List Folders For Agent

client.agents.folders.list(, ?, ?): ArrayPage<FolderListResponse { id, embedding_config, name, 8 more } >

get/v1/agents/{agent_id}/folders

Get the folders associated with an agent.

ParametersExpand Collapse

agentID: string

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

minLength42

maxLength42

query: FolderListParams { after, before, limit, 2 more }

after?: string | null

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

before?: string | null

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

limit?: number | null

Maximum number of sources to return

order?: "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?: "created_at"

Field to sort by

ReturnsExpand Collapse

FolderListResponse { id, embedding_config, name, 8 more }

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

id: string

The human-friendly ID of the Source

embedding_config: EmbeddingConfig { embedding_dim, embedding_endpoint_type, embedding_model, 7 more }

The embedding configuration used by the source.

embedding_dim: number

The dimension of the embedding.

embedding_endpoint_type: "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: string

The model for the embedding.

azure_deployment?: string | null

The Azure deployment for the model.

azure_endpoint?: string | null

The Azure endpoint for the model.

azure_version?: string | null

The Azure version for the model.

batch_size?: number

The maximum batch size for processing embeddings.

embedding_chunk_size?: number | null

The chunk size of the embedding.

embedding_endpoint?: string | null

The endpoint for the model (None if local).

handle?: string | null

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

name: string

The name of the source.

created_at?: string | null

The timestamp when the source was created.

formatdate-time

created_by_id?: string | null

The id of the user that made this Tool.

description?: string | null

The description of the source.

instructions?: string | null

Instructions for how to use the source.

last_updated_by_id?: string | null

The id of the user that made this Tool.

metadata?: Record<string, unknown> | null

Metadata associated with the source.

updated_at?: string | null

The timestamp when the source was last updated.

formatdate-time

vector_db_provider?: VectorDBProvider

The vector database provider used for this source's passages

Accepts one of the following:

"native"

"tpuf"

"pinecone"

List Folders For Agent

import Letta from '@letta-ai/letta-client';

const client = new Letta({
  apiKey: process.env['LETTA_API_KEY'], // This is the default and can be omitted
});

// Automatically fetches more pages as needed.
for await (const folderListResponse of client.agents.folders.list(
  'agent-123e4567-e89b-42d3-8456-426614174000',
)) {
  console.log(folderListResponse.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"
  }
]