---
title: "List Folder Passages"
method: GET
path: "/v1/folders/{folder_id}/passages"
tags: ["folders"]
---

# List Folder Passages

`GET /v1/folders/{folder_id}/passages`

List all passages associated with a data folder.

## Path parameters

- `folder_id` string, required

## Query parameters

- `before` string, nullable — Passage ID cursor for pagination. Returns passages that come before this passage ID in the specified sort order
- `after` string, nullable — Passage ID cursor for pagination. Returns passages that come after this passage ID in the specified sort order
- `limit` integer, nullable — Maximum number of passages to return
- `order` 'asc' | 'desc' — Sort order for passages by creation time. 'asc' for oldest first, 'desc' for newest first
- `order_by` 'created_at' — Field to sort by

## Response `200`

Successful Response

- Passage[]
  - `created_by_id` string, nullable — The id of the user that made this object.
  - `last_updated_by_id` string, nullable — The id of the user that made this object.
  - `created_at` string, date-time — The creation date of the passage.
  - `updated_at` string, date-time, nullable — The timestamp when the object was last updated.
  - `is_deleted` boolean — Whether this passage is deleted or not.
  - `archive_id` string, nullable — The unique identifier of the archive containing this passage.
  - `source_id` string, nullable — The data source of the passage.
  - `file_id` string, nullable — The unique identifier of the file associated with the passage.
  - `file_name` string, nullable — The name of the file (only for source passages).
  - `metadata` object, nullable — The metadata of the passage.
  - `tags` string[], nullable — Tags associated with this passage.
  - `id` string — The human-friendly ID of the Passage
  - `text` string, required — The text of the passage.
  - `embedding` number[], nullable, required — The embedding of the passage.
  - `embedding_config` EmbeddingConfig, required — Configuration for embedding model connection and processing parameters.
    - `embedding_endpoint_type` 'openai' | 'anthropic' | 'bedrock' | 'google_ai' | 'google_vertex' | 'azure' | 'groq' | 'ollama' | 'webui' | 'webui-legacy' | 'lmstudio' | 'lmstudio-legacy' | 'llamacpp' | 'koboldcpp' | 'vllm' | 'hugging-face' | 'mistral' | 'together' | 'pinecone', required — The endpoint type for the model.
    - `embedding_endpoint` string, nullable — The endpoint for the model (`None` if local).
    - `embedding_model` string, required — The model for the embedding.
    - `embedding_dim` integer, required — The dimension of the embedding.
    - `embedding_chunk_size` integer, nullable — The chunk size of the embedding.
    - `handle` string, nullable — The handle for this config, in the format provider/model-name.
    - `batch_size` integer — The maximum batch size for processing embeddings.
    - `azure_endpoint` string, nullable — The Azure endpoint for the model.
    - `azure_version` string, nullable — The Azure version for the model.
    - `azure_deployment` string, nullable — The Azure deployment for the model.

## Other responses

- `422` — Validation Error

---

[API](https://skmtc.dev/yu-code666/apis/letta-api.md) · [All operations](https://skmtc.dev/yu-code666/apis/letta-api/llms.txt) · [OpenAPI document](https://skmtc-service-production.skmtc.workers.dev/v1/apis/yu-code666/letta-api/revisions/6cec99480c13/schema)