Get Agent Archival Memory

GET

/v1/agents/:agent_id/archival

Retrieve the memories in an agent’s archival memory store (paginated query).

Path parameters

agent_idstringRequired

Query parameters

afterintegerOptional

Unique ID of the memory to start the query range at.

beforeintegerOptional

Unique ID of the memory to end the query range at.

limitintegerOptional

How many results to include in the response.

Response

Successful Response

textstring

The text of the passage.

created_by_idstringOptional

The id of the user that made this object.

last_updated_by_idstringOptional

The id of the user that made this object.

created_atdatetimeOptional

The creation date of the passage.

updated_atdatetimeOptional

The timestamp when the object was last updated.

is_deletedbooleanOptionalDefaults to false

Whether this passage is deleted or not.

agent_idstringOptional

The unique identifier of the agent associated with the passage.

source_idstringOptional

The data source of the passage.

file_idstringOptional

The unique identifier of the file associated with the passage.

metadata_map from strings to anyOptional

The metadata of the passage.

idstringOptionalformat: "^passage-[a-fA-F0-9]{8}"

The human-friendly ID of the Passage

embeddinglist of doublesOptional

The embedding of the passage.

embedding_configobjectOptional

The embedding configuration used by the passage.

Errors

GET

1 curl https://app.letta.com/v1/agents/agent_id/archival \
2      -H "Authorization: Bearer <token>"

1 [
2   {
3     "text": "text",
4     "created_by_id": "created_by_id",
5     "last_updated_by_id": "last_updated_by_id",
6     "created_at": "2024-01-15T09:30:00Z",
7     "updated_at": "2024-01-15T09:30:00Z",
8     "is_deleted": true,
9     "agent_id": "agent_id",
10     "source_id": "source_id",
11     "file_id": "file_id",
12     "metadata_": {
13       "key": "value"
14     },
15     "id": "passage-123e4567-e89b-12d3-a456-426614174000",
16     "embedding": [
17       1.1
18     ],
19     "embedding_config": {
20       "embedding_endpoint_type": "openai",
21       "embedding_model": "embedding_model",
22       "embedding_dim": 1,
23       "embedding_endpoint": "embedding_endpoint",
24       "embedding_chunk_size": 1,
25       "handle": "handle",
26       "azure_endpoint": "azure_endpoint",
27       "azure_version": "azure_version",
28       "azure_deployment": "azure_deployment"
29     }
30   }
31 ]

Insert Agent Archival Memory

Up Next

Built with

1	curl https://app.letta.com/v1/agents/agent_id/archival \
2	-H "Authorization: Bearer <token>"

1	[
2	{
3	"text": "text",
4	"created_by_id": "created_by_id",
5	"last_updated_by_id": "last_updated_by_id",
6	"created_at": "2024-01-15T09:30:00Z",
7	"updated_at": "2024-01-15T09:30:00Z",
8	"is_deleted": true,
9	"agent_id": "agent_id",
10	"source_id": "source_id",
11	"file_id": "file_id",
12	"metadata_": {
13	"key": "value"
14	},
15	"id": "passage-123e4567-e89b-12d3-a456-426614174000",
16	"embedding": [
17	1.1
18	],
19	"embedding_config": {
20	"embedding_endpoint_type": "openai",
21	"embedding_model": "embedding_model",
22	"embedding_dim": 1,
23	"embedding_endpoint": "embedding_endpoint",
24	"embedding_chunk_size": 1,
25	"handle": "handle",
26	"azure_endpoint": "azure_endpoint",
27	"azure_version": "azure_version",
28	"azure_deployment": "azure_deployment"
29	}
30	}
31	]