for how to use the source.

last_updated_by_id: Optional[str]

The id of the user that made this Tool.

metadata: Optional[Dict[str, object]]

Metadata associated with the source.

updated_at: Optional[datetime]

The timestamp when the source was last updated.

formatdate-time

vector_db_provider: Optional[VectorDBProvider]

The vector database provider used for this source's passages

Accepts one of the following:

"native"

"tpuf"

"pinecone"

List Folders For Agent

from letta_client import Letta

client = Letta(
    api_key="My API Key",
)
page = client.agents.folders.list(
    agent_id="agent-123e4567-e89b-42d3-8456-426614174000",
)
page = page.items[0]
print(page.id)

[
  {
    "id": "source-123e4567-e89b-12d3-a456-426614174000",
    "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_at": "2019-12-27T18:11:19.117Z",
    "created_by_id": "created_by_id",
    "description": "description",
    "instructions": "instructions",
    "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": "source-123e4567-e89b-12d3-a456-426614174000",
    "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_at": "2019-12-27T18:11:19.117Z",
    "created_by_id": "created_by_id",
    "description": "description",
    "instructions": "instructions",
    "last_updated_by_id": "last_updated_by_id",
    "metadata": {
      "foo": "bar"
    },
    "updated_at": "2019-12-27T18:11:19.117Z",
    "vector_db_provider": "native"
  }
]